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- affiliation: University of Waterloo, ON, Canada
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2020 – today
- 2024
[j16]Yudong Luo, Yangchen Pan, Han Wang, Philip Torr, Pascal Poupart:
A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimization. RLJ 2: 573-592 (2024)- [j15]Ehsan Imani, Guojun Zhang, Runjia Li, Jun Luo, Pascal Poupart, Philip H. S. Torr, Yangchen Pan:
Label Alignment Regularization for Distribution Shift. J. Mach. Learn. Res. 25: 247:1-247:32 (2024) - [c136]Mohsin Hasan, Guojun Zhang, Kaiyang Guo, Xi Chen, Pascal Poupart:
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space. AAAI 2024: 12313-12321 - [c135]Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart:
Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks. AISTATS 2024: 3034-3042 - [c134]Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss:
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? ICML 2024 - [c133]Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart:
Confidence Aware Inverse Constrained Reinforcement Learning. ICML 2024 - [i72]Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss:
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? CoRR abs/2402.05015 (2024) - [i71]Oliver Schulte, Pascal Poupart:
Why Online Reinforcement Learning is Causal. CoRR abs/2403.04221 (2024) - [i70]Yudong Luo, Yangchen Pan, Han Wang, Philip Torr, Pascal Poupart:
A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimization. CoRR abs/2403.11062 (2024) - [i69]Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss:
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? CoRR abs/2406.06459 (2024) - [i68]Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi, Pascal Poupart:
A Critical Look At Tokenwise Reward-Guided Text Generation. CoRR abs/2406.07780 (2024) - [i67]Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart:
Confidence Aware Inverse Constrained Reinforcement Learning. CoRR abs/2406.16782 (2024) - [i66]Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi:
Uncertainty-Guided Optimization on Large Language Model Search Trees. CoRR abs/2407.03951 (2024) - [i65]Haolin Yu, Guojun Zhang, Pascal Poupart:
FedLog: Personalized Federated Classification with Less Communication and More Flexibility. CoRR abs/2407.08337 (2024) - [i64]Yanting Miao, William Loh, Suraj Kothawade, Pascal Poupart, Abdullah Rashwan, Yeqing Li:
Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning. CoRR abs/2407.12164 (2024) - [i63]Guiliang Liu, Sheng Xu, Shicheng Liu, Ashish Gaurav, Sriram Ganapathi Subramanian, Pascal Poupart:
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges. CoRR abs/2409.07569 (2024) - [i62]Niloufar Saeidi Mobarakeh, Behzad Khamidehi, Chunlin Li, Hamidreza Mirkhani, Fazel Arasteh, Mohammed Elmahgiubi, Weize Zhang, Kasra Rezaee, Pascal Poupart:
Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories. CoRR abs/2412.05717 (2024) - 2023
- [c132]Runcheng Liu, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart:
Attribute Controlled Dialogue Prompting. ACL (Findings) 2023: 2380-2389 - [c131]Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart:
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge. AISTATS 2023: 1942-1964 - [c130]Liam Hebert, Lukasz Golab, Pascal Poupart, Robin Cohen:
FedFormer: Contextual Federation with Attention in Reinforcement Learning. AAMAS 2023: 810-818 - [c129]Cynthia Huang, Pascal Poupart:
Defensive Collaborative Learning: Protecting Objective Privacy in Data Sharing. AAMAS 2023: 2845-2847 - [c128]Ivan Kobyzev, Aref Jafari, Mehdi Rezagholizadeh, Tianda Li, Alan Do-Omri, Peng Lu, Pascal Poupart, Ali Ghodsi:
Do we need Label Regularization to Fine-tune Pre-trained Language Models? EACL 2023: 166-177 - [c127]Amur Ghose, Pascal Poupart:
Contrastive Deterministic Autoencoders For Language Modeling. EMNLP (Findings) 2023: 8458-8476 - [c126]Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart:
Learning Soft Constraints From Constrained Expert Demonstrations. ICLR 2023 - [c125]Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart:
Benchmarking Constraint Inference in Inverse Reinforcement Learning. ICLR 2023 - [c124]Amur Ghose, Apurv Gupta, Yaoliang Yu, Pascal Poupart:
Batchnorm Allows Unsupervised Radial Attacks. NeurIPS 2023 - [c123]Yudong Luo, Guiliang Liu, Pascal Poupart, Yangchen Pan:
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient. NeurIPS 2023 - [c122]Guanren Qiao, Guiliang Liu, Pascal Poupart, Zhiqiang Xu:
Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations. NeurIPS 2023 - [i61]Runcheng Liu, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart:
Attribute Controlled Dialogue Prompting. CoRR abs/2307.05228 (2023) - [i60]Yudong Luo, Guiliang Liu, Pascal Poupart, Yangchen Pan
