World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
8347
World Ranking
4922
National Ranking
194

Overview

Pascal Poupart is affiliated with the University of Waterloo in Canada and has contributed extensively to the field of Computer Science. Their research spans multiple subfields, predominantly focusing on Artificial Intelligence, with notable work also in Computer Vision and Pattern Recognition, Management Science and Operations Research, Automotive Engineering, and Computational Theory and Mathematics.

Their research topics cover a broad range of advanced themes including Topic Modeling, Natural Language Processing Techniques, Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning, Gaussian Processes and Bayesian Inference, Privacy-Preserving Technologies in Data, and Autonomous Vehicle Technology and Safety.

Pascal Poupart's publication record includes numerous papers, with selected recent works being:

  • Learning Dynamic Belief Graphs to Generalize on Text-Based Games, 2020, arXiv (Cornell University)
  • Diachronic Embedding for Temporal Knowledge Graph Completion, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation, 2022, Findings of the Association for Computational Linguistics: NAACL 2022
  • Hierarchical Double Dirichlet Process Mixture of Gaussian Processes, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Newton-type Methods for Minimax Optimization, 2020, arXiv (Cornell University)

They frequently publish in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • International Journal of Approximate Reasoning
  • Findings of the Association for Computational Linguistics: NAACL 2022
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Pascal Poupart collaborates regularly with a number of coauthors, including:

  • Guojun Zhang
  • Mehdi Rezagholizadeh
  • Sriram Ganapathi Subramanian
  • Agustinus Kristiadi
  • Ahmad Rashid

Best Publications

  • An analytic solution to discrete Bayesian reinforcement learning

    Pascal Poupart;Nikos Vlassis;Jesse Hoey;Kevin Regan

  • Representation Learning for Dynamic Graphs: A Survey

    Seyed Mehran Kazemi;Rishab Goel;Kshitij Jain;Ivan Kobyzev

  • Exploiting structure to efficiently solve large scale partially observable markov decision processes

    Pascal Poupart

  • Point-Based Value Iteration for Continuous POMDPs

    Josep M. Porta;Nikos Vlassis;Matthijs T.J. Spaan;Pascal Poupart

  • Diachronic Embedding for Temporal Knowledge Graph Completion

    Rishab Goel;Seyed Mehran Kazemi;Marcus Brubaker;Pascal Poupart

  • Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process

    Jesse Hoey;Pascal Poupart;Axel von Bertoldi;Tammy Craig

  • Bounded Finite State Controllers

    Pascal Poupart;Craig Boutilier

  • A planning system based on Markov decision processes to guide people with dementia through activities of daily living

    J. Boger;J. Hoey;P. Poupart;C. Boutilier

  • A decision-theoretic approach to task assistance for persons with dementia

    Jennifer Boger;Pascal Poupart;Jesse Hoey;Craig Boutilier

  • Learning Rate Based Branching Heuristic for SAT Solvers

    Jia Hui Liang;Vijay Ganesh;Pascal Poupart;Krzysztof Czarnecki

  • Affective Neural Response Generation

    Nabiha Asghar;Pascal Poupart;Jesse Hoey;Xin Jiang

  • Constraint-based optimization and utility elicitation using the minimax decision criterion

    Craig Boutilier;Relu Patrascu;Pascal Poupart;Dale Schuurmans

  • Assisting persons with dementia during handwashing using a partially observable Markov decision process.

    Jesse Hoey;Axel von Bertoldi;Pascal Poupart;Alex Mihailidis

  • Self-adaptive hierarchical sentence model

    Han Zhao;Zhengdong Lu;Pascal Poupart

  • Value-Directed Compression of POMDPs

    Pascal Poupart;Craig Boutilier

  • Factored partially observable Markov decision processes for dialogue management

    J Williams;P Poupart;SJ Young

  • Solving POMDPs with continuous or large discrete observation spaces

    Jesse Hoey;Pascal Poupart

  • Bayesian reputation modeling in E-marketplaces sensitive to subjecthity, deception and change

    Kevin Regan;Pascal Poupart;Robin Cohen

  • PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES WITH CONTINUOUS OBSERVATIONS FOR DIALOGUE MANAGEMENT

    Jason D. Williams;Pascal Poupart;Steve J. Young

  • VDCBPI: an Approximate Scalable Algorithm for Large POMDPs

    Pascal Poupart;Craig Boutilier

  • Time2Vec: Learning a Vector Representation of Time

    Seyed Mehran Kazemi;Rishab Goel;Sepehr Eghbali;Janahan Ramanan

  • Partially Observable Markov Decision Processes.

    Pascal Poupart

Frequent Co-Authors

Craig Boutilier
Craig Boutilier Google (United States)
Jesse Hoey
Jesse Hoey University of Waterloo
Lili Mou
Lili Mou University of Alberta
Alex Mihailidis
Alex Mihailidis University of Toronto
Eric A. Roy
Eric A. Roy University of Waterloo
Dale Schuurmans
Dale Schuurmans University of Alberta
Sandra E. Black
Sandra E. Black University of Toronto
Steve Young
Steve Young University of Cambridge
Jason D. Williams
Jason D. Williams Apple (United States)
Marc Toussaint
Marc Toussaint Technical University of Berlin

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