240 citations · 284 across the 3 of their papers we have counts for
4 papers · 1 filter
Investigating Power laws in Deep Representation Learning
Arna Ghosh, Arnab Kumar Mondal, Kumar Krishna Agrawal +1
Representation learning that leverages large-scale labelled datasets, is central to recent progress in machine learning. Access to task relevant labels at scale is often scarce or…
Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal +2
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. In th…
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi +2
We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework. The first problem is implicit bias present in the reward functions used…
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis
Surya Bhupatiraju, Kumar Krishna Agrawal, Rishabh Singh
Deep reinforcement learning has led to several recent breakthroughs, though the learned policies are often based on black-box neural networks. This makes them difficult to interpre…