Pascal Poupart

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 138

Top Venues

CoRR

NeurIPS

AAAI

AAMAS

Publications

Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart
Confidence Aware Inverse Constrained Reinforcement Learning. CoRR (2024)
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 (2024)
Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi, Pascal Poupart
A Critical Look At Tokenwise Reward-Guided Text Generation. CoRR (2024)
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)
Yudong Luo, Yangchen Pan, Han Wang, Philip H. S. Torr, Pascal Poupart
A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimization. CoRR (2024)
Yanting Miao, William Loh, Suraj Kothawade, Pascal Poupart, Abdullah Rashwan, Yeqing Li
Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning. CoRR (2024)
Haolin Yu, Guojun Zhang, Pascal Poupart
FedLog: Personalized Federated Classification with Less Communication and More Flexibility. CoRR (2024)
Oliver Schulte, Pascal Poupart
Why Online Reinforcement Learning is Causal. CoRR (2024)
Mohsin Hasan, Guojun Zhang, Kaiyang Guo, Xi Chen, Pascal Poupart
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space. AAAI (2024)
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 (2024)
Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi
Uncertainty-Guided Optimization on Large Language Model Search Trees. CoRR (2024)
Yudong Luo, Guiliang Liu, Pascal Poupart, Yangchen Pan
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient. NeurIPS (2023)
Mohsin Hasan, Guojun Zhang, Kaiyang Guo, Xi Chen, Pascal Poupart
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space. CoRR (2023)
Cynthia Huang, Pascal Poupart
Defensive Collaborative Learning: Protecting Objective Privacy in Data Sharing. AAMAS (2023)
Amur Ghose, Pascal Poupart
Contrastive Deterministic Autoencoders For Language Modeling. EMNLP (Findings) (2023)
Runcheng Liu, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart
Attribute Controlled Dialogue Prompting. ACL (Findings) (2023)
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)
Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart
Learning Soft Constraints From Constrained Expert Demonstrations. ICLR (2023)
Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge. AISTATS (2023)
Yudong Luo, Guiliang Liu, Pascal Poupart, Yangchen Pan
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient. CoRR (2023)
Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart
Benchmarking Constraint Inference in Inverse Reinforcement Learning. ICLR (2023)
Guanren Qiao, Guiliang Liu, Pascal Poupart, Zhiqiang Xu
Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations. NeurIPS (2023)
Runcheng Liu, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart
Attribute Controlled Dialogue Prompting. CoRR (2023)
Liam Hebert, Lukasz Golab, Pascal Poupart, Robin Cohen
FedFormer: Contextual Federation with Attention in Reinforcement Learning. AAMAS (2023)
Amur Ghose, Apurv Gupta, Yaoliang Yu, Pascal Poupart
Batchnorm Allows Unsupervised Radial Attacks. NeurIPS (2023)
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 (2023)
Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart
Distributional Reinforcement Learning with Monotonic Splines. ICLR (2022)
Md. Akmal Haidar, Mehdi Rezagholizadeh, Abbas Ghaddar, Khalil Bibi, Philippe Langlais, Pascal Poupart
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation. CoRR (2022)
Kira A. Selby, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart
Learning functions on multiple sets using multi-set transformers. UAI (2022)
Md. Akmal Haidar, Mehdi Rezagholizadeh, Abbas Ghaddar, Khalil Bibi, Philippe Langlais, Pascal Poupart
CILDA: Contrastive Data Augmentation Using Intermediate Layer Knowledge Distillation. COLING (2022)
Aref Jafari, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart, Ali Ghodsi
Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization. CoRR (2022)
Guiliang Liu, Yudong Luo, Oliver Schulte, Pascal Poupart
Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game. NeurIPS (2022)
