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ORCIDAndrew McCallum
Andrew Kachites McCallum – R. Andrew McCallum
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- affiliation: University of Massachusetts Amherst, Department of Computer Science, Amherst, MA, USA
- affiliation (PhD 1995): University of Rochester, Department of Computer Science, Rochester, NY, USA
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2020 – today
- 2025
[i128]Shib Sankar Dasgupta, Michael Boratko, Andrew McCallum:
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings. CoRR abs/2502.10875 (2025)- 2024
- [j23]Somnath Basu Roy Chowdhury, Nicholas Monath, Kumar Avinava Dubey, Manzil Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi:
Incremental Extractive Opinion Summarization Using Cover Trees. Trans. Mach. Learn. Res. 2024 (2024) - [c279]Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O'Gorman, Nalini Singh, Andrew McCallum, Xiang Li:
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures. ACL (1) 2024: 493-506 - [c278]Jiachen Zhao, Wenlong Zhao, Andrew Drozdov, Benjamin Rozonoyer, Md. Arafat Sultan, Jay-Yoon Lee, Mohit Iyyer, Andrew McCallum:
Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation. ACL (1) 2024: 14201-14214 - [c277]Ameya Godbole, Nicholas Monath, Seungyeon Kim, Ankit Singh Rawat, Andrew McCallum, Manzil Zaheer:
Analysis of Plan-based Retrieval for Grounded Text Generation. EMNLP 2024: 13101-13119 - [c276]Jonghyun Song, Cheyon Jin, Wenlong Zhao, Andrew McCallum, Jay-Yoon Lee:
Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrieval. EMNLP 2024: 22255-22269 - [c275]Nishant Yadav, Nicholas Monath, Manzil Zaheer, Rob Fergus, Andrew McCallum:
Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders. ICLR 2024 - [c274]Rico Angell, Andrew McCallum:
Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching. ICML 2024 - [c273]Nicholas Monath, Will Sussman Grathwohl, Michael Boratko, Rob Fergus, Andrew McCallum, Manzil Zaheer:
A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks. ICML 2024 - [c272]Benjamin Rozonoyer, Michael Boratko, Dhruvesh Patel, Wenlong Zhao, Shib Sankar Dasgupta, Hung Le, Andrew McCallum:
Learning Representations for Hierarchies with Minimal Support. NeurIPS 2024 - [c271]Haw-Shiuan Chang
, Nikhil Agarwal, Andrew McCallum:
To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders. WSDM 2024: 67-76 - [i127]Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Manzil Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi:
Incremental Extractive Opinion Summarization Using Cover Trees. CoRR abs/2401.08047 (2024) - [i126]Nishant Yadav, Nicholas Monath, Manzil Zaheer, Rob Fergus, Andrew McCallum:
Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders. CoRR abs/2405.03651 (2024) - [i125]Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O'Gorman, Nalini Singh, Andrew McCallum, Xiang Lorraine Li:
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures. CoRR abs/2406.04145 (2024) - [i124]Garima Dhanania, Sheshera Mysore, Chau Minh Pham, Mohit Iyyer, Hamed Zamani, Andrew McCallum:
Interactive Topic Models with Optimal Transport. CoRR abs/2406.19928 (2024) - [i123]Ameya Godbole, Nicholas Monath, Seungyeon Kim, Ankit Singh Rawat, Andrew McCallum, Manzil Zaheer:
Analysis of Plan-based Retrieval for Grounded Text Generation. CoRR abs/2408.10490 (2024) - [i122]Nicholas Monath, Will Grathwohl, Michael Boratko, Rob Fergus, Andrew McCallum, Manzil Zaheer:
A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks. CoRR abs/2409.01890 (2024) - [i121]Sheshera Mysore, Garima Dhanania, Kishor Patil, Surya Kallumadi, Andrew McCallum, Hamed Zamani:
Memory Augmented Cross-encoders for Controllable Personalized Search. CoRR abs/2411.02790 (2024) - 2023
- [c270]Haw-Shiuan Chang, Ruei-Yao Sun, Kathryn Ricci, Andrew McCallum:
Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling. ACL (1) 2023: 821-854 - [c269]Raymond Zhang, Neha Nayak Kennard, Daniel Scott Smith, Daniel A. McFarland, Andrew McCallum, Katherine Keith:
Causal Matching with Text Embeddings: A Case Study in Estimating the Causal Effects of Peer Review Policies. ACL (Findings) 2023: 1284-1297 - [c268]Haw-Shiuan Chang, Zonghai Yao, Alolika Gon, Hong Yu, Andrew McCallum:
Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond. ACL (Findings) 2023: 12707-12730 - [c267]Nicholas Monath, Manzil Zaheer, Kelsey Allen, Andrew McCallum:
Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining. AISTATS 2023: 9308-9330 - [c266]Kumar Shridhar, Nicholas Monath, Raghuveer Thirukovalluru, Alessandro Stolfo, Manzil Zaheer, Andrew McCallum, Mrinmaya Sachan:
Longtonotes: OntoNotes with Longer Coreference Chains. EACL (Findings) 2023: 1398-1412 - [c265]Subendhu Rongali, Mukund Sridhar, Haidar Khan, Konstantine Arkoudas, Wael Hamza, Andrew McCallum:
Low-Resource Compositional Semantic Parsing with Concept Pretraining. EACL 2023: 1402-1411 - [c264]Nishant Yadav, Nicholas Monath, Manzil Zaheer, Andrew McCallum:
Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR Decomposition. EMNLP (Findings) 2023: 8088-8103 - [c263]Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Wenlong Zhao, Rajarshi Das, Jay-Yoon Lee, Hannaneh Hajishirzi, Manzil Zaheer, Andrew McCallum:
Machine Reading Comprehension using Case-based Reasoning. EMNLP (Findings) 2023: 8414-8428 - [c262]Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui:
PaRaDe: Passage Ranking using Demonstrations with LLMs. EMNLP (Findings) 2023: 14242-14252 - [c261]Sandeep Silwal, Sara Ahmadian, Andrew Nystrom, Andrew McCallum, Deepak Ramachandran, Seyed Mehran Kazemi:
KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals. ICLR 2023 - [c260]Nicholas Monath, Manzil Zaheer, Andrew McCallum:
Online Level-wise Hierarchical Clustering. KDD 2023: 1733-1745 - [c259]Sheshera Mysore, Andrew McCallum, Hamed Zamani:
Large Language Model Augmented Narrative Driven Recommendations. RecSys 2023: 777-783 - [c258]Sheshera Mysore, Mahmood Jasim, Andrew McCallum, Hamed Zamani:
Editable User Profiles for Controllable Text Recommendations. SIGIR 2023: 993-1003 - [c257]Sandeep Silwal, Sara Ahmadian, Andrew Nystrom, Andrew McCallum, Deepak Ramachandran, Seyed Mehran Kazemi:
KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals. SustaiNLP 2023: 1-31 - [i120]Subendhu Rongali, Mukund Sridhar, Haidar Khan, Konstantine Arkoudas, Wael Hamza, Andrew McCallum:
Low-Resource Compositional Semantic Parsing with Concept Pretraining. CoRR abs/2301.09809 (2023) - [i119]Nicholas Monath, Manzil Zaheer, Kelsey Allen, Andrew McCallum:
Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining. CoRR abs/2303.15311 (2023) - [i118]Sheshera Mysore, Mahmood Jasim, Andrew McCallum, Hamed Zamani:
Editable User Profiles for Controllable Text Recommendation. CoRR abs/2304.04250 (2023) - [i117]Nishant Yadav, Nicholas Monath, Manzil Zaheer, Andrew McCallum:
Adaptive Selection of Anchor Items for CUR-based k-NN search with Cross-Encoders. CoRR abs/2305.02996 (2023) - [i116]Haw-Shiuan Chang, Zonghai Yao, Alolika Gon, Hong Yu, Andrew McCallum:
Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond. CoRR abs/2305.12289 (2023) - [i115]Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Rajarshi Das, Manzil Zaheer, Jay-Yoon Lee, Hannaneh Hajishirzi, Andrew McCallum:
Machine Reading Comprehension using Case-based Reasoning. CoRR abs/2305.14815 (2023) - [i114]Sheshera Mysore, Andrew McCallum, Hamed Zamani:
Large Language Model Augmented Narrative Driven Recommendations. CoRR abs/2306.02250 (2023) - [i113]Shib Sankar Dasgupta, Andrew McCallum, Steffen Rendle, Li Zhang:
Answering Compositional Queries with Set-Theoretic Embeddings. CoRR abs/2306.04133 (2023) - [i112]Ronald Seoh, Haw-Shiuan Chang, Andrew McCallum:
Encoding Multi-Domain Scientific Papers by Ensembling Multiple CLS Tokens. CoRR abs/2309.04333 (2023) - [i111]Haw-Shiuan Chang, Nikhil Agarwal, Andrew McCallum:
To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders. CoRR abs/2310.14079 (2023) - [i110]Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui:
PaRaDe: Passage Ranking using Demonstrations with Large Language Models. CoRR abs/2310.14408 (2023) - [i109]Jiachen Zhao, Wenlong Zhao, Andrew Drozdov, Benjamin Rozonoyer, Md. Arafat Sultan, Jay-Yoon Lee, Mohit Iyyer, Andrew McCallum:
Multistage Collaborative Knowledge Distillation from Large Language Models. CoRR abs/2311.08640 (2023) - [i108]Rico Angell, Andrew McCallum:
Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching. CoRR abs/2312.11801 (2023) - 2022
- [c256]Siddhartha Mishra, Nicholas Monath, Michael Boratko, Ariel Kobren, Andrew McCallum:
An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity. AAAI 2022: 7788-7796 - [c255]Archan Ray, Nicholas Monath, Andrew McCallum, Cameron Musco:
Sublinear Time Approximation of Text Similarity Matrices. AAAI 2022: 8072-8080 - [c254]EunJeong Hwang, Jay-Yoon Lee, Tianyi Yang, Dhruvesh Patel, Dongxu Zhang, Andrew McCallum:
Event-Event Relation Extraction using Probabilistic Box Embedding. ACL (2) 2022: 235-244 - [c253]Shib Sankar Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, Andrew McCallum:
