NeurIPS 2015

403 papers

Reflection, Refraction, and Hamiltonian Monte Carlo

Hadi Mohasel Afshar, Justin Domke

Regularization Path of Cross-Validation Error Lower Bounds

Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, Ichiro Takeuchi

Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction

Been Kim, Julie A Shah, Finale Doshi-velez

Sample Efficient Path Integral Control under Uncertainty

Yunpeng Pan, Evangelos Theodorou, Michail Kontitsis

Sample Complexity of Learning Mahalanobis Distance Metrics

Nakul Verma, Kristin Branson

Probabilistic Line Searches for Stochastic Optimization

Maren Mahsereci, Philipp Hennig

Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings

Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin

Communication Complexity of Distributed Convex Learning and Optimization

Yossi Arjevani, Ohad Shamir

Fast Convergence of Regularized Learning in Games

Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire

A Gaussian Process Model of Quasar Spectral Energy Distributions

Andrew Miller, Albert Wu, Jeff Regier, Jon McAuliffe, Dustin Lang

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier

Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach

Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier

Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path

Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári

Space-Time Local Embeddings

Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet

A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice

Tasuku Soma, Yuichi Yoshida

A Market Framework for Eliciting Private Data

Bo Waggoner, Rafael Frongillo, Jacob D. Abernethy

Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients

Bo Xie, Yingyu Liang, Le Song

Bandits with Unobserved Confounders: A Causal Approach

Elias Bareinboim, Andrew Forney, Judea Pearl

A Structural Smoothing Framework For Robust Graph Comparison

Pinar Yanardag, S.v.n. Vishwanathan

Statistical Topological Data Analysis - A Kernel Perspective

Roland Kwitt, Stefan Huber, Marc Niethammer, Weili Lin, Ulrich Bauer

Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems

Yuxin Chen, Emmanuel Candes

Online Learning with Adversarial Delays

Kent Quanrud, Daniel Khashabi

Private Graphon Estimation for Sparse Graphs

Christian Borgs, Jennifer Chayes, Adam Smith

Lifted Symmetry Detection and Breaking for MAP Inference

Timothy Kopp, Parag Singla, Henry Kautz

Non-convex Statistical Optimization for Sparse Tensor Graphical Model

Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng

Semi-supervised Sequence Learning

Andrew M Dai, Quoc V Le

Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling

Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos J. Storkey

Biologically Inspired Dynamic Textures for Probing Motion Perception

Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré

Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm

Qinqing Zheng, Ryota Tomioka

3D Object Proposals for Accurate Object Class Detection

Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G Berneshawi, Huimin Ma

When are Kalman-Filter Restless Bandits Indexable?

Christopher R Dance, Tomi Silander

Unified View of Matrix Completion under General Structural Constraints

Suriya Gunasekar, Arindam Banerjee, Joydeep Ghosh

Action-Conditional Video Prediction using Deep Networks in Atari Games

Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder Singh

MCMC for Variationally Sparse Gaussian Processes

James Hensman, Alexander G Matthews, Maurizio Filippone, Zoubin Ghahramani

Predtron: A Family of Online Algorithms for General Prediction Problems

Prateek Jain, Nagarajan Natarajan, Ambuj Tewari

Measuring Sample Quality with Stein's Method

Jackson Gorham, Lester Mackey

The Pareto Regret Frontier for Bandits

Tor Lattimore

GAP Safe screening rules for sparse multi-task and multi-class models

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

On some provably correct cases of variational inference for topic models

Pranjal Awasthi, Andrej Risteski

Tensorizing Neural Networks

Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, Dmitry P Vetrov

From random walks to distances on unweighted graphs

Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola

Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection

Jie Wang, Jieping Ye

SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk

Guillaume Papa, Stéphan Clémençon, Aurélien Bellet

Convergence Analysis of Prediction Markets via Randomized Subspace Descent

Rafael Frongillo, Mark D. Reid

Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number

Pekka Parviainen, Janne H. