| 2017 | "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford |
| 2017 | A Birth-Death Process for Feature Allocation. Konstantina Palla, David A. Knowles, Zoubin Ghahramani |
| 2017 | A Closer Look at Memorization in Deep Networks. Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien |
| 2017 | A Distributional Perspective on Reinforcement Learning. Marc G. Bellemare, Will Dabney, Rémi Munos |
| 2017 | A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI. Justin Domke |
| 2017 | A Laplacian Framework for Option Discovery in Reinforcement Learning. Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling |
| 2017 | A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. Tianbao Yang, Qihang Lin, Lijun Zhang |
| 2017 | A Semismooth Newton Method for Fast, Generic Convex Programming. Alnur Ali, Eric Wong, J. Zico Kolter |
| 2017 | A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency. Ron Appel, Pietro Perona |
| 2017 | A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization. Jianbo Ye, James Ze Wang, Jia Li |
| 2017 | A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions. Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh |
| 2017 | A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery. Lingxiao Wang, Xiao Zhang, Quanquan Gu |
| 2017 | A Unified View of Multi-Label Performance Measures. Xi-Zhu Wu, Zhi-Hua Zhou |
| 2017 | Accelerating Eulerian Fluid Simulation With Convolutional Networks. Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin |
| 2017 | Active Heteroscedastic Regression. Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan |
| 2017 | Active Learning for Accurate Estimation of Linear Models. Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric |
| 2017 | Active Learning for Cost-Sensitive Classification. Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford |
| 2017 | Active Learning for Top-K Rank Aggregation from Noisy Comparisons. Soheil Mohajer, Changho Suh, Adel M. Elmahdy |
| 2017 | AdaNet: Adaptive Structural Learning of Artificial Neural Networks. Corinna Cortes, Xavier Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang |
| 2017 | Adapting Kernel Representations Online Using Submodular Maximization. Matthew Schlegel, Yangchen Pan, Jiecao Chen, Martha White |
| 2017 | Adaptive Consensus ADMM for Distributed Optimization. Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein |
| 2017 | Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál |
| 2017 | Adaptive Multiple-Arm Identification. Jiecao Chen, Xi Chen, Qin Zhang, Yuan Zhou |
| 2017 | Adaptive Neural Networks for Efficient Inference. Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama |
| 2017 | Adaptive Sampling Probabilities for Non-Smooth Optimization. Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi |
| 2017 | Adversarial Feature Matching for Text Generation. Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, Lawrence Carin |
| 2017 | Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks. Lars M. Mescheder, Sebastian Nowozin, Andreas Geiger |
| 2017 | Algebraic Variety Models for High-Rank Matrix Completion. Greg Ongie, Rebecca Willett, Robert D. Nowak, Laura Balzano |
| 2017 | Algorithmic Stability and Hypothesis Complexity. Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao |
| 2017 | Algorithms for $\ell_p$ Low-Rank Approximation. Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff |
| 2017 | An Adaptive Test of Independence with Analytic Kernel Embeddings. Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton |
| 2017 | An Alternative Softmax Operator for Reinforcement Learning. Kavosh Asadi, Michael L. Littman |
| 2017 | An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis. Yuandong Tian |
| 2017 | An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation. David G. Anderson, Ming Gu |
| 2017 | An Infinite Hidden Markov Model With Similarity-Biased Transitions. Colin R. Dawson, Chaofan Huang, Clayton T. Morrison |
| 2017 | Analogical Inference for Multi-relational Embeddings. Hanxiao Liu, Yuexin Wu, Yiming Yang |
| 2017 | Analysis and Optimization of Graph Decompositions by Lifted Multicuts. Andrea Hornáková, Jan-Hendrik Lange, Bjoern Andres |
| 2017 | Analytical Guarantees on Numerical Precision of Deep Neural Networks. Charbel Sakr, Yongjune Kim, Naresh R. Shanbhag |
| 2017 | Approximate Newton Methods and Their Local Convergence. Haishan Ye, Luo Luo, Zhihua Zhang |
| 2017 | Approximate Steepest Coordinate Descent. Sebastian U. Stich, Anant Raj, Martin Jaggi |
| 2017 | Asymmetric Tri-training for Unsupervised Domain Adaptation. Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada |
| 2017 | Asynchronous Distributed Variational Gaussian Process for Regression. Hao Peng, Shandian Zhe, Xiao Zhang, Yuan Qi |
| 2017 | Asynchronous Stochastic Gradient Descent with Delay Compensation. Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhiming Ma, Tie-Yan Liu |
| 2017 | Attentive Recurrent Comparators. Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati |
| 2017 | Automated Curriculum Learning for Neural Networks. Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu |
| 2017 | Automatic Discovery of the Statistical Types of Variables in a Dataset. Isabel Valera, Zoubin Ghahramani |
| 2017 | Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning. Oron Anschel, Nir Baram, Nahum Shimkin |
| 2017 | Axiomatic Attribution for Deep Networks. Mukund Sundararajan, Ankur Taly, Qiqi Yan |
| 2017 | Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli |
| 2017 | Bayesian Boolean Matrix Factorisation. Tammo Rukat, Christopher C. Holmes, Michalis K. Titsias, Christopher Yau |
| 2017 | Bayesian Models of Data Streams with Hierarchical Power Priors. Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen |
| 2017 | Bayesian Optimization with Tree-structured Dependencies. Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias W. Seeger |
| 2017 | Bayesian inference on random simple graphs with power law degree distributions. Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi |
| 2017 | Being Robust (in High Dimensions) Can Be Practical. Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart |
| 2017 | Beyond Filters: Compact Feature Map for Portable Deep Model. Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao |
| 2017 | Bidirectional Learning for Time-series Models with Hidden Units. Takayuki Osogami, Hiroshi Kajino, Taro Sekiyama |
| 2017 | Boosted Fitted Q-Iteration. Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli |
| 2017 | Bottleneck Conditional Density Estimation. Rui Shu, Hung Hai Bui, Mohammad Ghavamzadeh |
| 2017 | Breaking Locality Accelerates Block Gauss-Seidel. Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht |
| 2017 | Canopy Fast Sampling with Cover Trees. Manzil Zaheer, Satwik Kottur, Amr Ahmed, José M. F. Moura, Alexander J. Smola |
| 2017 | Capacity Releasing Diffusion for Speed and Locality. Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao |
| 2017 | ChoiceRank: Identifying Preferences from Node Traffic in Networks. Lucas Maystre, Matthias Grossglauser |
| 2017 | Clustering High Dimensional Dynamic Data Streams. Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang |
| 2017 | Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery. Ashkan Panahi, Devdatt P. Dubhashi, Fredrik D. Johansson, Chiranjib Bhattacharyya |
| 2017 | Co-clustering through Optimal Transport. Charlotte Laclau, Ievgen Redko, Basarab Matei, Younès Bennani, Vincent Brault |
| 2017 | Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. Samuel Ritter, David G. T. Barrett, Adam Santoro, Matt M. Botvinick |
| 2017 | Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery. Mostafa Rahmani, George K. Atia |
| 2017 | Coherent Probabilistic Forecasts for Hierarchical Time Series. Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman |
| 2017 | Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible. Kai Zheng, Wenlong Mou, Liwei Wang |
| 2017 | Combined Group and Exclusive Sparsity for Deep Neural Networks. Jaehong Yoon, Sung Ju Hwang |
| 2017 | Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav S. Sukhatme, Stefan Schaal, Sergey Levine |
| 2017 | Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. Dan Garber, Ohad Shamir, Nathan Srebro |
| 2017 | Composing Tree Graphical Models with Persistent Homology Features for Clustering Mixed-Type Data. Xiuyan Ni, Novi Quadrianto, Yusu Wang, Chao Chen |
| 2017 | Compressed Sensing using Generative Models. Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis |
| 2017 | Conditional Accelerated Lazy Stochastic Gradient Descent. Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink |
| 2017 | Conditional Image Synthesis with Auxiliary Classifier GANs. Augustus Odena, Christopher Olah, Jonathon Shlens |
| 2017 | Confident Multiple Choice Learning. Kimin Lee, Changho Hwang, KyoungSoo Park, Jinwoo Shin |
| 2017 | Connected Subgraph Detection with Mirror Descent on SDPs. Cem Aksoylar, Lorenzo Orecchia, Venkatesh Saligrama |
| 2017 | Consistency Analysis for Binary Classification Revisited. Krzysztof Dembczynski, Wojciech Kotlowski, Oluwasanmi Koyejo, Nagarajan Natarajan |
| 2017 | Consistent On-Line Off-Policy Evaluation. Assaf Hallak, Shie Mannor |
| 2017 | Consistent k-Clustering. Silvio Lattanzi, Sergei Vassilvitskii |
| 2017 | Constrained Policy Optimization. Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel |
| 2017 | Contextual Decision Processes with low Bellman rank are PAC-Learnable. Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire |
| 2017 | Continual Learning Through Synaptic Intelligence. Friedemann Zenke, Ben Poole, Surya Ganguli |
| 2017 | Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization. Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney |
| 2017 | Convex Phase Retrieval without Lifting via PhaseMax. Tom Goldstein, Christoph Studer |
| 2017 | Convexified Convolutional Neural Networks. Yuchen Zhang, Percy Liang, Martin J. Wainwright |
| 2017 | Convolutional Sequence to Sequence Learning. Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin |
| 2017 | Coordinated Multi-Agent Imitation Learning. Hoang Minh Le, Yisong Yue, Peter Carr, Patrick Lucey |
| 2017 | Coresets for Vector Summarization with Applications to Network Graphs. Dan Feldman, Sedat Ozer, Daniela Rus |
| 2017 | Cost-Optimal Learning of Causal Graphs. Murat Kocaoglu, Alex Dimakis, Sriram Vishwanath |
| 2017 | Count-Based Exploration with Neural Density Models. Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, Rémi Munos |
| 2017 | Counterfactual Data-Fusion for Online Reinforcement Learners. Andrew Forney, Judea Pearl, Elias Bareinboim |
| 2017 | Coupling Distributed and Symbolic Execution for Natural Language Queries. Lili Mou, Zhengdong Lu, Hang Li, Zhi Jin |
| 2017 | Curiosity-driven Exploration by Self-supervised Prediction. Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell |
| 2017 | DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner |
| 2017 | Dance Dance Convolution. Chris Donahue, Zachary C. Lipton, Julian J. McAuley |
| 2017 | Data-Efficient Policy Evaluation Through Behavior Policy Search. Josiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum |
| 2017 | Deciding How to Decide: Dynamic Routing in Artificial Neural Networks. Mason McGill, Pietro Perona |
| 2017 | Decoupled Neural Interfaces using Synthetic Gradients. Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu |
| 2017 | Deep Bayesian Active Learning with Image Data. Yarin Gal, Riashat Islam, Zoubin Ghahramani |
| 2017 | Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability. Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian |
| 2017 | Deep Generative Models for Relational Data with Side Information. Changwei Hu, Piyush Rai, Lawrence Carin |
| 2017 | Deep IV: A Flexible Approach for Counterfactual Prediction. Jason S. Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy |
| 2017 | Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC. Yulai Cong, Bo Chen, Hongwei Liu, Mingyuan Zhou |
| 2017 | Deep Spectral Clustering Learning. Marc T. Law, Raquel Urtasun, Richard S. Zemel |
| 2017 | Deep Tensor Convolution on Multicores. David M. Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit |
| 2017 | Deep Transfer Learning with Joint Adaptation Networks. Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan |
| 2017 | Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs. Michael Gygli, Mohammad Norouzi, Anelia Angelova |
