雷锋网AI科技评论按:NIPS 2017将于今年12月在美国长滩举办。自 1987 年到 2000 年,NIPS都在美国丹佛举办,虽然后来也曾经在加拿大温哥华、西班牙的格兰纳达、加拿大蒙特利尔举办,但不得不承认的是,美国一直是全球科研的主要阵地。

近日,雷锋网(公众号:雷锋网)AI科技评论发现NIPS录用结果已经出炉。今年NIPS上,美国计算机四大名校(CMU、MIT、UC伯克利、斯坦福)“理所当然”地霸屏,仅以第一作者所属机构统计的录用论文就达92篇,雷锋网AI科技评论统计了详细名单,整理如下(统计中排除了一篇双机构的):

CMU,37篇

    Attentional Pooling for Action Recognition

    Rohit Girdhar (Carnegie Mellon University) · Deva Ramanan (Carnegie Mellon University)

    On the Power of Truncated SVD for General High-rank Matrix Estimation Problems

    Simon Du (Carnegie Mellon University) · Yining Wang (Carnegie Mellon University) · Aarti Singh (CMU)

    Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions

    Ryan J Tibshirani (Carnegie Mellon University)

    Hypothesis Transfer Learning via Transformation Functions

    Simon Du (Carnegie Mellon University) · Jayanth Koushik (Carnegie Mellon University) · Aarti Singh (CMU) · Barnabas Poczos (Carnegie Mellon University)

    Adversarial Invariant Feature Learning

    Qizhe Xie (Carnegie Mellon University) · Zihang Dai (CMU) · Yulun Du (CMU) · Eduard Hovy (CMU) · Graham Neubig (Carnegie Mellon University)

    Safe and Nested Subgame Solving for Imperfect-Information Games

    Noam Brown (Carnegie Mellon University) · Tuomas Sandholm (Carnegie Mellon University)

    Coded Distributed Computing for Inverse Problems

    Yaoqing Yang (Carnegie Mellon University) · Pulkit Grover (CMU) · Soummya Kar (Carnegie Mellon University)

    Gradient Descent Can Take Exponential Time to Escape Saddle Points

    Simon Du (Carnegie Mellon University) · Chi Jin (UC Berkeley) · Jason D Lee (USC) · Michael Jordan (UC Berkeley) · Aarti Singh (CMU) · Barnabas Poczos (Carnegie Mellon University)

    Predictive-State Decoders: Encoding the Future into Recurrent Networks

    Arun Venkatraman (Carnegie Mellon University) · Nicholas Rhinehart (Carnegie Mellon University) · Wen Sun (Carnegie Mellon University) · Lerrel Pinto () · Martial Hebert (cmu) · Byron Boots (Georgia Tech / Google Brain) · Kris Kitani (Carnegie Mellon University) · J. Bagnell (Carnegie Mellon University)

    Deanonymization in the Bitcoin P2P Network

    Giulia Fanti (Carnegie Mellon University) · Pramod Viswanath (UIUC)

    Adaptive sampling for a population of neurons

    Benjamin Cowley (Carnegie Mellon University) · Ryan Williamson (Carnegie Mellon University) · Katerina Clemens (University of Pittsburgh) · Matthew Smith (University of Pittsburgh) · Byron M Yu (Carnegie Mellon University)

    Max-Margin Invariant Features from Transformed Unlabelled Data

    Dipan Pal (Carnegie Mellon University) · Ashwin Kannan (Carnegie Mellon University) · Gautam Arakalgud (Carnegie Mellon University) · Marios Savvides (Carnegie Mellon University)

    MMD GAN: Towards Deeper Understanding of Moment Matching Network

    Chun-Liang Li (Carnegie Mellon University) · Wei-Cheng Chang (Carnegie Mellon University) · Yu Cheng (AI Foundations, IBM Research) · Yiming Yang (CMU) · Barnabas Poczos (Carnegie Mellon University)

    Differentiable Learning of Logical Rules for Knowledge Base Reasoning

    Fan Yang (Carnegie Mellon University) · Zhilin Yang (Carnegie Mellon University) · William W Cohen (Carnegie Mellon University)

