Neil Zhu，簡書ID Not_GOD，University AI 創(chuàng)始人 & Chief Scientist，致力于推進(jìn)世界人工智能化進(jìn)程。制定并實施 UAI 中長期增長戰(zhàn)略和目標(biāo)，帶領(lǐng)團(tuán)隊快速成長為人工智能領(lǐng)域最專業(yè)的力量。
作為行業(yè)領(lǐng)導(dǎo)者，他和UAI一起在2014年創(chuàng)建了TASA（中國最早的人工智能社團(tuán)）, DL Center（深度學(xué)習(xí)知識中心全球價值網(wǎng)絡(luò)），AI growth（行業(yè)智庫培訓(xùn)）等，為中國的人工智能人才建設(shè)輸送了大量的血液和養(yǎng)分。此外，他還參與或者舉辦過各類國際性的人工智能峰會和活動，產(chǎn)生了巨大的影響力，書寫了60萬字的人工智能精品技術(shù)內(nèi)容，生產(chǎn)翻譯了全球第一本深度學(xué)習(xí)入門書《神經(jīng)網(wǎng)絡(luò)與深度學(xué)習(xí)》，生產(chǎn)的內(nèi)容被大量的專業(yè)垂直公眾號和媒體轉(zhuǎn)載與連載。曾經(jīng)受邀為國內(nèi)頂尖大學(xué)制定人工智能學(xué)習(xí)規(guī)劃和教授人工智能前沿課程，均受學(xué)生和老師好評。
ICML 16-全部接受論文
ICML 2016 - 強(qiáng)化學(xué)習(xí)相關(guān)論文 如下：
1. Inverse Optimal Control with Deep Networks via Policy Optimization
Chelsea Finn, UC Berkeley; Sergey Levine, ; Pieter Abbeel, Berkeley
摘要：
http://arxiv.org/abs/1603.00448
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Nan Jiang, University of Michigan; Lihong Li, Microsoft
http://arxiv.org/abs/1511.03722
Smooth Imitation Learning
Hoang Le, Caltech; Andrew Kang, ; Yisong Yue, Caltech; Peter Carr,
PAC Lower Bounds and Efficient Algorithms for The Max KK-Armed Bandit Problem
Yahel David, Technion; Nahum Shimkin, Technion
Anytime Exploration for Multi-armed Bandits using Confidence Information
Kwang-Sung Jun, UW-Madison; Robert Nowak,
The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks
Yingfei Wang, Princeton University; Chu Wang, ; Warren Powell,
https://arxiv.org/abs/1510.02354
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm
Junpei Komiyama, The University of Tokyo; Junya Honda, The University of Tokyo; Hiroshi Nakagawa, The University of Tokyo
https://arxiv.org/abs/1605.01677
Benchmarking Deep Reinforcement Learning for Continuous Control
Yan Duan, University of California, Berk; Xi Chen, University of California, Berkeley; Rein Houthooft, Ghent University; John Schulman, University of California, Berkeley; Pieter Abbeel, Berkeley
https://arxiv.org/abs/1604.06778
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
Prashanth L.A., University of Maryland ; Cheng Jie, University of Maryland – College Park; Michael Fu, University of Maryland – College Park; Steve Marcus, University of Maryland – College Park; Csaba Szepesvari, Alberta
http://arxiv.org/abs/1506.02632
An optimal algorithm for the Thresholding Bandit Problem
Andrea LOCATELLI, University of Potsdam; Maurilio Gutzeit, Universit?t Potsdam; Alexandra Carpentier,
Sequential decision making under uncertainty: Are most decisions easy?
Ozgur Simsek, ; Simon Algorta, ; Amit Kothiyal,
Opponent Modeling in Deep Reinforcement Learning
He He, ; Jordan , ; Hal Daume, Maryland
Softened Approximate Policy Iteration for Markov Games
Julien Pérolat, Univ. Lille; Bilal Piot, Univ. Lille; Matthieu Geist, ; Bruno Scherrer, ; Olivier Pietquin, Univ. Lille, CRIStAL, UMR 9189, SequeL Team, Villeneuve d’Ascq, 59650, FRANCE
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Google DeepMind; Adria Puigdomenech Badia, Google DeepMind; Mehdi Mirza, ; Alex Graves, Google DeepMind; Timothy Lillicrap, Google DeepMind; Tim Harley, Google DeepMind; David , ; Koray Kavukcuoglu, Google Deepmind
https://arxiv.org/abs/1602.01783
Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Google Inc.; Nando de Freitas, University of Oxford; Tom Schaul, Google Inc.; Matteo Hessel, Google Deepmind; Hado van Hasselt, Google DeepMind; Marc Lanctot, Google Deepmind
http://arxiv.org/abs/1511.06581 Cited by 10
Differentially Private Policy Evaluation
Borja Balle, Lancaster University; Maziar Gomrokchi, McGill University; Doina Precup, McGill
https://arxiv.org/abs/1603.02010
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
Philip Thomas, CMU; Emma ,
https://arxiv.org/abs/1604.00923
Hierarchical Decision Making In Electricity Grid Management
Gal Dalal, Technion; Elad Gilboa, Technion; Shie Mannor, Technion
http://arxiv.org/abs/1603.01840
Generalization and Exploration via Randomized Value Functions
Ian Osband, Stanford; Ben , ; Zheng Wen, Adobe Research
https://arxiv.org/abs/1402.0635 Cited by 9
Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Yutian Chen, University of Cambridge; Zoubin ,
摘要
Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods. Like other sampling algorithms, discrete sampling suffers from the high computational burden in large-scale inference problems. We study the problem of sampling a discrete random variable with a high degree of dependency that is typical in large-scale Bayesian inference and graphical models, and propose an efficient approximate solution with a subsampling approach. We make a novel connection between the discrete sampling and Multi-Armed Bandits problems with a finite reward population and provide three algorithms with theoretical guarantees. Empirical evaluations show the robustness and efficiency of the approximate algorithms in both synthetic and real-world large-scale problems.
http://arxiv.org/abs/1506.09039
Model-Free Imitation Learning with Policy Optimization
Jonathan Ho, Stanford; Jayesh Gupta, Stanford University; Stefano Ermon,
Improving the Efficiency of Deep Reinforcement Learning with Normalized Advantage Functions and Synthetic Experience
Shixiang Gu, University of Cambridge; Sergey Levine, Google; Timothy Lillicrap, Google DeepMind; Ilya Sutskever, OpenAI
http://arxiv.org/abs/1603.00748
Near Optimal Behavior via Approximate State Abstraction
David Abel, Brown University; David Hershkowitz, Brown University; Michael Littman,
https://cs.brown.edu/~dabel/papers/abel_approx_abstraction.pdf
Model-Free Trajectory Optimization for Reinforcement Learning of Motor Skills
Riad Akrour, TU Darmstadt; Gerhard Neumann