:
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient. CoRR abs/2307.08873 (2023) - [i59]Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart:
Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks. CoRR abs/2311.03683 (2023) - [i58]Mohsin Hasan, Guojun Zhang, Kaiyang Guo, Xi Chen, Pascal Poupart:
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space. CoRR abs/2312.09817 (2023) - 2022
- [j14]Guojun Zhang, Pascal Poupart, Yaoliang Yu:
Optimality and Stability in Non-Convex Smooth Games. J. Mach. Learn. Res. 23: 35:1-35:71 (2022) - [c121]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Decentralized Mean Field Games. AAAI 2022: 9439-9447 - [c120]Kashif Khan, Ruizhe Wang, Pascal Poupart:
WatClaimCheck: A new Dataset for Claim Entailment and Inference. ACL (1) 2022: 1293-1304 - [c119]Md. Akmal Haidar, Mehdi Rezagholizadeh, Abbas Ghaddar, Khalil Bibi, Philippe Langlais, Pascal Poupart:
CILDA: Contrastive Data Augmentation Using Intermediate Layer Knowledge Distillation. COLING 2022: 4707-4713 - [c118]Aref Jafari, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart, Ali Ghodsi:
Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization. EMNLP (Findings) 2022: 5260-5269 - [c117]Guiliang Liu, Ashutosh Adhikari, Amir-massoud Farahmand, Pascal Poupart:
Learning Object-Oriented Dynamics for Planning from Text. ICLR 2022 - [c116]Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart:
Distributional Reinforcement Learning with Monotonic Splines. ICLR 2022 - [c115]Md. Akmal Haidar, Nithin Anchuri, Mehdi Rezagholizadeh, Abbas Ghaddar, Philippe Langlais, Pascal Poupart:
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation. NAACL-HLT (Findings) 2022: 1389-1400 - [c114]Guiliang Liu, Yudong Luo, Oliver Schulte, Pascal Poupart:
Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game. NeurIPS 2022 - [c113]Elliot Nelson, Debarun Bhattacharjya, Tian Gao, Miao Liu, Djallel Bouneffouf, Pascal Poupart:
Linearizing contextual bandits with latent state dynamics. UAI 2022: 1477-1487 - [c112]Kira A. Selby, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart:
Learning functions on multiple sets using multi-set transformers. UAI 2022: 1760-1770 - [i57]Md. Akmal Haidar, Mehdi Rezagholizadeh, Abbas Ghaddar, Khalil Bibi, Philippe Langlais, Pascal Poupart:
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation. CoRR abs/2204.07674 (2022) - [i56]Ivan Kobyzev, Aref Jafari, Mehdi Rezagholizadeh, Tianda Li, Alan Do-Omri, Peng Lu, Ali Ghodsi, Pascal Poupart:
Towards Understanding Label Regularization for Fine-tuning Pre-trained Language Models. CoRR abs/2205.12428 (2022) - [i55]Liam Hebert, Lukasz Golab, Pascal Poupart, Robin Cohen:
FedFormer: Contextual Federation with Attention in Reinforcement Learning. CoRR abs/2205.13697 (2022) - [i54]Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart:
Learning Soft Constraints From Constrained Expert Demonstrations. CoRR abs/2206.01311 (2022) - [i53]Haolin Yu, Kaiyang Guo, Mahdi Karami, Xi Chen, Guojun Zhang, Pascal Poupart:
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process. CoRR abs/2206.06357 (2022) - [i52]Mohsin Hasan, Zehao Zhang, Kaiyang Guo, Mahdi Karami, Guojun Zhang, Xi Chen, Pascal Poupart:
Robust One Round Federated Learning with Predictive Space Bayesian Inference. CoRR abs/2206.09526 (2022) - [i51]Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart:
Benchmarking Constraint Inference in Inverse Reinforcement Learning. CoRR abs/2206.09670 (2022) - [i50]Kira A. Selby, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart:
Learning Functions on Multiple Sets using Multi-Set Transformers. CoRR abs/2206.15444 (2022) - [i49]Ehsan Imani, Guojun Zhang, Jun Luo, Pascal Poupart, Yangchen Pan:
Label Alignment Regularization for Distribution Shift. CoRR abs/2211.14960 (2022) - [i48]Aref Jafari, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart, Ali Ghodsi:
Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization. CoRR abs/2212.05998 (2022) - 2021
- [c111]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Partially Observable Mean Field Reinforcement Learning. AAMAS 2021: 537-545 - [c110]Elmira Amirloo Abolfathi, Mohsen Rohani, Ershad Banijamali, Jun Luo, Pascal Poupart:
Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map. CVPR 2021: 8494-8503 - [c109]Ershad Banijamali, Mohsen Rohani, Elmira Amirloo Abolfathi, Jun Luo, Pascal Poupart:
Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning. ICCV 2021: 15601-15610 - [c108]Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart:
Quantifying and Improving Transferability in Domain Generalization. NeurIPS 2021: 10957-10970 - [c107]Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart:
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning. NeurIPS 2021: 19622-19636 - [i47]Elmira Amirloo Abolfathi, Mohsen Rohani, Ershad Banijamali, Jun Luo, Pascal Poupart:
Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map. CoRR abs/2103.01039 (2021) - [i46]Kira A. Selby, Yinong Wang, Ruizhe Wang, Peyman Passban, Ahmad Rashid, Mehdi Rezagholizadeh, Pascal Poupart:
Robust Embeddings Via Distributions. CoRR abs/2104.08420 (2021) - [i45]Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart:
Quantifying and Improving Transferability in Domain Generalization. CoRR abs/2106.03632 (2021) - [i44]Xiangyu Sun, Guiliang Liu, Pascal Poupart, Oliver Schulte:
NTS-NOTEARS: Learning Nonparametric Temporal DAGs With Time-Series Data and Prior Knowledge. CoRR abs/2109.04286 (2021) - [i43]Md. Akmal Haidar, Nithin Anchuri, Mehdi Rezagholizadeh, Abbas Ghaddar, Philippe Langlais, Pascal Poupart:
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation. CoRR abs/2109.10164 (2021) - [i42]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Decentralized Mean Field Games. CoRR abs/2112.09099 (2021) - [i41]Xiangle Cheng, James He, Shihan Xiao, Yingxue Zhang, Zhitang Chen, Pascal Poupart, Fenglin Li:
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks. CoRR abs/2112.12321 (2021) - 2020
- [j13]Amur Ghose, Priyank Jaini, Pascal Poupart:
Learning directed acyclic graph SPNs in sub-quadratic time. Int. J. Approx. Reason. 120: 48-73 (2020) - [j12]Haonan Duan, Abdullah Rashwan, Pascal Poupart, Zhitang Chen:
Discriminative training of feed-forward and recurrent sum-product networks by extended Baum-Welch. Int. J. Approx. Reason. 124: 66-81 (2020) - [j11]Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart:
Representation Learning for Dynamic Graphs: A Survey. J. Mach. Learn. Res. 21: 70:1-70:73 (2020) - [c106]Rishab Goel, Seyed Mehran Kazemi, Marcus A. Brubaker, Pascal Poupart:
Diachronic Embedding for Temporal Knowledge Graph Completion. AAAI 2020: 3988-3995 - [c105]Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde:
Multi Type Mean Field Reinforcement Learning. AAMAS 2020: 411-419 - [c104]Nabiha Asghar, Lili Mou, Kira A. Selby, Kevin D. Pantasdo, Pascal Poupart, Xin Jiang:
Progressive Memory Banks for Incremental Domain Adaptation. ICLR 2020 - [c103]Haonan Duan, Saeed Nejati, George Trimponias, Pascal Poupart, Vijay Ganesh:
Online Bayesian Moment Matching based SAT Solver Heuristics. ICML 2020: 2710-2719 - [c102]Yudong Luo, Oliver Schulte, Pascal Poupart:
Inverse Reinforcement Learning for Team Sports: Valuing Actions and Players. IJCAI 2020: 3356-3363 - [c101]Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu:
Unsupervised Multilingual Alignment using Wasserstein Barycenter. IJCAI 2020: 3702-3708 - [c100]Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikulas Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, William L. Hamilton:
Learning Dynamic Belief Graphs to Generalize on Text-Based Games. NeurIPS 2020 - [c99]Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan:
Learning Agent Representations for Ice Hockey. NeurIPS 2020 - [c98]Amur Ghose, Abdullah Rashwan, Pascal Poupart:
Batch norm with entropic regularization turns deterministic autoencoders into generative models. UAI 2020: 1079-1088 - [i40]Abdullah Rashwan, Rishav Agarwal, Agastya Kalra, Pascal Poupart:
MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection. CoRR abs/2001.03194 (2020) - [i39]Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu:
Unsupervised Multilingual Alignment using Wasserstein Barycenter. CoRR abs/2002.00743 (2020) - [i38]Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde:
Multi Type Mean Field Reinforcement Learning. CoRR abs/2002.02513 (2020) - [i37]Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikulas Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, William L. Hamilton:
Learning Dynamic Knowledge Graphs to Generalize on Text-Based Games. CoRR abs/2002.09127 (2020) - [i36]Amur Ghose, Abdullah Rashwan, Pascal Poupart:
Batch norm with entropic regularization turns deterministic autoencoders into generative models. CoRR abs/2002.10631 (2020) - [i35]Guojun Zhang, Pascal Poupart, Yaoliang Yu:
Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization. CoRR abs/2002.11875 (2020) - [i34]Nabiha Asghar, Ivan Kobyzev, Jesse Hoey, Pascal Poupart, Muhammad Bilal Sheikh:
Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory. CoRR abs/2003.03645 (2020) - [i33]Allen Houze Wang, Priyank Jaini, Yaoliang Yu, Pascal Poupart:
Complete Hierarchy of Relaxation for Constrained Signomial Positivity. CoRR abs/2003.03731 (2020) - [i32]Guojun Zhang, Kaiwen Wu, Pascal Poupart, Yaoliang Yu:
