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- affiliation: University of Waterloo, ON, Canada
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2020 – today
- 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 - [i70]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) - [i69]Oliver Schulte, Pascal Poupart:
Why Online Reinforcement Learning is Causal. CoRR abs/2403.04221 (2024) - [i68]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) - [i67]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) - [i66]Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi, Pascal Poupart:
A Critical Look At Tokenwise Reward-Guided Text Generation. CoRR abs/2406.07780 (2024) - [i65]Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart:
Confidence Aware Inverse Constrained Reinforcement Learning. CoRR abs/2406.16782 (2024) - [i64]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) - [i63]Haolin Yu, Guojun Zhang, Pascal Poupart:
FedLog: Personalized Federated Classification with Less Communication and More Flexibility. CoRR abs/2407.08337 (2024) - [i62]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) - 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,