ABSTRACT: In many real-world applications of reinforcement learning (RL) such as healthcare, dialogue systems and robotics, running a new policy on humans or robots can [...]

ABSTRACT: I will discuss new provably efficient algorithms for reinforcement in rich observation environments with arbitrarily large state spaces. Both algorithms operate by learning succinct [...]

ABSTRACT: Offline reinforcement learning (RL) is aiming for exploiting the tremendous historical experiences for future decision making. In this talk, we summarize our recent work [...]

UCLA Ph.D. student Aayush Jain, collaborator Rachel Lin, and Professor Amit Sahai are giving an invited plenary tutorial at FOCS 2020 on their recent work.

ABSTRACT: Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and [...]

ABSTRACT: The autonomous vehicle (AV) stack implements a mapping from sensor data to vehicle controls. In this talk I discuss two aspects of this mapping: [...]

ABSTRACT:  Tables are used to store and retrieve structured information in the databases. They are used to communicate business statistics, scientific results, sports results, demographic data, [...]

ABSTRACT: It has been known for decades that a polynomial-size training sample suffices for learning neural networks.  Most theoretical results, however, indicate that these learning [...]

ABSTRACT: Humans mentally model the causal structure of the world, and manipulate these models to reason: we infer plausible causes of events, construct explanations, and [...]