Newton-type Methods for Minimax Optimization. CoRR abs/2006.14592 (2020) - [i31]Ershad Banijamali, Mohsen Rohani, Elmira Amirloo Abolfathi, Jun Luo, Pascal Poupart:
Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning. CoRR abs/2012.13478 (2020) - [i30]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Partially Observable Mean Field Reinforcement Learning. CoRR abs/2012.15791 (2020)
2010 – 2019
- 2019
- [c97]Bolin Wei, Shuai Lu, Lili Mou, Hao Zhou, Pascal Poupart, Ge Li, Zhi Jin:
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? a Comparison between Dialog and Translation. ICASSP 2019: 7290-7294 - [c96]Abdullah Rashwan, Agastya Kalra, Pascal Poupart:
Matrix Nets: A New Deep Architecture for Object Detection. ICCV Workshops 2019: 2025-2028 - [c95]Guojun Zhang, Pascal Poupart, George Trimponias:
Comparing EM with GD in Mixture Models of Two Components. UAI 2019: 164-174 - [c94]Ricardo Salmon, Pascal Poupart:
On the Relationship Between Satisfiability and Markov Decision Processes. UAI 2019: 1105-1115 - [i29]Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Pranav Subramani, Nicola Di Mauro, Pascal Poupart, Kristian Kersting:
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks. CoRR abs/1901.03704 (2019) - [i28]Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart:
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey. CoRR abs/1905.11485 (2019) - [i27]Rishab Goel, Seyed Mehran Kazemi, Marcus A. Brubaker, Pascal Poupart:
Diachronic Embedding for Temporal Knowledge Graph Completion. CoRR abs/1907.03143 (2019) - [i26]Guojun Zhang, Pascal Poupart, George Trimponias:
Comparing EM with GD in Mixture Models of Two Components. CoRR abs/1907.03783 (2019) - [i25]Seyed Mehran Kazemi, Rishab Goel, Sepehr Eghbali, Janahan Ramanan, Jaspreet Sahota, Sanjay Thakur, Stella Wu, Cathal Smyth, Pascal Poupart, Marcus A. Brubaker:
Time2Vec: Learning a Vector Representation of Time. CoRR abs/1907.05321 (2019) - [i24]Abdullah Rashwan, Agastya Kalra, Pascal Poupart:
Matrix Nets: A New Deep Architecture for Object Detection. CoRR abs/1908.04646 (2019) - 2018
- [c93]Lei Sha, Lili Mou, Tianyu Liu, Pascal Poupart, Sujian Li, Baobao Chang, Zhifang Sui:
Order-Planning Neural Text Generation From Structured Data. AAAI 2018: 5414-5421 - [c92]Zhuoshu Li, Zhitang Chen, Pascal Poupart, Sanmay Das, Yanhui Geng:
Faster Policy Adaptation in Environments with Exogeneity: A State Augmentation Approach. AAMAS 2018: 1035-1043 - [c91]Hareesh Bahuleyan, Lili Mou, Olga Vechtomova, Pascal Poupart:
Variational Attention for Sequence-to-Sequence Models. COLING 2018: 1672-1682 - [c90]Nabiha Asghar, Pascal Poupart, Jesse Hoey, Xin Jiang, Lili Mou:
Affective Neural Response Generation. ECIR 2018: 154-166 - [c89]Jia Liang, Hari Govind V. K., Pascal Poupart, Krzysztof Czarnecki, Vijay Ganesh:
An Empirical Study of Branching Heuristics through the Lens of Global Learning Rate. IJCAI 2018: 5319-5323 - [c88]Vikash Goel, Jameson Weng, Pascal Poupart:
Unsupervised Video Object Segmentation for Deep Reinforcement Learning. NeurIPS 2018: 5688-5699 - [c87]Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias:
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks. NeurIPS 2018: 6944-6954 - [c86]Priyank Jaini, Pascal Poupart, Yaoliang Yu:
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation. NeurIPS 2018: 7136-7145 - [c85]Jongmin Lee, Geon-hyeong Kim, Pascal Poupart, Kee-Eung Kim:
Monte-Carlo Tree Search for Constrained POMDPs. NeurIPS 2018: 7934-7943 - [c84]Cory J. Butz, Jhonatan de S. Oliveira, André E. dos Santos, André L. Teixeira, Pascal Poupart, Agastya Kalra:
An Empirical Study of Methods for SPN Learning and Inference. PGM 2018: 49-60 - [c83]Priyank Jaini, Amur Ghose, Pascal Poupart:
Prometheus : Directly Learning Acyclic Directed Graph Structures for Sum-Product Networks. PGM 2018: 181-192 - [c82]Abdullah Rashwan, Pascal Poupart, Zhitang Chen:
Discriminative Training of Sum-Product Networks by Extended Baum-Welch. PGM 2018: 356-367 - [i23]Vik Goel, Jameson Weng, Pascal Poupart:
Unsupervised Video Object Segmentation for Deep Reinforcement Learning. CoRR abs/1805.07780 (2018) - [i22]Nabiha Asghar, Lili Mou, Kira A. Selby, Kevin D. Pantasdo, Pascal Poupart, Xin Jiang:
Progressive Memory Banks for Incremental Domain Adaptation. CoRR abs/1811.00239 (2018) - 2017
- [c81]Wenchao Du, Pascal Poupart, Wei Xu:
Discovering Conversational Dependencies between Messages in Dialogs. AAAI 2017: 4917-4918 - [c80]Wilson Hsu, Agastya Kalra, Pascal Poupart:
Online Structure Learning for Sum-Product Networks with Gaussian Leaves. ICLR (Workshop) 2017 - [c79]Priyank Jaini, Zhitang Chen, Pablo Carbajal, Edith Law, Laura Middleton, Kayla Regan, Mike Schaekermann, George Trimponias, James Tung, Pascal Poupart:
Online Bayesian Transfer Learning for Sequential Data Modeling. ICLR (Poster) 2017 - [c78]Jongmin Lee, Youngsoo Jang, Pascal Poupart, Kee-Eung Kim:
Constrained Bayesian Reinforcement Learning via Approximate Linear Programming. IJCAI 2017: 2088-2095 - [c77]Ershad Banijamali, Ali Ghodsi, Pascal Poupart:
Generative mixture of networks. IJCNN 2017: 3753-3760 - [c76]Jia Hui Liang, Hari Govind V. K., Pascal Poupart, Krzysztof Czarnecki, Vijay Ganesh:
An Empirical Study of Branching Heuristics Through the Lens of Global Learning Rate. SAT 2017: 119-135 - [c75]Saeed Nejati, Zack Newsham, Joseph Scott, Jia Hui Liang, Catherine H. Gebotys, Pascal Poupart, Vijay Ganesh:
A Propagation Rate Based Splitting Heuristic for Divide-and-Conquer Solvers. SAT 2017: 251-260 - [c74]Nabiha Asghar, Pascal Poupart, Xin Jiang, Hang Li:
Deep Active Learning for Dialogue Generation. *SEM 2017: 78-83 - [r4]Pascal Poupart:
Bayesian Reinforcement Learning. Encyclopedia of Machine Learning and Data Mining 2017: 116-120 - [r3]Pascal Poupart:
Partially Observable Markov Decision Processes. Encyclopedia of Machine Learning and Data Mining 2017: 959-966 - [i21]Wilson Hsu, Agastya Kalra, Pascal Poupart:
Online Structure Learning for Sum-Product Networks with Gaussian Leaves. CoRR abs/1701.05265 (2017) - [i20]Ershad Banijamali, Ali Ghodsi, Pascal Poupart:
Generative Mixture of Networks. CoRR abs/1702.03307 (2017) - [i19]Pengfei Zhu, Xin Li, Pascal Poupart:
On Improving Deep Reinforcement Learning for POMDPs. CoRR abs/1704.07978 (2017) - [i18]Lei Sha, Lili Mou, Tianyu Liu, Pascal Poupart, Sujian Li, Baobao Chang, Zhifang Sui:
Order-Planning Neural Text Generation From Structured Data. CoRR abs/1709.00155 (2017) - [i17]Nabiha Asghar, Pascal Poupart, Jesse Hoey, Xin Jiang, Lili Mou:
Affective Neural Response Generation. CoRR abs/1709.03968 (2017) - [i16]Bolin Wei, Shuai Lu, Lili Mou, Hao Zhou, Pascal Poupart, Ge Li, Zhi Jin:
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation. CoRR abs/1712.02250 (2017) - [i15]Hareesh Bahuleyan, Lili Mou, Olga Vechtomova, Pascal Poupart:
Variational Attention for Sequence-to-Sequence Models. CoRR abs/1712.08207 (2017) - 2016
- [c73]Jia Hui Liang, Vijay Ganesh, Pascal Poupart, Krzysztof Czarnecki:
Exponential Recency Weighted Average Branching Heuristic for SAT Solvers. AAAI 2016: 3434-3440 - [c72]Mazen Melibari, Pascal Poupart, Prashant Doshi:
Decision Sum-Product-Max Networks. AAAI 2016: 4234-4235 - [c71]Zhitang Chen, Pascal Poupart, Yanhui Geng:
Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings. AISTATS 2016: 573-581 - [c70]Abdullah Rashwan, Han Zhao, Pascal Poupart:
Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks. AISTATS 2016: 1469-1477 - [c69]Mazen A. Melibari, Pascal Poupart, Prashant Doshi:
Sum-Product-Max Networks for Tractable Decision Making: (Extended Abstract). AAMAS 2016: 1419-1420 - [c68]Pascal Poupart, Zhitang Chen, Priyank Jaini, Fred Fung, Hengky Susanto, Yanhui Geng, Li Chen, Kai Chen, Hao Jin:
Online flow size prediction for improved network routing. ICNP 2016: 1-6 - [c67]Mazen Melibari, Pascal Poupart, Prashant Doshi:
Sum-Product-Max Networks for Tractable Decision Making. IJCAI 2016: 1846-1852 - [c66]Han Zhao, Pascal Poupart, Geoffrey J. Gordon:
A Unified Approach for Learning the Parameters of Sum-Product Networks. NIPS 2016: 433-441 - [c65]Wei-Shou Hsu, Pascal Poupart:
Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics. NIPS 2016: 4529-4537 - [c64]Priyank Jaini, Abdullah Rashwan, Han Zhao, Yue Liu, Ershad Banijamali, Zhitang Chen, Pascal Poupart:
Online Algorithms for Sum-Product Networks with Continuous Variables. Probabilistic Graphical Models 2016: 228-239 - [c63]Mazen Melibari, Pascal Poupart, Prashant Doshi, George Trimponias:
Dynamic Sum Product Networks for Tractable Inference on Sequence Data. Probabilistic Graphical Models 2016: 345-355 - [c62]Jia Hui Liang, Vijay Ganesh, Pascal Poupart, Krzysztof Czarnecki:
Learning Rate Based Branching Heuristic for SAT Solvers. SAT 2016: 123-140 - [c61]Hujie Wang, Pascal Poupart:
Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings. SemEval@NAACL-HLT 2016: 861-865 - [i14]Han Zhao, Pascal Poupart, Geoffrey J. Gordon:
A Unified Approach for Learning the Parameters of Sum-Product Networks. CoRR abs/1601.00318 (2016) - [i13]Priyank Jaini, Pascal Poupart:
Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching. CoRR abs/1609.05881 (2016) - [i12]Wenchao Du, Pascal Poupart, Wei Xu:
Discovering Conversational Dependencies between Messages in Dialogs. CoRR abs/1612.02801 (2016) - [i11]Nabiha Asghar, Pascal Poupart, Xin Jiang, Hang Li:
Online Sequence-to-Sequence Active Learning for Open-Domain Dialogue Generation. CoRR abs/1612.03929 (2016) - 2015
