首页 » 漏洞 » NIPS 2017美国四大名校霸屏,92篇论文抢先看 | NIPS 2017

NIPS 2017美国四大名校霸屏,92篇论文抢先看 | NIPS 2017

 
文章目录

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 BitcoinP2P 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 H?ne (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)

雷锋网原创文章,未经授权禁止转载。详情见转载须知。

原文链接:NIPS 2017美国四大名校霸屏,92篇论文抢先看 | NIPS 2017,转载请注明来源!

0