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Jay Pujara
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- affiliation: University of California, Santa Cruz, USA
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2020 – today
- 2024
- [j7]Ju-Hyung Lee
, Dong-Ho Lee, Joohan Lee
, Jay Pujara:
Integrating Pre-Trained Language Model With Physical Layer Communications. IEEE Trans. Wirel. Commun. 23(11): 17266-17278 (2024) - [c67]Saurav Joshi, Filip Ilievski
, Jay Pujara:
Knowledge-Powered Recommendation for an Improved Diet Water Footprint. AAAI 2024: 23805-23807 - [c66]Pegah Jandaghi, XiangHai Sheng, Xinyi Bai, Jay Pujara, Hakim Sidahmed:
Faithful Persona-based Conversational Dataset Generation with Large Language Models. ACL (Findings) 2024: 15245-15270 - [c65]Kexuan Sun, Nicolaas Paul Jedema, Karishma Sharma, Ruben Janssen, Jay Pujara, Pedro A. Szekely, Alessandro Moschitti:
Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering. LREC/COLING 2024: 5585-5595 - [c64]Kian Ahrabian, Alon Benhaim, Barun Patra, Jay Pujara, Saksham Singhal, Xia Song:
On the Adaptation of Unlimiformer for Decoder-Only Transformers. LREC/COLING 2024: 12395-12402 - [i41]Kian Ahrabian, Zhivar Sourati
, Kexuan Sun, Jiarui Zhang, Yifan Jiang, Fred Morstatter, Jay Pujara:
The Curious Case of Nonverbal Abstract Reasoning with Multi-Modal Large Language Models. CoRR abs/2401.12117 (2024) - [i40]Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou, Swaroop Mishra, Huaixiu Steven Zheng:
Self-Discover: Large Language Models Self-Compose Reasoning Structures. CoRR abs/2402.03620 (2024) - [i39]Ju-Hyung Lee, Dong-Ho Lee, Joohan Lee, Jay Pujara:
Integrating Pre-Trained Language Model with Physical Layer Communications. CoRR abs/2402.11656 (2024) - [i38]Saurav Joshi, Filip Ilievski, Jay Pujara:
Knowledge-Powered Recommendation for an Improved Diet Water Footprint. CoRR abs/2403.17426 (2024) - [i37]Yifan Jiang, Jiarui Zhang, Kexuan Sun, Zhivar Sourati, Kian Ahrabian, Kaixin Ma, Filip Ilievski, Jay Pujara:
MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning. CoRR abs/2404.13591 (2024) - [i36]Kian Ahrabian, Xihui Lin, Barun Patra, Vishrav Chaudhary, Alon Benhaim, Jay Pujara, Xia Song:
The Hitchhiker's Guide to Human Alignment with *PO. CoRR abs/2407.15229 (2024) - [i35]Kian Ahrabian, Casandra Rusti, Ziao Wang, Jay Pujara, Kristina Lerman:
Surprising Resilience of Science During a Global Pandemic: A Large-Scale Descriptive Analysis. CoRR abs/2409.07710 (2024) - [i34]Yifan Jiang, Kriti Aggarwal, Tanmay Laud, Kashif Munir, Jay Pujara, Subhabrata Mukherjee:
RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking. CoRR abs/2409.17458 (2024) - [i33]Kian Ahrabian, Alon Benhaim, Barun Patra, Jay Pujara, Saksham Singhal, Xia Song:
On The Adaptation of Unlimiformer for Decoder-Only Transformers. CoRR abs/2410.01637 (2024) - 2023
- [c63]Xinwei Du, Kian Ahrabian, Arun Baalaaji Sankar Ananthan, Richard Delwin Myloth, Jay Pujara:
Citation Intent Classification Through Weakly Supervised Knowledge Graphs. SDU@AAAI 2023 - [c62]Richard Delwin Myloth, Kian Ahrabian, Arun Baalaaji Sankar Ananthan, Xinwei Du, Jay Pujara:
Is Dynamicity All You Need? SDU@AAAI 2023 - [c61]Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya
, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren:
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models. ACL (demo) 2023: 264-273 - [c60]Pei Zhou, Andrew Zhu
, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj Ammanabrolu:
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons. ACL (1) 2023: 11136-11155 - [c59]Ju-Hyung Lee, Dong-Ho Lee, Eunsoo Sheen, Thomas Choi, Jay Pujara:
SEQ2SEQ-SC: End-To-End Semantic Communication Systems with Pre-Trained Language Model. ACSSC 2023: 260-264 - [c58]Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren:
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction. EACL 2023: 3003-3017 - [c57]Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, Jay Pujara:
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning. EMNLP 2023: 544-557 - [c56]Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Young Park, Minwoo Kim, Jonathan May
, Jay Pujara, Sungjoon Park:
Analyzing Norm Violations in Live-Stream Chat. EMNLP 2023: 852-868 - [c55]Avijit Thawani, Saurabh Ghanekar, Xiaoyuan Zhu, Jay Pujara:
Learn Your Tokens: Word-Pooled Tokenization for Language Modeling. EMNLP (Findings) 2023: 9883-9893 - [c54]Dong-Ho Lee, Jay Pujara, Mohit Sewak, Ryen White, Sujay Kumar Jauhar:
Making Large Language Models Better Data Creators. EMNLP 2023: 15349-15360 - [c53]Ana Iglesias-Molina
, Kian Ahrabian
, Filip Ilievski
, Jay Pujara
, Óscar Corcho
:
Comparison of Knowledge Graph Representations for Consumer Scenarios. ISWC 2023: 271-289 - [p1]Filip Ilievski, Kaixin Ma, Alessandro Oltramari, Peifeng Wang, Jay Pujara:
Building Robust and Explainable AI with Commonsense Knowledge Graphs and Neural Models. Compendium of Neurosymbolic Artificial Intelligence 2023: 178-209 - [i32]Kian Ahrabian, Xinwei Du, Richard Delwin Myloth, Arun Baalaaji Sankar Ananthan, Jay Pujara:
PubGraph: A Large Scale Scientific Temporal Knowledge Graph. CoRR abs/2302.02231 (2023) - [i31]Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, Jay Pujara:
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning. CoRR abs/2305.10613 (2023) - [i30]Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Young Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park:
Analyzing Norm Violations in Live-Stream Chat. CoRR abs/2305.10731 (2023) - [i29]Lee Kezar, Jay Pujara:
Finding Pragmatic Differences Between Disciplines. CoRR abs/2310.00204 (2023) - [i28]Pei Zhou, Aman Madaan, Srividya Pranavi Potharaju, Aditya Gupta, Kevin R. McKee, Ari Holtzman, Jay Pujara, Xiang Ren, Swaroop Mishra, Aida Nematzadeh, Shyam Upadhyay, Manaal Faruqui:
How FaR Are Large Language Models From Agents with Theory-of-Mind? CoRR abs/2310.03051 (2023) - [i27]Avijit Thawani, Jay Pujara, Ashwin Kalyan:
Estimating Numbers without Regression. CoRR abs/2310.06204 (2023) - [i26]Avijit Thawani, Saurabh Ghanekar
, Xiaoyuan Zhu, Jay Pujara:
Learn Your Tokens: Word-Pooled Tokenization for Language Modeling. CoRR abs/2310.11628 (2023) - [i25]Dong-Ho Lee, Jay Pujara, Mohit Sewak, Ryen W. White, Sujay Kumar Jauhar:
Making Large Language Models Better Data Creators. CoRR abs/2310.20111 (2023) - [i24]Pegah Jandaghi, XiangHai Sheng, Xinyi Bai, Jay Pujara, Hakim Sidahmed:
Faithful Persona-based Conversational Dataset Generation with Large Language Models. CoRR abs/2312.10007 (2023) - 2022
- [c52]Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur:
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation. ACL (1) 2022: 1237-1252 - [c51]Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya
, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren:
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER. ACL (1) 2022: 2687-2700 - [c50]Woojeong Jin, Dong-Ho Lee, Chenguang Zhu, Jay Pujara, Xiang Ren:
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer. ACL (1) 2022: 2750-2762 - [c49]Pei Zhou, Hyundong Cho, Pegah Jandaghi, Dong-Ho Lee, Bill Yuchen Lin, Jay Pujara, Xiang Ren:
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality. EMNLP 2022: 10450-10468 - [c48]Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang:
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue. EMNLP 2022: 10936-10953 - [c47]Filip Ilievski, Jay Pujara, Kartik Shenoy
:
Does Wikidata Support Analogical Reasoning? KGSWC 2022: 178-191 - [c46]Eriq Augustine, Connor Pryor, Charles Dickens, Jay Pujara, William Yang Wang, Lise Getoor:
Visual Sudoku Puzzle Classification: A Suite of Collective Neuro-Symbolic Tasks. NeSy 2022: 15-29 - [c45]Kexuan Sun, Zhiqiang Qiu, Abel Salinas, Yuzhong Huang, Dong-Ho Lee, Daniel Benjamin, Fred Morstatter, Xiang Ren, Kristina Lerman, Jay Pujara:
Assessing Scientific Research Papers with Knowledge Graphs. SIGIR 2022: 2467-2472 - [i23]Ehsan Qasemi, Lee Kezar, Jay Pujara, Pedro A. Szekely:
Evaluating Machine Common Sense via Cloze Testing. CoRR abs/2201.07902 (2022) - [i22]Woojeong Jin, Dong-Ho Lee, Chenguang Zhu, Jay Pujara, Xiang Ren:
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer. CoRR abs/2203.07519 (2022) - [i21]Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang:
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue. CoRR abs/2205.06262 (2022) - [i20]Thiloshon Nagarajah, Filip Ilievski, Jay Pujara:
Understanding Narratives through Dimensions of Analogy. CoRR abs/2206.07167 (2022) - [i19]Filip Ilievski, Jay Pujara, Kartik Shenoy:
Does Wikidata Support Analogical Reasoning? CoRR abs/2210.00620 (2022) - [i18]Ju-Hyung Lee, Dong-Ho Lee, Eunsoo Sheen, Thomas Choi, Jay Pujara, Joongheon Kim:
Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model. CoRR abs/2210.15237 (2022) - [i17]Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya
, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren:
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models. CoRR abs/2210.16978 (2022) - [i16]Pei Zhou, Hyundong Cho, Pegah Jandaghi, Dong-Ho Lee, Bill Yuchen Lin, Jay Pujara, Xiang Ren:
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality. CoRR abs/2211.09267 (2022) - [i15]Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj Ammanabrolu:
An AI Dungeon Master's Guide: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons. CoRR abs/2212.10060 (2022) - 2021
- [j6]Majid Ghasemi-Gol
, Jay Pujara, Pedro A. Szekely
:
Learning cell embeddings for understanding table layouts. Knowl. Inf. Syst. 63(1): 39-64 (2021) - [j5]Yolanda Gil
, Daniel Garijo, Deborah Khider, Craig A. Knoblock, Varun Ratnakar
, Maximiliano Osorio, Hernán Vargas, Minh Pham, Jay Pujara, Basel Shbita, Binh Vu, Yao-Yi Chiang, Dan Feldman, Yijun Lin, Hayley Song
, Vipin Kumar, Ankush Khandelwal, Michael S. Steinbach
, Kshitij Tayal, Shaoming Xu, Suzanne A. Pierce
, Lissa Pearson, Daniel Hardesty-Lewis, Ewa Deelman, Rafael Ferreira da Silva
, Rajiv Mayani, Armen R. Kemanian, Yuning Shi, Lorne Leonard, Scott D. Peckham, Maria Stoica, Kelly M. Cobourn, Zeya Zhang, Christopher J. Duffy, Lele Shu:
Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making. ACM Trans. Interact. Intell. Syst. 11(2): 11:1-11:49 (2021) - [c44]Kexuan Sun, Harsha Rayudu, Jay Pujara:
A Hybrid Probabilistic Approach for Table Understanding. AAAI 2021: 4366-4374 - [c43]Kexuan Sun, Fei Wang, Muhao Chen, Jay Pujara:
Tabular Functional Block Detection with Embedding-based Agglomerative Cell Clustering. CIKM 2021: 1744-1753 - [c42]Fei Wang, Kexuan Sun, Jay Pujara, Pedro A. Szekely, Muhao Chen:
Table-based Fact Verification With Salience-aware Learning. EMNLP (Findings) 2021: 4025-4036 - [c41]Pei Zhou, Pegah Jandaghi, Hyundong Cho, Bill Yuchen Lin, Jay Pujara, Xiang Ren:
Probing Commonsense Explanation in Dialogue Response Generation. EMNLP (Findings) 2021: 4132-4146 - [c40]Ninareh Mehrabi, Pei Zhou, Fred Morstatter, Jay Pujara, Xiang Ren, Aram Galstyan:
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources. EMNLP (1) 2021: 5016-5033 - [c39]Avijit Thawani, Jay Pujara, Filip Ilievski:
Numeracy enhances the Literacy of Language Models. EMNLP (1) 2021: 6960-6967 - [c38]Pei Zhou, Rahul Khanna, Seyeon Lee, Bill Yuchen Lin, Daniel Ho, Jay Pujara, Xiang Ren:
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms. EMNLP (1) 2021: 7560-7579 - [c37]Minh Pham, Craig A. Knoblock, Muhao Chen, Binh Vu, Jay Pujara:
SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables. IJCAI 2021: 3543-3551 - [c36]Jay Pujara, Pedro A. Szekely, Huan Sun, Muhao Chen:
From Tables to Knowledge: Recent Advances in Table Understanding. KDD 2021: 4060-4061 - [c35]Avijit Thawani
, Jay Pujara, Filip Ilievski, Pedro A. Szekely:
Representing Numbers in NLP: a Survey and a Vision. NAACL-HLT 2021: 644-656 - [c34]Binh Vu, Craig A. Knoblock, Pedro A. Szekely, Minh Pham, Jay Pujara:
A Graph-Based Approach for Inferring Semantic Descriptions of Wikipedia Tables. ISWC 2021: 304-320 - [c33]Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tür:
Commonsense-Focused Dialogues for Response Generation: An Empirical Study. SIGDIAL 2021: 121-132 - [c32]Fei Wang, Kexuan Sun, Muhao Chen, Jay Pujara, Pedro A. Szekely:
Retrieving Complex Tables with Multi-Granular Graph Representation Learning. SIGIR 2021: 1472-1482 - [i14]Ninareh Mehrabi, Pei Zhou, Fred Morstatter, Jay Pujara, Xiang Ren, Aram Galstyan:
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources. CoRR abs/2103.11320 (2021) - [i13]Avijit Thawani, Jay Pujara, Pedro A. Szekely, Filip Ilievski:
Representing Numbers in NLP: a Survey and a Vision. CoRR abs/2103.13136 (2021) - [i12]Pei Zhou, Pegah Jandaghi, Bill Yuchen Lin, Justin Cho, Jay Pujara, Xiang Ren:
Probing Causal Common Sense in Dialogue Response Generation. CoRR abs/2104.09574 (2021) - [i11]Fei Wang, Kexuan Sun, Muhao Chen, Jay Pujara, Pedro A. Szekely:
Retrieving Complex Tables with Multi-Granular Graph Representation Learning. CoRR abs/2105.01736 (2021) - [i10]Fei Wang, Kexuan Sun, Jay Pujara, Pedro A. Szekely, Muhao Chen:
Table-based Fact Verification with Salience-aware Learning. CoRR abs/2109.04053 (2021) - [i9]Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Mahak Agarwal, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren:
AutoTriggER: Named Entity Recognition with Auxiliary Trigger Extraction. CoRR abs/2109.04726 (2021) - [i8]Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur:
Commonsense-Focused Dialogues for Response Generation: An Empirical Study. CoRR abs/2109.06427 (2021) - [i7]Dong-Ho Lee, Mahak Agarwal, Akshen Kadakia, Jay Pujara, Xiang Ren:
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER. CoRR abs/2110.08454 (2021) - [i6]Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur:
Think Before You Speak: Using Self-talk to Generate Implicit Commonsense Knowledge for Response Generation. CoRR abs/2110.08501 (2021) - 2020
- [j4]Pigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, Lise Getoor:
Generating and Understanding Personalized Explanations in Hybrid Recommender Systems. ACM Trans. Interact. Intell. Syst. 10(4): 31:1-31:40 (2020) - [e2]Douglas Burdick, Jay Pujara:
Proceedings of the Sixth International Workshop on Data Science for Macro-Modeling, DSMM 2020, In conjunction with the ACM SIGMOD/PODS Conference, Portland, OR, USA, June 14, 2020. ACM 2020, ISBN 978-1-4503-8030-0 [contents] - [i5]Pegah Jandaghi, Jay Pujara:
Human-like Time Series Summaries via Trend Utility Estimation. CoRR abs/2001.05665 (2020) - [i4]Pei Zhou, Rahul Khanna, Bill Yuchen Lin, Daniel Ho, Xiang Ren, Jay Pujara:
Can BERT Reason? Logically Equivalent Probes for Evaluating the Inference Capabilities of Language Models. CoRR abs/2005.00782 (2020)
2010 – 2019
- 2019
- [j3]Jay Pujara, Kartik D. Kothari
, Ashish V. Gohil:
Statistical investigation of surface roughness and kerf on wire electrical discharge machining performance. Int. J. Manuf. Res. 14(3): 231-244 (2019) - [j2]Pigi Kouki
, Jay Pujara, Christopher Marcum
, Laura M. Koehly, Lise Getoor:
Collective entity resolution in multi-relational familial networks. Knowl. Inf. Syst. 61(3): 1547-1581 (2019) - [c31]Minh Pham, Craig A. Knoblock, Jay Pujara:
Learning Data Transformations with Minimal User Effort. IEEE BigData 2019: 657-664 - [c30]Majid Ghasemi-Gol, Jay Pujara, Pedro A. Szekely
:
Tabular Cell Classification Using Pre-Trained Cell Embeddings. ICDM 2019: 230-239 - [c29]Daniel Garijo, Deborah Khider, Varun Ratnakar
, Yolanda Gil
, Ewa Deelman, Rafael Ferreira da Silva
, Craig A. Knoblock, Yao-Yi Chiang, Minh Pham, Jay Pujara, Binh Vu, Dan Feldman, Rajiv Mayani, Kelly M. Cobourn, Christopher J. Duffy, Armen R. Kemanian, Lele Shu
, Vipin Kumar, Ankush Khandelwal, Kshitij Tayal, Scott D. Peckham, Maria Stoica, Anna Dabrowski, Daniel Hardesty-Lewis, Suzanne A. Pierce
:
An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models. IUI Companion 2019: 111-112 - [c28]Pigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, Lise Getoor:
Personalized explanations for hybrid recommender systems. IUI 2019: 379-390 - [c27]Binh Vu, Jay Pujara, Craig A. Knoblock:
D-REPR: A Language for Describing and Mapping Diversely-Structured Data Sources to RDF. K-CAP 2019: 189-196 - [c26]Pedro A. Szekely
, Daniel Garijo, Divij Bhatia
, Jiasheng Wu, Yixiang Yao, Jay Pujara:
T2WML: Table To Wikidata Mapping Language. K-CAP 2019: 267-270 - [c25]Avijit Thawani, Minda Hu, Erdong Hu, Husain Zafar, Naren Teja Divvala, Amandeep Singh, Ehsan Qasemi, Pedro A. Szekely, Jay Pujara:
Entity Linking to Knowledge Graphs to Infer Column Types and Properties. SemTab@ISWC 2019: 25-32 - [c24]Pedro A. Szekely, Daniel Garijo, Jay Pujara, Divij Bhatia, Jiasheng Wu:
T2WML: A Cell-Based Language to Map Tables into Wikidata Records. ISWC (Satellites) 2019: 45-48 - [c23]Jay Pujara, Arunkumar Rajendran, Majid Ghasemi-Gol, Pedro A. Szekely:
A Common Framework for Developing Table Understanding Models. ISWC (Satellites) 2019: 133-136 - [c22]Louiqa Raschid, Douglas Burdick, Cesar de Pablo, Mark D. Flood
, John Grant, Joe Langsam, Jay Pujara, Elena Tomas, Ian Soboroff:
Financial Entity Identification and Information Integration (FEIII) 2019 Challenge: The Report of the Organizing Committee. DSMM@SIGMOD 2019: 6:1-6:3 - [c21]Yixiang Yao, Pedro A. Szekely
, Jay Pujara:
Extensible and Scalable Entity Resolution for Financial Datasets Using RLTK. DSMM@SIGMOD 2019: 11:1 - [c20]Binh Vu, Craig A. Knoblock, Jay Pujara:
Learning Semantic Models of Data Sources Using Probabilistic Graphical Models. WWW 2019: 1944-1953 - 2018
- [c19]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Scalable Probabilistic Causal Structure Discovery. IJCAI 2018: 5112-5118 - [c18]Rahul Gupta, Jay Pujara, Craig A. Knoblock, Shushyam M. Sharanappa, Bharat Pulavarti, Gerard Hoberg, Gordon Phillips:
Feature Selection Methods For Understanding Business Competitor Relationships. DSMM@SIGMOD 2018: 2:1-2:6 - [c17]Louiqa Raschid, Douglas Burdick, John Grant, Joe Langsam, Jay Pujara, Elizabeth Roman, Ian Soboroff, Mohammed J. Zaki, Elena Zotkina:
Financial Entity Identification and Information Integration (FEIII) 2018 Challenge: The Report of the Organizing Committee. DSMM@SIGMOD 2018: 9:1-9:3 - [c16]Jay Pujara:
Hybrid Link Prediction for Competitor Relationships. DSMM@SIGMOD 2018: 14:1-14:4 - [c15]Jay Pujara, Sameer Singh:
Mining Knowledge Graphs From Text. WSDM 2018: 789-790 - 2017
- [c14]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Using Noisy Extractions to Discover Causal Knowledge. AKBC@NIPS 2017 - [c13]Jay Pujara, Eriq Augustine, Lise Getoor:
Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short. EMNLP 2017: 1751-1756 - [c12]Pigi Kouki, Jay Pujara, Christopher Marcum, Laura M. Koehly, Lise Getoor:
Collective Entity Resolution in Familial Networks. ICDM 2017: 227-236 - [c11]Sabina Tomkins, Jay Pujara, Lise Getoor:
Disambiguating Energy Disaggregation: A Collective Probabilistic Approach. IJCAI 2017: 2857-2863 - [c10]Pigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, Lise Getoor:
User Preferences for Hybrid Explanations. RecSys 2017: 84-88 - [c9]Jay Pujara:
Extracting Knowledge Graphs from Financial Filings: Extended Abstract. DSMM@SIGMOD 2017: 5:1-5:2 - [c8]Sungchul Kim, Nikhil Kini, Jay Pujara, Eunyee Koh, Lise Getoor:
Probabilistic Visitor Stitching on Cross-Device Web Logs. WWW 2017: 1581-1589 - [i3]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Using Noisy Extractions to Discover Causal Knowledge. CoRR abs/1711.05900 (2017) - 2016
- [b1]Jay Pujara:
Probabilistic Models for Scalable Knowledge Graph Construction. University of Maryland, College Park, MD, USA, 2016 - [c7]Shachi H. Kumar, Jay Pujara, Lise Getoor, David Mares, Dipak Gupta, Ellen Riloff:
Unsupervised models for predicting strategic relations between organizations. ASONAM 2016: 711-718 - [e1]Jay Pujara, Tim Rocktäschel, Danqi Chen, Sameer Singh:
Proceedings of the 5th Workshop on Automated Knowledge Base Construction, AKBC@NAACL-HLT 2016, San Diego, CA, USA, June 17, 2016. The Association for Computer Linguistics 2016, ISBN 978-1-941643-53-2 [contents] - [i2]Shobeir Fakhraei, Dhanya Sridhar, Jay Pujara, Lise Getoor:
Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks. CoRR abs/1607.00474 (2016) - [i1]Jay Pujara, Lise Getoor:
Generic Statistical Relational Entity Resolution in Knowledge Graphs. CoRR abs/1607.00992 (2016) - 2015
- [j1]