    Collaborative PAC Learning

    Avrim Blum (CMU) · Nika Haghtalab (Carnegie Mellon University) · Ariel D Procaccia (Carnegie Mellon University) · IIIS Mingda Qiao (IIIS, Tsinghua University)

    Noise-Tolerant Interactive Learning Using Pairwise Comparisons

    Yichong Xu (Carnegie Mellon University) · Hongyang Zhang (Carnegie Mellon University) · Aarti Singh (Carnegie Mellon University) · Artur Dubrawski (Carnegie Mellon University) · Kyle Miller (Carnegie Mellon University)

    Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation

    Zhaohan Guo (Carnegie Mellon University/Stanford) · Philip S. Thomas (CMU) · Emma Brunskill (CMU)

    Deep Sets

    Manzil Zaheer (Carnegie Mellon University) · Satwik Kottur (Carnegie Mellon University) · Siamak Ravanbakhsh (CMU/UBC) · Barnabas Poczos (Carnegie Mellon University) · Ruslan Salakhutdinov () · Alexander Smola (Amazon - We are hiring!)

    Structured Generative Adversarial Networks

    Hao Zhang (Carnegie Mellon University) · Zhijie Deng (Tsinghua University) · Xiaodan Liang (Carnegie Mellon University) · Jun Zhu (Tsinghua University) · Eric P Xing (Carnegie Mellon University)

    On-the-fly Operation Batching in Dynamic Computation Graphs

    Graham Neubig (Carnegie Mellon University) · Yoav Goldberg (Bar-Ilan University) · Chris Dyer (DeepMind)

    Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach

    Emmanouil Platanios (Carnegie Mellon University) · Hoifung Poon (Microsoft Research) · Tom M Mitchell (Carnegie Mellon University) · Eric J Horvitz (Microsoft Research)

    The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities

    Arun Suggala (Carnegie Mellon University) · Mladen Kolar (University of Chicago) · Pradeep Ravikumar (Carnegie Mellon University)

    Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs

    Sanjiban Choudhury (Carnegie Mellon University) · Shervin Javdani (Carnegie Mellon University) · Siddhartha Srinivasa (Carnegie Mellon University) · Sebastian Scherer (Carnegie Mellon University)

    Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions

    Maria-Florina Balcan (Carnegie Mellon University) · Hongyang Zhang (Carnegie Mellon University)

    Self-supervised Learning of Motion Capture

    Hsiao-Yu Tung (Carnegie Mellon University) · Hsiao-Wei Tung (University of Pittsburgh) · Ersin Yumer (Adobe Research) · Katerina Fragkiadaki ()

    Task-based End-to-end Model Learning in Stochastic Optimization

    Priya Donti (Carnegie Mellon University) · J. Zico Kolter (Carnegie Mellon University) · Brandon Amos (Carnegie Mellon University)

    Gradient descent GAN optimization is locally stable

    Vaishnavh Nagarajan (Carnegie Mellon University) · J. Zico Kolter (Carnegie Mellon University)

    Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning

    Christoph Dann (Carnegie Mellon University) · Tor Lattimore (DeepMind) · Emma Brunskill (Stanford University)

    Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods

    Veeranjaneyulu Sadhanala (CMU) · Yu-Xiang Wang (CMU / Amazon AI) · James Sharpnack () · Ryan J Tibshirani (Carnegie Mellon University)

    Predictive State Recurrent Neural Networks

    Carlton Downey (Carnegie Mellon University) · Ahmed Hefny (Carnegie Mellon University) · Byron Boots (Georgia Tech / Google Brain) · Geoffrey Gordon (CMU) · Boyue Li (Carnegie Mellon University)

    Predictive State Recurrent Neural Networks

    Carlton Downey (Carnegie Mellon University) · Ahmed Hefny (Carnegie Mellon University) · Byron Boots (Georgia Tech / Google Brain) · Geoffrey Gordon (CMU) · Boyue Li (Carnegie Mellon University)

    Good Semi-supervised Learning That Requires a Bad GAN

    Zihang Dai (Carnegie Mellon University) · Zhilin Yang (Carnegie Mellon University) · Fan Yang (Carnegie Mellon University) · William W Cohen (Carnegie Mellon University) · Ruslan Salakhutdinov ()

