7 papers
How Accurately Can a Gaussian Approximate Stochastic Approximation Iterates?
Shaan Ul Haque, Zedong Wang, Zixuan Zhang +1
Stochastic approximation (SA) is a method for finding the root of an operator perturbed by noise. The focus of this paper is studying the distribution of SA iterates in finite time…
Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
Gautham Govind Anil, Shaan Ul Haque, Nithish Kannen +3
Diffusion models are widely used for generative tasks across domains. Given a pre-trained diffusion model, it is often desirable to fine-tune it further either to correct for error…
Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
Shaan Ul Haque, Siva Theja Maguluri
Motivated by engineering applications such as resource allocation in networks and inventory systems, we consider average-reward Reinforcement Learning with unbounded state space an…
Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Message
Shaan ul Haque, Siddharth Chandak, Federico Chiariotti +2
The rapid development of Internet of Things (IoT) technologies has not only enabled new applications, but also presented new challenges for reliable communication with limited reso…
Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise
Siddharth Chandak, Shaan Ul Haque, Nicholas Bambos
Two-time-scale Stochastic Approximation (SA) is an iterative algorithm with applications in reinforcement learning and optimization. Prior finite time analysis of such algorithms h…
Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise
Shaan Ul Haque, Sajad Khodadadian, Siva Theja Maguluri
Stochastic approximation (SA) is an iterative algorithm for finding the fixed point of an operator using noisy samples and widely used in optimization and Reinforcement Learning (R…