Baihe Huang - RankMe

38 papers A* 13A 1Journal 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	HuPER: A Human-Inspired Framework for Phonetic Perception. CoRR Chenxu Guo, Jiachen Lian, Yisi Liu, Baihe Huang, Shriyaa Narayanan, Cheol Jun Cho, Gopala Anumanchipalli
2026	J	`jnl`	PivotRL: High Accuracy Agentic Post-Training at Low Compute Cost. CoRR Junkeun Yi, Damon Mosk-Aoyama, Baihe Huang, Ritu Gala, Charles Wang, Sugam Dipak Devare, Khushi Bhardwaj, Abhibha Gupta, Oleksii Kuchaiev, Jiantao Jiao, Jian Zhang, Venkat Srinivasan
2026	J	`jnl`	Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models. CoRR Miao Li, Hanyang Jiang, Sikai Cheng, Hengyu Fu, Yuhang Cai, Baihe Huang, Tinghan Ye, Xuanzhou Chen, Pascal Van Hentenryck
2026	J	`jnl`	Towards Anytime-Valid Statistical Watermarking. CoRR Baihe Huang, Eric Xu, Kannan Ramchandran, Jiantao Jiao, Michael I. Jordan
2025	J	`jnl`	From Bits to Rounds: Parallel Decoding with Exploration for Diffusion Language Models. CoRR Hengyu Fu, Baihe Huang, Virginia Adams, Charles Wang, Venkat Srinivasan, Jiantao Jiao
2025	J	`jnl`	Sample Complexity and Representation Ability of Test-time Scaling Paradigms. CoRR Baihe Huang, Shanda Li, Tianhao Wu, Yiming Yang, Ameet Talwalkar, Kannan Ramchandran, Michael I. Jordan, Jiantao Jiao
2025	A*	`conf`	Sounding that Object: Interactive Object-Aware Image to Audio Generation. ICML Tingle Li, Baihe Huang, Xiaobin Zhuang, Dongya Jia, Jiawei Chen, Yuping Wang, Zhuo Chen, Gopala Anumanchipalli, Yuxuan Wang
2025	J	`jnl`	Sounding that Object: Interactive Object-Aware Image to Audio Generation. CoRR Tingle Li, Baihe Huang, Xiaobin Zhuang, Dongya Jia, Jiawei Chen, Yuping Wang, Zhuo Chen, Gopala Anumanchipalli, Yuxuan Wang
2024	A*	`conf`	Data Acquisition via Experimental Design for Data Markets. NeurIPS Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
2024	J	`jnl`	Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
2024	A*	`conf`	Enhancing Language Model Alignment: A Confidence-Based Approach to Label Smoothing. EMNLP Baihe Huang, Hiteshi Sharma, Yi Mao
2024	A*	`conf`	On Representation Complexity of Model-based and Model-free Reinforcement Learning. ICLR Hanlin Zhu, Baihe Huang, Stuart Russell
2024	A*	`conf`	Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity. NeurIPS Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee
2024	J	`jnl`	Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity. CoRR Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee
2024	A*	`conf`	Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. NeurIPS Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart J. Russell
2024	J	`jnl`	Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. CoRR Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart Russell
2023	J	`jnl`	Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. CoRR Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan
2023	J	`jnl`	On Representation Complexity of Model-based and Model-free Reinforcement Learning. CoRR Hanlin Zhu, Baihe Huang, Stuart Russell
2023	A	`conf`	Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition. AISTATS Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee
2023	J	`jnl`	Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence. SIAM J. Optim. Wenhao Zhan, Shicong Cen, Baihe Huang, Yuxin Chen, Jason D. Lee, Yuejie Chi
2023	A*	`conf`	Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms. NeurIPS Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee
2023	J	`jnl`	Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms. CoRR Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee
2023	J	`jnl`	Towards Optimal Statistical Watermarking. CoRR Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Michael I. Jordan
2022	J	`jnl`	A Dynamic Fast Gaussian Transform. CoRR Baihe Huang, Zhao Song, Omri Weinstein, Hengjie Zhang, Ruizhe Zhang
2022	A*	`conf`	Offline Reinforcement Learning with Realizability and Single-policy Concentrability. COLT Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee
2022	J	`jnl`	Offline Reinforcement Learning with Realizability and Single-policy Concentrability. CoRR Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee
2022	A*	`conf`	Solving SDP Faster: A Robust IPM Framework and Efficient Implementation. FOCS Baihe Huang, Shunhua Jiang, Zhao Song, Runzhou Tao, Ruizhe Zhang
2022	A*	`conf`	Towards General Function Approximation in Zero-Sum Markov Games. ICLR Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang
2021	A*	`conf`	FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis. ICML Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang
2021	J	`jnl`	FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis. CoRR Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang
2021	A*	`conf`	Going Beyond Linear RL: Sample Efficient Neural Function Approximation. NeurIPS Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
2021	J	`jnl`	Going Beyond Linear RL: Sample Efficient Neural Function Approximation. CoRR Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
2021	A*	`conf`	Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. NeurIPS Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
2021	J	`jnl`	Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. CoRR Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
2021	J	`jnl`	Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence. CoRR Wenhao Zhan, Shicong Cen, Baihe Huang, Yuxin Chen, Jason D. Lee, Yuejie Chi
2021	J	`jnl`	Solving Tall Dense SDPs in the Current Matrix Multiplication Time. CoRR Baihe Huang, Shunhua Jiang, Zhao Song, Runzhou Tao
2021	J	`jnl`	Towards General Function Approximation in Zero-Sum Markov Games. CoRR Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang
2020	J	`jnl`	InstaHide's Sample Complexity When Mixing Two Private Images. CoRR Baihe Huang, Zhao Song, Runzhou Tao, Ruizhe Zhang, Danyang Zhuo

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.