12 citations · 14 across the 3 of their papers we have counts for
6 papers
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…
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…
Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning
Adithya M. Devraj, Sean P. Meyn
Sample complexity bounds are a common performance metric in the Reinforcement Learning literature. In the discounted cost, infinite horizon setting, all of the known bounds have a…
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…
Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow
Yue Chen, Andrey Bernstein, Adithya Devraj +1
This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit…
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization
Adithya M. Devraj, Jianshu Chen
We consider a generic empirical composition optimization problem, where there are empirical averages present both outside and inside nonlinear loss functions. Such a problem is of…