⚡ Single-pass algorithms for statistics

Code for Paper (Policy Optimization in RLHF: The Impact of Out-of-preference Data)

Stochastic Approximation Cut Algorithm for Inference in Modularised Bayesian Models

Code of the paper "Multi-Task Meta-Learning Modification with Stochastic Approximation".

Stochastic approximation with gym

Published at TMLR in 2026. Focusing on the general stochastic approximation framework, it proposes a federated algorithm that finds the optimum of an average of contractive operators.

Python implementations and some experiments for stochastic approximation algorithms

A kernel-based stochastic approximation (KBSA) framework for contextual optimization.

Hands-on implementations of Reinforcement Learning algorithms from scratch, progressing from classical methods to deep RL with practical experiments.

Published at IEEE Asilomar 2025. We study a general distributed heterogeneous stochastic approximation problem with M agents. The proposed DisSACC method converges to the desiderata with linear speedups, no heterogeneity bias, and near constant communication.