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings. ACL (1) 2022: 2263-2276 - [c252]Haw-Shiuan Chang, Andrew McCallum:
Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions. ACL (1) 2022: 8048-8073 - [c251]Trapit Bansal, Salaheddin Alzubi, Tong Wang, Jay-Yoon Lee, Andrew McCallum:
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning. AutoML 2022: 19/1-18 - [c250]Kathryn Ricci, Haw-Shiuan Chang, Purujit Goyal, Andrew McCallum:
Unsupervised Partial Sentence Matching for Cited Text Identification. SDP@COLING 2022: 95-104 - [c249]Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum:
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization. EMNLP 2022: 2171-2194 - [c248]Andrew Drozdov, Shufan Wang, Razieh Rahimi, Andrew McCallum, Hamed Zamani, Mohit Iyyer:
You can't pick your neighbors, or can you? When and How to Rely on Retrieval in the kNN-LM. EMNLP (Findings) 2022: 2997-3007 - [c247]Dhruvesh Patel, Pavitra Dangati, Jay-Yoon Lee, Michael Boratko, Andrew McCallum:
Modeling Label Space Interactions in Multi-label Classification using Box Embeddings. ICLR 2022 - [c246]Rico Angell, Nicholas Monath, Nishant Yadav, Andrew McCallum:
Interactive Correlation Clustering with Existential Cluster Constraints. ICML 2022: 703-716 - [c245]Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, Andrew McCallum:
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs. ICML 2022: 4777-4793 - [c244]Dongxu Zhang, Sunil Mohan, Michaela Torkar, Andrew McCallum:
A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes. LREC 2022: 1073-1082 - [c243]Jui Shah, Dongxu Zhang, Sam Brody, Andrew McCallum:
Enhanced Distant Supervision with State-Change Information for Relation Extraction. LREC 2022: 5573-5579 - [c242]Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramón Fernandez Astudillo:
Inducing and Using Alignments for Transition-based AMR Parsing. NAACL-HLT 2022: 1086-1098 - [c241]Neha Nayak Kennard, Tim O'Gorman, Rajarshi Das, Akshay Sharma, Chhandak Bagchi, Matthew Clinton, Pranay Kumar Yelugam, Hamed Zamani, Andrew McCallum:
DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions. NAACL-HLT 2022: 1234-1249 - [c240]Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum:
Entity Linking via Explicit Mention-Mention Coreference Modeling. NAACL-HLT 2022: 4644-4658 - [c239]Jay Yoon Lee, Dhruvesh Patel, Purujit Goyal, Wenlong Zhao, Zhiyang Xu, Andrew McCallum:
Structured Energy Network As a Loss. NeurIPS 2022 - [c238]Dongxu Zhang, Michael Boratko, Cameron Musco, Andrew McCallum:
Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings. NeurIPS 2022 - [i107]Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Robin Jia, Manzil Zaheer, Hannaneh Hajishirzi, Andrew McCallum:
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs. CoRR abs/2202.10610 (2022) - [i106]Dongxu Zhang, Sunil Mohan, Michaela Torkar, Andrew McCallum:
A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes. CoRR abs/2204.06584 (2022) - [i105]Dung Thai, Srinivas Ravishankar, Ibrahim Abdelaziz, Mudit Chaudhary, Nandana Mihindukulasooriya, Tahira Naseem, Rajarshi Das, Pavan Kapanipathi, Achille Fokoue, Andrew McCallum:
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases. CoRR abs/2204.08554 (2022) - [i104]Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramón Fernandez Astudillo:
Inducing and Using Alignments for Transition-based AMR Parsing. CoRR abs/2205.01464 (2022) - [i103]Hyeonsu B. Kang, Sheshera Mysore, Kevin Huang, Haw-Shiuan Chang, Thorben Prein, Andrew McCallum, Aniket Kittur, Elsa Olivetti:
Augmenting Scientific Creativity with Retrieval across Knowledge Domains. CoRR abs/2206.01328 (2022) - [i102]Kumar Shridhar, Nicholas Monath, Raghuveer Thirukovalluru, Alessandro Stolfo, Manzil Zaheer, Andrew McCallum, Mrinmaya Sachan:
Longtonotes: OntoNotes with Longer Coreference Chains. CoRR abs/2210.03650 (2022) - [i101]Haw-Shiuan Chang, Ruei-Yao Sun, Kathryn Ricci, Andrew McCallum:
Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling. CoRR abs/2210.05043 (2022) - [i100]Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum:
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization. CoRR abs/2210.12579 (2022) - [i99]Andrew Drozdov, Shufan Wang, Razieh Rahimi, Andrew McCallum, Hamed Zamani, Mohit Iyyer:
You can't pick your neighbors, or can you? When and how to rely on retrieval in the kNN-LM. CoRR abs/2210.15859 (2022) - 2021
- [c237]Haw-Shiuan Chang, Amol Agrawal, Andrew McCallum:
Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications. AAAI 2021: 6956-6965 - [c236]Ahsaas Bajaj, Pavitra Dangati, Kalpesh Krishna, Pradhiksha Ashok Kumar, Rheeya Uppaal, Bradford Windsor, Eliot Brenner, Dominic Dotterrer, Rajarshi Das, Andrew McCallum:
Long Document Summarization in a Low Resource Setting using Pretrained Language Models. ACL (student) 2021: 71-80 - [c235]Nicholas FitzGerald, Daniel M. Bikel, Jan A. Botha, Daniel Gillick, Tom Kwiatkowski, Andrew McCallum:
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network. ACL/IJCNLP (2) 2021: 278-285 - [c234]Yasumasa Onoe, Michael Boratko, Andrew McCallum, Greg Durrett:
Modeling Fine-Grained Entity Types with Box Embeddings. ACL/IJCNLP (1) 2021: 2051-2064 - [c233]Raghuveer Thirukovalluru, Nicholas Monath, Kumar Shridhar, Manzil Zaheer, Mrinmaya Sachan, Andrew McCallum:
Scaling Within Document Coreference to Long Texts. ACL/IJCNLP (Findings) 2021: 3921-3931 - [c232]Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, Andrew McCallum:
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models. ACL/IJCNLP (1) 2021: 4528-4537 - [c231]Robert L. Logan IV, Andrew McCallum, Sameer Singh, Daniel M. Bikel:
Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference. ACL/IJCNLP (1) 2021: 4717-4731 - [c230]Sebastian Macaluso, Craig S. Greenberg, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum:
Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering. AISTATS 2021: 2467-2475 - [c229]Nicholas Monath, Manzil Zaheer, Kumar Avinava Dubey, Amr Ahmed, Andrew McCallum:
DAG-Structured Clustering by Nearest Neighbors. AISTATS 2021: 2854-2862 - [c228]Sunil Mohan, Rico Angell, Nicholas Monath, Andrew McCallum:
Low resource recognition and linking of biomedical concepts from a large ontology. BCB 2021: 54:1-54:10 - [c227]Rohan Paul, Haw-Shiuan Chang, Andrew McCallum:
Multi-facet Universal Schema. EACL 2021: 909-919 - [c226]Haw-Shiuan Chang, Jiaming Yuan, Mohit Iyyer, Andrew McCallum:
Changing the Mind of Transformers for Topically-Controllable Language Generation. EACL 2021: 2601-2611 - [c225]Tejas Chheda, Purujit Goyal, Trang Tran, Dhruvesh Patel, Michael Boratko, Shib Sankar Dasgupta, Andrew McCallum:
Box Embeddings: An open-source library for representation learning using geometric structures. EMNLP (Demos) 2021: 203-211 - [c224]Tim O'Gorman, Zach Jensen, Sheshera Mysore, Kevin Huang, Rubayyat Mahbub, Elsa Olivetti, Andrew McCallum:
MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text. EMNLP (1) 2021: 1337-1352 - [c223]Zhiyang Xu, Andrew Drozdov, Jay-Yoon Lee, Tim O'Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum:
Improved Latent Tree Induction with Distant Supervision via Span Constraints. EMNLP (1) 2021: 4818-4831 - [c222]Trapit Bansal, Karthick Prasad Gunasekaran, Tong Wang, Tsendsuren Munkhdalai, Andrew McCallum:
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP. EMNLP (1) 2021: 5812-5824 - [c221]Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum:
Case-based Reasoning for Natural Language Queries over Knowledge Bases. EMNLP (1) 2021: 9594-9611 - [c220]Nicholas Monath, Kumar Avinava Dubey, Guru Guruganesh, Manzil Zaheer, Amr Ahmed, Andrew McCallum, Gökhan Mergen, Marc Najork, Mert Terzihan, Bryon Tjanaka, Yuan Wang, Yuchen Wu:
Scalable Hierarchical Agglomerative Clustering. KDD 2021: 1245-1255 - [c219]Xuelu Chen, Michael Boratko, Muhao Chen, Shib Sankar Dasgupta, Xiang Lorraine Li, Andrew McCallum:
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning. NAACL-HLT 2021: 882-893 - [c218]Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum:
Clustering-based Inference for Biomedical Entity Linking. NAACL-HLT 2021: 2598-2608 - [c217]Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L. Clarkson, Andrew McCallum:
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs. NeurIPS 2021: 16423-16436 - [c216]Sheshera Mysore, Tim O'Gorman, Andrew McCallum, Hamed Zamani:
CSFCube - A Test Collection of Computer Science Research Articles for Faceted Query by Example. NeurIPS Datasets and Benchmarks 2021 - [c215]Raghuveer Thirukovalluru, Mukund Sridhar, Dung Thai, Shruti Chanumolu, Nicholas Monath, Sankaranarayanan Ananthakrishnan, Andrew McCallum:
Knowledge Informed Semantic Parsing for Conversational Question Answering. RepL4NLP@ACL-IJCNLP 2021: 231-240 - [c214]Dung Thai, Raghuveer Thirukovalluru, Trapit Bansal, Andrew McCallum:
Simultaneously Self-Attending to Text and Entities for Knowledge-Informed Text Representations. RepL4NLP@ACL-IJCNLP 2021: 241-247 - [c213]Shib Sankar Dasgupta, Xiang Lorraine Li, Michael Boratko, Dongxu Zhang, Andrew McCallum:
Box-To-Box Transformations for Modeling Joint Hierarchies. RepL4NLP@ACL-IJCNLP 2021: 277-288 - [c212]Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum:
Exact and approximate hierarchical clustering using A. UAI 2021: 2061-2071 - [c211]Michael Boratko, Javier Burroni, Shib Sankar Dasgupta, Andrew McCallum:
Min/max stability and box distributions. UAI 2021: 2146-2155 - [e4]Danqi Chen, Jonathan Berant, Andrew McCallum, Sameer Singh:
3rd Conference on Automated Knowledge Base Construction, AKBC 2021, Virtual, October 4-8, 2021. 2021 [contents] - [i98]Sunil Mohan, Rico Angell, Nicholas Monath, Andrew McCallum:
Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology. CoRR abs/2101.10587 (2021) - [i97]Ahsaas Bajaj, Pavitra Dangati, Kalpesh Krishna, Pradhiksha Ashok Kumar, Rheeya Uppaal, Bradford Windsor, Eliot Brenner, Dominic Dotterrer, Rajarshi Das, Andrew McCallum:
Long Document Summarization in a Low Resource Setting using Pretrained Language Models. CoRR abs/2103.00751 (2021) - [i96]Sheshera Mysore, Tim O'Gorman, Andrew McCallum, Hamed Zamani:
CSFCube - A Test Collection of Computer Science Research Articles for Faceted Query by Example. CoRR abs/2103.12906 (2021) - [i95]Haw-Shiuan Chang, Amol Agrawal, Andrew McCallum:
Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications. CoRR abs/2103.15330 (2021) - [i94]Haw-Shiuan Chang, Jiaming Yuan, Mohit Iyyer, Andrew McCallum:
Changing the Mind of Transformers for Topically-Controllable Language Generation. CoRR abs/2103.15335 (2021) - [i93]Rohan Paul, Haw-Shiuan Chang, Andrew McCallum:
Multi-facet Universal Schema. CoRR abs/2103.15339 (2021) - [i92]Xuelu Chen, Michael Boratko, Muhao Chen, Shib Sankar Dasgupta, Xiang Lorraine Li, Andrew McCallum:
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning. CoRR abs/2104.04597 (2021) - [i91]Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum:
Exact and Approximate Hierarchical Clustering Using A. CoRR abs/2104.07061 (2021) - [i90]Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay-Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum:
Case-based Reasoning for Natural Language Queries over Knowledge Bases. CoRR abs/2104.08762 (2021) - [i89]Nicholas FitzGerald, Jan A. Botha, Daniel Gillick, Daniel M. Bikel, Tom Kwiatkowski, Andrew McCallum:
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network. CoRR abs/2106.07352 (2021) - [i88]Shib Sankar Dasgupta, Michael Boratko, Shriya Atmakuri, Xiang Lorraine Li, Dhruvesh Patel, Andrew McCallum:
Word2Box: Learning Word Representation Using Box Embeddings. CoRR abs/2106.14361 (2021) - [i87]Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum:
Entity Linking and Discovery via Arborescence-based Supervised Clustering. CoRR abs/2109.01242 (2021) - [i86]Tejas Chheda, Purujit Goyal, Trang Tran, Dhruvesh Patel, Michael Boratko, Shib Sankar Dasgupta, Andrew McCallum:
Box Embeddings: An open-source library for representation learning using geometric structures. CoRR abs/2109.04997 (2021) - [i85]Zhiyang Xu, Andrew Drozdov, Jay-Yoon Lee, Tim O'Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum:
Improved Latent Tree Induction with Distant Supervision via Span Constraints. CoRR abs/2109.05112 (2021) - [i84]Neha Nayak Kennard, Tim O'Gorman, Akshay Sharma, Chhandak Bagchi, Matthew Clinton, Pranay Kumar Yelugam, Rajarshi Das, Hamed Zamani, Andrew McCallum:
A Dataset for Discourse Structure in Peer Review Discussions. CoRR abs/2110.08520 (2021) - [i83]Trapit Bansal, Karthick Gunasekaran, Tong Wang, Tsendsuren Munkhdalai, Andrew McCallum:
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP. CoRR abs/2111.01322 (2021) - [i82]Archan Ray, Nicholas Monath, Andrew McCallum, Cameron Musco:
Sublinear Time Approximation of Text Similarity Matrices. CoRR abs/2112.09631 (2021) - 2020
- [j22]Edward Kim, Zach Jensen, Alexander van Grootel, Kevin Huang, Matthew Staib, Sheshera Mysore, Haw-Shiuan Chang, Emma Strubell, Andrew McCallum, Stefanie Jegelka, Elsa Olivetti:
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks. J. Chem. Inf. Model. 60(3): 1194-1201 (2020) - [j21]Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan, Rheeya Uppaal, Andrew McCallum:
Using error decay prediction to overcome practical issues of deep active learning for named entity recognition. Mach. Learn. 109(9-10): 1749-1778 (2020) - [c210]Trapit Bansal, Patrick Verga, Neha Choudhary, Andrew McCallum:
Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision. AAAI 2020: 7407-7414 - [c209]Emma Strubell, Ananya Ganesh, Andrew McCallum:
Energy and Policy Considerations for Modern Deep Learning Research. AAAI 2020: 13693-13696 - [c208]Rajarshi Das, Ameya Godbole, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum:
A Simple Approach to Case-Based Reasoning in Knowledge Bases. AKBC 2020 - [c207]Dhruvesh Patel, Shib Sankar Dasgupta, Michael Boratko, Xiang Li, Luke Vilnis, Andrew McCallum:
Representing Joint Hierarchies with Box Embeddings. AKBC 2020 - [c206]Derek Tam, Nicholas Monath, Ari Kobren, Andrew McCallum:
Predicting Institution Hierarchies with Set-based Models. AKBC 2020 - [c205]Dung Thai, Zhiyang Xu, Nicholas Monath, Boris Veytsman, Andrew McCallum:
Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction. AKBC 2020 - [c204]Vaishnavi Kommaraju, Karthick Gunasekaran, Kun Li, Trapit Bansal, Andrew McCallum, Ivana Williams, Ana-Maria Istrate:
Unsupervised Pre-training for Biomedical Question Answering. CLEF (Working Notes) 2020 - [c203]Trapit Bansal, Rishikesh Jha, Andrew McCallum:
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks. COLING 2020: 5108-5123 - [c202]Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai, Andrew McCallum:
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks. EMNLP (1) 2020: 522-534 - [c201]Michael Boratko, Xiang Li, Tim O'Gorman, Rajarshi Das, Dan Le, Andrew McCallum:
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning. EMNLP (1) 2020: 1122-1136 - [c200]Daivik Swarup, Ahsaas Bajaj, Sheshera Mysore, Tim O'Gorman, Rajarshi Das, Andrew McCallum:
An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text. EMNLP (Findings) 2020: 3010-3017 - [c199]Rajarshi Das, Ameya Godbole, Nicholas Monath, Manzil Zaheer, Andrew McCallum:
Probabilistic Case-based Reasoning in Knowledge Bases. EMNLP (Findings) 2020: 4752-4765 - [c198]Andrew Drozdov, Subendhu Rongali, Yi-Pei Chen, Tim O'Gorman, Mohit Iyyer, Andrew McCallum:
Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders. EMNLP (1) 2020: 4832-4845 - [c197]Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos Faloutsos, Andrew McCallum, Jiawei Han:
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types. KDD 2020: 2724-2734 - [c196]Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum:
Improving Local Identifiability in Probabilistic Box Embeddings. NeurIPS 2020 - [e3]Dipanjan Das, Hannaneh Hajishirzi, Andrew McCallum, Sameer Singh:
Conference on Automated Knowledge Base Construction, AKBC 2020, Virtual, June 22-24, 2020. 2020 [contents] - [i81]Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael R. Glass, Andrew McCallum:
Scalable Hierarchical Clustering with Tree Grafting. CoRR abs/2001.00076 (2020) - [i80]Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum:
Compact Representation of Uncertainty in Hierarchical Clustering. CoRR abs/2002.11661 (2020) - [i79]Michael Boratko, Xiang Lorraine Li, Rajarshi Das, Tim O'Gorman, Dan Le, Andrew McCallum:
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning. CoRR abs/2005.00771 (2020) - [i78]Dung Thai, Zhiyang Xu, Nicholas Monath, Boris Veytsman, Andrew McCallum:
Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction. CoRR abs/2006.05563 (2020) - [i77]Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos Faloutsos, Andrew McCallum, Jiawei Han:
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types. CoRR abs/2006.13473 (2020) - [i76]Rajarshi Das, Ameya Godbole, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum:
A Simple Approach to Case-Based Reasoning in Knowledge Bases. CoRR abs/2006.14198 (2020) - [i75]Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai, Andrew McCallum:
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks. CoRR abs/2009.08445 (2020) - [i74]Vaishnavi Kommaraju, Karthick Gunasekaran, Kun Li, Trapit Bansal, Andrew McCallum, Ivana Williams, Ana-Maria Istrate:
Unsupervised Pre-training for Biomedical Question Answering. CoRR abs/2009.12952 (2020) - [i73]Subhajit Naskar, Amirmohammad Rooshenas, Simeng Sun, Mohit Iyyer, Andrew McCallum:
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models. CoRR abs/2009.13267 (2020) - [i72]Rajarshi Das, Ameya Godbole, Nicholas Monath, Manzil Zaheer, Andrew McCallum:
Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion. CoRR abs/2010.03548 (2020) - [i71]Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Lorraine Li, Andrew McCallum:
Improving Local Identifiability in Probabilistic Box Embeddings. CoRR abs/2010.04831 (2020) - [i70]Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum:
