Michael Gimelfarb

About Michael Gimelfarb

Michael Gimelfarb, With an exceptional h-index of 5 and a recent h-index of 5 (since 2020), a distinguished researcher at University of Toronto, specializes in the field of machine learning, deep learning, reinforcement learning, Bayesian statistics.

His recent articles reflect a diverse array of research interests and contributions to the field:

The 2023 International Planning Competition

Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs

Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions

Who Should I Trust?: Uncertainty and Risk for Knowledge Transfer from Multiple Sources in Reinforcement Learning Domains

Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization

pyrddlgym: From rddl to gym environments

A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs

End-to-End Risk-Aware Planning by Gradient Descent

Michael Gimelfarb Information

University

Position

Mechanical and Industrial Engineering ; Vector Institute

Citations(all)

76

Citations(since 2020)

74

Cited By

14

hIndex(all)

5

hIndex(since 2020)

5

i10Index(all)

3

i10Index(since 2020)

3

Email

University Profile Page

Google Scholar

Michael Gimelfarb Skills & Research Interests

machine learning

deep learning

reinforcement learning

Bayesian statistics

Top articles of Michael Gimelfarb

Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs

arXiv preprint arXiv:2401.12243

2024/1/20

Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions

arXiv preprint arXiv:2305.07844

2023/5/13

Michael Gimelfarb
Michael Gimelfarb

H-Index: 1

Who Should I Trust?: Uncertainty and Risk for Knowledge Transfer from Multiple Sources in Reinforcement Learning Domains

2023

Michael Gimelfarb
Michael Gimelfarb

H-Index: 1

Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization

arXiv preprint arXiv:2210.03802

2022/10/7

pyrddlgym: From rddl to gym environments

arXiv preprint arXiv:2211.05939

2022/11/11

A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs

Proceedings of the AAAI Conference on Artificial Intelligence

2022/6/28

End-to-End Risk-Aware Planning by Gradient Descent

2021

Risk-Aware Transfer in Reinforcement Learning using Successor Features

Advances in Neural Information Processing Systems

2021/12/6

Bayesian Experience Reuse for Learning from Multiple Demonstrators

arXiv preprint arXiv:2006.05725

2020/6/10

Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts

2021/12/1

See List of Professors in Michael Gimelfarb University(University of Toronto)

Co-Authors

academic-engine