| 2017 | Deep Voice: Real-time Neural Text-to-Speech. Sercan Ömer Arik, Mike Chrzanowski, Adam Coates, Gregory Frederick Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Y. Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi |
| 2017 | DeepBach: a Steerable Model for Bach Chorales Generation. Gaëtan Hadjeres, François Pachet, Frank Nielsen |
| 2017 | Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell |
| 2017 | Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten". Baharan Mirzasoleiman, Amin Karbasi, Andreas Krause |
| 2017 | Delta Networks for Optimized Recurrent Network Computation. Daniel Neil, Junhaeng Lee, Tobi Delbruck, Shih-Chii Liu |
| 2017 | Density Level Set Estimation on Manifolds with DBSCAN. Heinrich Jiang |
| 2017 | Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks. Itay Safran, Ohad Shamir |
| 2017 | Deriving Neural Architectures from Sequence and Graph Kernels. Tao Lei, Wengong Jin, Regina Barzilay, Tommi S. Jaakkola |
| 2017 | Developing Bug-Free Machine Learning Systems With Formal Mathematics. Daniel Selsam, Percy Liang, David L. Dill |
| 2017 | Device Placement Optimization with Reinforcement Learning. Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean |
| 2017 | Diameter-Based Active Learning. Christopher Tosh, Sanjoy Dasgupta |
| 2017 | Dictionary Learning Based on Sparse Distribution Tomography. Pedram Pad, Farnood Salehi, L. Elisa Celis, Patrick Thiran, Michael Unser |
| 2017 | Differentiable Programs with Neural Libraries. Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow |
| 2017 | Differentially Private Chi-squared Test by Unit Circle Mechanism. Kazuya Kakizaki, Kazuto Fukuchi, Jun Sakuma |
| 2017 | Differentially Private Clustering in High-Dimensional Euclidean Spaces. Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang |
| 2017 | Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau |
| 2017 | Differentially Private Ordinary Least Squares. Or Sheffet |
| 2017 | Differentially Private Submodular Maximization: Data Summarization in Disguise. Marko Mitrovic, Mark Bun, Andreas Krause, Amin Karbasi |
| 2017 | Discovering Discrete Latent Topics with Neural Variational Inference. Yishu Miao, Edward Grefenstette, Phil Blunsom |
| 2017 | Dissipativity Theory for Nesterov's Accelerated Method. Bin Hu, Laurent Lessard |
| 2017 | Distributed Batch Gaussian Process Optimization. Erik A. Daxberger, Bryan Kian Hsiang Low |
| 2017 | Distributed Mean Estimation with Limited Communication. Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan |
| 2017 | Distributed and Provably Good Seedings for k-Means in Constant Rounds. Olivier Bachem, Mario Lucic, Andreas Krause |
| 2017 | Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition. Zeyuan Allen-Zhu, Yuanzhi Li |
| 2017 | Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization. Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon, Pradeep Ravikumar |
| 2017 | Dropout Inference in Bayesian Neural Networks with Alpha-divergences. Yingzhen Li, Yarin Gal |
| 2017 | Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization. Bo Liu, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu, Dimitris N. Metaxas |
| 2017 | Dual Supervised Learning. Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu |
| 2017 | Dueling Bandits with Weak Regret. Bangrui Chen, Peter I. Frazier |
| 2017 | Dynamic Word Embeddings. Robert Bamler, Stephan Mandt |
| 2017 | Efficient Distributed Learning with Sparsity. Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang |
| 2017 | Efficient Nonmyopic Active Search. Shali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley, Roman Garnett |
| 2017 | Efficient Online Bandit Multiclass Learning with Õ(√T) Regret. Alina Beygelzimer, Francesco Orabona, Chicheng Zhang |
| 2017 | Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections. Zakaria Mhammedi, Andrew D. Hellicar, Ashfaqur Rahman, James Bailey |
| 2017 | Efficient Regret Minimization in Non-Convex Games. Elad Hazan, Karan Singh, Cyril Zhang |
| 2017 | Efficient softmax approximation for GPUs. Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, Hervé Jégou |
| 2017 | Emulating the Expert: Inverse Optimization through Online Learning. Andreas Bärmann, Sebastian Pokutta, Oskar Schneider |
| 2017 | End-to-End Differentiable Adversarial Imitation Learning. Nir Baram, Oron Anschel, Itai Caspi, Shie Mannor |
| 2017 | End-to-End Learning for Structured Prediction Energy Networks. David Belanger, Bishan Yang, Andrew McCallum |
| 2017 | Enumerating Distinct Decision Trees. Salvatore Ruggieri |
| 2017 | Equivariance Through Parameter-Sharing. Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos |
| 2017 | Estimating individual treatment effect: generalization bounds and algorithms. Uri Shalit, Fredrik D. Johansson, David A. Sontag |
| 2017 | Estimating the unseen from multiple populations. Aditi Raghunathan, Gregory Valiant, James Zou |
| 2017 | Evaluating Bayesian Models with Posterior Dispersion Indices. Alp Kucukelbir, Yixin Wang, David M. Blei |
| 2017 | Evaluating the Variance of Likelihood-Ratio Gradient Estimators. Seiya Tokui, Issei Sato |
| 2017 | Exact Inference for Integer Latent-Variable Models. Kevin Winner, Debora Sujono, Daniel Sheldon |
| 2017 | Exact MAP Inference by Avoiding Fractional Vertices. Erik M. Lindgren, Alexandros G. Dimakis, Adam R. Klivans |
| 2017 | Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms. Jialei Wang, Lin Xiao |
| 2017 | Failures of Gradient-Based Deep Learning. Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah |
| 2017 | Fairness in Reinforcement Learning. Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth |
| 2017 | Fake News Mitigation via Point Process Based Intervention. Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit S. Trivedi, Elias B. Khalil, Shuang Li, Le Song, Hongyuan Zha |
| 2017 | Fast Bayesian Intensity Estimation for the Permanental Process. Christian J. Walder, Adrian N. Bishop |
| 2017 | Fast k-Nearest Neighbour Search via Prioritized DCI. Ke Li, Jitendra Malik |
| 2017 | Faster Greedy MAP Inference for Determinantal Point Processes. Insu Han, Prabhanjan Kambadur, KyoungSoo Park, Jinwoo Shin |
| 2017 | Faster Principal Component Regression and Stable Matrix Chebyshev Approximation. Zeyuan Allen-Zhu, Yuanzhi Li |
