Link

Online Learning and Game Theory

• Gabriele Farina: MIT, Libratus: game theory, machine learning (especially online learning), optimization, and statistics. Mingyang Liu

• Ellen Vitercik: Stanford, machine learning theory, algorithm design, and the interface between economics and computation.

• Constantinos Daskalakis: MIT, Theory of Computation, and its interface with Economics, Game Theory, Machine Learning, Statistics and Probability Theory, Noah Golowich

• Tuomas Sandholm: CMU, Libratus, computational game theory

• Sasha Rakhlin: MIT, Online Learning, Statistical Learning, Reinforcement Learning and Decision Making

• Jon Schneider: Google Research, the overlap between game theory and online learning.

• Ian Gemp: DeepMind

• Sam Ganzfried: Independent researcher

• Yiling Chen: The interface between computer science and economics, lies in the emerging area of social computing, where human creativity and resources are harnessed for the purpose of computational tasks.

• Yang Cai: Yale, Economics and Computation

• Binghui Peng (彭炳辉): understanding learning and intelligence through the lens of computation

• Aviad Rubinstein: Stanford, online algorithm, approximate algorithm, mechanism design, game theory.

• Manolis Zampetakis: Yale, Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, Mechanism Design

• Matt Weinberg: Princeton, Game theory and algorithms under uncertainty

• Andre Wibisono: Yale, design and analysis of algorithms for machine learning, in particular for problems in optimization, sampling, and game dynamics

• David C. Parkes: Harvard, Multi-agent AI, Bounded rationality, Machine learning and decisions

• Emma Brunskill: Stanford, RL, Online Learning

• Haipeng Luo: online learning, reinforcement learning, learning in games, fast and scalable optimization methods, Introduction to Online Optimization/Learning (Fall 2022)

• Simon Shaolei Du (杜少雷)

• Jacob Abernethy: University of Michigan, discovering connections between Optimization, Statistics, and Economics

• Nika Haghtalab: UC Berkeley, the mathematical foundation for learning and decision-making systems

• Zhengyuan Zhou: New York University, data-driven decision-making problems.

• Marc Lanctot: DeepMind, general multiagent learning (and planning), computational game theory, reinforcement learning, and game-tree search.

• Noam Brown: OpenAI

• Tianyi Peng: Columbia, machine learning and causal inference algorithms for large-scale dynamic decision-making systems in the real world.

• Stephen Bates: MIT, understand uncertainty and reliable decision-making with data.

• Aleksander Mądry: MIT, forge a decision-making toolkit that is reliable and well-understood enough to be safely and responsibly deployed in the real world.

• Patrick Jaillet: MIT, Operation Research

• Manish Raghavan: MIT, online platforms, algorithmic fairness, and behavioral economics

• Asu Ozdaglar: MIT, optimization, machine learning, economics, and networks

• John N. Tsitsiklis: MIT, Optimization and Game Theory, Systems Theory, Control, and Autonomy

• Suvrit Sra: MIT, Optimization and Game Theory; Tiancheng Yu (余天呈)

• Christian Kroer: Columbia, a focus on how optimization and AI methods enable large-scale economic solution concepts

• Tim Roughgarden

• Chara Podimata: MIT, mostly on the intersection of Theoretical Computer Science, Economics and Machine Learning and specifically on incentive-aware machine learning, social computing, online learning, and mechanism design. Recently, I have started thinking about policy questions related to AI and recommendation systems.

• Negin Golrezaei: MIT, Statistical learning theory Mechanism design Optimization algorithms Game theory

• Manish Raghavan: MIT, online platforms, algorithmic fairness, and behavioral economics

• Jiarui Gan: computational game theory, and more broadly, in the fields of multi-agent systems, EconCS, and AI

• Andrea Celli: Bocconi University, online learning and computational game theory.

• Adam Wierman: Caltech, Sustainable Computing, Online Algorithms, Online Optimization, Stochastic Networks, Network Economics.

• Santiago R. Balseiro: Columbia, mechanism design.

• Pradeep Ravikumar: CMU, next-generation machine learning systems.

• Zico Kolter: CMU, making deep learning algorithms safer, more robust, and more explainable.

• Vincent Conitzer: CMU

• MIT Operations Research Center, MIT Center for Computational Science and Engineering, MIT Admissions, MIT Laboratory for Information and Decision Systems, MIT Institute for Data, Systems, and Society, MIT EECS, Massachusetts Institute of Technology Laboratory, MIT Optimization and Game Theory faculty, MIT graduate-program-requirements, MSR Asia Theory Lecture Series, MIT EECS GAAP, MIT HACK

• CS SOP

Explainable Decision Making

• Causality Boot Camp

• Lilian Weng: Lil’Log

• Steven Wu: CMU, privacy, fairness, causal inference, RL, game theory

• Fei Fang: CMU, integrating machine learning with game theory. Her work contributes to the theme of AI for Social Good. Ryan Shi: research AI for nonprofits, with nonprofits, which is actually used by nonprofits. Stephanie Milani: create intelligent agents that can learn quickly, explain their decisions, and work harmoniously with people and other artificially intelligent agents.