- [j10]Marek Grzes, Pascal Poupart, Xiao Yang, Jesse Hoey:
Energy Efficient Execution of POMDP Policies. IEEE Trans. Cybern. 45(11): 2484-2497 (2015) - [c60]Han Zhao, Pascal Poupart, Yongfeng Zhang, Martin Lysy:
SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering. AAAI 2015: 3188-3195 - [c59]Pascal Poupart, Aarti Malhotra, Pei Pei, Kee-Eung Kim, Bongseok Goh, Michael Bowling:
Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes. AAAI 2015: 3342-3348 - [c58]Marek Grzes, Pascal Poupart:
Incremental Policy Iteration with Guaranteed Escape from Local Optima in POMDP Planning. AAMAS 2015: 1249-1257 - [c57]Han Zhao, Mazen Melibari, Pascal Poupart:
On the Relationship between Sum-Product Networks and Bayesian Networks. ICML 2015: 116-124 - [c56]Han Zhao, Zhengdong Lu, Pascal Poupart:
Self-Adaptive Hierarchical Sentence Model. IJCAI 2015: 4069-4076 - [c55]Pascal Poupart:
Think fast - resource constrained reasoning and planning under uncertainty. TAAI 2015: 30 - [i10]Han Zhao, Mazen Melibari, Pascal Poupart:
On the Relationship between Sum-Product Networks and Bayesian Networks. CoRR abs/1501.01239 (2015) - [i9]Han Zhao, Zhengdong Lu, Pascal Poupart:
Self-Adaptive Hierarchical Sentence Model. CoRR abs/1504.05070 (2015) - [i8]Mazen Melibari, Pascal Poupart, Prashant Doshi:
Dynamic Sum Product Networks for Tractable Inference on Sequence Data. CoRR abs/1511.04412 (2015) - 2014
- [c54]Igor Kiselev, Pascal Poupart:
A Novel Single-DBN Generative Model for Optimizing POMDP Controllers by Probabilistic Inference. AAAI 2014: 3112-3113 - [c53]Marek Grzes, Pascal Poupart:
POMDP planning and execution in an augmented space. AAMAS 2014: 757-764 - [c52]Igor Kiselev, Pascal Poupart:
Policy optimization by marginal-map probabilistic inference in generative models. AAMAS 2014: 1611-1612 - [i7]Wei Li, Pascal Poupart, Peter van Beek:
Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference. CoRR abs/1401.3886 (2014) - [i6]Han Zhao, Pascal Poupart:
A Sober Look at Spectral Learning. CoRR abs/1406.4631 (2014) - 2013
- [c51]Marek Grzes, Pascal Poupart, Jesse Hoey:
Controller Compilation and Compression for Resource Constrained Applications. ADT 2013: 193-207 - [c50]Ehsan Abbasnejad, Scott Sanner, Edwin V. Bonilla, Pascal Poupart:
Learning Community-Based Preferences via Dirichlet Process Mixtures of Gaussian Processes. IJCAI 2013: 1213-1219 - [c49]Marek Grzes, Pascal Poupart, Jesse Hoey:
Isomorph-Free Branch and Bound Search for Finite State Controllers. IJCAI 2013: 2282-2290 - [c48]Omar Zia Khan, Pascal Poupart, John Mark Agosta:
Iterative Model Refinement of Recommender MDPs Based on Expert Feedback. ECML/PKDD (1) 2013: 162-177 - [i5]Pascal Poupart, Craig Boutilier:
Vector-space Analysis of Belief-state Approximation for POMDPs. CoRR abs/1301.2304 (2013) - [i4]Pascal Poupart, Luis E. Ortiz, Craig Boutilier:
Value-Directed Sampling Methods for POMDPs. CoRR abs/1301.2305 (2013) - [i3]Pascal Poupart, Craig Boutilier:
Value-Directed Belief State Approximation for POMDPs. CoRR abs/1301.3887 (2013) - 2012
- [j9]Jason D. Williams, Kai Yu, Brahim Chaib-draa, Oliver Lemon, Roberto Pieraccini, Olivier Pietquin, Pascal Poupart, Steve J. Young:
Introduction to the Issue on Advances in Spoken Dialogue Systems and Mobile Interface. IEEE J. Sel. Top. Signal Process. 6(8): 889-890 (2012) - [j8]Jesse Hoey, Craig Boutilier, Pascal Poupart, Patrick Olivier, Andrew Monk, Alex Mihailidis:
People, sensors, decisions: Customizable and adaptive technologies for assistance in healthcare. ACM Trans. Interact. Intell. Syst. 2(4): 20:1-20:36 (2012) - [c47]Aditya Tayal, Pascal Poupart, Yuying Li:
Hierarchical Double Dirichlet Process Mixture of Gaussian Processes. AAAI 2012: 1126-1133 - [c46]Meena Abdelmaseeh, Pascal Poupart, Benn Smith, Daniel W. Stashuk:
Muscle Categorization Using Quantitative Needle Electromyography: A 2-Stage Gaussian Mixture Model Based Approach. ICMLA (1) 2012: 548-553 - [c45]Zahra Zamani, Scott Sanner, Pascal Poupart, Kristian Kersting:
Symbolic Dynamic Programming for Continuous State and Observation POMDPs. NIPS 2012: 1403-1411 - [c44]Dongho Kim, Kee-Eung Kim, Pascal Poupart:
Cost-Sensitive Exploration in Bayesian Reinforcement Learning. NIPS 2012: 3077-3085 - [p1]Nikos Vlassis, Mohammad Ghavamzadeh, Shie Mannor, Pascal Poupart:
Bayesian Reinforcement Learning. Reinforcement Learning 2012: 359-386 - [i2]Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Yungjen Tung, Allen Caine:
Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker. CoRR abs/1203.3500 (2012) - [i1]Marc Toussaint, Laurent Charlin, Pascal Poupart:
Hierarchical POMDP Controller Optimization by Likelihood Maximization. CoRR abs/1206.3291 (2012) - 2011