Clustering-based Inference for Zero-Shot Biomedical Entity Linking. CoRR abs/2010.11253 (2020) - [i69]Nicholas Monath, Avinava Dubey, Guru Guruganesh, Manzil Zaheer, Amr Ahmed, Andrew McCallum, Gökhan Mergen, Marc Najork, Mert Terzihan, Bryon Tjanaka, Yuan Wang, Yuchen Wu:
Scalable Bottom-Up Hierarchical Clustering. CoRR abs/2010.11821 (2020)
2010 – 2019
- 2019
- [c195]Emma Strubell, Ananya Ganesh, Andrew McCallum:
Energy and Policy Considerations for Deep Learning in NLP. ACL (1) 2019: 3645-3650 - [c194]Trapit Bansal, Da-Cheng Juan, Sujith Ravi, Andrew McCallum:
A2N: Attending to Neighbors for Knowledge Graph Inference. ACL (1) 2019: 4387-4392 - [c193]Derek Tam, Nicholas Monath, Ari Kobren, Aaron Traylor, Rajarshi Das, Andrew McCallum:
Optimal Transport-based Alignment of Learned Character Representations for String Similarity. ACL (1) 2019: 5907-5917 - [c192]Rajarshi Das, Ameya Godbole, Dilip Kavarthapu, Zhiyu Gong, Abhishek Singhal, Mo Yu, Xiaoxiao Guo, Tian Gao, Hamed Zamani, Manzil Zaheer, Andrew McCallum:
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering. MRQA@EMNLP 2019: 113-118 - [c191]Sheshera Mysore, Zach Jensen, Edward Kim, Kevin Huang, Haw-Shiuan Chang, Emma Strubell, Jeffrey Flanigan, Andrew McCallum, Elsa Olivetti:
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures. LAW@ACL 2019: 56-64 - [c190]Ari Kobren, Nicholas Monath, Andrew McCallum:
Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction. AKBC 2019 - [c189]Pallavi Patil, Kriti Myer, Ronak Zala, Arpit Singh, Sheshera Mysore, Andrew McCallum, Adrian Benton, Amanda Stent:
Roll Call Vote Prediction with Knowledge Augmented Models. CoNLL 2019: 574-581 - [c188]Andrew Drozdov, Patrick Verga, Yi-Pei Chen, Mohit Iyyer, Andrew McCallum:
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders. EMNLP/IJCNLP (1) 2019: 1507-1512 - [c187]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum:
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering. ICLR (Poster) 2019 - [c186]Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan, Adam Trischler, Andrew McCallum:
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension. ICLR (Poster) 2019 - [c185]Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum:
Smoothing the Geometry of Probabilistic Box Embeddings. ICLR 2019 - [c184]Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum:
Supervised Hierarchical Clustering with Exponential Linkage. ICML 2019: 6973-6983 - [c183]Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, Amr Ahmed:
Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. KDD 2019: 714-722 - [c182]Ari Kobren, Barna Saha, Andrew McCallum:
Paper Matching with Local Fairness Constraints. KDD 2019: 1247-1257 - [c181]Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael R. Glass, Andrew McCallum:
Scalable Hierarchical Clustering with Tree Grafting. KDD 2019: 1438-1448 - [c180]Dongxu Zhang, Subhabrata Mukherjee, Colin Lockard, Xin Luna Dong, Andrew McCallum:
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference. NAACL-HLT (1) 2019: 762-772 - [c179]Andrew Drozdov, Patrick Verga, Mohit Yadav, Mohit Iyyer, Andrew McCallum:
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders. NAACL-HLT (1) 2019: 1129-1141 - [c178]Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum:
Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks. NeurIPS 2019: 13522-13532 - [c177]Rajarshi Das, Ameya Godbole, Manzil Zaheer, Shehzaad Dhuliawala, Andrew McCallum:
Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference. TextGraphs@EMNLP 2019: 101-117 - [i68]Edward Kim, Zach Jensen, Alexander van Grootel, Kevin Huang, Matthew Staib, Sheshera Mysore, Haw-Shiuan Chang, Emma Strubell, Andrew McCallum, Stefanie Jegelka, Elsa Olivetti:
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks. CoRR abs/1901.00032 (2019) - [i67]Andrew Drozdov, Patrick Verga, Mohit Yadav, Mohit Iyyer, Andrew McCallum:
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders. CoRR abs/1904.02142 (2019) - [i66]Dongxu Zhang, Subhabrata Mukherjee, Colin Lockard, Xin Luna Dong, Andrew McCallum:
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference. CoRR abs/1904.12606 (2019) - [i65]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum:
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering. CoRR abs/1905.05733 (2019) - [i64]Sheshera Mysore, Zach Jensen, Edward Kim, Kevin Huang, Haw-Shiuan Chang, Emma Strubell, Jeffrey Flanigan, Andrew McCallum, Elsa Olivetti:
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures. CoRR abs/1905.06939 (2019) - [i63]Ari Kobren, Barna Saha, Andrew McCallum:
Paper Matching with Local Fairness Constraints. CoRR abs/1905.11924 (2019) - [i62]Emma Strubell, Ananya Ganesh, Andrew McCallum:
Energy and Policy Considerations for Deep Learning in NLP. CoRR abs/1906.02243 (2019) - [i61]Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum:
Supervised Hierarchical Clustering with Exponential Linkage. CoRR abs/1906.07859 (2019) - [i60]Derek Tam, Nicholas Monath, Ari Kobren, Aaron Traylor, Rajarshi Das, Andrew McCallum:
Optimal Transport-based Alignment of Learned Character Representations for String Similarity. CoRR abs/1907.10165 (2019) - [i59]Ameya Godbole, Dilip Kavarthapu, Rajarshi Das, Zhiyu Gong, Abhishek Singhal, Hamed Zamani, Mo Yu, Tian Gao, Xiaoxiao Guo, Manzil Zaheer, Andrew McCallum:
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering. CoRR abs/1909.07598 (2019) - [i58]Trapit Bansal, Rishikesh Jha, Andrew McCallum:
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks. CoRR abs/1911.03863 (2019) - [i57]Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan, Rheeya Uppaal, Andrew McCallum:
Overcoming Practical Issues of Deep Active Learning and its Applications on Named Entity Recognition. CoRR abs/1911.07335 (2019) - [i56]Trapit Bansal, Patrick Verga, Neha Choudhary, Andrew McCallum:
Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision. CoRR abs/1912.01070 (2019) - 2018
- [c176]Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue-Nkoutche, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, Michael Witbrock:
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset. QA@ACL 2018: 60-70 - [c175]Shikhar Murty, Patrick Verga, Luke Vilnis, Irena Radovanovic, Andrew McCallum:
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking. ACL (1) 2018: 97-109 - [c174]Luke Vilnis, Xiang Li, Shikhar Murty, Andrew McCallum:
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures. ACL (1) 2018: 263-272 - [c173]Dung Thai, Sree Harsha Ramesh, Shikhar Murty, Luke Vilnis, Andrew McCallum:
Embedded-State Latent Conditional Random Fields for Sequence Labeling. CoNLL 2018: 1-10 - [c172]Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, Michael Witbrock:
An Interface for Annotating Science Questions. EMNLP (Demonstration) 2018: 102-107 - [c171]Nathan Greenberg, Trapit Bansal, Patrick Verga, Andrew McCallum:
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets. EMNLP 2018: 2824-2829 - [c170]Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum:
Linguistically-Informed Self-Attention for Semantic Role Labeling. EMNLP 2018: 5027-5038 - [c169]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. ICLR (Poster) 2018 - [c168]Amirmohammad Rooshenas, Aishwarya Kamath, Andrew McCallum:
Training Structured Prediction Energy Networks with Indirect Supervision. NAACL-HLT (2) 2018: 130-135 - [c167]Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis, Andrew McCallum:
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection. NAACL-HLT 2018: 485-495 - [c166]Patrick Verga, Emma Strubell, Andrew McCallum:
Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction. NAACL-HLT 2018: 872-884 - [c165]Craig S. Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew McGregor, Andrew McCallum:
Compact Representation of Uncertainty in Clustering. NeurIPS 2018: 8639-8649 - [c164]Haw-Shiuan Chang, Amol Agrawal, Ananya Ganesh, Anirudha Desai, Vinayak Mathur, Alfred Hough, Andrew McCallum:
Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings. TextGraphs@NAACL-HLT 2018: 38-48 - [i55]Patrick Verga, Emma Strubell, Andrew McCallum:
Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction. CoRR abs/1802.10569 (2018) - [i54]Haw-Shiuan Chang, Amol Agrawal, Ananya Ganesh, Anirudha Desai, Vinayak Mathur, Alfred Hough, Andrew McCallum:
Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings. CoRR abs/1804.03257 (2018) - [i53]Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum:
Linguistically-Informed Self-Attention for Semantic Role Labeling. CoRR abs/1804.08199 (2018) - [i52]Luke Vilnis, Xiang Li, Shikhar Murty, Andrew McCallum:
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures. CoRR abs/1805.06627 (2018) - [i51]Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue-Nkoutche, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, Michael Witbrock:
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset. CoRR abs/1806.00358 (2018) - [i50]Shikhar Murty, Patrick Verga, Luke Vilnis, Irena Radovanovic, Andrew McCallum:
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking. CoRR abs/1807.05127 (2018) - [i49]Dung Thai, Sree Harsha Ramesh, Shikhar Murty, Luke Vilnis, Andrew McCallum:
Embedded-State Latent Conditional Random Fields for Sequence Labeling. CoRR abs/1809.10835 (2018) - [i48]Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan, Adam Trischler, Andrew McCallum:
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension. CoRR abs/1810.05682 (2018) - [i47]Emma Strubell, Andrew McCallum:
Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL? CoRR abs/1811.04773 (2018) - [i46]Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum:
Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks. CoRR abs/1812.09603 (2018) - 2017
- [c163]Rajarshi Das, Manzil Zaheer, Siva Reddy, Andrew McCallum:
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks. ACL (2) 2017: 358-365 - [c162]Trapit Bansal, Arvind Neelakantan, Andrew McCallum:
RelNet: End-to-end Modeling of Entities & Relations. AKBC@NIPS 2017 - [c161]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning. AKBC@NIPS 2017 - [c160]Ari Kobren, Nicholas Monath, Andrew McCallum:
Entity-centric Attribute Feedback for Interactive Knowledge Bases. AKBC@NIPS 2017 - [c159]Shikhar Murty, Patrick Verga, Luke Vilnis, Andrew McCallum:
Finer Grained Entity Typing with TypeNet. AKBC@NIPS 2017 - [c158]Aaron Traylor, Nicholas Monath, Rajarshi Das, Andrew McCallum:
Learning String Alignments for Entity Aliases. AKBC@NIPS 2017 - [c157]Patrick Verga, Emma Strubell, Ofer Shai, Andrew McCallum:
Attending to All Mention Pairs for Full Abstract Biological Relation Extraction. AKBC@NIPS 2017 - [c156]Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum:
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks. EACL (1) 2017: 132-141 - [c155]Patrick Verga, Arvind Neelakantan, Andrew McCallum:
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema. EACL (1) 2017: 613-622 - [c154]Emma Strubell, Andrew McCallum:
Dependency Parsing with Dilated Iterated Graph CNNs. SPNLP@EMNLP 2017: 1-6 - [c153]Emma Strubell, Patrick Verga, David Belanger, Andrew McCallum:
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions. EMNLP 2017: 2670-2680 - [c152]Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, Dario Amodei:
Learning a Natural Language Interface with Neural Programmer. ICLR (Poster) 2017 - [c151]David Belanger, Bishan Yang, Andrew McCallum:
End-to-End Learning for Structured Prediction Energy Networks. ICML 2017: 429-439 - [c150]Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum:
A Hierarchical Algorithm for Extreme Clustering. KDD 2017: 255-264 - [c149]Haw-Shiuan Chang, Erik G. Learned-Miller, Andrew McCallum:
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples. NIPS 2017: 1002-1012 - [c148]Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman, Andrew McCallum:
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications. SemEval@ACL 2017: 546-555 - [i45]Emma Strubell, Patrick Verga, David Belanger, Andrew McCallum:
Fast and Accurate Sequence Labeling with Iterated Dilated Convolutions. CoRR abs/1702.02098 (2017) - [i44]David Belanger, Bishan Yang, Andrew McCallum:
End-to-End Learning for Structured Prediction Energy Networks. CoRR abs/1703.05667 (2017) - [i43]Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum:
An Online Hierarchical Algorithm for Extreme Clustering. CoRR abs/1704.01858 (2017) - [i42]Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman, Andrew McCallum:
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications. CoRR abs/1704.02853 (2017) - [i41]Haw-Shiuan Chang, Erik G. Learned-Miller, Andrew McCallum:
Active Bias: Training a More Accurate Neural Network by Emphasizing High Variance Samples. CoRR abs/1704.07433 (2017) - [i40]Rajarshi Das, Manzil Zaheer, Siva Reddy, Andrew McCallum:
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks. CoRR abs/1704.08384 (2017) - [i39]Emma Strubell, Andrew McCallum:
Dependency Parsing with Dilated Iterated Graph CNNs. CoRR abs/1705.00403 (2017) - [i38]Trapit Bansal, Arvind Neelakantan, Andrew McCallum:
RelNet: End-to-end Modeling of Entities & Relations. CoRR abs/1706.07179 (2017) - [i37]Xiang Li, Luke Vilnis, Andrew McCallum:
Improved Representation Learning for Predicting Commonsense Ontologies. CoRR abs/1708.00549 (2017) - [i36]Dung Thai, Shikhar Murty, Trapit Bansal, Luke Vilnis, David Belanger, Andrew McCallum:
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling. CoRR abs/1708.00553 (2017) - [i35]Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis, Andrew McCallum:
Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding. CoRR abs/1710.00880 (2017) - [i34]Patrick Verga, Emma Strubell, Ofer Shai, Andrew McCallum:
Attending to All Mention Pairs for Full Abstract Biological Relation Extraction. CoRR abs/1710.08312 (2017) - [i33]Shikhar Murty, Patrick Verga, Luke Vilnis, Andrew McCallum:
Finer Grained Entity Typing with TypeNet. CoRR abs/1711.05795 (2017) - [i32]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alexander J. Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. CoRR abs/1711.05851 (2017) - [i31]Sheshera Mysore, Edward Kim, Emma Strubell, Ao Liu, Haw-Shiuan Chang, Srikrishna Kompella, Kevin Huang, Andrew McCallum, Elsa Olivetti:
Automatically Extracting Action Graphs from Materials Science Synthesis Procedures. CoRR abs/1711.06872 (2017) - 2016
- [c147]Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum:
Incorporating Selectional Preferences in Multi-hop Relation Extraction. AKBC@NAACL-HLT 2016: 18-23 - [c146]Patrick Verga, Andrew McCallum:
Row-less Universal Schema. AKBC@NAACL-HLT 2016: 63-68 - [c145]Teresa Martin, Fiete Botschen, Ajay Nagesh, Andrew McCallum:
Call for Discussion: Building a New Standard Dataset for Relation Extraction Tasks. AKBC@NAACL-HLT 2016: 92-96 - [c144]David Belanger, Andrew McCallum:
Structured Prediction Energy Networks. ICML 2016: 983-992 - [c143]Patrick Verga, David Belanger, Emma Strubell, Benjamin Roth, Andrew McCallum:
Multilingual Relation Extraction using Compositional Universal Schema. HLT-NAACL 2016: 886-896 - [c142]Trapit Bansal, David Belanger, Andrew McCallum:
Ask the GRU: Multi-task Learning for Deep Text Recommendations. RecSys 2016: 107-114 - [i30]Haw-Shiuan Chang, Abdurrahman Munir, Ao Liu, Johnny Tian-Zheng Wei, Aaron Traylor, Ajay Nagesh, Nicholas Monath, Patrick Verga, Emma Strubell, Andrew McCallum:
Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema. TAC 2016 - [i29]Patrick Verga, Andrew McCallum:
Row-less Universal Schema. CoRR abs/1604.06361 (2016) - [i28]Patrick Verga, Arvind Neelakantan, Andrew McCallum:
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema. CoRR abs/1606.05804 (2016) - [i27]Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum:
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks. CoRR abs/1607.01426 (2016) - [i26]Trapit Bansal, David Belanger, Andrew McCallum:
Ask the GRU: Multi-Task Learning for Deep Text Recommendations. CoRR abs/1609.02116 (2016) - [i25]Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, Dario Amodei:
Learning a Natural Language Interface with Neural Programmer. CoRR abs/1611.08945 (2016) - 2015
- [j20]Nitin Agarwal, Sean Andrist, Dan Bohus, Fei Fang, Laurie Fenstermacher, Lalana Kagal, Takashi Kido, Christopher Kiekintveld, William F. Lawless, Huan Liu, Andrew McCallum, Hemant Purohit, Oshani Seneviratne, Keiki Takadama, Gavin Taylor:
Reports on the 2015 AAAI Spring Symposium Series. AI Mag. 36(3): 113-119 (2015) - [c141]Arvind Neelakantan, Benjamin Roth, Andrew McCallum:
Compositional Vector Space Models for Knowledge Base Inference. AAAI Spring Symposia 2015 - [c140]Emma Strubell, Luke Vilnis, Kate Silverstein, Andrew McCallum:
Learning Dynamic Feature Selection for Fast Sequential Prediction. ACL (1) 2015: 146-155 - [c139]Arvind Neelakantan, Benjamin Roth, Andrew McCallum:
Compositional Vector Space Models for Knowledge Base Completion. ACL (1) 2015: 156-166 - [c138]Andrew McCallum:
Embedded Representations of Lexical and Knowledge-Base Semantics. ICTIR 2015: 1 - [c137]Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum:
Bethe Projections for Non-Local Inference. UAI 2015: 892-901 - [c136]Luke Vilnis, Andrew McCallum:
Word Representations via Gaussian Embedding. ICLR 2015 - [i24]Benjamin Roth, Nicholas Monath, David Belanger, Emma Strubell, Patrick Verga, Andrew McCallum:
Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches. TAC 2015 - [i23]Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum:
Bethe Projections for Non-Local Inference. CoRR abs/1503.01397 (2015) - [i22]Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, Andrew McCallum:
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space. CoRR abs/1504.06654 (2015) - [i21]Arvind Neelakantan, Benjamin Roth, Andrew McCallum:
Compositional Vector Space Models for Knowledge Base Completion. CoRR abs/1504.06662 (2015) - [i20]Emma Strubell, Luke Vilnis, Kate Silverstein, Andrew McCallum:
Learning Dynamic Feature Selection for Fast Sequential Prediction. CoRR abs/1505.06169 (2015) - [i19]David Belanger, Andrew McCallum:
Structured Prediction Energy Networks. CoRR abs/1511.06350 (2015) - [i18]Patrick Verga, David Belanger, Emma Strubell, Benjamin Roth, Andrew McCallum:
Multilingual Relation Extraction using Compositional Universal Schema. CoRR abs/1511.06396 (2015) - 2014
- [c135]Sam Anzaroot, Alexandre Passos, David Belanger, Andrew McCallum:
Learning Soft Linear Constraints with Application to Citation Field Extraction. ACL (1) 2014: 593-602 - [c134]Alexandre Passos, Vineet Kumar, Andrew McCallum:
Lexicon Infused Phrase Embeddings for Named Entity Resolution. CoNLL 2014: 78-86 - [c133]Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, Andrew McCallum:
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space. EMNLP 2014: 1059-1069 - [c132]David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum:
Message Passing for Soft Constraint Dual Decomposition. UAI 2014: 62-71 - [i17]Sam Anzaroot, Alexandre Passos, David Belanger, Andrew McCallum:
Learning Soft Linear Constraints with Application to Citation Field Extraction. CoRR abs/1403.1349 (2014) - [i16]Alexandre Passos, Vineet Kumar, Andrew McCallum:
Lexicon Infused Phrase Embeddings for Named Entity Resolution. CoRR abs/1404.5367 (2014) - [i15]Emma Strubell, Luke Vilnis, Andrew McCallum:
Training for Fast Sequential Prediction Using Dynamic Feature Selection. CoRR abs/1410.8498 (2014) - 2013
- [c131]Jinho D. Choi, Andrew McCallum:
Transition-based Dependency Parsing with Selectional Branching. ACL (1) 2013: 1052-1062 - [c130]Sameer Singh, Sebastian Riedel, Brian Martin, Jiaping Zheng, Andrew McCallum:
Joint inference of entities, relations, and coreference. AKBC@CIKM 2013: 1-6 - [c129]Michael L. Wick, Sameer Singh, Ari Kobren, Andrew McCallum:
Assessing confidence of knowledge base content with an experimental study in entity resolution. AKBC@CIKM 2013: 13-18 - [c128]Michael L. Wick, Sameer Singh, Harshal Pandya, Andrew McCallum:
A joint model for discovering and linking entities. AKBC@CIKM 2013: 67-72 - [c127]Limin Yao, Sebastian Riedel, Andrew McCallum:
Universal schema for entity type prediction. AKBC@CIKM 2013: 79-84 - [c126]Jiaping Zheng, Luke Vilnis, Sameer Singh, Jinho D. Choi, Andrew McCallum:
Dynamic Knowledge-Base Alignment for Coreference Resolution. CoNLL 2013: 153-162 - [c125]Sebastian Riedel, Limin Yao, Andrew McCallum, Benjamin M. Marlin:
Relation Extraction with Matrix Factorization and Universal Schemas. HLT-NAACL 2013: 74-84 - [c124]Sebastian Riedel, Limin Yao, Andrew McCallum:
Latent Relation Representations for Universal Schemas. ICLR (Workshop Poster) 2013 - [i14]Sameer Singh, Limin Yao, David Belanger, Ari Kobren, Sam Anzaroot, Mike Wick, Alexandre Passos, Harshal Pandya, Jinho D. Choi, Brian Martin, Andrew McCallum:
Universal Schema for Slot Filling and Cold Start: UMass IESL at TACKBP 2013. TAC 2013 - [i13]David M. Blei, J. Andrew Bagnell, Andrew McCallum:
Learning with Scope, with Application to Information Extraction and Classification. CoRR abs/1301.0556 (2013) - [i12]Sameer Singh, Sebastian Riedel, Andrew McCallum:
Anytime Belief Propagation Using Sparse Domains. CoRR abs/1311.3368 (2013) - 2012
- [j19]David McClosky, Sebastian Riedel, Mihai Surdeanu, Andrew McCallum, Christopher D. Manning:
Combining joint models for biomedical event extraction. BMC Bioinform. 13(S-11): S9 (2012) - [j18]Charles Sutton, Andrew McCallum:
An Introduction to Conditional Random Fields. Found. Trends Mach. Learn. 4(4): 267-373 (2012) - [c123]Michael L. Wick, Sameer Singh, Andrew McCallum:
A Discriminative Hierarchical Model for Fast Coreference at Large Scale. ACL (1) 2012: 379-388 - [c122]Limin Yao, Sebastian Riedel, Andrew McCallum:
Unsupervised Relation Discovery with Sense Disambiguation. ACL (1) 2012: 712-720 - [c121]Sebastian Riedel, David A. Smith, Andrew McCallum:
Parse, Price and Cut--Delayed Column and Row Generation for Graph Based Parsers. EMNLP-CoNLL 2012: 732-743 - [c120]Sameer Singh, Michael L. Wick, Andrew McCallum:
Monte Carlo MCMC: Efficient Inference by Approximate Sampling. EMNLP-CoNLL 2012: 1104-1113 - [c119]Anton Bakalov, Andrew McCallum, Hanna M. Wallach, David M. Mimno:
Topic models for taxonomies. JCDL 2012: 237-240 - [c118]Michael L. Wick, Karl Schultz, Andrew McCallum:
Human-Machine Cooperation: Supporting User Corrections to Automatically Constructed KBs. AKBC-WEKEX@NAACL-HLT 2012: 89-94 - [c117]Sameer Singh, Michael L. Wick, Andrew McCallum:
Monte Carlo MCMC: Efficient Inference by Sampling Factors. AKBC-WEKEX@NAACL-HLT 2012: 111-115 - [c116]Limin Yao, Sebastian Riedel, Andrew McCallum:
Probabilistic Databases of Universal Schema. AKBC-WEKEX@NAACL-HLT 2012: 116-121 - [c115]David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum:
MAP Inference in Chains using Column Generation. NIPS 2012: 1853-1861 - [c114]Pallika H. Kanani, Andrew McCallum:
Selecting actions for resource-bounded information extraction using reinforcement learning. WSDM 2012: 253-262 - [i11]Sebastian Riedel, David A. Smith, Andrew McCallum:
Inference by Minimizing Size, Divergence, or their Sum. CoRR abs/1203.3511 (2012) - [i10]Kedar Bellare, Gregory Druck, Andrew McCallum:
Alternating Projections for Learning with Expectation Constraints. CoRR abs/1205.2660 (2012) - [i9]David M. Mimno, Andrew McCallum:
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression. CoRR abs/1206.3278 (2012) - [i8]Wei Li, David M. Blei, Andrew McCallum:
Nonparametric Bayes Pachinko Allocation. CoRR abs/1206.5270 (2012) - [i7]Charles Sutton, Andrew McCallum:
Improved Dynamic Schedules for Belief Propagation. CoRR abs/1206.5291 (2012) - [i6]Andrew McCallum, Kedar Bellare, Fernando C. N. Pereira:
A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance. CoRR abs/1207.1406 (2012) - [i5]Charles Sutton, Andrew McCallum:
Piecewise Training for Undirected Models. CoRR abs/1207.1409 (2012) - [i4]Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay:
An Integrated, Conditional Model of Information Extraction and Coreference with Applications to Citation Matching. CoRR abs/1207.4157 (2012) - [i3]Andrew McCallum:
Efficiently Inducing Features of Conditional Random Fields. CoRR abs/1212.2504 (2012) - 2011
- [c113]Sameer Singh, Amarnag Subramanya, Fernando C. N. Pereira, Andrew McCallum:
Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models. ACL 2011: 793-803 - [c112]Sebastian Riedel, Andrew McCallum:
Robust Biomedical Event Extraction with Dual Decomposition and Minimal Domain Adaptation. BioNLP@ACL (Shared Task) 2011: 46-50 - [c111]Sebastian Riedel, David McClosky, Mihai Surdeanu, Andrew McCallum, Christopher D. Manning:
Model Combination for Event Extraction in BioNLP 2011. BioNLP@ACL (Shared Task) 2011: 51-55 - [c110]Gregory Druck, Andrew McCallum:
Toward interactive training and evaluation. CIKM 2011: 947-956 - [c109]Sebastian Riedel, Andrew McCallum:
Fast and Robust Joint Models for Biomedical Event Extraction. EMNLP 2011: 1-12 - [c108]David M. Mimno, Hanna M. Wallach, Edmund M. Talley, Miriam Leenders, Andrew McCallum:
Optimizing Semantic Coherence in Topic Models. EMNLP 2011: 262-272 - [c107]Limin Yao, Aria Haghighi, Sebastian Riedel, Andrew McCallum:
Structured Relation Discovery using Generative Models. EMNLP 2011: 1456-1466 - [c106]Michael L. Wick, Khashayar Rohanimanesh, Kedar Bellare, Aron Culotta, Andrew McCallum:
SampleRank: Training Factor Graphs with Atomic Gradients. ICML 2011: 777-784 - [c105]Michael L. Wick, Andrew McCallum:
Query-Aware MCMC. NIPS 2011: 2564-2572 - 2010
- [j17]Gideon S. Mann, Andrew McCallum:
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data. J. Mach. Learn. Res. 11: 955-984 (2010) - [j16]Michael L. Wick, Andrew McCallum, Gerome Miklau:
Scalable Probabilistic Databases with Factor Graphs and MCMC. Proc. VLDB Endow. 3(1): 794-804 (2010) - [c104]Benjamin Roth, Andrew McCallum, Marc Dymetman, Nicola Cancedda:
Machine Translation Using Overlapping Alignments and SampleRank. AMTA 2010 - [c103]Limin Yao, Sebastian Riedel, Andrew McCallum:
Collective Cross-Document Relation Extraction Without Labelled Data. EMNLP 2010: 1013-1023 - [c102]Gregory Druck, Andrew McCallum:
High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models. ICML 2010: 319-326 - [c101]Sameer Singh, Limin Yao, Sebastian Riedel, Andrew McCallum:
Constraint-Driven Rank-Based Learning for Information Extraction. HLT-NAACL 2010: 729-732 - [c100]Pallika H. Kanani, Andrew McCallum, Shaohan Hu:
Resource-Bounded Information Extraction: Acquiring Missing Feature Values on Demand. PAKDD (1) 2010: 415-427 - [c99]Sebastian Riedel, Limin Yao, Andrew McCallum:
Modeling Relations and Their Mentions without Labeled Text. ECML/PKDD (3) 2010: 148-163 - [c98]Sebastian Riedel, David A. Smith, Andrew McCallum:
Inference by Minimizing Size, Divergence, or their Sum. UAI 2010: 492-499 - [i2]Michael L. Wick, Andrew McCallum, Gerome Miklau:
Scalable Probabilistic Databases with Factor Graphs and MCMC. CoRR abs/1005.1934 (2010) - [i1]Sameer Singh, Michael L. Wick, Andrew McCallum:
Distantly Labeling Data for Large Scale Cross-Document Coreference. CoRR abs/1005.4298 (2010)
