Adithya M. Devraj

32 papers A* 2A 1C 4Journal 18Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs. ACL (1) Nicholas E. Corrado, Julian Katz-Samuels, Adithya M. Devraj, Hyokun Yun, Chao Zhang, Yi Xu, Yi Pan, Bing Yin, Trishul Chilimbi
2025	J	`jnl`	AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs. CoRR Nicholas E. Corrado, Julian Katz-Samuels, Adithya M. Devraj, Hyokun Yun, Chao Zhang, Yi Xu, Yi Pan, Bing Yin, Trishul Chilimbi
2022	J	`jnl`	Gaussian Imagination in Bandit Learning. CoRR Yueyang Liu, Adithya M. Devraj, Benjamin Van Roy, Kuang Xu
2022	J	`jnl`	Q-Learning With Uniformly Bounded Variance. IEEE Trans. Autom. Control. Adithya M. Devraj, Sean P. Meyn
2021	J	`jnl`	A Bit Better? Quantifying Information for Bandit Learning. CoRR Adithya M. Devraj, Benjamin Van Roy, Kuang Xu
2021	C	`conf`	Accelerating Optimization and Reinforcement Learning with Quasi Stochastic Approximation. ACC Shuhang Chen, Adithya M. Devraj, Andrey Bernstein, Sean P. Meyn
2021	J	`jnl`	Differential Temporal Difference Learning. IEEE Trans. Autom. Control. Adithya M. Devraj, Ioannis Kontoyiannis, Sean P. Meyn
2021	J	`jnl`	Revisiting the ODE Method for Recursive Algorithms: Fast Convergence Using Quasi Stochastic Approximation. J. Syst. Sci. Complex. Shuhang Chen, Adithya M. Devraj, Andrey Berstein, Sean P. Meyn
2021	J	`jnl`	The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning. CoRR Vivek S. Borkar, Shuhang Chen, Adithya M. Devraj, Ioannis Kontoyiannis, Sean P. Meyn
2020	J	`jnl`	Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation. CoRR Shuhang Chen, Adithya M. Devraj, Andrey Bernstein, Sean P. Meyn
2020	A	`conf`	Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation. AISTATS Shuhang Chen, Adithya M. Devraj, Ana Busic, Sean P. Meyn
2020	J	`jnl`	Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation. CoRR Shuhang Chen, Adithya M. Devraj, Ana Busic, Sean P. Meyn
2020	C	`conf`	Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow. ACC Yue Chen, Andrey Bernstein, Adithya M. Devraj, Sean P. Meyn
2020	J	`jnl`	Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning. CoRR Adithya M. Devraj, Sean P. Meyn
2020	C	`conf`	Reinforcement Learning for Control of Building HVAC Systems. ACC Naren Srivaths Raman, Adithya M. Devraj, Prabir Barooah, Sean P. Meyn
2020	A*	`conf`	Zap Q-Learning With Nonlinear Function Approximation. NeurIPS Shuhang Chen, Adithya M. Devraj, Fan Lu, Ana Busic, Sean P. Meyn
2020	C	`conf`	Zap Q-Learning for Optimal Stopping. ACC Shuhang Chen, Adithya M. Devraj, Ana Busic, Sean P. Meyn
2019	J	`jnl`	Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow. CoRR Yue Chen, Andrey Bernstein, Adithya M. Devraj, Sean P. Meyn
2019	—	`conf`	On Matrix Momentum Stochastic Approximation and Applications to Q-learning. Allerton Adithya M. Devraj, Ana Busic, Sean P. Meyn
2019	A*	`conf`	Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization. NeurIPS Adithya M. Devraj, Jianshu Chen
2019	J	`jnl`	Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization. CoRR Adithya M. Devraj, Jianshu Chen
2019	J	`jnl`	Zap Q-Learning With Nonlinear Function Approximation. CoRR Shuhang Chen, Adithya M. Devraj, Ana Busic, Sean P. Meyn
2019	J	`jnl`	Zap~Q-Learning for Optimal Stopping Time Problems. CoRR Shuhang Chen, Adithya M. Devraj, Ana Busic, Sean P. Meyn
2018	J	`jnl`	Differential Temporal Difference Learning. CoRR Adithya M. Devraj, Ioannis Kontoyiannis, Sean P. Meyn
2018	J	`jnl`	Zap Meets Momentum: Stochastic Approximation Algorithms with Optimal Convergence Rate. CoRR Adithya M. Devraj, Ana Busic, Sean P. Meyn
2017	J	`jnl`	Fastest Convergence for Q-learning. CoRR Adithya M. Devraj, Sean P. Meyn
2017	—	`conf`	Zap Q-Learning. NIPS Adithya M. Devraj, Sean P. Meyn
2016	J	`jnl`	Differential TD Learning for Value Function Approximation. CoRR Adithya M. Devraj, Sean P. Meyn
2016	—	`conf`	Differential TD learning for value function approximation. CDC Adithya M. Devraj, Sean P. Meyn
2016	—	`conf`	Learning techniques for feedback particle filter design. CDC Anand Radhakrishnan, Adithya M. Devraj, Sean P. Meyn
2015	—	`conf`	Array processing with known waveform and steering vector but unknown diagonal noise covariance matrix. CAMSAP Adithya M. Devraj, Chris Gianelli, Jian Li
2014	—	`conf`	Power allocation in energy harvesting sensors with ARQ: A convex optimization approach. GlobalSIP Adithya M. Devraj, Mohit K. Sharma, Chandra R. Murthy

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.