| 2017 | FeUdal Networks for Hierarchical Reinforcement Learning. Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu |
| 2017 | Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU. Zeyuan Allen-Zhu, Yuanzhi Li |
| 2017 | Follow the Moving Leader in Deep Learning. Shuai Zheng, James T. Kwok |
| 2017 | Forest-type Regression with General Losses and Robust Forest. Alexander Hanbo Li, Andrew Martin |
| 2017 | Forward and Reverse Gradient-Based Hyperparameter Optimization. Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil |
| 2017 | Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo. Umut Simsekli |
| 2017 | Frame-based Data Factorizations. Sebastian Mair, Ahcène Boubekki, Ulf Brefeld |
| 2017 | From Patches to Images: A Nonparametric Generative Model. Geng Ji, Michael C. Hughes, Erik B. Sudderth |
| 2017 | GSOS: Gauss-Seidel Operator Splitting Algorithm for Multi-Term Nonsmooth Convex Composite Optimization. Li Shen, Wei Liu, Ganzhao Yuan, Shiqian Ma |
| 2017 | Generalization and Equilibrium in Generative Adversarial Nets (GANs). Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang |
| 2017 | Geometry of Neural Network Loss Surfaces via Random Matrix Theory. Jeffrey Pennington, Yasaman Bahri |
| 2017 | Global optimization of Lipschitz functions. Cédric Malherbe, Nicolas Vayatis |
| 2017 | Globally Induced Forest: A Prepruning Compression Scheme. Jean-Michel Begon, Arnaud Joly, Pierre Geurts |
| 2017 | Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs. Alon Brutzkus, Amir Globerson |
| 2017 | Gradient Boosted Decision Trees for High Dimensional Sparse Output. Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh |
| 2017 | Gradient Coding: Avoiding Stragglers in Distributed Learning. Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis |
| 2017 | Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares. Junqi Tang, Mohammad Golbabaee, Mike E. Davies |
| 2017 | Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling. Hairong Liu, Zhenyao Zhu, Xiangang Li, Sanjeev Satheesh |
| 2017 | Grammar Variational Autoencoder. Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato |
| 2017 | Graph-based Isometry Invariant Representation Learning. Renata Khasanova, Pascal Frossard |
| 2017 | Guarantees for Greedy Maximization of Non-submodular Functions with Applications. Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek |
| 2017 | Hierarchy Through Composition with Multitask LMDPs. Andrew M. Saxe, Adam Christopher Earle, Benjamin Rosman |
| 2017 | High Dimensional Bayesian Optimization with Elastic Gaussian Process. Santu Rana, Cheng Li, Sunil Gupta, Vu Nguyen, Svetha Venkatesh |
| 2017 | High-Dimensional Structured Quantile Regression. Vidyashankar Sivakumar, Arindam Banerjee |
| 2017 | High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. Rongda Zhu, Lingxiao Wang, ChengXiang Zhai, Quanquan Gu |
| 2017 | High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation. Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu |
| 2017 | How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices? Andreas Loukas |
| 2017 | How to Escape Saddle Points Efficiently. Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan |
| 2017 | Hyperplane Clustering via Dual Principal Component Pursuit. Manolis C. Tsakiris, René Vidal |
| 2017 | Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables. Bryant Chen, Daniel Kumor, Elias Bareinboim |
| 2017 | Identify the Nash Equilibrium in Static Games with Random Payoffs. Yichi Zhou, Jialian Li, Jun Zhu |
| 2017 | Identifying Best Interventions through Online Importance Sampling. Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai |
| 2017 | Image-to-Markup Generation with Coarse-to-Fine Attention. Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, Alexander M. Rush |
| 2017 | Improved Variational Autoencoders for Text Modeling using Dilated Convolutions. Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, Taylor Berg-Kirkpatrick |
| 2017 | Improving Gibbs Sampler Scan Quality with DoGS. Ioannis Mitliagkas, Lester W. Mackey |
| 2017 | Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution. Po-Wei Chou, Daniel Maturana, Sebastian A. Scherer |
| 2017 | Improving Viterbi is Hard: Better Runtimes Imply Faster Clique Algorithms. Arturs Backurs, Christos Tzamos |
| 2017 | Innovation Pursuit: A New Approach to the Subspace Clustering Problem. Mostafa Rahmani, George K. Atia |
| 2017 | Input Convex Neural Networks. Brandon Amos, Lei Xu, J. Zico Kolter |
| 2017 | Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo |
| 2017 | Interactive Learning from Policy-Dependent Human Feedback. James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman |
| 2017 | Iterative Machine Teaching. Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, Le Song |
| 2017 | Joint Dimensionality Reduction and Metric Learning: A Geometric Take. Mehrtash Tafazzoli Harandi, Mathieu Salzmann, Richard I. Hartley |
| 2017 | Just Sort It! A Simple and Effective Approach to Active Preference Learning. Lucas Maystre, Matthias Grossglauser |
| 2017 | Kernelized Support Tensor Machines. Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin |
| 2017 | Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. Rakshit S. Trivedi, Hanjun Dai, Yichen Wang, Le Song |
| 2017 | Language Modeling with Gated Convolutional Networks. Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier |
| 2017 | Large-Scale Evolution of Image Classifiers. Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka I. Leon-Suematsu, Jie Tan, Quoc V. Le, Alexey Kurakin |
| 2017 | Latent Feature Lasso. Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar |
| 2017 | Latent Intention Dialogue Models. Tsung-Hsien Wen, Yishu Miao, Phil Blunsom, Steve J. Young |
| 2017 | Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data. Manzil Zaheer, Amr Ahmed, Alexander J. Smola |
| 2017 | Lazifying Conditional Gradient Algorithms. Gábor Braun, Sebastian Pokutta, Daniel Zink |
| 2017 | Learned Optimizers that Scale and Generalize. Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein |
| 2017 | Learning Algorithms for Active Learning. Philip Bachman, Alessandro Sordoni, Adam Trischler |
