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ORCIDScott Sanner
Person information
- affiliation: University of Toronto, Department of Mechanical and Industrial Engineering, Canada
- affiliation: Australian National University, Acton, USA
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Books and Theses
- 2008
[b1]Scott Sanner:
First-order Decision-theoretic Planning in Structured Relational Environments. University of Toronto, Canada, 2008
Journal Articles
- 2023
- [j20]Tianshu Shen
, Jiaru Li, Mohamed Reda Bouadjenek, Zheda Mai, Scott Sanner:
Towards understanding and mitigating unintended biases in language model-driven conversational recommendation. Inf. Process. Manag. 60(1): 103139 (2023) - [j19]Ruiwen Li, Zheda Mai, Zhibo Zhang, Jongseong Jang, Scott Sanner:
TransCAM: Transformer attention-based CAM refinement for Weakly supervised semantic segmentation. J. Vis. Commun. Image Represent. 92: 103800 (2023) - [j18]Mohamed Reda Bouadjenek, Scott Sanner, Ga Wu:
A User-Centric Analysis of Social Media for Stock Market Prediction. ACM Trans. Web 17(2): 9:1-9:22 (2023) - 2022
- [j17]Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, Scott Sanner:
Online continual learning in image classification: An empirical survey. Neurocomputing 469: 28-51 (2022) - [j16]Ga Wu, Justin Domke, Scott Sanner:
Arbitrary conditional inference in variational autoencoders via fast prior network training. Mach. Learn. 111(7): 2537-2559 (2022) - [j15]Mohamed Reda Bouadjenek, Scott Sanner, Zahra Iman, Lexing Xie, Daniel Xiaoliang Shi:
A longitudinal study of topic classification on Twitter. PeerJ Comput. Sci. 8: e991 (2022) - 2021
- [j14]Yew Meng Khaw, Amir Abiri Jahromi, Mohammadreza Fakhari Moghaddam Arani, Scott Sanner, Deepa Kundur, Marthe Kassouf:
A Deep Learning-Based Cyberattack Detection System for Transmission Protective Relays. IEEE Trans. Smart Grid 12(3): 2554-2565 (2021) - 2020
- [j13]Buser Say, Scott Sanner:
Compact and efficient encodings for planning in factored state and action spaces with learned Binarized Neural Network transition models. Artif. Intell. 285: 103291 (2020) - [j12]Dusan Sovilj, Paul Budnarain, Scott Sanner, Geoff Salmon, Mohan Rao:
A comparative evaluation of unsupervised deep architectures for intrusion detection in sequential data streams. Expert Syst. Appl. 159: 113577 (2020) - [j11]Mohamed Reda Bouadjenek, Scott Sanner, Yihao Du:
Relevance- and interface-driven clustering for visual information retrieval. Inf. Syst. 94: 101592 (2020) - [j10]Ga Wu, Buser Say, Scott Sanner:
Scalable Planning with Deep Neural Network Learned Transition Models. J. Artif. Intell. Res. 68: 571-606 (2020) - [j9]Bohan Zhang, Scott Sanner, Mohamed Reda Bouadjenek, Shagun Gupta:
Bayesian Networks for Data Integration in the Absence of Foreign Keys. IEEE Trans. Knowl. Data Eng. 32(4): 803-808 (2020) - 2018
- [j8]Sean W. Kortschot, Dusan Sovilj, Greg A. Jamieson, Scott Sanner, Chelsea Carrasco, Harold Soh:
Measuring and Mitigating the Costs of Attentional Switches in Active Network Monitoring for Cybersecurity. Hum. Factors 60(7): 962-977 (2018) - 2017
- [j7]Monica Anderson, Roman Barták, John S. Brownstein, David L. Buckeridge, Hoda Eldardiry, Christopher W. Geib, Maria L. Gini, Aaron Isaksen, Sarah Keren, Robert Laddaga, Viliam Lisý, Rodney Martin, David R. Martinez, Martin Michalowski, Loizos Michael, Reuth Mirsky, Thanh Hai Nguyen, Michael J. Paul, Enrico Pontelli, Scott Sanner, Arash Shaban-Nejad, Arunesh Sinha, Shirin Sohrabi, Kumar Sricharan, Biplav Srivastava, Mark Stefik, William W. Streilein, Nathan R. Sturtevant, Kartik Talamadupula, Michael Thielscher, Julian Togelius, Tran Cao Son, Long Tran-Thanh, Neal Wagner, Byron C. Wallace, Szymon Wilk, Jichen Zhu:
Reports of the Workshops of the Thirty-First AAAI Conference on Artificial Intelligence. AI Mag. 38(3): 72-82 (2017) - 2016
- [j6]Karina Valdivia Delgado, Leliane N. de Barros, Daniel B. Dias, Scott Sanner:
Real-time dynamic programming for Markov decision processes with imprecise probabilities. Artif. Intell. 230: 192-223 (2016) - 2015
- [j5]Mauro Vallati, Lukás Chrpa, Marek Grzes, Thomas Leo McCluskey, Mark Roberts, Scott Sanner:
The 2014 International Planning Competition: Progress and Trends. AI Mag. 36(3): 90-98 (2015) - 2012
- [j4]Amanda Jane Coles, Andrew Coles, Angel García Olaya, Sergio Jiménez Celorrio, Carlos Linares López, Scott Sanner, Sungwook Yoon:
A Survey of the Seventh International Planning Competition. AI Mag. 33(1): 83-88 (2012) - 2011
- [j3]Karina Valdivia Delgado, Scott Sanner, Leliane Nunes de Barros:
Efficient solutions to factored MDPs with imprecise transition probabilities. Artif. Intell. 175(9-10): 1498-1527 (2011) - [j2]Karina Valdivia Delgado, Leliane Nunes de Barros, Fábio Gagliardi Cozman, Scott Sanner:
Using mathematical programming to solve Factored Markov Decision Processes with Imprecise Probabilities. Int. J. Approx. Reason. 52(7): 1000-1017 (2011) - 2009
- [j1]Scott Sanner, Craig Boutilier:
Practical solution techniques for first-order MDPs. Artif. Intell. 173(5-6): 748-788 (2009)
Conference and Workshop Papers
- 2024
- [c127]Armin Toroghi, Scott Sanner:
Bayesian Inference with Complex Knowledge Graph Evidence. AAAI 2024: 20550-20558 - 2023
- [c126]Yudong Xu, Elias B. Khalil, Scott Sanner:
Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus. AAAI 2023: 4115-4122 - [c125]Aravinth Chembu, Scott Sanner, Hassan Khurram, Akshat Kumar:
Scalable and Globally Optimal Generalized L₁ K-center Clustering via Constraint Generation in Mixed Integer Linear Programming. AAAI 2023: 7015-7023 - [c124]Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang, Ali Pesaranghader, Manasa Bharadwaj, Scott Sanner:
DiffuDetox: A Mixed Diffusion Model for Text Detoxification. ACL (Findings) 2023: 7566-7574 - [c123]Siow Meng Low, Akshat Kumar, Scott Sanner:
Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects. ICAPS 2023: 596-604 - [c122]Jihwan Jeong, Scott Sanner, Akshat Kumar:
A Mixed-Integer Linear Programming Reduction of Disjoint Bilinear Programs via Symbolic Variable Elimination. CPAIOR 2023: 79-95 - [c121]Aravinth Chembu, Scott Sanner, Elias B. Khalil:
Scalable and Near-Optimal ε-Tube Clusterwise Regression. CPAIOR 2023: 254-263 - [c120]Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Armin Toroghi, Anton Korikov, Ali Pesaranghader, Touqir Sajed, Manasa Bharadwaj, Borislav Mavrin, Scott Sanner:
Self-supervised Contrastive BERT Fine-tuning for Fusion-Based Reviewed-Item Retrieval. ECIR (1) 2023: 3-17 - [c119]Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Manasa Bharadwaj, Nikhil Verma, Ali Pesaranghader, Scott Sanner:
COUNT: COntrastive UNlikelihood Text Style Transfer for Text Detoxification. EMNLP (Findings) 2023: 8658-8666 - [c118]Tongzi Wu, Yuhao Zhou, Wang Ling, Hojin Yang, Joana Veloso, Lin Sun, Ruixin Huang, Norberto Guimaraes, Scott Sanner:
Towards Dialogue Modeling Beyond Text. ICASSP 2023: 1-5 - [c117]Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb, Hyunwoo Kim, Baher Abdulhai, Scott Sanner:
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization. ICLR 2023 - [c116]Xiaocan Li, Ray Coden Mercurius, Ayal Taitler, Xiaoyu Wang, Mohammad Noaeen, Scott Sanner, Baher Abdulhai:
Perimeter Control Using Deep Reinforcement Learning: A Model-Free Approach Towards Homogeneous Flow Rate Optimization. ITSC 2023: 1474-1479 - [c115]Ilia Smirnov, Scott Sanner, Baher Abdulhai:
A Case for Monte Carlo Tree Search in Adaptive Traffic Signal Control: Modifiability, Interpretability and Generalization. ITSC 2023: 1480-1486 - [c114]Xiaoyu Wang, Ayal Taitler, Ilia Smirnov, Scott Sanner, Baher Abdulhai:
eMARLIN+: Overcoming Partial-Observability Caused by Sensor Limitations and Short Detection Ranges in Traffic Signal Control. ITSC 2023: 2337-2342 - [c113]Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, Lucas Dixon:
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences. RecSys 2023: 890-896 - [c112]Armin Toroghi, Griffin Floto, Zhenwei Tang, Scott Sanner:
Bayesian Knowledge-driven Critiquing with Indirect Evidence. SIGIR 2023: 1838-1842 - [c111]Zhenwei Tang, Griffin Floto, Armin Toroghi, Shichao Pei, Xiangliang Zhang, Scott Sanner:
LogicRec: Recommendation with Users' Logical Requirements. SIGIR 2023: 2129-2133 - [c110]Haochen Zhang, Anton Korikov, Parsa Farinneya, Mohammad Mahdi Abdollah Pour, Manasa Bharadwaj, Ali Pesaranghader, Xi Yu Huang, Yi Xin Lok, Zhaoqi Wang, Nathan Jones, Scott Sanner:
Recipe-MPR: A Test Collection for Evaluating Multi-aspect Preference-based Natural Language Retrieval. SIGIR 2023: 2744-2753 - 2022
- [c109]Siow Meng Low, Akshat Kumar, Scott Sanner:
Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. AAAI 2022: 9840-9848 - [c108]Noah Patton, Jihwan Jeong, Mike Gimelfarb, Scott Sanner:
A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs. AAAI 2022: 9894-9901 - [c107]Jihwan Jeong, Parth Jaggi, Andrew Butler, Scott Sanner:
An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming. ICML 2022: 10053-10067 - [c106]Mathieu Tuli, Andrew C. Li, Pashootan Vaezipoor, Toryn Q. Klassen, Scott Sanner, Sheila A. McIlraith:
Learning to Follow Instructions in Text-Based Games. NeurIPS 2022 - [c105]Riley Moher, Michael Gruninger, Scott Sanner:
What's in a (Data) Type? Meaningful Type Safety for Data Science. RCIS 2022: 20-38 - [c104]Zhaolin Gao, Tianshu Shen, Zheda Mai, Mohamed Reda Bouadjenek, Isaac Waller, Ashton Anderson, Ron Bodkin, Scott Sanner:
Mitigating the Filter Bubble While Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems. SIGIR 2022: 2524-2531 - [c103]Tianshu Shen, Zheda Mai, Ga Wu, Scott Sanner:
Distributional Contrastive Embedding for Clarification-based Conversational Critiquing. WWW 2022: 2422-2432 - 2021
- [c102]Dongsub Shim, Zheda Mai, Jihwan Jeong, Scott Sanner, Hyunwoo Kim, Jongseong Jang:
Online Class-Incremental Continual Learning with Adversarial Shapley Value. AAAI 2021: 9630-9638 - [c101]Zheda Mai, Ruiwen Li, Hyunwoo Kim, Scott Sanner:
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning. CVPR Workshops 2021: 3589-3599 - [c100]Mike Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Bayesian Experience Reuse for Learning from Multiple Demonstrators. IJCAI 2021: 2425-2431 - [c99]Jihwan Jeong, Parth Jaggi, Scott Sanner:
Symbolic Dynamic Programming for Continuous State MDPs with Linear Program Transitions. IJCAI 2021: 4083-4089 - [c98]Ta Jiun Ting, Xiaocan Li, Scott Sanner, Baher Abdulhai:
Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction. ITSC 2021: 1259-1265 - [c97]Parth Jaggi, Xiaoyu Wang, Nicolas Carrara, Scott Sanner, Baher Abdulhai:
Microscopic Model-Based RL Approaches for Traffic Signal Control Generalize Better than Model-Free RL Approaches. ITSC 2021: 2525-2532 - [c96]Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee:
Risk-Aware Transfer in Reinforcement Learning using Successor Features. NeurIPS 2021: 17298-17310 - [c95]Yi Sui, Ga Wu, Scott Sanner:
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models. NeurIPS 2021: 23347-23358 - [c94]Hojin Yang, Tianshu Shen, Scott Sanner:
Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems. SIGIR 2021: 2111-2115 - [c93]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts. UAI 2021: 1787-1797 - [c92]Hojin Yang, Scott Sanner, Ga Wu, Jin Peng Zhou:
Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation. UMAP 2021: 55-64 - [c91]Shengnan Lyu, Arpit Rana, Scott Sanner, Mohamed Reda Bouadjenek:
A Workflow Analysis of Context-driven Conversational Recommendation. WWW 2021: 866-877 - 2020
- [c90]Zheda Mai, Ga Wu, Kai Luo, Scott Sanner:
Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering. ICDM (Workshops) 2020: 165-172 - [c89]Hanze Li, Scott Sanner, Kai Luo, Ga Wu:
A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems. RecSys 2020: 13-22 - [c88]Kai Luo, Hojin Yang, Ga Wu, Scott Sanner:
Deep Critiquing for VAE-based Recommender Systems. SIGIR 2020: 1269-1278 - [c87]Kai Luo, Scott Sanner, Ga Wu, Hanze Li, Hojin Yang:
Latent Linear Critiquing for Conversational Recommender Systems. WWW 2020: 2535-2541 - 2019
- [c86]Thiago Pereira Bueno, Leliane N. de Barros, Denis Deratani Mauá, Scott Sanner:
Deep Reactive Policies for Planning in Stochastic Nonlinear Domains. AAAI 2019: 7530-7537 - [c85]Mohamed Reda Bouadjenek, Scott Sanner:
Relevance-driven Clustering for Visual Information Retrieval on Twitter. CHIIR 2019: 349-353 - [c84]Buser Say, Scott Sanner, Sylvie Thiébaux:
Reward Potentials for Planning with Learned Neural Network Transition Models. CP 2019: 674-689 - [c83]Buser Say, Scott Sanner:
Metric Hybrid Factored Planning in Nonlinear Domains with Constraint Generation. CPAIOR 2019: 502-518 - [c82]Ga Wu, Kai Luo, Scott Sanner, Harold Soh:
Deep language-based critiquing for recommender systems. RecSys 2019: 137-145 - [c81]Yakun Wang, Ga Wu, Mohamed Reda Bouadjenek, Scott Sanner, Sen Su, Zhongbao Zhang:
A Novel Regularizer for Temporally Stable Learning with an Application to Twitter Topic Classification. SDM 2019: 217-225 - [c80]Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai:
Noise Contrastive Estimation for One-Class Collaborative Filtering. SIGIR 2019: 135-144 - [c79]Ga Wu, Mohamed Reda Bouadjenek, Scott Sanner:
One-Class Collaborative Filtering with the Queryable Variational Autoencoder. SIGIR 2019: 921-924 - [c78]Yew Meng Khaw, Amir Abiri Jahromi, Mohammadreza Fakhari Moghaddam Arani, Deepa Kundur, Scott Sanner, Marthe Kassouf:
Preventing False Tripping Cyberattacks Against Distance Relays: A Deep Learning Approach. SmartGridComm 2019: 1-6 - [c77]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning. UAI 2019: 476-485 - 2018
- [c76]Zhijiang Ye, Buser Say, Scott Sanner:
Symbolic Bucket Elimination for Piecewise Continuous Constrained Optimization. CPAIOR 2018: 585-594 - [c75]Buser Say, Scott Sanner:
Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models. IJCAI 2018: 4815-4821 - [c74]Samuel Kolb, Martin Mladenov, Scott Sanner, Vaishak Belle, Kristian Kersting:
Efficient Symbolic Integration for Probabilistic Inference. IJCAI 2018: 5031-5037 - [c73]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach. NeurIPS 2018: 9549-9559 - [c72]Maksims Volkovs, Himanshu Rai, Zhaoyue Cheng, Ga Wu, Yichao Lu, Scott Sanner:
Two-stage Model for Automatic Playlist Continuation at Scale. RecSys Challenge 2018: 9:1-9:6 - [c71]Dusan Sovilj, Scott Sanner, Harold Soh, Hanze Li:
Collaborative Filtering with Behavioral Models. UMAP 2018: 91-99 - 2017
- [c70]Shamin Kinathil, Harold Soh, Scott Sanner:
Nonlinear Optimization and Symbolic Dynamic Programming for Parameterized Hybrid Markov Decision Processes. AAAI Workshops 2017 - [c69]Daniela Rosu, Dionne M. Aleman, J. Christopher Beck, Mark H. Chignell, Mariano P. Consens, Mark S. Fox, Michael Gruninger, Chang Liu, Yi Ru, Scott Sanner:
Knowledge-Based Provision of Goods and Services for People with Social Needs: Towards a Virtual Marketplace. AAAI Workshops 2017 - [c68]Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Lexing Xie, Darius Braziunas:
Low-Rank Linear Cold-Start Recommendation from Social Data. AAAI 2017: 1502-1508 - [c67]Aswin Raghavan, Scott Sanner, Roni Khardon, Prasad Tadepalli, Alan Fern:
Hindsight Optimization for Hybrid State and Action MDPs. AAAI 2017: 3790-3796 - [c66]Daniela Rosu, Dionne M. Aleman, J. Christopher Beck, Mark H. Chignell, Mariano P. Consens, Mark S. Fox, Michael Gruninger, Chang Liu, Yi Ru, Scott Sanner:
Knowledge-Based Provisioning of Goods and Services: Towards a Virtual Social Needs Marketplace. AAAI Spring Symposia 2017 - [c65]Shamin Kinathil, Harold Soh, Scott Sanner:
Analytic Decision Analysis via Symbolic Dynamic Programming for Parameterized Hybrid MDPs. ICAPS 2017: 181-185 - [c64]Zahra Iman, Scott Sanner, Mohamed Reda Bouadjenek, Lexing Xie:
A Longitudinal Study of Topic Classification on Twitter. ICWSM 2017: 552-555 - [c63]Daniela Rosu, Dionne M. Aleman, J. Christopher Beck, Mark H. Chignell, Mariano P. Consens, Mark S. Fox, Michael Gruninger, Chang Liu, Yi Ru, Scott Sanner:
A virtual marketplace for goods and services for people with social needs. IHTC 2017: 202-206 - [c62]Buser Say, Ga Wu, Yu Qing Zhou, Scott Sanner:
Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming. IJCAI 2017: 750-756 - [c61]Harold Soh, Scott Sanner, Madeleine White, Greg A. Jamieson:
Deep Sequential Recommendation for Personalized Adaptive User Interfaces. IUI 2017: 589-593 - [c60]Ga Wu, Buser Say, Scott Sanner:
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains. NIPS 2017: 6273-6283 - [c59]Sean W. Kortschot, Dusan Sovilj, Harold Soh, Greg A. Jamieson, Scott Sanner, Chelsea Carrasco, Scott Ralph, Scott Langevin:
An open source adaptive user interface for network monitoring. SMC 2017: 1535-1539 - [c58]Alberto Camacho, Oscar Chen, Scott Sanner, Sheila A. McIlraith:
Non-Markovian Rewards Expressed in LTL: Guiding Search Via Reward Shaping. SOCS 2017: 159-160 - [c57]Marian-Andrei Rizoiu, Lexing Xie, Scott Sanner, Manuel Cebrián, Honglin Yu, Pascal Van Hentenryck:
Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity. WWW 2017: 735-744 - 2016
- [c56]Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Darius Braziunas:
On the Effectiveness of Linear Models for One-Class Collaborative Filtering. AAAI 2016: 229-235 - [c55]Hadi Mohasel Afshar, Scott Sanner, Christfried Webers:
Closed-Form Gibbs Sampling for Graphical Models with Algebraic Constraints. AAAI 2016: 3287-3293 - [c54]Shamin Kinathil, Scott Sanner, Sanmay Das, Nicolás Della Penna:
A Symbolic Closed-Form Solution to Sequential Market Making with Inventory. IJCAI 2016: 3609-3615 - [c53]Suvash Sedhain, Hung Bui, Jaya Kawale, Nikos Vlassis, Branislav Kveton, Aditya Krishna Menon, Trung Bui, Scott Sanner:
Practical Linear Models for Large-Scale One-Class Collaborative Filtering. IJCAI 2016: 3854-3860 - 2015
- [c52]Ga Wu, Scott Sanner, Rodrigo F. S. C. Oliveira:
Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time. AAAI 2015: 3094-3100 - [c51]Luis Gustavo Rocha Vianna, Leliane N. de Barros, Scott Sanner:
Real-Time Symbolic Dynamic Programming. AAAI 2015: 3402-3408 - [c50]Ehsan Abbasnejad, Justin Domke, Scott Sanner:
Loss-Calibrated Monte Carlo Action Selection. AAAI 2015: 3447-3453 - [c49]Hadi Mohasel Afshar, Scott Sanner, Ehsan Abbasnejad:
Linear-Time Gibbs Sampling in Piecewise Graphical Models. AAAI 2015: 3461-3467 - [c48]Mohamed Reda Bouadjenek, Scott Sanner, Gabriela Ferraro:
A study of query reformulation for patent prior art search with partial patent applications. ICAIL 2015: 23-32 - [c47]Honglin Yu, Lexing Xie, Scott Sanner:
The Lifecyle of a Youtube Video: Phases, Content and Popularity. ICWSM 2015: 533-542 - [c46]Khoi-Nguyen Tran, Peter Christen, Scott Sanner, Lexing Xie:
Context-Aware Detection of Sneaky Vandalism on Wikipedia Across Multiple Languages. PAKDD (1) 2015: 380-391 - [c45]Mona Golestan Far, Scott Sanner, Mohamed Reda Bouadjenek, Gabriela Ferraro, David Hawking:
On Term Selection Techniques for Patent Prior Art Search. SIGIR 2015: 803-806 - [c44]Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Lexing Xie:
AutoRec: Autoencoders Meet Collaborative Filtering. WWW (Companion Volume) 2015: 111-112 - 2014
- [c43]Luis Gustavo Rocha Vianna, Scott Sanner, Leliane Nunes de Barros:
Continuous Real Time Dynamic Programming for Discrete and Continuous State MDPs. BRACIS 2014: 134-139 - [c42]Honglin Yu, Lexing Xie, Scott Sanner:
Twitter-driven YouTube Views: Beyond Individual Influencers. ACM Multimedia 2014: 869-872 - [c41]Suvash Sedhain, Scott Sanner, Darius Braziunas, Lexing Xie, Jordan Christensen:
Social collaborative filtering for cold-start recommendations. RecSys 2014: 345-348 - [c40]Shamin Kinathil, Scott Sanner, Nicolás Della Penna:
Closed-form Solutions to a Subclass of Continuous Stochastic Games via Symbolic Dynamic Programming. UAI 2014: 390-399 - [c39]Román Marchant, Fabio Ramos, Scott Sanner:
Sequential Bayesian Optimisation for Spatial-Temporal Monitoring. UAI 2014: 553-562 - 2013
- [c38]Suvash Sedhain, Scott Sanner, Lexing Xie, Riley Kidd, Khoi-Nguyen Tran, Peter Christen:
Social affinity filtering: recommendation through fine-grained analysis of user interactions and activities. COSN 2013: 51-62 - [c37]Tan Nguyen, Scott Sanner:
Algorithms for Direct 0-1 Loss Optimization in Binary Classification. ICML (3) 2013: 1085-1093 - [c36]Ehsan Abbasnejad, Scott Sanner, Edwin V. Bonilla, Pascal Poupart:
Learning Community-Based Preferences via Dirichlet Process Mixtures of Gaussian Processes. IJCAI 2013: 1213-1219 - [c35]Zahra Zamani, Scott Sanner, Karina Valdivia Delgado, Leliane Nunes de Barros:
Robust Optimization for Hybrid MDPs with State-Dependent Noise. IJCAI 2013: 2437-2443 - [c34]M. Ehsan Abbasnejad, Edwin V. Bonilla, Scott Sanner:
Decision-Theoretic Sparsification for Gaussian Process Preference Learning. ECML/PKDD (2) 2013: 515-530 - [c33]Rishabh Mehrotra, Scott Sanner, Wray L. Buntine, Lexing Xie:
Improving LDA topic models for microblogs via tweet pooling and automatic labeling. SIGIR 2013: 889-892 - [c32]Luis Gustavo Vianna, Scott Sanner, Leliane Nunes de Barros:
Bounded Approximate Symbolic Dynamic Programming for Hybrid MDPs. UAI 2013 - 2012
- [c31]Zahra Zamani, Scott Sanner, Cheng Fang:
Symbolic Dynamic Programming for Continuous State and Action MDPs. AAAI 2012: 1839-1845 - [c30]Scott Sanner, Ehsan Abbasnejad:
Symbolic Variable Elimination for Discrete and Continuous Graphical Models. AAAI 2012: 1954-1960 - [c29]Zahra Zamani, Scott Sanner, Pascal Poupart, Kristian Kersting:
Symbolic Dynamic Programming for Continuous State and Observation POMDPs. NIPS 2012: 1403-1411 - [c28]Shengbo Guo, Scott Sanner, Thore Graepel, Wray L. Buntine:
Score-Based Bayesian Skill Learning. ECML/PKDD (1) 2012: 106-121 - [c27]Kar Wai Lim, Scott Sanner, Shengbo Guo:
On the mathematical relationship between expected n-call@k and the relevance vs. diversity trade-off. SIGIR 2012: 1117-1118 - [c26]Joseph Noel, Scott Sanner, Khoi-Nguyen Tran, Peter Christen, Lexing Xie, Edwin V. Bonilla, Ehsan Abbasnejad, Nicolás Della Penna:
New objective functions for social collaborative filtering. WWW 2012: 859-868 - 2011
- [c25]Scott Sanner, Shengbo Guo, Thore Graepel, Sadegh Kharazmi, Sarvnaz Karimi:
Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model. CIKM 2011: 1977-1980 - [c24]Babak Ahmadi, Kristian Kersting, Scott Sanner:
Multi-Evidence Lifted Message Passing, with Application to PageRank and the Kalman Filter. IJCAI 2011: 1152-1158 - [c23]Babak Ahmadi, Martin Mladenov, Kristian Kersting, Scott Sanner:
On Lifted PageRank, Kalman Filter and Towards Lifted Linear Program Solving. LWA 2011: 35-42 - [c22]Matthew W. Robards, Peter Sunehag, Scott Sanner, Bhaskara Marthi:
Sparse Kernel-SARSA(λ) with an Eligibility Trace. ECML/PKDD (3) 2011: 1-17 - [c21]Scott Sanner, Karina Valdivia Delgado, Leliane Nunes de Barros:
Symbolic Dynamic Programming for Discrete and Continuous State MDPs. UAI 2011: 643-652 - 2010
- [c20]Scott Sanner, Kristian Kersting:
Symbolic Dynamic Programming for First-order POMDPs. AAAI 2010: 1140-1146 - [c19]Scott Sanner, William T. B. Uther, Karina Valdivia Delgado:
Approximate dynamic programming with affine ADDs. AAMAS 2010: 1349-1356 - [c18]Carlton Downey, Scott Sanner:
Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda. ICML 2010: 311-318 - [c17]Shengbo Guo, Scott Sanner:
Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries. ISNN (1) 2010: 396-403 - [c16]Edwin V. Bonilla, Shengbo Guo, Scott Sanner:
Gaussian Process Preference Elicitation. NIPS 2010: 262-270 - [c15]Karina Valdivia Delgado, Cheng Fang, Scott Sanner, Leliane Nunes de Barros:
Symbolic Bounded Real-Time Dynamic Programming. SBIA 2010: 193-202 - [c14]Shengbo Guo, Scott Sanner:
Probabilistic latent maximal marginal relevance. SIGIR 2010: 833-834 - [c13]Shengbo Guo, Scott Sanner:
Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries. AISTATS 2010: 289-296 - 2009
- [c12]Karina Valdivia Delgado, Scott Sanner, Leliane Nunes de Barros, Fábio Gagliardi Cozman:
Efficient Solutions to Factored MDPs with Imprecise Transition Probabilities. ICAPS 2009 - [c11]Scott Sanner, Robby Goetschalckx, Kurt Driessens, Guy Shani:
Bayesian Real-Time Dynamic Programming. IJCAI 2009: 1784-1789 - 2008
- [c10]Robby Goetschalckx, Scott Sanner, Kurt Driessens:
Reinforcement Learning with the Use of Costly Features. ECAI 2008: 779-780 - [c9]Robby Goetschalckx, Scott Sanner, Kurt Driessens:
Reinforcement Learning with the Use of Costly Features. EWRL 2008: 124-135 - [c8]Robby Goetschalckx, Kurt Driessens, Scott Sanner:
Cost-Sensitive Parsimonious Linear Regression. ICDM 2008: 809-814 - 2007
- [c7]Scott Sanner, Craig Boutilier:
Approximate Solution Techniques for Factored First-Order MDPs. ICAPS 2007: 288-295 - 2006
- [c6]Scott Sanner, Sheila A. McIlraith:
An Ordered Theory Resolution Calculus for Hybrid Reasoning in First-Order Extensions of Description Logic. KR 2006: 100-111 - [c5]Scott Sanner, Craig Boutilier:
Practical Linear Value-approximation Techniques for First-order MDPs. UAI 2006 - 2005
- [c4]Scott Sanner, David A. McAllester:
Affine Algebraic Decision Diagrams (AADDs) and their Application to Structured Probabilistic Inference. IJCAI 2005: 1384-1390 - [c3]Scott Sanner, Craig Boutilier:
Approximate Linear Programming for First-order MDPs. UAI 2005: 509-517 - 2002
- [c2]Rahul Biswas, Benson Limketkai, Scott Sanner, Sebastian Thrun:
Towards object mapping in non-stationary environments with mobile robots. IROS 2002: 1014-1019 - 2000
- [c1]Scott Sanner, John R. Anderson, Christian Lebiere, Marsha C. Lovett:
Achieving Efficient and Cognitively Plausible Learning in Backgammon. ICML 2000: 823-830
Editorship
- 2016
- [e3]Daniele Magazzeni, Scott Sanner, Sylvie Thiébaux:
Planning for Hybrid Systems, Papers from the 2016 AAAI Workshop, Phoenix, Arizona, USA, February 13, 2016. AAAI Technical Report WS-16-12, AAAI Press 2016 [contents] - [e2]Amanda Jane Coles, Andrew Coles, Stefan Edelkamp, Daniele Magazzeni, Scott Sanner:
Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling, ICAPS 2016, London, UK, June 12-17, 2016. AAAI Press 2016, ISBN 978-1-57735-757-5 [contents] - 2012
- [e1]Scott Sanner, Marcus Hutter:
Recent Advances in Reinforcement Learning - 9th European Workshop, EWRL 2011, Athens, Greece, September 9-11, 2011, Revised Selected Papers. Lecture Notes in Computer Science 7188, Springer 2012, ISBN 978-3-642-29945-2 [contents]
Reference Works
- 2017
- [r2]Scott Sanner, Kristian Kersting:
Symbolic Dynamic Programming. Encyclopedia of Machine Learning and Data Mining 2017: 1220-1228 - 2010
- [r1]Scott Sanner, Kristian Kersting:
Symbolic Dynamic Programming. Encyclopedia of Machine Learning 2010: 946-954
