1 citations · 1 across the 3 of their papers we have counts for
7 papers
Convergence of Steepest Descent and Adam under Non-Uniform Smoothness
Sharan Vaswani, Yifan Sun, Reza Babanezhad
Recent work has analyzed the convergence of first-order methods under non-uniform smoothness assumptions that better model the loss landscape in machine learning tasks. We generali…
Dissecting Discrete Soft Actor-Critic: Limitations and Principled Alternatives
Reza Asad, Reza Babanezhad, Sharan Vaswani
While Soft Actor-Critic (SAC) is highly effective in continuous control, its discrete counterpart (DSAC) performs poorly on challenging discrete-action domains such as Atari. Conse…
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad
We aim to make stochastic gradient descent (SGD) adaptive to (i) the noise in the stochastic gradients and (ii) problem-dependent constants. When minimizing smooth, strongly…
Towards Parameter-Free Temporal Difference Learning
Yunxiang Li, Mark Schmidt, Reza Babanezhad +1
Temporal difference (TD) learning is a fundamental algorithm for estimating value functions in reinforcement learning. Recent finite-time analyses of TD with linear function approx…
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
Sharan Vaswani, Reza Babanezhad
Armijo line-search (Armijo-LS) is a standard method to set the step-size for gradient descent (GD). For smooth functions, Armijo-LS alleviates the need to know the global smoothnes…
Fast Convergence of Softmax Policy Mirror Ascent
Reza Asad, Reza Babanezhad, Issam Laradji +2
Natural policy gradient (NPG) is a common policy optimization algorithm and can be viewed as mirror ascent in the space of probabilities. Recently, Vaswani et al. [2021] introduced…