2000 – 2009
- 2009
- [j15]Charles Sutton, Andrew McCallum:
Piecewise training for structured prediction. Mach. Learn. 77(2-3): 165-194 (2009) - [c97]Gregory Druck, Gideon S. Mann, Andrew McCallum:
Semi-supervised Learning of Dependency Parsers using Generalized Expectation Criteria. ACL/IJCNLP 2009: 360-368 - [c96]Andrew McCallum:
Joint Inference for Natural Language Processing. CoNLL 2009: 1 - [c95]Gregory Druck, Burr Settles, Andrew McCallum:
Active Learning by Labeling Features. EMNLP 2009: 81-90 - [c94]Kedar Bellare, Andrew McCallum:
Generalized Expectation Criteria for Bootstrapping Extractors using Record-Text Alignment. EMNLP 2009: 131-140 - [c93]David M. Mimno, Hanna M. Wallach, Jason Naradowsky, David A. Smith, Andrew McCallum:
Polylingual Topic Models. EMNLP 2009: 880-889 - [c92]Limin Yao, David M. Mimno, Andrew McCallum:
Efficient methods for topic model inference on streaming document collections. KDD 2009: 937-946 - [c91]Andrew McCallum, Karl Schultz, Sameer Singh:
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs. NIPS 2009: 1249-1257 - [c90]Hanna M. Wallach, David M. Mimno, Andrew McCallum:
Rethinking LDA: Why Priors Matter. NIPS 2009: 1973-1981 - [c89]Michael L. Wick, Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum:
Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference. NIPS 2009: 2044-2052 - [c88]Sameer Singh, Karl Schultz, Andrew McCallum:
Bi-directional Joint Inference for Entity Resolution and Segmentation Using Imperatively-Defined Factor Graphs. ECML/PKDD (2) 2009: 414-429 - [c87]Michael L. Wick, Aron Culotta, Khashayar Rohanimanesh, Andrew McCallum:
An Entity Based Model for Coreference Resolution. SDM 2009: 365-376 - [c86]Kedar Bellare, Gregory Druck, Andrew McCallum:
Alternating Projections for Learning with Expectation Constraints. UAI 2009: 43-50 - [e2]Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen:
Advances in Web Mining and Web Usage Analysis, 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop on Social Networks Analysis, SNA-KDD 2007, San Jose, CA, USA, August 12-15, 2007. Revised Papers. Lecture Notes in Computer Science 5439, Springer 2009, ISBN 978-3-642-00527-5 [contents] - 2008
- [c85]Gideon S. Mann, Andrew McCallum:
Generalized Expectation Criteria for Semi-Supervised Learning of Conditional Random Fields. ACL 2008: 870-878 - [c84]Rebecca Reznik-Zellen, Bob Stevens, Michael Thorn, Jeff Morse, Mark D. Smucker, James Allan, David M. Mimno, Andrew McCallum, Mark Tuominen:
InterNano: e-Science for the Nanomanufacturing Community. eScience 2008: 382-383 - [c83]Robert J. Hall, Charles Sutton, Andrew McCallum:
Unsupervised deduplication using cross-field dependencies. KDD 2008: 310-317 - [c82]Michael L. Wick, Khashayar Rohanimanesh, Karl Schultz, Andrew McCallum:
A unified approach for schema matching, coreference and canonicalization. KDD 2008: 722-730 - [c81]Michael L. Wick, Khashayar Rohanimanesh, Andrew McCallum, AnHai Doan:
A Discriminative Approach to Ontology Mapping. NTII 2008: 16-19 - [c80]Gregory Druck, Gideon S. Mann, Andrew McCallum:
Learning from labeled features using generalized expectation criteria. SIGIR 2008: 595-602 - [c79]David M. Mimno, Andrew McCallum:
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression. UAI 2008: 411-418 - [e1]William W. Cohen, Andrew McCallum, Sam T. Roweis:
Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008. ACM International Conference Proceeding Series 307, ACM 2008, ISBN 978-1-60558-205-4 [contents] - 2007
- [j14]Andrew McCallum, Xuerui Wang, Andrés Corrada-Emmanuel:
Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email. J. Artif. Intell. Res. 30: 249-272 (2007) - [j13]Charles Sutton, Andrew McCallum, Khashayar Rohanimanesh:
Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data. J. Mach. Learn. Res. 8: 693-723 (2007) - [j12]Haizheng Zhang, John Yen, C. Lee Giles, Bamshad Mobasher, Myra Spiliopoulou, Jaideep Srivastava, Olfa Nasraoui, Andrew McCallum:
WebKDD/SNAKDD 2007: web mining and social network analysis post-workshop report. SIGKDD Explor. 9(2): 87-92 (2007) - [c78]Pallika H. Kanani, Andrew McCallum:
Resource-Bounded Information Gathering for Correlation Clustering. COLT 2007: 625-627 - [c77]Vidit Jain, Erik G. Learned-Miller, Andrew McCallum:
People-LDA: Anchoring Topics to People using Face Recognition. ICCV 2007: 1-8 - [c76]Gary B. Huang, Erik G. Learned-Miller, Andrew McCallum:
Cryptogram Decoding for OCR Using Numerization Strings. ICDAR 2007: 208-212 - [c75]Xuerui Wang, Andrew McCallum, Xing Wei:
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval. ICDM 2007: 697-702 - [c74]Gideon S. Mann, Andrew McCallum:
Simple, robust, scalable semi-supervised learning via expectation regularization. ICML 2007: 593-600 - [c73]David M. Mimno, Wei Li, Andrew McCallum:
Mixtures of hierarchical topics with Pachinko allocation. ICML 2007: 633-640 - [c72]Charles Sutton, Andrew McCallum:
Piecewise pseudolikelihood for efficient training of conditional random fields. ICML 2007: 863-870 - [c71]Pallika H. Kanani, Andrew McCallum, Chris Pal:
Improving Author Coreference by Resource-Bounded Information Gathering from the Web. IJCAI 2007: 429-434 - [c70]David M. Mimno, Andrew McCallum:
Mining a digital library for influential authors. JCDL 2007: 105-106 - [c69]David M. Mimno, Andrew McCallum:
Organizing the OCA: learning faceted subjects from a library of digital books. JCDL 2007: 376-385 - [c68]Aron Culotta, Michael L. Wick, Robert J. Hall, Matthew Marzilli, Andrew McCallum:
Canonicalization of database records using adaptive similarity measures. KDD 2007: 201-209 - [c67]Gregory Druck, Chris Pal, Andrew McCallum, Xiaojin Zhu:
Semi-supervised classification with hybrid generative/discriminative methods. KDD 2007: 280-289 - [c66]David M. Mimno, Andrew McCallum:
Expertise modeling for matching papers with reviewers. KDD 2007: 500-509 - [c65]Xuerui Wang, Chris Pal, Andrew McCallum:
Generalized component analysis for text with heterogeneous attributes. KDD 2007: 794-803 - [c64]Mary P. Harper, Alex Acero, Srinivas Bangalore, Jaime Carbonell, Jordan Cohen, Barbara Cuthill, Carol Y. Espy-Wilson, Christiane Fellbaum, John Garofolo, Chin-Hui Lee, Jim Lester, Andrew McCallum, Nelson Morgan, Michael Picheney, Joe Picone, Lance Ramshaw, Jeffrey C. Reynar, Hadar Shemtov, Clare Voss:
Report on the NSF-sponsored Human Language Technology Workshop on Industrial Centers. MTSummit 2007 - [c63]Aron Culotta, Michael L. Wick, Andrew McCallum:
First-Order Probabilistic Models for Coreference Resolution. HLT-NAACL 2007: 81-88 - [c62]Gideon S. Mann, Andrew McCallum:
Efficient Computation of Entropy Gradient for Semi-Supervised Conditional Random Fields. HLT-NAACL (Short Papers) 2007: 109-112 - [c61]Wei Li, David M. Blei, Andrew McCallum:
Nonparametric Bayes Pachinko Allocation. UAI 2007: 243-250 - [c60]Charles Sutton, Andrew McCallum:
Improved Dynamic Schedules for Belief Propagation. UAI 2007: 376-383 - 2006
- [j11]Aron Culotta, Trausti T. Kristjansson, Andrew McCallum, Paul A. Viola:
Corrective feedback and persistent learning for information extraction. Artif. Intell. 170(14-15): 1101-1122 (2006) - [j10]Fuchun Peng, Andrew McCallum:
Information extraction from research papers using conditional random fields. Inf. Process. Manag. 42(4): 963-979 (2006) - [j9]Xing Wei, W. Bruce Croft, Andrew McCallum:
Table extraction for answer retrieval. Inf. Retr. 9(5): 589-611 (2006) - [c59]Andrew McCallum, Chris Pal, Gregory Druck, Xuerui Wang:
Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification. AAAI 2006: 433-439 - [c58]Shaolei Feng, R. Manmatha, Andrew McCallum:
Exploring the Use of Conditional Random Field Models and HMMs for Historical Handwritten Document Recognition. DIAL 2006: 30-37 - [c57]Michael L. Wick, Aron Culotta, Andrew McCallum:
Learning Field Compatibilities to Extract Database Records from Unstructured Text. EMNLP 2006: 603-611 - [c56]Chris Pal, Charles Sutton, Andrew McCallum:
Sparse Forward-Backward Using Minimum Divergence Beams for Fast Training Of Conditional Random Fields. ICASSP (5) 2006: 581-584 - [c55]Andrew McCallum, Xuerui Wang, Natasha Mohanty:
Joint Group and Topic Discovery from Relations and Text. SNA@ICML 2006: 28-44 - [c54]Wei Li, Andrew McCallum:
Pachinko allocation: DAG-structured mixture models of topic correlations. ICML 2006: 577-584 - [c53]B. Michael Kelm, Chris Pal, Andrew McCallum:
Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning. ICPR (2) 2006: 828-832 - [c52]Gideon S. Mann, David M. Mimno, Andrew McCallum:
Bibliometric impact measures leveraging topic analysis. JCDL 2006: 65-74 - [c51]Xuerui Wang, Andrew McCallum:
Topics over time: a non-Markov continuous-time model of topical trends. KDD 2006: 424-433 - [c50]Andrew McCallum:
Information extraction, data mining and joint inference. KDD 2006: 835 - [c49]Aron Culotta, Andrew McCallum, Jonathan Betz:
Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text. HLT-NAACL 2006 - [c48]Charles Sutton, Michael Sindelar, Andrew McCallum:
Reducing Weight Undertraining in Structured Discriminative Learning. HLT-NAACL 2006 - [p1]Kamal Nigam, Andrew McCallum, Tom M. Mitchell:
Semi-Supervised Text Classification Using EM. Semi-Supervised Learning 2006: 32-55 - 2005
- [j8]Andrew McCallum:
Information extraction: distilling structured data from unstructured text. ACM Queue 3(9): 48-57 (2005) - [c47]Aron Culotta, Andrew McCallum:
Reducing Labeling Effort for Structured Prediction Tasks. AAAI 2005: 746-751 - [c46]Wei Li, Andrew McCallum:
Semi-Supervised Sequence Modeling with Syntactic Topic Models. AAAI 2005: 813-818 - [c45]Nadia Ghamrawi, Andrew McCallum:
Collective multi-label classification. CIKM 2005: 195-200 - [c44]Aron Culotta, Andrew McCallum:
Joint deduplication of multiple record types in relational data. CIKM 2005: 257-258 - [c43]Charles Sutton, Andrew McCallum:
Joint Parsing and Semantic Role Labeling. CoNLL 2005: 225-228 - [c42]Ron Bekkerman, Ran El-Yaniv, Andrew McCallum:
Multi-way distributional clustering via pairwise interactions. ICML 2005: 41-48 - [c41]Andrew McCallum, Andrés Corrada-Emmanuel, Xuerui Wang:
Topic and Role Discovery in Social Networks. IJCAI 2005: 786-791 - [c40]Yu Gu, Andrew McCallum, Donald F. Towsley:
Detecting Anomalies in Network Traffic Using Maximum Entropy Estimation. Internet Measurement Conference 2005: 345-350 - [c39]Xuerui Wang, Natasha Mohanty, Andrew McCallum:
Group and topic discovery from relations and text. LinkKDD 2005: 28-35 - [c38]Charles Sutton, Andrew McCallum:
Composition of Conditional Random Fields for Transfer Learning. HLT/EMNLP 2005: 748-754 - [c37]Xuerui Wang, Natasha Mohanty, Andrew McCallum:
Group and Topic Discovery from Relations and Their Attributes. NIPS 2005: 1449-1456 - [c36]Andrew McCallum, Kedar Bellare, Fernando C. N. Pereira:
A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance. UAI 2005: 388-395 - [c35]Charles Sutton, Andrew McCallum:
Piecewise Training for Undirected Models. UAI 2005: 568-575 - [c34]Ron Bekkerman, Andrew McCallum:
Disambiguating Web appearances of people in a social network. WWW 2005: 463-470 - 2004
- [c33]Trausti T. Kristjansson, Aron Culotta, Paul A. Viola, Andrew McCallum:
Interactive Information Extraction with Constrained Conditional Random Fields. AAAI 2004: 412-418 - [c32]Aron Culotta, Ron Bekkerman, Andrew McCallum:
Extracting social networks and contact information from email and the Web. CEAS 2004 - [c31]Fuchun Peng, Fangfang Feng, Andrew McCallum:
Chinese Segmentation and New Word Detection using Conditional Random Fields. COLING 2004 - [c30]Charles Sutton, Khashayar Rohanimanesh, Andrew McCallum:
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. ICML 2004 - [c29]Aron Culotta, Andrew McCallum:
Confidence Estimation for Information Extraction. HLT-NAACL (Short Papers) 2004 - [c28]Fuchun Peng, Andrew McCallum:
Accurate Information Extraction from Research Papers using Conditional Random Fields. HLT-NAACL 2004: 329-336 - [c27]Andrew McCallum, Ben Wellner:
Conditional Models of Identity Uncertainty with Application to Noun Coreference. NIPS 2004: 905-912 - [c26]Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay:
An Integrated, Conditional Model of Information Extraction and Coreference with Appli. UAI 2004: 593-601 - 2003
- [j7]James Allan, Jay Aslam, Nicholas J. Belkin, Chris Buckley, James P. Callan, W. Bruce Croft, Susan T. Dumais, Norbert Fuhr, Donna Harman, David J. Harper, Djoerd Hiemstra, Thomas Hofmann, Eduard H. Hovy, Wessel Kraaij, John D. Lafferty, Victor Lavrenko, David D. Lewis, Liz Liddy, R. Manmatha, Andrew McCallum, Jay M. Ponte, John M. Prager, Dragomir R. Radev, Philip Resnik, Stephen E. Robertson, Ronald Rosenfeld, Salim Roukos, Mark Sanderson, Richard M. Schwartz, Amit Singhal, Alan F. Smeaton, Howard R. Turtle, Ellen M. Voorhees, Ralph M. Weischedel, Jinxi Xu, ChengXiang Zhai:
Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002. SIGIR Forum 37(1): 31-47 (2003) - [j6]Wei Li, Andrew McCallum:
Rapid development of Hindi named entity recognition using conditional random fields and feature induction. ACM Trans. Asian Lang. Inf. Process. 2(3): 290-294 (2003) - [c25]Andrew McCallum, Wei Li:
Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons. CoNLL 2003: 188-191 - [c24]David Pinto, Andrew McCallum, Xing Wei, W. Bruce Croft:
Table Extraction Using Conditional Random Fields. DG.O 2003 - [c23]Andrew McCallum, Ben Wellner:
Toward Conditional Models of Identity Uncertainty with Application to Proper Noun Coreference. IIWeb 2003: 79-84 - [c22]Rajat Raina, Yirong Shen, Andrew Y. Ng, Andrew McCallum:
Classification with Hybrid Generative/Discriminative Models. NIPS 2003: 545-552 - [c21]David Pinto, Andrew McCallum, Xing Wei, W. Bruce Croft:
Table extraction using conditional random fields. SIGIR 2003: 235-242 - [c20]Andrew McCallum:
Efficiently Inducing Features of Conditional Random Fields. UAI 2003: 403-410 - 2002
- [c19]David M. Blei, J. Andrew Bagnell, Andrew Kachites McCallum:
Learning with Scope, with Application to Information Extraction and Classification. UAI 2002: 53-60 - 2001
- [c18]John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira:
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. ICML 2001: 282-289 - [c17]Nicholas Roy, Andrew McCallum:
Toward Optimal Active Learning through Sampling Estimation of Error Reduction. ICML 2001: 441-448 - 2000
- [j5]Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, Kamal Nigam, Seán Slattery:
Learning to construct knowledge bases from the World Wide Web. Artif. Intell. 118(1-2): 69-113 (2000) - [j4]William W. Cohen, Andrew McCallum, Dallan Quass:
Learning to Understand the Web. IEEE Data Eng. Bull. 23(3): 17-24 (2000) - [j3]Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore:
Automating the Construction of Internet Portals with Machine Learning. Inf. Retr. 3(2): 127-163 (2000) - [j2]Kamal Nigam, Andrew Kachites McCallum, Sebastian Thrun, Tom M. Mitchell:
Text Classification from Labeled and Unlabeled Documents using EM. Mach. Learn. 39(2/3): 103-134 (2000) - [c16]Dayne Freitag, Andrew McCallum:
Information Extraction with HMM Structures Learned by Stochastic Optimization. AAAI/IAAI 2000: 584-589 - [c15]Huan Chang, David Cohn, Andrew McCallum:
Learning to Create Customized Authority Lists. ICML 2000: 127-134 - [c14]Andrew McCallum, Dayne Freitag, Fernando C. N. Pereira:
Maximum Entropy Markov Models for Information Extraction and Segmentation. ICML 2000: 591-598 - [c13]Andrew McCallum, Kamal Nigam, Lyle H. Ungar:
Efficient clustering of high-dimensional data sets with application to reference matching. KDD 2000: 169-178
1990 – 1999
- 1999
- [c12]Jason Rennie, Andrew Kachites McCallum:
Using Reinforcement Learning to Spider the Web Efficiently. ICML 1999: 335-343 - [c11]Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore:
A Machine Learning Approach to Building Domain-Specific Search Engines. IJCAI 1999: 662-667 - 1998
- [c10]Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, Kamal Nigam, Seán Slattery:
Learning to Extract Symbolic Knowledge from the World Wide Web. AAAI/IAAI 1998: 509-516 - [c9]Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom M. Mitchell:
Learning to Classify Text from Labeled and Unlabeled Documents. AAAI/IAAI 1998: 792-799 - [c8]Andrew Kachites McCallum, Kamal Nigam:
Employing EM and Pool-Based Active Learning for Text Classification. ICML 1998: 350-358 - [c7]Andrew McCallum, Ronald Rosenfeld, Tom M. Mitchell, Andrew Y. Ng:
Improving Text Classification by Shrinkage in a Hierarchy of Classes. ICML 1998: 359-367 - [c6]L. Douglas Baker, Andrew Kachites McCallum:
Distributional Clustering of Words for Text Classification. SIGIR 1998: 96-103 - 1996
- [j1]R. Andrew McCallum:
Hidden state and reinforcement learning with instance-based state identification. IEEE Trans. Syst. Man Cybern. Part B 26(3): 464-473 (1996) - 1995
- [c5]R. Andrew McCallum:
Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State. ICML 1995: 387-395 - 1994
- [c4]R. Andrew McCallum:
Instance-Based State Identification for Reinforcement Learning. NIPS 1994: 377-384 - 1993
- [c3]R. Andrew McCallum:
Overcoming Incomplete Perception with Utile Distinction Memory. ICML 1993: 190-196 - 1992
- [c2]R. Andrew McCallum:
Using Transitional Proximity for Faster Reinforcement Learning. ML 1992: 316-321 - 1990
- [c1]R. Andrew McCallum, Kent A. Spackman:
Using Genetic Algorithms to Learn Disjunctive Rules from Examples. ML 1990: 149-152
Coauthor Index
Ari Kobren
aka: Ariel Kobren
aka: Ariel Kobren
Jay-Yoon Lee
aka: Jay Yoon Lee
aka: Jay Yoon Lee
Xiang Li 0069
aka: Xiang Lorraine Li
aka: Xiang Lorraine Li
[j23] [c277] [c275] [c273] [i127] [i126] [i123] [i122] [c267] [c266] [c264] [c260] [i119] [i117] [c256] [c255] [c249] [c246] [c240] [i102] [i100] [c233] [c230] [c229] [c228] [c220] [c218] [c217] [c215] [c212] [i98] [i91] [i87] [i82] [c206] [c205] [c199] [i81] [i80] [i78] [i72] [i70] [i69] [c193] [c190] [c184] [c183] [c181] [i61] [i60] [c165] [c160] [c158] [c150] [i43] [i30] [i24]
Alexandre Tachard Passos
aka: Alexandre Passos
aka: Alexandre Passos
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