Informal and Other Publications
- 2024
- [i54]Michael Gimelfarb, Ayal Taitler, Scott Sanner:
Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs. CoRR abs/2401.12243 (2024) - [i53]Armin Toroghi, Willis Guo, Mohammad Mahdi Abdollah Pour, Scott Sanner:
Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering. CoRR abs/2403.01390 (2024) - [i52]Willis Guo, Armin Toroghi, Scott Sanner:
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge. CoRR abs/2403.01395 (2024) - 2023
- [i51]Siow Meng Low, Akshat Kumar, Scott Sanner:
Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects. CoRR abs/2304.03081 (2023) - [i50]Zhenwei Tang, Griffin Floto, Armin Toroghi, Shichao Pei, Xiangliang Zhang, Scott Sanner:
LogicRec: Recommendation with Users' Logical Requirements. CoRR abs/2304.11722 (2023) - [i49]Aravinth Chembu, Scott Sanner:
A Generalized Framework for Predictive Clustering and Optimization. CoRR abs/2305.04364 (2023) - [i48]Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner, Elias B. Khalil:
LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations. CoRR abs/2305.18354 (2023) - [i47]Xiaocan Li, Ray Coden Mercurius, Ayal Taitler, Xiaoyu Wang, Mohammad Noaeen, Scott Sanner, Baher Abdulhai:
Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization. CoRR abs/2305.19291 (2023) - [i46]Ta Jiun Ting, Xiaocan Li, Scott Sanner, Baher Abdulhai:
Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction. CoRR abs/2305.19292 (2023) - [i45]Armin Toroghi, Griffin Floto, Zhenwei Tang, Scott Sanner:
Bayesian Knowledge-driven Critiquing with Indirect Evidence. CoRR abs/2306.05636 (2023) - [i44]Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang, Ali Pesaranghader, Manasa Bharadwaj, Scott Sanner:
DiffuDetox: A Mixed Diffusion Model for Text Detoxification. CoRR abs/2306.08505 (2023) - [i43]Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, Lucas Dixon:
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences. CoRR abs/2307.14225 (2023) - [i42]Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Armin Toroghi, Anton Korikov, Ali Pesaranghader, Touqir Sajed, Manasa Bharadwaj, Borislav Mavrin, Scott Sanner:
Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval. CoRR abs/2308.00762 (2023) - [i41]Griffin Floto, Thorsteinn Jonsson, Mihai Nica, Scott Sanner, Eric Zhengyu Zhu:
Diffusion on the Probability Simplex. CoRR abs/2309.02530 (2023) - [i40]Sebastian Junges, Joost-Pieter Katoen, Scott Sanner, Guy Van den Broeck, Bahare Salmani:
Scalable Analysis of Probabilistic Models and Programs (Dagstuhl Seminar 23241). Dagstuhl Reports 13(6): 1-21 (2023) - 2022
- [i39]Tianshu Shen, Jiaru Li, Mohamed Reda Bouadjenek, Zheda Mai, Scott Sanner:
Unintended Bias in Language Model-driven Conversational Recommendation. CoRR abs/2201.06224 (2022) - [i38]Ruiwen Li, Zheda Mai, Chiheb Trabelsi, Zhibo Zhang, Jongseong Jang, Scott Sanner:
TransCAM: Transformer Attention-based CAM Refinement for Weakly Supervised Semantic Segmentation. CoRR abs/2203.07239 (2022) - [i37]Siow Meng Low, Akshat Kumar, Scott Sanner:
Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. CoRR abs/2203.12679 (2022) - [i36]Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb, Hyunwoo Kim, Baher Abdulhai, Scott Sanner:
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization. CoRR abs/2210.03802 (2022) - [i35]Yudong Xu, Elias B. Khalil, Scott Sanner:
Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus. CoRR abs/2210.09880 (2022) - [i34]Mathieu Tuli, Andrew C. Li, Pashootan Vaezipoor, Toryn Q. Klassen, Scott Sanner, Sheila A. McIlraith:
Learning to Follow Instructions in Text-Based Games. CoRR abs/2211.04591 (2022) - [i33]Ayal Taitler, Michael Gimelfarb, Sriram Gopalakrishnan, Xiaotian Liu, Scott Sanner:
pyRDDLGym: From RDDL to Gym Environments. CoRR abs/2211.05939 (2022) - [i32]Xiaoyu Wang, Scott Sanner, Baher Abdulhai:
A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control. CoRR abs/2211.14426 (2022) - 2021
- [i31]Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, Scott Sanner:
Online Continual Learning in Image Classification: An Empirical Survey. CoRR abs/2101.10423 (2021) - [i30]Zheda Mai, Ruiwen Li, Hyunwoo Kim, Scott Sanner:
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning. CoRR abs/2103.13885 (2021) - [i29]Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee:
Risk-Aware Transfer in Reinforcement Learning using Successor Features. CoRR abs/2105.14127 (2021) - [i28]Ruiwen Li, Zhibo Zhang, Jiani Li, Scott Sanner, Jongseong Jang, Yeonjeong Jeong, Dongsub Shim:
EDDA: Explanation-driven Data Augmentation to Improve Model and Explanation Alignment. CoRR abs/2105.14162 (2021) - [i27]Noah Patton, Jihwan Jeong, Michael Gimelfarb, Scott Sanner:
RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation. CoRR abs/2106.07260 (2021) - [i26]Buser Say, Scott Sanner, Jo Devriendt, Jakob Nordström, Peter J. Stuckey:
Planning with Learned Binarized Neural Networks Benchmarks for MaxSAT Evaluation 2021. CoRR abs/2108.00633 (2021) - [i25]Yi Sui, Ga Wu, Scott Sanner:
Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction. CoRR abs/2110.01794 (2021) - [i24]Zhibo Zhang, Jongseong Jang, Chiheb Trabelsi, Ruiwen Li, Scott Sanner, Yeonjeong Jeong, Dongsub Shim:
ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification. CoRR abs/2111.14271 (2021) - 2020
- [i23]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Contextual Policy Reuse using Deep Mixture Models. CoRR abs/2003.00203 (2020) - [i22]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Bayesian Experience Reuse for Learning from Multiple Demonstrators. CoRR abs/2006.05725 (2020) - [i21]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning. CoRR abs/2007.00869 (2020) - [i20]Zheda Mai, Hyunwoo Kim, Jihwan Jeong, Scott Sanner:
Batch-level Experience Replay with Review for Continual Learning. CoRR abs/2007.05683 (2020) - [i19]Jin Peng Zhou, Ga Wu, Zheda Mai, Scott Sanner:
Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering. CoRR abs/2008.01246 (2020) - [i18]Zheda Mai, Dongsub Shim, Jihwan Jeong, Scott Sanner, Hyunwoo Kim, Jongseong Jang:
Adversarial Shapley Value Experience Replay for Task-Free Continual Learning. CoRR abs/2009.00093 (2020) - [i17]Zheda Mai, Ga Wu, Kai Luo, Scott Sanner:
Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering. CoRR abs/2010.12803 (2020) - 2019
- [i16]Ga Wu, Buser Say, Scott Sanner:
Scalable Nonlinear Planning with Deep Neural Network Learned Transition Models. CoRR abs/1904.02873 (2019) - [i15]Buser Say, Scott Sanner, Sylvie Thiébaux:
Reward Potentials for Planning with Learned Neural Network Transition Models. CoRR abs/1904.09366 (2019) - [i14]Kasra Safari, Scott Sanner:
Optimizing Search API Queries for Twitter Topic Classifiers Using a Maximum Set Coverage Approach. CoRR abs/1904.10403 (2019) - 2018
- [i13]Ga Wu, Justin Domke, Scott Sanner:
Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding. CoRR abs/1805.07785 (2018) - [i12]Yu Qing Zhou, Ga Wu, Scott Sanner, Putra Manggala:
Aesthetic Features for Personalized Photo Recommendation. CoRR abs/1809.00060 (2018) - [i11]Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai:
Noise Contrastive Estimation for Scalable Linear Models for One-Class Collaborative Filtering. CoRR abs/1811.00697 (2018) - [i10]Buser Say, Scott Sanner:
Compact and Efficient Encodings for Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models. CoRR abs/1811.10433 (2018) - 2017
- [i9]Roni Khardon, Scott Sanner:
Stochastic Planning and Lifted Inference. CoRR abs/1701.01048 (2017) - [i8]Ga Wu, Buser Say, Scott Sanner:
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains. CoRR abs/1704.07511 (2017) - 2016
- [i7]Marian-Andrei Rizoiu, Lexing Xie, Scott Sanner, Manuel Cebrián, Honglin Yu, Pascal Van Hentenryck:
Can this video be promoted? - Endogenous and exogenous popularity processes in social media. CoRR abs/1602.06033 (2016) - [i6]Kar Wai Lim, Scott Sanner, Shengbo Guo:
On the Mathematical Relationship between Expected n-call@k and the Relevance vs. Diversity Trade-off. CoRR abs/1609.06568 (2016) - 2014
- [i5]Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, Scott Sanner:
Preference Learning (Dagstuhl Seminar 14101). Dagstuhl Reports 4(3): 1-27 (2014) - 2013
- [i4]Luis Gustavo Vianna, Scott Sanner, Leliane Nunes de Barros:
Bounded Approximate Symbolic Dynamic Programming for Hybrid MDPs. CoRR abs/1309.6871 (2013) - 2012
- [i3]Scott Sanner, Karina Valdivia Delgado, Leliane Nunes de Barros:
Symbolic Dynamic Programming for Discrete and Continuous State MDPs. CoRR abs/1202.3762 (2012) - [i2]Scott Sanner, Craig Boutilier:
Practical Linear Value-approximation Techniques for First-order MDPs. CoRR abs/1206.6879 (2012) - [i1]Scott Sanner, Craig Boutilier:
Approximate Linear Programming for First-order MDPs. CoRR abs/1207.1415 (2012)
Coauthor Index
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