• Michael I. Jordan: UC Berkeley

• Lily Xu: Machine learning and game theory applied to challenges in sustainability

• Siddhartha Banerjee: Cornell University, Associate Professor

• Jason D. Lee: Princeton, Foundations of Deep Reinforcement Learning

• Jun Wang

• Danijar Hafner: dreamer

• Michael Bowling: Deepstack

• Dengji Zhao (赵登吉): algorithmic game theory and multi-agent systems, especially mechanism design and its applications on social networks

• Yaodong Yang (杨耀东): game theory, reinforcement learning and multi-agent systems

• Xiaotie Deng (邓小铁): algorithmic game theory, blockchain, internet economy, online algorithms and parallel computing

• Jiachen T. Wang: Princeton, differential privacy and data valuation.

Famous People

• Mark Newman: Networks

• Martin Wainwright: MIT, statistics

• Tommi S. Jaakkola: MIT, ML

• Dina Katabi: MIT, Wireless and Mobile Systems

• Ankur Moitra: MIT, give algorithms with provable guarantees for various problems in machine learning

• Manish Raghavan: MIT, the application of computational techniques to domains of social concern, including online platforms, algorithmic fairness, and behavioral economics

• STEFANIE JEGELKA: MIT, exploiting mathematical structure for discrete and combinatorial machine learning problems, for robustness and for scaling machine learning algorithms.

• Song Han: MIT, efficient deep learning computing；Ligeng Zhu

• Ali Jadbabaie: MIT, investigates the behavior and evolution of complex interconnected systems

• REX (ZHITAO) YING: graph neural networks, geometric embeddings, explainable models, and more recently, multi-modal foundation models involving relational reasoning

• Weijie Su (苏炜杰)

• Xingyu Zhou: focus on bandits and reinforcement learning with a focus on differential privacy; Online Decision Making

• Giorgia Ramponi: RL, privacy, online learning

• Elad Hazan: online learning

• Shishir Patil: Machine learning for the edge

• Jingzhao Zhang (张景昭)

• Omar Montasser: Yale, robust PAC learning

• Causal Inference: Judea Pearl, Donald B. Rubin

• Yuejie Chi: CMU ECE, He Wang

• Jie Wang:CUHK(SZ), optimization and statistics

• John C.S. Lui: CUHK Professor, Online Machine Learning, Network Science and Economics

• Chen Liu: deep neural networks and study it from an optimization perspective

• Kolmogorov complexity: Ming Li, Marcus Hutter

• (苏剑林)

• Lesi Chen (陈乐偲): 优化博客

• Zhenzhe Zheng (郑臻哲): SJTU, Mobile Computing and On-device Machine Learning, Network Economics and Online Advertising

• james-zou: Stanford Assistant Professor, Lingjiao Chen, Shirley Wu

• CS PhD Statements of Purpose, How I got into Graduate School

• Jiliang Tang (汤继良)

• 北京大学前沿计算中心

• Zaiwen Wen (文再文)

• Jesse Cai

• Xudong HU (胡旭东)

• Yao Liu (刘垚)

• Bin Dong (董彬)

• NLPR (模式识别国家重点实验室)

• Fangming Liu (刘方明)

• Jeff Erickson

• Quanshi Zhang (张拳石)

• Hailiang Zhao (赵海亮)

• Yi-Fan Zhang (张一帆):自动化所, Causal Inference, Out of Distribution Generalization, Domain Adaptation

• Chong Chen (陈冲)

• Yuchen Shi (史雨晨)

• Minghua Chen

• Jackie Baek (公平)

• Ju Sun (非凸优化)

• Bolei Zhou (周博磊)

• Hongyang Zhang (张弘扬)

• HUNG-YI LEE (李宏毅)

• (蒋炎岩), 科研入门的书籍/在线课程/论文的推荐列表, (科研与英文学术论文写作指南), (阮一峰的网络日志), 刘建平, 基础知识入门 (廖雪峰)

People who got their B.S. degree from Sun Yat-Sen University

• Canhui Chen (陈灿辉)

• Ling Pan (潘玲)

• Longbo Huang (黄隆波)

• Hongzheng Chen (陈鸿峥)

• Kan Wu (吴坎)