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 (2022)
Aref Jafari, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart, Ali Ghodsi
Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization. EMNLP (Findings) (2022)
Kira A. Selby, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart
Learning Functions on Multiple Sets using Multi-Set Transformers. CoRR (2022)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
Decentralized Mean Field Games. AAAI (2022)
Guojun Zhang, Pascal Poupart, Yaoliang Yu
Optimality and Stability in Non-Convex Smooth Games. J. Mach. Learn. Res. 23 (2022)
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)
Haolin Yu, Kaiyang Guo, Mahdi Karami, Xi Chen, Guojun Zhang, Pascal Poupart
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process. CoRR (2022)
Liam Hebert, Lukasz Golab, Pascal Poupart, Robin Cohen
FedFormer: Contextual Federation with Attention in Reinforcement Learning. CoRR (2022)
Ehsan Imani, Guojun Zhang, Jun Luo, Pascal Poupart, Yangchen Pan
Label Alignment Regularization for Distribution Shift. CoRR (2022)
Kashif Khan, Ruizhe Wang, Pascal Poupart
WatClaimCheck: A new Dataset for Claim Entailment and Inference. ACL (1) (2022)
Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart
Learning Soft Constraints From Constrained Expert Demonstrations. CoRR (2022)
Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart
Benchmarking Constraint Inference in Inverse Reinforcement Learning. CoRR (2022)
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 (2022)
Elliot Nelson, Debarun Bhattacharjya, Tian Gao, Miao Liu, Djallel Bouneffouf, Pascal Poupart
Linearizing contextual bandits with latent state dynamics. UAI (2022)
Guiliang Liu, Ashutosh Adhikari, Amir-massoud Farahmand, Pascal Poupart
Learning Object-Oriented Dynamics for Planning from Text. ICLR (2022)
Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart
Quantifying and Improving Transferability in Domain Generalization. NeurIPS (2021)
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 (2021)
Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart
Quantifying and Improving Transferability in Domain Generalization. CoRR (2021)
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)
Xiangyu Sun, Guiliang Liu, Pascal Poupart, Oliver Schulte
NTS-NOTEARS: Learning Nonparametric Temporal DAGs With Time-Series Data and Prior Knowledge. CoRR (2021)
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)
Md. Akmal Haidar, Nithin Anchuri, Mehdi Rezagholizadeh, Abbas Ghaddar, Philippe Langlais, Pascal Poupart
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation. CoRR (2021)
Kira A. Selby, Yinong Wang, Ruizhe Wang, Peyman Passban, Ahmad Rashid, Mehdi Rezagholizadeh, Pascal Poupart
Robust Embeddings Via Distributions. CoRR (2021)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
Partially Observable Mean Field Reinforcement Learning. AAMAS (2021)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
Decentralized Mean Field Games. CoRR (2021)
Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning. NeurIPS (2021)
Elmira Amirloo Abolfathi, Mohsen Rohani, Ershad Banijamali, Jun Luo, Pascal Poupart
Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map. CoRR (2021)
Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, Pascal Poupart
Diachronic Embedding for Temporal Knowledge Graph Completion. AAAI (2020)
Amur Ghose, Abdullah Rashwan, Pascal Poupart
Batch norm with entropic regularization turns deterministic autoencoders into generative models. UAI (2020)
Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde
Multi Type Mean Field Reinforcement Learning. AAMAS (2020)
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 (2020)
Amur Ghose, Abdullah Rashwan, Pascal Poupart
Batch norm with entropic regularization turns deterministic autoencoders into generative models. CoRR (2020)
Nabiha Asghar, Lili Mou, Kira A. Selby, Kevin D. Pantasdo, Pascal Poupart, Xin Jiang
Progressive Memory Banks for Incremental Domain Adaptation. ICLR (2020)
Allen Houze Wang, Priyank Jaini, Yaoliang Yu, Pascal Poupart
