Kannan Ramchandran

Data Curation for Web-Scale Datasets


Data curation is a promising direction for improving the efficiency and performance of large-scale models. Current efforts towards curation are ad-hoc and disconnected. We propose to develop new principled approaches for data curation inspired by Sorscher et al...

Dynamic Compression Techniques for Efficient Transformers


Transformers are a class of deep neural networks that have achieved state-of-the-art results across a wide range of domains, including natural language processing, computer vision, and computational biology. The widespread success of these models has been attributed to the attention mechanism, which identifies complex dependencies between elements of each input sequence. While the attention mechanism is incredibly...

Statistically Efficient Offline RL with General Function Approximation


Offline reinforcement learning (RL) aims at learning effective policies from only a previously-collected dataset of interactions without access to further interactions with the environment. To handle datasets with partial coverage, conservatism is recently shown to be necessary, both in practice and theory, for offline RL. Existing offline RL algorithms, however, either do not offer theoretical guarantees or are not practical due to strong assumptions (such as tabular or linear parameterization) or computational intractability. We propose...