• Tianlong Wang (王天龙)

• Zhihong Deng (邓智鸿)

• Yunkai Liang (梁允楷)

• dawang (小土刀)

• Shengyuan Ye (叶盛源)

• Liekang Zeng (曾烈康)

• Haohua Liu

• Xuanteng Huang (黄軒騰)

• Yao Fu(符尧)

• Youzhi Zhang，安波学生

• Zhenyu Hu: the National University of Singapore, pricing, Game theory

• Xiang Li (李响), Niao He, ETH Zurich

People who got their B.S. degree from Tsinghua University

• Zhixuan Fang (房智轩)

• Qingsong Liu: Constrained Online Learning, Multi-agent Online Learning, Online Learning in Computer Systems and Communication Networks

• Hongxun Wu (IIIS)

• wu-ys (IIIS), 笔记

• Shi Feng (IIIS): algorithmic game theory, online learning, and social networks

• Lijie Chen (陈立杰)

• Haike Xu (徐海珂), IIIS, MIT

• Kunhe Yang (杨坤禾), IIIS

• Yanjun Han (韩衍隽)

• Mingda Qiao (乔明达): Theoretical aspects of prediction, learning, and decision-making in online settings

• IIIS seminar

• Shengqi Chen (陈晟祺)

• laekov

• Hanzhang Qin (覃含章)

• Jiayi Weng (翁家翌)

• Mingrui Zhang (张明瑞), Blog

• Kaichao You (游凯超)

• (上海交通大学生存手册)

• (北大信科自学指南), (南京大学计算机系统基础), (深入理解计算机系统中文版), 浏览器工作原理与实践

• 友链

• BY (Blog)

• UNIDY (Blog)

• 课程笔记

• 旅行日记

FOCS, STOC, SODA, COLT

• Shai Shalev-Shwartz: Machine Learning algorithms, Online Learning and Online Convex Optimization

• Sébastien Bubeck: Machine Learning Foundations, Microsoft Research

• Steve Hanneke: the statistical analysis of machine learning

• Mehryar Mohri (森): Foundations of Machine Learning.

• Idan Attias: theoretical machine learning, and its intersection with game theory, optimization, statistics and private data analysis.

• Peng Zhao: Machine Learning in Open and Non-stationary Environments, Advanced Optimization (2022 Fall)

• 华为理论计算机实验室

• (优化理论)

• Zhi-Qin John Xu (许志钦), understanding dl

• (自动化所李凯老师)

• 南京大学高级算法 (Fall 2022)

• SHUCHI CHAWLA, approximation and online algorithms, algorithmic mechanism design

• Jin-Yi Cai (蔡进一)

• Boaz Barak: Harvard University

• Yang Liu

• Chi-Ning Chou (周紀寧)

• Pinyan Lu (陆品燕), ITCS (理论计算机科学研究中心), Zhihao Gavin Tang(唐志皓), Hu Fu (伏虎), Nick Gravin

• Lin Yang: 南京大学, online learning

• Zhiyi Huang(黄志毅)

• Zeyuan Allen-Zhu (朱泽园)

• Xiaohui Bei: fairness

• Yuhao Zhang (张宇昊)

• Sigma Lab

• Yitong Yin (尹一通)

• Chao Xu (许超), Problem

• (姚班科研简报), IIIS Seminar

• Billy Jin

• Michael J. Neely

• Aaron Sidford

• Lijun Zhang (张利军)

• Zhouchen Lin (林宙辰)

• Jian Li (李建), paper

• Liwei Wang (王立威)

• Tianle Cai (蔡天乐)

• Hsuan-Tien Lin (林轩田), HsuanTienLin_MachineLearning github

• (钥匙书)

• 机器学习，概率模型和深度学习的讲义(2000+页)和视频链接

• Haihao (Sean) Lu

• Tengyu Ma (马腾宇)

• Tong Zhang (张潼)

• Tianyuan Jin, Online Learning and Bandits, Submodularity, and Graph Algorithms

• Mengxiao Zhang (张梦晓): Online learning and bandits

• yuanyang (袁洋), Jiaye Teng (滕佳烨)，Generalization Theory

• Yuanzhi Li: Assistant Professor in the Machine Learning Department at CMU, working on Deep Learning (Theory).

Economics

• Selected Topics in Microeconomics

• Zhentao Shi (史震涛)

• Wei He (何暐): Mechanism Design

• difference-in-difference, synthetic control method

• Matthew O. Jackson: Stanford, network, game theory

• Rakesh Vohra: University of Pennsylvania, mechanism design

• Robert Wilson: Stanford

• Paul Milgrom: Stanford

• vahab mirrokni, Song Zuo, Yuan Deng (邓原)