Complete Hierarchy of Relaxation for Constrained Signomial Positivity. CoRR (2020)
Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan
Learning Agent Representations for Ice Hockey. NeurIPS (2020)
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)
Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu
Unsupervised Multilingual Alignment using Wasserstein Barycenter. CoRR (2020)
Haonan Duan, Saeed Nejati, George Trimponias, Pascal Poupart, Vijay Ganesh
Online Bayesian Moment Matching based SAT Solver Heuristics. ICML (2020)
Guojun Zhang, Pascal Poupart, Yaoliang Yu
Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization. CoRR (2020)
Ershad Banijamali, Mohsen Rohani, Elmira Amirloo Abolfathi, Jun Luo, Pascal Poupart
Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning. CoRR (2020)
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 (2020)
Nabiha Asghar, Ivan Kobyzev, Jesse Hoey, Pascal Poupart, Muhammad Bilal Sheikh
Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory. CoRR (2020)
Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde
Multi Type Mean Field Reinforcement Learning. CoRR (2020)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
Partially Observable Mean Field Reinforcement Learning. CoRR (2020)
Guojun Zhang, Kaiwen Wu, Pascal Poupart, Yaoliang Yu
Newton-type Methods for Minimax Optimization. CoRR (2020)
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 (2020)
Yudong Luo, Oliver Schulte, Pascal Poupart
Inverse Reinforcement Learning for Team Sports: Valuing Actions and Players. IJCAI (2020)
Abdullah Rashwan, Rishav Agarwal, Agastya Kalra, Pascal Poupart
MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection. CoRR (2020)
Amur Ghose, Priyank Jaini, Pascal Poupart
Learning directed acyclic graph SPNs in sub-quadratic time. Int. J. Approx. Reason. 120 (2020)
Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu
Unsupervised Multilingual Alignment using Wasserstein Barycenter. IJCAI (2020)
Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, Pascal Poupart
Diachronic Embedding for Temporal Knowledge Graph Completion. CoRR (2019)
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)
Guojun Zhang, Pascal Poupart, George Trimponias
Comparing EM with GD in Mixture Models of Two Components. UAI (2019)
Seyed Mehran Kazemi, Rishab Goel, Sepehr Eghbali, Janahan Ramanan, Jaspreet Sahota, Sanjay Thakur, Stella Wu, Cathal Smyth, Pascal Poupart, Marcus Brubaker
Time2Vec: Learning a Vector Representation of Time. CoRR (2019)
Abdullah Rashwan, Agastya Kalra, Pascal Poupart
Matrix Nets: A New Deep Architecture for Object Detection. ICCV Workshops (2019)
Abdullah Rashwan, Agastya Kalra, Pascal Poupart
Matrix Nets: A New Deep Architecture for Object Detection. CoRR (2019)
Guojun Zhang, Pascal Poupart, George Trimponias
Comparing EM with GD in Mixture Models of Two Components. CoRR (2019)
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 (2019)
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 (2019)
Ricardo Salmon, Pascal Poupart
On the Relationship Between Satisfiability and Markov Decision Processes. UAI (2019)
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)
Priyank Jaini, Amur Ghose, Pascal Poupart
Prometheus : Directly Learning Acyclic Directed Graph Structures for Sum-Product Networks. PGM (2018)
Nabiha Asghar, Pascal Poupart, Jesse Hoey, Xin Jiang, Lili Mou
Affective Neural Response Generation. ECIR (2018)
Hareesh Bahuleyan, Lili Mou, Olga Vechtomova, Pascal Poupart
Variational Attention for Sequence-to-Sequence Models. COLING (2018)
Priyank Jaini, Pascal Poupart, Yaoliang Yu
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation. NeurIPS (2018)
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)
Vikash Goel, Jameson Weng, Pascal Poupart
Unsupervised Video Object Segmentation for Deep Reinforcement Learning. NeurIPS (2018)
Vik Goel, Jameson Weng, Pascal Poupart
Unsupervised Video Object Segmentation for Deep Reinforcement Learning. CoRR (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Pascal Poupart