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ORCIDSeyed Kamyar Seyed Ghasemipour
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Conference and Workshop Papers
- 2024
[c9]Sherry Yang, Yilun Du, Seyed Kamyar Seyed Ghasemipour, Jonathan Tompson, Leslie Pack Kaelbling, Dale Schuurmans, Pieter Abbeel:
Learning Interactive Real-World Simulators. ICLR 2024- 2022
- [c8]Seyed Kamyar Seyed Ghasemipour, Satoshi Kataoka, Byron David, Daniel Freeman, Shixiang Shane Gu, Igor Mordatch:
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning. ICML 2022: 7435-7469 - [c7]Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum:
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters. NeurIPS 2022 - [c6]Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, Mohammad Norouzi:
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. NeurIPS 2022 - 2021
- [c5]Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu:
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL. ICML 2021: 3682-3691 - 2019
- [c4]Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu:
A Divergence Minimization Perspective on Imitation Learning Methods. CoRL 2019: 1259-1277 - [c3]Seyed Kamyar Seyed Ghasemipour, Shane Gu, Richard S. Zemel:
Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning. DGS@ICLR 2019 - [c2]Seyed Kamyar Seyed Ghasemipour, Shixiang Gu, Richard S. Zemel:
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies. NeurIPS 2019: 7879-7889 - 2018
- [c1]Will Grathwohl, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel:
Gradient-based Optimization of Neural Network Architecture. ICLR (Workshop) 2018
Informal and Other Publications
- 2023
- [i9]Satoshi Kataoka, Youngseog Chung, Seyed Kamyar Seyed Ghasemipour, Pannag Sanketi, Shixiang Shane Gu, Igor Mordatch:
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning. CoRR abs/2303.14870 (2023) - [i8]Mengjiao Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Dale Schuurmans, Pieter Abbeel:
Learning Interactive Real-World Simulators. CoRR abs/2310.06114 (2023) - 2022
- [i7]Satoshi Kataoka, Seyed Kamyar Seyed Ghasemipour, C. Daniel Freeman, Igor Mordatch:
Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning. CoRR abs/2203.08277 (2022) - [i6]Seyed Kamyar Seyed Ghasemipour, Daniel Freeman, Byron David, Shixiang Gu, Satoshi Kataoka, Igor Mordatch:
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning. CoRR abs/2203.13733 (2022) - [i5]Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Raphael Gontijo Lopes, Tim Salimans, Jonathan Ho, David J. Fleet, Mohammad Norouzi:
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. CoRR abs/2205.11487 (2022) - [i4]Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum:
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters. CoRR abs/2205.13703 (2022) - 2021
- [i3]Shixiang Shane Gu, Manfred Diaz, C. Daniel Freeman, Hiroki Furuta, Seyed Kamyar Seyed Ghasemipour, Anton Raichuk, Byron David, Erik Frey, Erwin Coumans, Olivier Bachem:
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization. CoRR abs/2110.04686 (2021) - 2020
- [i2]Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu:
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL. CoRR abs/2007.11091 (2020) - 2019
- [i1]Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu:
A Divergence Minimization Perspective on Imitation Learning Methods. CoRR abs/1911.02256 (2019)
Coauthor Index
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