Haque Ishfaq

I am a final year PhD Student at Mila and McGill University, advised by Prof. Doina Precup. My current research focuses on exploration in reinforcement learning and reinforcement learning with human feedback.

I am on the job market and actively looking for Research Scientist or Postdoctoral Researcher positions in the USA/Canada!

Previously, I obtained my BS (Mathematical and Computational Science) in 2015 and MS (Statistics) in 2018 from Stanford University. There I am grateful to have my research supervised by Prof. Daniel Rubin. Previously, I also did research internships at Meta AI, Microsoft, IBM Research and Nvidia.

Feel free to reach out to me in case you have any questions or want to chat about my work!

I co-organize the RLHF reading group at Mila. If you want to present your work, feel free to reach out!

Email / CV / Google Scholar / Github / Twitter / LinkedIn

News

April 2024: I will be attending ICLR 2024 in person. Let me know if you want to meet and chat!
April 2024: Invited to give a long talk at the 25th International Symposium on Mathematical Programming, Montreal, Canada.
April 2023: Invited to attend and present at the Citadel PhD Summit in Miami, USA.
Summer 2022: Research internship at Meta AI with Applied Reinforcement Learning Team
September 2021: Invited talk at ML-Seminar, Purdue University. Host: Kamyar Azizzadenesheli
September 2021: Invited talk at Jiantao Jiao Group meeting, University of California, Berkeley. Host: Jiantao Jiao
February 2021: Invited talk at Benjamin Van Roy Group meeting, Stanford University. Host: Benjamin Van Roy
January 2021: Contributed talk at Microsoft Research Reinforcement Learning Day 2021.
November 2020: Contributed talk at Northeast Reinforcement Learning and Decision Making Symposium.

Conference Publications

More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
Haque Ishfaq*, Yixin Tan*, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup and Pan Xu
Submitted to Reinforcement Learning Conference (RLC), 2024

Offline Multitask Representation Learning for Reinforcement Learning
Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin and Doina Precup
Submitted to Reinforcement Learning Conference (RLC), 2024
[Paper]

Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Haque Ishfaq*, Qingfeng Lan*, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar and Kamyar Azizzadenesheli
International Conference on Learning Representations (ICLR), 2024
[Paper], [Code], [Poster]

Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Haque Ishfaq*, Qiwen Cui*, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, and Lin F. Yang
International Conference on Machine Learning (ICML), 2021
[Paper], [Slide]

Preprints/Workshop Publications

Randomized Least Squares Policy Optimization
Haque Ishfaq, Zhuoran Yang, Andrei Lupu, Viet Nguyen, Lewis Liu, Riashat Islam, Zhaoran Wang and Doina Precup
ICML Workshop on Reinforcement Learning Theory , 2021
[Paper]

Path-Based Contextualization of Knowledge Graphs for Textual Entailment
Kshitij Fadnis, Kartik Talamadupula, Pavan Kapanipathi, Haque Ishfaq, Salim Roukos and Achille Fokoue
Preprint, 2019
[Paper]

TVAE: Triplet-Based Variational Autoencoder using Metric Learning
Haque Ishfaq, Assaf Hoogi and Daniel Rubin
Preprint, 2018
[Paper]

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