    A Sharp Error Analysis for the Fused Lasso, with Implications to Broader Settings and Approximate Screening

    Kevin Lin (Carnegie Mellon University) · James Sharpnack () · Alessandro Rinaldo (CMU) · Ryan Tibshirani (Carnegie Mellon University)

    Efficient Computation of Moments in Sum-Product Networks

    Han Zhao (Carnegie Mellon University)

    Active Learning from Peers

    Keerthiram Murugesan (Carnegie Mellon University) · Jaime Carbonell (CMU)

    Learning to Model the Tail

    Yu-Xiong Wang (Carnegie Mellon University) · Deva Ramanan (Carnegie Mellon University) · Martial Hebert (cmu)

    On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models

    Adarsh Prasad (Carnegie Mellon University) · Pradeep Ravikumar (Carnegie Mellon University)

    MIT,20篇

      Concentration of Multilinear Functions of the Ising Model with Applications to Network Data

      Constantinos Daskalakis (MIT) · Nishanth Dikkala (MIT) · Gautam Kamath (MIT)

      Scene Physics Acquisition via Visual De-animation

      Jiajun Wu (MIT) · Erika Lu (University of Oxford) · Pushmeet Kohli (DeepMind) · Bill Freeman (MIT/Google) · Josh Tenenbaum (MIT)

      3D Shape Reconstruction by Modeling 2.5D Sketch

      Jiajun Wu (MIT) · Yifan Wang (ShanghaiTech University) · Tianfan Xue (MIT CSAIL) · Xingyuan Sun (Shanghai Jiao Tong University) · Bill Freeman (MIT/Google) · Josh Tenenbaum (MIT)

      Shape and Material from Sound

      zhoutong zhang (MIT) · Qiujia Li (University of Cambridge) · Zhengjia Huang () · Jiajun Wu (MIT) · Josh Tenenbaum (MIT) · Bill Freeman (MIT/Google)

      Lookahead Bayesian Optimization with Inequality Constraints

      Remi Lam (MIT) · Karen Willcox (MIT)

      Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration

      Jason Altschuler (MIT) · Jonathan Weed (MIT) · Philippe Rigollet (MIT)

      Elementary Symmetric Polynomials for Optimal Experimental Design

      Zelda E. Mariet (MIT) · Suvrit Sra (MIT)

      Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems

      Yonatan Belinkov (MIT)

      Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications

      Linus Hamilton (MIT) · Frederic Koehler (MIT) · Ankur Moitra ()

      Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

      Wengong Jin (MIT CSAIL) · Connor W Coley (MIT Department of Chemical Engineering) · Regina Barzilay (Massachusetts Institute of Technology) · Tommi Jaakkola (MIT)

      Practical Data-Dependent Metric Compression with Provable Guarantees

      Piotr Indyk (MIT) · Ilya Razenshteyn (Columbia University) · Tal Wagner (MIT)

      Parallel Streaming Wasserstein Barycenters

      Matthew Staib (MIT) · Sebastian Claici (MIT) · Justin M Solomon (MIT) · Stefanie Jegelka (MIT)

      On Optimal Generalizability in Parametric Learning

      Ahmad Beirami (Harvard University & MIT) · Meisam Razaviyayn (University of Southern California) · Shahin Shahrampour (Harvard University) · Vahid Tarokh (Harvard University) On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks

      On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks

      Arturs Backurs (MIT) · Piotr Indyk (MIT) · Ludwig Schmidt (MIT)

      Polynomial time algorithms for dual volume sampling

      Chengtao Li (MIT) · Stefanie Jegelka (MIT) · Suvrit Sra (MIT)

      Real-Time Bidding with Side Information

      arthur flajolet (MIT) · Patrick Jaillet (Massachusetts Institute of Technology)

      Local Aggregative Games

      Vikas Garg (MIT) · Tommi Jaakkola (MIT)

      Permutation-based Causal Inference Algorithms with Interventions

      Yuhao Wang (MIT) · Liam Solus (KTH Royal Institute of Technology) · Karren Yang (MIT) · Caroline Uhler (MIT)