- [j7]Wei Li, Pascal Poupart, Peter van Beek:
Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference. J. Artif. Intell. Res. 40: 729-765 (2011) - [c43]James Yungjen Tung, Jonathan F. L. Semple, Wei X. Woo, Wei-Shou Hsu, Mathieu Sinn, Eric A. Roy, Pascal Poupart:
Ambulatory Assessment of Lifestyle Factors for Alzheimer's Disease and Related Dementias. AAAI Spring Symposium: Computational Physiology 2011 - [c42]Pascal Poupart, Kee-Eung Kim, Dongho Kim:
Closing the Gap: Improved Bounds on Optimal POMDP Solutions. ICAPS 2011 - [c41]Mathieu Sinn, Pascal Poupart:
Smart walkers!: enhancing the mobility of the elderly. AAMAS 2011: 1133-1134 - [c40]Pascal Poupart, Tobias Lang, Marc Toussaint:
Escaping local optima in POMDP planning as inference. AAMAS 2011: 1263-1264 - [c39]Richard Zhi-Ling Hu, Adam Hartfiel, James Yungjen Tung, Adel H. Fakih, Jesse Hoey, Pascal Poupart:
3D Pose tracking of walker users' lower limb with a structured-light camera on a moving platform. CVPR Workshops 2011: 29-36 - [c38]Muhammad Bilal Sheikh, Umar Farooq Minhas, Omar Zia Khan, Ashraf Aboulnaga, Pascal Poupart, David J. Taylor:
A bayesian approach to online performance modeling for database appliances using gaussian models. ICAC 2011: 121-130 - [c37]Mathieu Sinn, Pascal Poupart:
Error Bounds for Online Predictions of Linear-Chain Conditional Random Fields: Application to Activity Recognition for Users of Rolling Walkers. ICMLA (2) 2011: 1-6 - [c36]Robby Goetschalckx, Pascal Poupart, Jesse Hoey:
Continuous Correlated Beta Processes. IJCAI 2011: 1269-1274 - [c35]Dongho Kim, Jaesong Lee, Kee-Eung Kim, Pascal Poupart:
Point-Based Value Iteration for Constrained POMDPs. IJCAI 2011: 1968-1974 - [c34]Omar Zia Khan, Pascal Poupart, John Mark Agosta:
Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints. NIPS 2011: 2591-2599 - [c33]Pascal Poupart, Tobias Lang, Marc Toussaint:
Analyzing and Escaping Local Optima in Planning as Inference for Partially Observable Domains. ECML/PKDD (2) 2011: 613-628 - [c32]Mathieu Sinn, Pascal Poupart:
Asymptotic Theory for Linear-Chain Conditional Random Fields. AISTATS 2011: 679-687 - 2010
- [j6]Jesse Hoey, Pascal Poupart, Axel von Bertoldi, Tammy Craig, Craig Boutilier, Alex Mihailidis:
Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process. Comput. Vis. Image Underst. 114(5): 503-519 (2010) - [c31]Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Yungjen Tung, Allen Caine:
Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker. UAI 2010: 392-400 - [r2]Pascal Poupart:
Bayesian Reinforcement Learning. Encyclopedia of Machine Learning 2010: 90-93 - [r1]Pascal Poupart:
Partially Observable Markov Decision Processes. Encyclopedia of Machine Learning 2010: 754-760
2000 – 2009
- 2009
- [j5]Razvan C. Bunescu, Vitor R. Carvalho, Jan Chomicki, Vincent Conitzer, Michael T. Cox, Virginia Dignum, Zachary Dodds, Mark Dredze, David Furcy, Evgeniy Gabrilovich, Mehmet H. Göker, Hans W. Guesgen, Haym Hirsh, Dietmar Jannach, Ulrich Junker, Wolfgang Ketter, Alfred Kobsa, Sven Koenig, Tessa A. Lau, Lundy Lewis, Eric T. Matson, Ted Metzler, Rada Mihalcea, Bamshad Mobasher, Joelle Pineau, Pascal Poupart, Anita Raja, Wheeler Ruml, Norman M. Sadeh, Guy Shani, Daniel G. Shapiro, Sarabjot Singh Anand, Matthew E. Taylor, Kiri Wagstaff, Trey Smith, William E. Walsh, Rong Zhou:
AAAI 2008 Workshop Reports. AI Mag. 30(1): 108-118 (2009) - [c30]Omar Zia Khan, Pascal Poupart, James P. Black:
Minimal Sufficient Explanations for Factored Markov Decision Processes. ICAPS 2009 - [c29]Samantha Ng, Adel H. Fakih, Adam Fourney, Pascal Poupart, John S. Zelek:
Probabilistic 3D Tracking: Rollator Users' Leg Pose from Coronal Images. CRV 2009: 53-60 - [c28]Omar Zia Khan, Pascal Poupart, James P. Black:
Minimal Sufficient Explanations for MDPs. ExaCt 2009: 48-59 - 2008
- [c27]Wei Li, Pascal Poupart, Peter van Beek:
Exploiting Causal Independence Using Weighted Model Counting. AAAI 2008: 337-343 - [c26]Guy Shani, Pascal Poupart, Ronen I. Brafman, Solomon Eyal Shimony:
Efficient ADD Operations for Point-Based Algorithms. ICAPS 2008: 330-337 - [c25]Omar Zia Khan, Pascal Poupart, James P. Black:
Explaining recommendations generated by MDPs. ExaCt 2008: 13-24 - [c24]Pascal Poupart, Nikos Vlassis:
Model-based Bayesian Reinforcement Learning in Partially Observable Domains. ISAIM 2008 - [c23]Marc Toussaint, Laurent Charlin, Pascal Poupart:
Hierarchical POMDP Controller Optimization by Likelihood Maximization. UAI 2008: 562-570 - [c22]Taehyun Park, Edward Lank, Pascal Poupart, Michael A. Terry:
Is the sky pure today? AwkChecker: an assistive tool for detecting and correcting collocation errors. UIST 2008: 121-130 - 2007
- [c21]Andy Chiu, Pascal Poupart, Chrysanne DiMarco:
Generating Lexical Analogies Using Dependency Relations. EMNLP-CoNLL 2007: 561-570 - 2006
- [j4]Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans:
Constraint-based optimization and utility elicitation using the minimax decision criterion. Artif. Intell. 170(8-9): 686-713 (2006) - [j3]Wolfgang Achtner, Esma Aïmeur, Sarabjot Singh Anand, Douglas E. Appelt, Naveen Ashish, Tiffany Barnes, Joseph E. Beck, M. Bernardine Dias, Prashant Doshi, Chris Drummond, William Elazmeh, Ariel Felner, Dayne Freitag, Hector Geffner, Christopher W. Geib, Richard Goodwin, Robert C. Holte, Frank Hutter, Fair Isaac, Nathalie Japkowicz, Gal A. Kaminka, Sven Koenig, Michail G. Lagoudakis, David B. Leake, Lundy Lewis, Hugo Liu, Ted Metzler, Rada Mihalcea, Bamshad Mobasher, Pascal Poupart, David V. Pynadath, Thomas Roth-Berghofer, Wheeler Ruml, Stefan Schulz, Sven Schwarz, Stephanie Seneff, Amit P. Sheth, Ron Sun, Michael Thielscher, Afzal Upal, Jason D. Williams, Steve J. Young, Dmitry Zelenko:
Reports on the Twenty-First National Conference on Artificial Intelligence (AAAI-06) Workshop Program. AI Mag. 27(4): 92-102 (2006) - [j2]Josep M. Porta, Nikos Vlassis, Matthijs T. J. Spaan, Pascal Poupart:
Point-Based Value Iteration for Continuous POMDPs. J. Mach. Learn. Res. 7: 2329-2367 (2006) - [j1]Jennifer Boger, Jesse Hoey, Pascal Poupart, Craig Boutilier, Geoff R. Fernie, Alex Mihailidis:
A Planning System Based on Markov Decision Processes to Guide People With Dementia Through Activities of Daily Living. IEEE Trans. Inf. Technol. Biomed. 10(2): 323-333 (2006) - [c20]Wei Li, Peter van Beek, Pascal Poupart:
Performing Incremental Bayesian Inference by Dynamic Model Counting. AAAI 2006: 1173-1179 - [c19]Kevin Regan, Pascal Poupart, Robin Cohen:
Bayesian Reputation Modeling in E-Marketplaces Sensitive to Subjectivity, Deception and Change. AAAI 2006: 1206-1212 - [c18]Tao Wang, Pascal Poupart, Michael H. Bowling, Dale Schuurmans:
Compact, Convex Upper Bound Iteration for Approximate POMDP Planning. AAAI 2006: 1245-1252 - [c17]Pascal Poupart, Nikos Vlassis, Jesse Hoey, Kevin Regan:
An analytic solution to discrete Bayesian reinforcement learning. ICML 2006: 697-704 - [c16]Laurent Charlin, Pascal Poupart, Romy Shioda:
Automated Hierarchy Discovery for Planning in Partially Observable Environments. NIPS 2006: 225-232 - 2005
- [b1]Pascal Poupart:
Exploiting structure to efficiently solve large scale partially observable Markov decision processes. University of Toronto, Canada, 2005 - [c15]Jesse Hoey, Pascal Poupart, Craig Boutilier, Alex Mihailidis:
POMDP Models for Assistive Technology. AAAI Fall Symposium: Caring Machines 2005: 51-58 - [c14]Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans:
Regret-based Utility Elicitation in Constraint-based Decision Problems. IJCAI 2005: 929-934 - [c13]Jennifer Boger, Pascal Poupart, Jesse Hoey, Craig Boutilier, Geoff R. Fernie, Alex Mihailidis:
A Decision-Theoretic Approach to Task Assistance for Persons with Dementia. IJCAI 2005: 1293-1299 - [c12]Jesse Hoey, Pascal Poupart:
Solving POMDPs with Continuous or Large Discrete Observation Spaces. IJCAI 2005: 1332-1338 - [c11]Kevin Regan, Robin Cohen, Pascal Poupart:
The Advisor-POMDP: A Principled Approach to Trust through Reputation in Electronic Markets. PST 2005 - [c10]Jason D. Williams, Pascal Poupart, Steve J. Young:
Partially Observable Markov Decision Processes with Continuous Observations for Dialogue Management. SIGDIAL Workshop 2005: 25-34 - 2004
- [c9]Pascal Poupart, Craig Boutilier:
VDCBPI: an Approximate Scalable Algorithm for Large POMDPs. NIPS 2004: 1081-1088 - 2003
- [c8]Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans:
Constraint-Based Optimization with the Minimax Decision Criterion. CP 2003: 168-182 - [c7]Pascal Poupart, Craig Boutilier:
Bounded Finite State Controllers. NIPS 2003: 823-830 - 2002
- [c6]Relu Patrascu, Pascal Poupart, Dale Schuurmans, Craig Boutilier, Carlos Guestrin:
Greedy Linear Value-Approximation for Factored Markov Decision Processes. AAAI/IAAI 2002: 285-291 - [c5]Pascal Poupart, Craig Boutilier, Relu Patrascu, Dale Schuurmans:
Piecewise Linear Value Function Approximation for Factored MDPs. AAAI/IAAI 2002: 292-299 - [c4]Pascal Poupart, Craig Boutilier:
Value-Directed Compression of POMDPs. NIPS 2002: 1547-1554 - 2001
- [c3]Pascal Poupart, Craig Boutilier:
Vector-space Analysis of Belief-state Approximation for POMDPs. UAI 2001: 445-452 - [c2]Pascal Poupart, Luis E. Ortiz, Craig Boutilier:
Value-Directed Sampling Methods for POMDPs. UAI 2001: 453-461 - 2000
- [c1]Pascal Poupart, Craig Boutilier:
Value-Directed Belief State Approximation for POMDPs. UAI 2000: 497-506
Coauthor Index
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