| 2017 | Learning Continuous Semantic Representations of Symbolic Expressions. Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton |
| 2017 | Learning Deep Architectures via Generalized Whitened Neural Networks. Ping Luo |
| 2017 | Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo. Matthew D. Hoffman |
| 2017 | Learning Determinantal Point Processes with Moments and Cycles. John Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet |
| 2017 | Learning Discrete Representations via Information Maximizing Self-Augmented Training. Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama |
| 2017 | Learning Gradient Descent: Better Generalization and Longer Horizons. Kaifeng Lv, Shunhua Jiang, Jian Li |
| 2017 | Learning Hawkes Processes from Short Doubly-Censored Event Sequences. Hongteng Xu, Dixin Luo, Hongyuan Zha |
| 2017 | Learning Hierarchical Features from Deep Generative Models. Shengjia Zhao, Jiaming Song, Stefano Ermon |
| 2017 | Learning Important Features Through Propagating Activation Differences. Avanti Shrikumar, Peyton Greenside, Anshul Kundaje |
| 2017 | Learning Infinite Layer Networks Without the Kernel Trick. Roi Livni, Daniel Carmon, Amir Globerson |
| 2017 | Learning Latent Space Models with Angular Constraints. Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing |
| 2017 | Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture. Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi |
| 2017 | Learning Stable Stochastic Nonlinear Dynamical Systems. Jonas Umlauft, Sandra Hirche |
| 2017 | Learning Texture Manifolds with the Periodic Spatial GAN. Urs Bergmann, Nikolay Jetchev, Roland Vollgraf |
| 2017 | Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis. Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar |
| 2017 | Learning in POMDPs with Monte Carlo Tree Search. Sammie Katt, Frans A. Oliehoek, Christopher Amato |
| 2017 | Learning the Structure of Generative Models without Labeled Data. Stephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré |
| 2017 | Learning to Aggregate Ordinal Labels by Maximizing Separating Width. Guangyong Chen, Shengyu Zhang, Di Lin, Hui Huang, Pheng-Ann Heng |
| 2017 | Learning to Align the Source Code to the Compiled Object Code. Dor Levy, Lior Wolf |
| 2017 | Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier. Joseph Futoma, Sanjay Hariharan, Katherine A. Heller |
| 2017 | Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim |
| 2017 | Learning to Discover Sparse Graphical Models. Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew B. Blaschko |
| 2017 | Learning to Generate Long-term Future via Hierarchical Prediction. Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee |
| 2017 | Learning to Learn without Gradient Descent by Gradient Descent. Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew M. Botvinick, Nando de Freitas |
| 2017 | Leveraging Node Attributes for Incomplete Relational Data. He Zhao, Lan Du, Wray L. Buntine |
| 2017 | Leveraging Union of Subspace Structure to Improve Constrained Clustering. John Lipor, Laura Balzano |
| 2017 | Local Bayesian Optimization of Motor Skills. Riad Akrour, Dmitry Sorokin, Jan Peters, Gerhard Neumann |
| 2017 | Local-to-Global Bayesian Network Structure Learning. Tian Gao, Kshitij P. Fadnis, Murray Campbell |
| 2017 | Logarithmic Time One-Against-Some. Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro |
| 2017 | Lost Relatives of the Gumbel Trick. Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller |
| 2017 | MEC: Memory-efficient Convolution for Deep Neural Network. Minsik Cho, Daniel Brand |
| 2017 | Magnetic Hamiltonian Monte Carlo. Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner |
| 2017 | Max-value Entropy Search for Efficient Bayesian Optimization. Zi Wang, Stefanie Jegelka |
| 2017 | Maximum Selection and Ranking under Noisy Comparisons. Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh |
| 2017 | McGan: Mean and Covariance Feature Matching GAN. Youssef Mroueh, Tom Sercu, Vaibhava Goel |
| 2017 | Measuring Sample Quality with Kernels. Jackson Gorham, Lester W. Mackey |
| 2017 | Meritocratic Fairness for Cross-Population Selection. Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu |
| 2017 | Meta Networks. Tsendsuren Munkhdalai, Hong Yu |
| 2017 | Minimax Regret Bounds for Reinforcement Learning. Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos |
| 2017 | Minimizing Trust Leaks for Robust Sybil Detection. János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz |
| 2017 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Chelsea Finn, Pieter Abbeel, Sergey Levine |
| 2017 | Model-Independent Online Learning for Influence Maximization. Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt |
| 2017 | Modular Multitask Reinforcement Learning with Policy Sketches. Jacob Andreas, Dan Klein, Sergey Levine |
| 2017 | Multi-Class Optimal Margin Distribution Machine. Teng Zhang, Zhi-Hua Zhou |
| 2017 | Multi-fidelity Bayesian Optimisation with Continuous Approximations. Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos |
| 2017 | Multi-objective Bandits: Optimizing the Generalized Gini Index. Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Shie Mannor |
| 2017 | Multi-task Learning with Labeled and Unlabeled Tasks. Anastasia Pentina, Christoph H. Lampert |
| 2017 | Multichannel End-to-end Speech Recognition. Tsubasa Ochiai, Shinji Watanabe, Takaaki Hori, John R. Hershey |
| 2017 | Multilabel Classification with Group Testing and Codes. Shashanka Ubaru, Arya Mazumdar |
| 2017 | Multilevel Clustering via Wasserstein Means. Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Q. Phung |
| 2017 | Multiple Clustering Views from Multiple Uncertain Experts. Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy |
| 2017 | Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. Christos Louizos, Max Welling |
| 2017 | Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter. Zeyuan Allen-Zhu |
| 2017 | Near-Optimal Design of Experiments via Regret Minimization. Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang |
| 2017 | Nearly Optimal Robust Matrix Completion. Yeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain |
| 2017 | Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders. Jesse H. Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Mohammad Norouzi, Douglas Eck, Karen Simonyan |
| 2017 | Neural Episodic Control. Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell |
| 2017 | Neural Message Passing for Quantum Chemistry. Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl |
| 2017 | Neural Networks and Rational Functions. Matus Telgarsky |
| 2017 | Neural Optimizer Search with Reinforcement Learning. Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le |
| 2017 | Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks. David Balduzzi, Brian McWilliams, Tony Butler-Yeoman |
| 2017 | No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis. Rong Ge, Chi Jin, Yi Zheng |
| 2017 | Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates. Jiali Mei, Yohann de Castro, Yannig Goude, Georges Hébrail |
| 2017 | Nonparanormal Information Estimation. Shashank Singh, Barnabás Póczos |
| 2017 | Nyström Method with Kernel K-means++ Samples as Landmarks. Dino Oglic, Thomas Gärtner |
| 2017 | On Approximation Guarantees for Greedy Low Rank Optimization. Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand N. Negahban |
| 2017 | On Calibration of Modern Neural Networks. Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger |
| 2017 | On Context-Dependent Clustering of Bandits. Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Giovanni Zappella, Evans Etrue |
| 2017 | On Kernelized Multi-armed Bandits. Sayak Ray Chowdhury, Aditya Gopalan |
| 2017 | On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations. Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti |
| 2017 | On Relaxing Determinism in Arithmetic Circuits. Arthur Choi, Adnan Darwiche |
| 2017 | On The Projection Operator to A Three-view Cardinality Constrained Set. Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, Ji Liu |
| 2017 | On orthogonality and learning recurrent networks with long term dependencies. Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal |
| 2017 | On the Expressive Power of Deep Neural Networks. Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein |
| 2017 | On the Iteration Complexity of Support Recovery via Hard Thresholding Pursuit. Jie Shen, Ping Li |
| 2017 | On the Sampling Problem for Kernel Quadrature. François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami |
| 2017 | Online Learning to Rank in Stochastic Click Models. Masrour Zoghi, Tomás Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvári, Zheng Wen |
| 2017 | Online Learning with Local Permutations and Delayed Feedback. Ohad Shamir, Liran Szlak |
| 2017 | Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability. Zhehui Chen, Lin F. Yang, Chris Junchi Li, Tuo Zhao |
| 2017 | Online and Linear-Time Attention by Enforcing Monotonic Alignments. Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck |
| 2017 | OptNet: Differentiable Optimization as a Layer in Neural Networks. Brandon Amos, J. Zico Kolter |
| 2017 | Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks. Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié |
| 2017 | Optimal Densification for Fast and Accurate Minwise Hashing. Anshumali Shrivastava |
| 2017 | Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík |
| 2017 | Oracle Complexity of Second-Order Methods for Finite-Sum Problems. Yossi Arjevani, Ohad Shamir |
| 2017 | Ordinal Graphical Models: A Tale of Two Approaches. Arun Sai Suggala, Eunho Yang, Pradeep Ravikumar |
| 2017 | Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use. Vatsal Sharan, Gregory Valiant |
| 2017 | Pain-Free Random Differential Privacy with Sensitivity Sampling. Benjamin I. P. Rubinstein, Francesco Aldà |
| 2017 | Parallel Multiscale Autoregressive Density Estimation. Scott E. Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gomez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, Nando de Freitas |
| 2017 | Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space. José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alán Aspuru-Guzik |
| 2017 | Parseval Networks: Improving Robustness to Adversarial Examples. Moustapha Cissé, Piotr Bojanowski, Edouard Grave, Yann N. Dauphin, Nicolas Usunier |
| 2017 | Partitioned Tensor Factorizations for Learning Mixed Membership Models. Zilong Tan, Sayan Mukherjee |
| 2017 | PixelCNN Models with Auxiliary Variables for Natural Image Modeling. Alexander Kolesnikov, Christoph H. Lampert |
| 2017 | Post-Inference Prior Swapping. Willie Neiswanger, Eric P. Xing |
| 2017 | Practical Gauss-Newton Optimisation for Deep Learning. Aleksandar Botev, Hippolyt Ritter, David Barber |
| 2017 | Prediction and Control with Temporal Segment Models. Nikhil Mishra, Pieter Abbeel, Igor Mordatch |
| 2017 | Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control. Yunpeng Pan, Xinyan Yan, Evangelos A. Theodorou, Byron Boots |
| 2017 | Preferential Bayesian Optimization. Javier González, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence |
| 2017 | Priv'IT: Private and Sample Efficient Identity Testing. Bryan Cai, Constantinos Daskalakis, Gautam Kamath |
| 2017 | Probabilistic Path Hamiltonian Monte Carlo. Vu Dinh, Arman Bilge, Cheng Zhang, Frederick A. Matsen IV |
| 2017 | Probabilistic Submodular Maximization in Sub-Linear Time. Serban Stan, Morteza Zadimoghaddam, Andreas Krause, Amin Karbasi |
| 2017 | Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 Doina Precup, Yee Whye Teh |
| 2017 | Programming with a Differentiable Forth Interpreter. Matko Bosnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel |
| 2017 | Projection-free Distributed Online Learning in Networks. Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang |
| 2017 | ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices. Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain |
| 2017 | Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations. Yuanzhi Li, Yingyu Liang |
| 2017 | Provably Optimal Algorithms for Generalized Linear Contextual Bandits. Lihong Li, Yu Lu, Dengyong Zhou |
| 2017 | Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks. Mingyi Hong, Davood Hajinezhad, Ming-Min Zhao |
| 2017 | Random Feature Expansions for Deep Gaussian Processes. Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone |
| 2017 | Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh |
| 2017 | Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method. Chenzi Zhang, Shuguang Hu, Zhihao Gavin Tang, T.-H. Hubert Chan |
| 2017 | Real-Time Adaptive Image Compression. Oren Rippel, Lubomir D. Bourdev |
| 2017 | Recovery Guarantees for One-hidden-layer Neural Networks. Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon |
| 2017 | Recurrent Highway Networks. Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber |
| 2017 | Recursive Partitioning for Personalization using Observational Data. Nathan Kallus |
| 2017 | Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning. Noam Brown, Tuomas Sandholm |
| 2017 | Regret Minimization in Behaviorally-Constrained Zero-Sum Games. Gabriele Farina, Christian Kroer, Tuomas Sandholm |
| 2017 | Regularising Non-linear Models Using Feature Side-information. Amina Mollaysa, Pablo Strasser, Alexandros Kalousis |
| 2017 | Reinforcement Learning with Deep Energy-Based Policies. Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine |
| 2017 | Relative Fisher Information and Natural Gradient for Learning Large Modular Models. Ke Sun, Frank Nielsen |
| 2017 | Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things. Ashish Kumar, Saurabh Goyal, Manik Varma |
| 2017 | Risk Bounds for Transferring Representations With and Without Fine-Tuning. Daniel McNamara, Maria-Florina Balcan |
| 2017 | Robust Adversarial Reinforcement Learning. Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta |
| 2017 | Robust Budget Allocation via Continuous Submodular Functions. Matthew Staib, Stefanie Jegelka |
| 2017 | Robust Gaussian Graphical Model Estimation with Arbitrary Corruption. Lingxiao Wang, Quanquan Gu |
| 2017 | Robust Guarantees of Stochastic Greedy Algorithms. Avinatan Hassidim, Yaron Singer |
| 2017 | Robust Probabilistic Modeling with Bayesian Data Reweighting. Yixin Wang, Alp Kucukelbir, David M. Blei |
| 2017 | Robust Structured Estimation with Single-Index Models. Sheng Chen, Arindam Banerjee |
| 2017 | Robust Submodular Maximization: A Non-Uniform Partitioning Approach. Ilija Bogunovic, Slobodan Mitrovic, Jonathan Scarlett, Volkan Cevher |
| 2017 | RobustFill: Neural Program Learning under Noisy I/O. Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli |
| 2017 | Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement. Jonathan Eckstein, Noam Goldberg, Ai Kagawa |
| 2017 | SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác |
| 2017 | SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling. Junichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe |
| 2017 | Safety-Aware Algorithms for Adversarial Contextual Bandit. Wen Sun, Debadeepta Dey, Ashish Kapoor |
| 2017 | Scalable Bayesian Rule Lists. Hongyu Yang, Cynthia Rudin, Margo I. Seltzer |
| 2017 | Scalable Generative Models for Multi-label Learning with Missing Labels. Vikas Jain, Nirbhay Modhe, Piyush Rai |
| 2017 | Scalable Multi-Class Gaussian Process Classification using Expectation Propagation. Carlos Villacampa-Calvo, Daniel Hernández-Lobato |
| 2017 | Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction. Weizhong Zhang, Bin Hong, Wei Liu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang |
| 2017 | Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics. Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, D. Scott Phoenix, Dileep George |
| 2017 | Second-Order Kernel Online Convex Optimization with Adaptive Sketching. Daniele Calandriello, Alessandro Lazaric, Michal Valko |
| 2017 | Selective Inference for Sparse High-Order Interaction Models. Shinya Suzumura, Kazuya Nakagawa, Yuta Umezu, Koji Tsuda, Ichiro Takeuchi |
| 2017 | Self-Paced Co-training. Fan Ma, Deyu Meng, Qi Xie, Zina Li, Xuanyi Dong |
| 2017 | Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama |
| 2017 | Sequence Modeling via Segmentations. Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou, Li Deng |
| 2017 | Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control. Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck |
| 2017 | Sequence to Better Sequence: Continuous Revision of Combinatorial Structures. Jonas Mueller, David K. Gifford, Tommi S. Jaakkola |
| 2017 | Sharp Minima Can Generalize For Deep Nets. Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio |
| 2017 | Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation. Yacine Jernite, Anna Choromanska, David A. Sontag |
| 2017 | Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. Shusen Wang, Alex Gittens, Michael W. Mahoney |
| 2017 | Sliced Wasserstein Kernel for Persistence Diagrams. Mathieu Carrière, Marco Cuturi, Steve Oudot |
| 2017 | Soft-DTW: a Differentiable Loss Function for Time-Series. Marco Cuturi, Mathieu Blondel |
| 2017 | Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression. Pengfei Wei, Ramón Sagarna, Yiping Ke, Yew-Soon Ong, Chi-Keong Goh |
| 2017 | Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity. Eunho Yang, Aurélie C. Lozano |
| 2017 | Spectral Learning from a Single Trajectory under Finite-State Policies. Borja Balle, Odalric-Ambrym Maillard |
| 2017 | Spherical Structured Feature Maps for Kernel Approximation. Yueming Lyu |
| 2017 | SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization. Juyong Kim, Yookoon Park, Gunhee Kim, Sung Ju Hwang |
| 2017 | Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. Jakob N. Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H. S. Torr, Pushmeet Kohli, Shimon Whiteson |
| 2017 | State-Frequency Memory Recurrent Neural Networks. Hao Hu, Guo-Jun Qi |
| 2017 | Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening. Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso |
| 2017 | StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent. Tyler B. Johnson, Carlos Guestrin |
| 2017 | Stochastic Adaptive Quasi-Newton Methods for Minimizing Expected Values. Chaoxu Zhou, Wenbo Gao, Donald Goldfarb |
| 2017 | Stochastic Bouncy Particle Sampler. Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski |
| 2017 | Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. Yi Xu, Qihang Lin, Tianbao Yang |
