1 citations · 1 across the 4 of their papers we have counts for
4 papers
What Does Flow Matching Bring To TD Learning?
Bhavya Agrawalla, Michal Nauman, Aviral Kumar
Recent work shows that flow matching can be effective for scalar Q-value function estimation in reinforcement learning (RL), but it remains unclear why or how this approach differs…
floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
Bhavya Agrawalla, Michal Nauman, Khush Agrawal +1
A hallmark of modern large-scale machine learning techniques is the use of training objectives that provide dense supervision to intermediate computations, such as teacher forcing…
Statistical Inference for Linear Functionals of Online Least-squares SGD when
Bhavya Agrawalla, Krishnakumar Balasubramanian, Promit Ghosal
Stochastic Gradient Descent (SGD) has become a cornerstone method in modern data science. However, deploying SGD in high-stakes applications necessitates rigorous quantification of…
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression
Xuxing Chen, Krishnakumar Balasubramanian, Promit Ghosal +1
We conduct a comprehensive investigation into the dynamics of gradient descent using large-order constant step-sizes in the context of quadratic regression models. Within this fram…