Yinlam Chow
Yinlam Chow is a research scientist at Google Research. Prior to Google, he was a research scientist at DeepMind (from 2017 to 2019) and a research scientist at Osaro, Inc (from 2016 to 2017). He received a Ph.D. from Stanford Institute of Computational and Mathematical Engineering (ICME) in 2017. He has published over 30 papers in major machine learning and control journals and conferences. His research focuses have been on deriving algorithms for risk-sensitive, safe, robust control, sequential decision making, and (model-based and model-free) reinforcement learning, with applications to problems in robotics, power systems, and personalized recommendation.
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Demystifying Embedding Spaces using Large Language Models
Jihwan Jeong
Lior Shani
Martin Mladenov
The Twelfth International Conference on Learning Representations (2024)
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta Reinforcement Learning
Anthony Liang
Erdem Biyik
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS-24), Vancouver (2024)
Factual and Personalized Recommendation Language Modeling with Reinforcement Learning
Jihwan Jeong
Mohammad Ghavamzadeh
Proceedings of the First Conference on Language Modeling (COLM-24), Philadelphia (2024)
Embedding-Aligned Language Models
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS-24), Vancouver (2024)
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Christina Göpfert
Alex Haig
Ivan Vendrov
Tyler Lu
Hubert Pham
Mohammad Ghavamzadeh
ACM Transactions on Recommender Systems (2024)
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
Dhawal Gupta
Mohammad Ghavamzadeh
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS-23), New Orleans (2023)
A Mixture-of-Expert Approach to RL-based Dialogue Management
Ofir Nachum
Dhawal Gupta
Moonkyung Ryu
Mohammad Ghavamzadeh
Proceedings of the Eleventh International Conference on Learning Representations (ICLR-23), Kigali, Rwanda (2023)
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Christina Göpfert
Ivan Vendrov
Tyler Lu
WWW22: The Web Conference 2022, Lyon, France, pp. 2411-2421
Non-Stationary Off-policy Optimization
Joey Hong
Branislav Kveton
Manzil Zaheer
International Conference on Artificial Intelligence and Statistics (AISTATS) (2021)
Safe Policy Learning for Continuous Control
Ofir Nachum
Edgar Duenez Guzman
Mohammad Ghavamzadeh
Conference on Robot Learning (CoRL) (2020)