      Learning to Generalize Intrinsic Images with a Structured Disentangling Autoencoder

      Michael Janner (MIT) · Jiajun Wu (MIT) · Tejas Kulkarni (DeepMind) · Ilker Yildirim (MIT) · Josh Tenenbaum (MIT)

      Style Transfer from Non-parallel Text by Cross-Alignment

      Tianxiao Shen (MIT) · Tao Lei (MIT) · Regina Barzilay (Massachusetts Institute of Technology) · Tommi Jaakkola (MIT)

      UC伯克利,16篇

        Learning a Multi-View Stereo Machine

        Abhishek Kar (UC Berkeley) · Jitendra Malik () · Christian Häne (UC Berkeley)

        Multimodal Image-to-Image Translation by Enforcing Bi-Cycle Consistency

        Jun-Yan Zhu (UC Berkeley) · Richard Zhang (University of California, Berkeley) · Deepak Pathak (UC Berkeley) · Trevor Darrell (UC Berkeley) · Oliver Wang (Adobe Research) · Eli Shechtman () · Alexei Efros (UC Berkeley)

        One-Shot Imitation Learning

        Yan Duan (UC Berkeley) · Marcin Andrychowicz (OpenAI) · Bradly Stadie (OpenAI) · OpenAI Jonathan Ho (OpenAI, UC Berkeley) · Jonas Schneider (OpenAI) · Ilya Sutskever () · Pieter Abbeel (OpenAI / UC Berkeley / Gradescope) · Wojciech Zaremba (OpenAI)

        Fast Alternating Minimization Algorithms for Dictionary Learning

        Niladri Chatterji (UC Berkeley) · Peter Bartlett (UC Berkeley)

        Consistent Robust Regression

        Kush Bhatia (UC Berkeley) · Prateek Jain (Microsoft Research) · Purushottam Kar (Indian Institute of Technology Kanpur)

        Non-convex Finite-Sum Optimization Via SCSG Methods

        Lihua Lei (UC Berkeley) · Cheng Ju (University of California, Berkeley) · Jianbo Chen (University of California, Berkeley) · Michael Jordan (UC Berkeley)

        Fast Black-box Variational Inference through Stochastic Trust-Region Optimization

        Jeffrey Regier (UC Berkeley) · Michael Jordan (UC Berkeley) · Jon McAuliffe (UC Berkeley)

        Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems

        Alyson Fletcher (UCLA, UCSC, & UC Berkeley) · Sundeep Rangan (NYU-Poly) · Mojtaba Sahraee-Ardakan (UCLA) · Philip Schniter (Ohio State University)

        EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

        Justin Fu (UC Berkeley) · John Co-Reyes (UC Berkeley) · Sergey Levine (UC Berkeley)

        #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

        Haoran Tang (UC Berkeley) · Pieter Abbeel (OpenAI / UC Berkeley / Gradescope) · Davis J Foote (UC Berkeley) · Yan Duan () · OpenAI Xi Chen (OpenAI, UC Berkeley) · Rein Houthooft (OpenAI) · Adam Stooke (UC Berkeley) · Filip DeTurck

        Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach

        Roel Dobbe (UC Berkeley) · David Fridovich-Keil (UC Berkeley) · Claire Tomlin (UC Berkeley)

        The Marginal Value of Adaptive Gradient Methods in Machine Learning

        Ashia C Wilson (UC Berkeley) · Rebecca D Roelofs (UC Berkeley) · Mitchell Stern (UC Berkeley) · Nati Srebro (TTI-Chicago) · Benjamin Recht (UC Berkeley)

        Federated Multi-Task Learning

        Virginia Smith (UC Berkeley) · Maziar Sanjabi (University of California, Los Angeles) · Chao-Kai Chiang (University of Southern California) · Ameet S Talwalkar (UCLA)

        Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities

        Michael Eickenberg (UC Berkeley) · Georgios Exarchakis (Ecole Normale Supérieure) · Matthew Hirn (Michigan State University) · Stephane Mallat (Ecole normale superieure)

