Power allocation in energy harvesting sensors with ARQ: A convex optimization approach. - Adithya M. Devraj; Mohit K. Sharma; Chandra R. Murthy (GlobalSIP, 2014)

Array processing with known waveform and steering vector but unknown diagonal noise covariance matrix. - Adithya M. Devraj; Chris Gianelli; Jian Li 001 (CAMSAP, 2015)

Differential TD learning for value function approximation. - Adithya M. Devraj; Sean P. Meyn (CDC, 2016)

Learning techniques for feedback particle filter design. - Anand Radhakrishnan; Adithya M. Devraj; Sean P. Meyn (CDC, 2016)

Zap Q-Learning. - Adithya M. Devraj; Sean P. Meyn (NIPS, 2017)

Fastest Convergence for Q-learning. - Adithya M. Devraj; Sean P. Meyn (CoRR, 2017)

Zap Meets Momentum: Stochastic Approximation Algorithms with Optimal Convergence Rate. - Adithya M. Devraj; Ana Busic; Sean P. Meyn (CoRR, 2018)

On Matrix Momentum Stochastic Approximation and Applications to Q-learning. - Adithya M. Devraj; Ana Busic; Sean P. Meyn (Allerton, 2019)

Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization. - Adithya M. Devraj; Jianshu Chen (NeurIPS, 2019)

Zap Q-Learning for Optimal Stopping Time Problems. - Shuhang Chen; Adithya M. Devraj; Ana Busic; Sean P. Meyn (CoRR, 2019)

Zap Q-Learning With Nonlinear Function Approximation. - Shuhang Chen; Adithya M. Devraj; Ana Busic; Sean P. Meyn (CoRR, 2019)

Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation. - Shuhang Chen; Adithya M. Devraj; Ana Busic; Sean P. Meyn (AISTATS, 2020)

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow. - Yue Chen; Andrey Bernstein; Adithya M. Devraj; Sean P. Meyn (ACC, 2020)

Zap Q-Learning for Optimal Stopping. - Shuhang Chen; Adithya M. Devraj; Ana Busic; Sean P. Meyn (ACC, 2020)

Reinforcement Learning for Control of Building HVAC Systems. - Naren Srivaths Raman; Adithya M. Devraj; Prabir Barooah; Sean P. Meyn (ACC, 2020)

Zap Q-Learning With Nonlinear Function Approximation. - Shuhang Chen; Adithya M. Devraj; Fan Lu; Ana Busic; Sean P. Meyn (NeurIPS, 2020)

Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning. - Adithya M. Devraj; Sean P. Meyn (CoRR, 2020)

Accelerating Optimization and Reinforcement Learning with Quasi Stochastic Approximation. - Shuhang Chen; Adithya M. Devraj; Andrey Bernstein; Sean P. Meyn (ACC, 2021)

A Bit Better? Quantifying Information for Bandit Learning. - Adithya M. Devraj; Benjamin Van Roy; Kuang Xu (CoRR, 2021)

The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning. - Vivek S. Borkar; Shuhang Chen; Adithya M. Devraj; Ioannis Kontoyiannis; Sean P. Meyn (CoRR, 2021)

Revisiting the ODE Method for Recursive Algorithms: Fast Convergence Using Quasi Stochastic Approximation. - Shuhang Chen; Adithya M. Devraj; Andrey Berstein; Sean P. Meyn (J. Syst. Sci. Complex., 2021)

Differential Temporal Difference Learning. - Adithya M. Devraj; Ioannis Kontoyiannis; Sean P. Meyn (IEEE Trans. Autom. Control., 2021)

Gaussian Imagination in Bandit Learning. - Yueyang Liu; Adithya M. Devraj; Benjamin Van Roy; Kuang Xu (CoRR, 2022)

Q-Learning With Uniformly Bounded Variance. - Adithya M. Devraj; Sean P. Meyn (IEEE Trans. Autom. Control., 2022)

AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs. - Nicholas E. Corrado; Julian Katz-Samuels; Adithya M. Devraj; Hyokun Yun; Chao Zhang; Yi Xu 011; Yi Pan; Bing Yin; Trishul Chilimbi (ACL (1), 2025)