| 2017 | Stochastic DCA for the Large-sum of Non-convex Functions Problem and its Application to Group Variable Selection in Classification. Hoai An Le Thi, Hoai Minh Le, Phan Duy Nhat, Bach Tran |
| 2017 | Stochastic Generative Hashing. Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song |
| 2017 | Stochastic Gradient MCMC Methods for Hidden Markov Models. Yi-An Ma, Nicholas J. Foti, Emily B. Fox |
| 2017 | Stochastic Gradient Monomial Gamma Sampler. Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin |
| 2017 | Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms. Qianxiao Li, Cheng Tai, Weinan E |
| 2017 | Stochastic Variance Reduction Methods for Policy Evaluation. Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou |
| 2017 | Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions. Yichen Chen, Dongdong Ge, Mengdi Wang, Zizhuo Wang, Yinyu Ye, Hao Yin |
| 2017 | Strongly-Typed Agents are Guaranteed to Interact Safely. David Balduzzi |
| 2017 | Sub-sampled Cubic Regularization for Non-convex Optimization. Jonas Moritz Kohler, Aurélien Lucchi |
| 2017 | Tensor Balancing on Statistical Manifold. Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda |
| 2017 | Tensor Belief Propagation. Andrew Wrigley, Wee Sun Lee, Nan Ye |
| 2017 | Tensor Decomposition via Simultaneous Power Iteration. Po-An Wang, Chi-Jen Lu |
| 2017 | Tensor Decomposition with Smoothness. Masaaki Imaizumi, Kohei Hayashi |
| 2017 | Tensor-Train Recurrent Neural Networks for Video Classification. Yinchong Yang, Denis Krompass, Volker Tresp |
| 2017 | The Loss Surface of Deep and Wide Neural Networks. Quynh Nguyen, Matthias Hein |
| 2017 | The Predictron: End-To-End Learning and Planning. David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris |
| 2017 | The Price of Differential Privacy for Online Learning. Naman Agarwal, Karan Singh |
| 2017 | The Sample Complexity of Online One-Class Collaborative Filtering. Reinhard Heckel, Kannan Ramchandran |
| 2017 | The Shattered Gradients Problem: If resnets are the answer, then what is the question? David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams |
| 2017 | The Statistical Recurrent Unit. Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider |
| 2017 | Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank. Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang, Bo Yuan |
| 2017 | Tight Bounds for Approximate Carathéodory and Beyond. Vahab S. Mirrokni, Renato Paes Leme, Adrian Vladu, Sam Chiu-wai Wong |
| 2017 | Toward Controlled Generation of Text. Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing |
| 2017 | Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data. Xixian Chen, Michael R. Lyu, Irwin King |
| 2017 | Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering. Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong |
| 2017 | Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs. Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Yann LeCun, Max Tegmark, Marin Soljacic |
| 2017 | Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. Aditya Chaudhry, Pan Xu, Quanquan Gu |
| 2017 | Uncorrelation and Evenness: a New Diversity-Promoting Regularizer. Pengtao Xie, Aarti Singh, Eric P. Xing |
| 2017 | Uncovering Causality from Multivariate Hawkes Integrated Cumulants. Massil Achab, Emmanuel Bacry, Stéphane Gaïffas, Iacopo Mastromatteo, Jean-François Muzy |
| 2017 | Understanding Black-box Predictions via Influence Functions. Pang Wei Koh, Percy Liang |
| 2017 | Understanding Synthetic Gradients and Decoupled Neural Interfaces. Wojciech Marian Czarnecki, Grzegorz Swirszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals, Koray Kavukcuoglu |
| 2017 | Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition. Tasha Nagamine, Nima Mesgarani |
| 2017 | Uniform Convergence Rates for Kernel Density Estimation. Heinrich Jiang |
| 2017 | Uniform Deviation Bounds for k-Means Clustering. Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause |
| 2017 | Unifying Task Specification in Reinforcement Learning. Martha White |
| 2017 | Unimodal Probability Distributions for Deep Ordinal Classification. Christopher Beckham, Christopher J. Pal |
| 2017 | Unsupervised Learning by Predicting Noise. Piotr Bojanowski, Armand Joulin |
| 2017 | Variants of RMSProp and Adagrad with Logarithmic Regret Bounds. Mahesh Chandra Mukkamala, Matthias Hein |
| 2017 | Variational Boosting: Iteratively Refining Posterior Approximations. Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams |
| 2017 | Variational Dropout Sparsifies Deep Neural Networks. Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov |
| 2017 | Variational Inference for Sparse and Undirected Models. John Ingraham, Debora S. Marks |
| 2017 | Variational Policy for Guiding Point Processes. Yichen Wang, Grady Williams, Evangelos A. Theodorou, Le Song |
| 2017 | Video Pixel Networks. Nal Kalchbrenner, Aäron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu |
| 2017 | Warped Convolutions: Efficient Invariance to Spatial Transformations. João F. Henriques, Andrea Vedaldi |
| 2017 | Wasserstein Generative Adversarial Networks. Martín Arjovsky, Soumith Chintala, Léon Bottou |
| 2017 | When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications. Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba, Vikas Singh |
| 2017 | Why is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband, Benjamin Van Roy |
| 2017 | World of Bits: An Open-Domain Platform for Web-Based Agents. Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, Percy Liang |
| 2017 | Zero-Inflated Exponential Family Embeddings. Li-Ping Liu, David M. Blei |
| 2017 | Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli |
| 2017 | ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning. Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang |
| 2017 | Zonotope Hit-and-run for Efficient Sampling from Projection DPPs. Guillaume Gautier, Rémi Bardenet, Michal Valko |
| 2017 | iSurvive: An Interpretable, Event-time Prediction Model for mHealth. Walter H. Dempsey, Alexander Moreno, Christy K. Scott, Michael L. Dennis, David H. Gustafson, Susan A. Murphy, James M. Rehg |
| 2017 | meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting. Xu Sun, Xuancheng Ren, Shuming Ma, Houfeng Wang |