activity
20192022
most citedExplicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation

12 citations · 13 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2022

PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training

Rogerio Bonatti, Sai Vemprala, Shuang Ma +3

Robotics has long been a field riddled with complex systems architectures whose modules and connections, whether traditional or learning-based, require significant human expertise…

eess.SP20211 cited

Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface

Shuhang Chen, Xiang Zhang, Xiang Shen +2

Brain-machine interfaces (BMIs) help the disabled restore body functions by translating neural activity into digital commands to control external devices. Neural adaptation, where…

math.OC2020

Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation

Shuhang Chen, Adithya Devraj, Andrey Bernstein +1

The ODE method has been a workhorse for algorithm design and analysis since the introduction of the stochastic approximation. It is now understood that convergence theory amounts t…

math.PR202012 cited

Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation

Shuhang Chen, Adithya M. Devraj, Ana Bušić +1

This paper concerns error bounds for recursive equations subject to Markovian disturbances. Motivating examples abound within the fields of Markov chain Monte Carlo (MCMC) and Rein…

cs.LG2019

Zap Q-Learning With Nonlinear Function Approximation

Shuhang Chen, Adithya M. Devraj, Fan Lu +2

Zap Q-learning is a recent class of reinforcement learning algorithms, motivated primarily as a means to accelerate convergence. Stability theory has been absent outside of two res…