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ORCIDScott Sanner
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- affiliation: University of Toronto, Department of Mechanical and Industrial Engineering, Canada
- affiliation: Australian National University, Acton, USA
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2020 – today
- 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) - [c112]Jihwan Jeong, Scott Sanner, Akshat Kumar:
A Mixed-Integer Linear Programming Reduction of Disjoint Bilinear Programs via Symbolic Variable Elimination. CPAIOR 2023: 79-95 - [c111]Aravinth Chembu, Scott Sanner, Elias B. Khalil:
Scalable and Near-Optimal ε-Tube Clusterwise Regression. CPAIOR 2023: 254-263 - [c110]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 - [i42]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) - [i41]Zhenwei Tang, Griffin Floto, Armin Toroghi, Shichao Pei, Xiangliang Zhang, Scott Sanner:
LogicRec: Recommendation with Users' Logical Requirements. CoRR abs/2304.11722 (2023) - [i40]Aravinth Chembu, Scott Sanner:
A Generalized Framework for Predictive Clustering and Optimization. CoRR abs/2305.04364 (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) - [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 - [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
- [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) - [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 - [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
- [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) - [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 - [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)
2010 – 2019
- 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 - [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
- [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) - [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 - [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
- [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) - [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 - [r2]Scott Sanner, Kristian Kersting:
Symbolic Dynamic Programming. Encyclopedia of Machine Learning and Data Mining 2017: 1220-1228 - [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
- [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) - [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 - [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] - [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) - 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) - [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]