<mark id="oztn4"></mark>
0
| 本文作者： 奕欣 | 2017-09-14 16:26 | 專題：NIPS 2017 |
雷鋒網AI科技評論按：NIPS 2017將于今年12月在美國長灘舉辦。自 1987 年到 2000 年，NIPS都在美國丹佛舉辦，雖然后來也曾經在加拿大溫哥華、西班牙的格蘭納達、加拿大蒙特利爾舉辦，但不得不承認的是，美國一直是全球科研的主要陣地。
近日，雷鋒網AI科技評論發現NIPS錄用結果已經出爐。今年NIPS上，美國計算機四大名校（CMU、MIT、UC伯克利、斯坦福）“理所當然”地霸屏，僅以第一作者所屬機構統計的錄用論文就達92篇，雷鋒網AI科技評論統計了詳細名單，整理如下（統計中排除了一篇雙機構的）：
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)
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)
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)
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)
雷峰網原創文章，未經授權禁止轉載。詳情見轉載須知。
專題