        Inverse Reward Design

        Dylan Hadfield-Menell (UC Berkeley) · Smitha Milli (UC Berkeley) · Stuart J Russell (UC Berkeley) · Pieter Abbeel (OpenAI / UC Berkeley / Gradescope) · Anca Dragan (UC Berkeley)

        Kernel Feature Selection via Conditional Covariance Minimization

        Jianbo Chen (University of California, Berkeley) · Mitchell Stern (UC Berkeley) · Martin J Wainwright (UC Berkeley) · Michael Jordan (UC Berkeley)

        斯坦福,20篇

          Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation

          Zhaohan Guo (Carnegie Mellon University/Stanford) · Philip S. Thomas (CMU) · Emma Brunskill (CMU)

          Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System

          Chengxu Zhuang (Stanford University) · Jonas Kubilius (Massachusetts Institute of Technology) · Mitra JZ Hartmann (Northwestern University) · Daniel Yamins (Stanford University)

          Robust Estimation of Neural Signals in Calcium Imaging

          Hakan Inan (Stanford University) · Murat Erdogdu (Microsoft Research) · Mark Schnitzer (Stanford University)

          Variance-based Regularization with Convex Objectives

          Hongseok Namkoong (Stanford University) · John C Duchi (Stanford)

          Variance-based Regularization with Convex Objectives

          Hongseok Namkoong (Stanford University) · John C Duchi (Stanford)

          An Applied Algorithmic Foundation for Hierarchical Clustering

          Joshua Wang (Stanford University) · Benjamin Moseley (Washington University in St Lo)

          Learning to Compose Domain-Specific Transformations for Data Augmentation

          Alexander Ratner (Stanford) · Henry Ehrenberg (Stanford University) · Zeshan Hussain (Stanford University) · Jared Dunnmon (Stanford University) · Christopher Ré (Stanford)

          Ensemble Sampling

          Xiuyuan Lu (Stanford University) · Benjamin Van Roy (Stanford University)

          Language modeling with recurrent highway hypernetworks

          Joseph Suarez (Stanford University)

          Certified Defenses for Data Poisoning Attacks

          Jacob Steinhardt (Stanford University) · Pang Wei W Koh (Stanford University) · Percy S Liang (Stanford University)

          Conservative Contextual Linear Bandits

          Abbas Kazerouni (Stanford University) · Mohammad Ghavamzadeh (DeepMind) · Yasin Abbasi (Adobe Research) · Benjamin Van Roy (Stanford University)

          Stochastic and Adversarial Online Learning without Hyperparameters

          Ashok Cutkosky (Stanford University) · Kwabena A Boahen (Stanford University)

          PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

          Charles Ruizhongtai Qi (Stanford University) · Li Yi (Stanford University) · Hao Su (Stanford) · Leonidas J Guibas (stanford.edu)

          A-NICE-MC: Adversarial Training for MCMC

          Jiaming Song (Stanford University) · Shengjia Zhao (Stanford University) · Stefano Ermon (Stanford)

          Optimally Learning Populations of Parameters

          Kevin Tian (Stanford University) · Weihao Kong (Stanford University) · Gregory Valiant (Stanford University)

          Gaussian Quadrature for Kernel Features

          Tri Dao (Stanford University) · Christopher M De Sa (Stanford) · Christopher Ré (Stanford)

          Delayed Mirror Descent in Continuous Games

          Zhengyuan Zhou (Stanford University) · Panayotis Mertikopoulos () · Nicholas Bambos () · Peter W Glynn (Stanford University) · Claire Tomlin (UC Berkeley)

          Learning Mixture of Gaussians with Streaming Data

          Aditi Raghunathan (Stanford University) · Prateek Jain (Microsoft Research) · Ravishankar Krishnawamy (Microsoft Research India)

          Neural Variational Inference and Learning in Undirected Graphical Models

          Volodymyr Kuleshov (Stanford University) · Stefano Ermon (Stanford)

          Stochastic Mirror Descent for Non-Convex Optimization

          Zhengyuan Zhou (Stanford University) · Panayotis Mertikopoulos () · Nicholas Bambos () · Stephen Boyd (Stanford University) · Peter W Glynn (Stanford University)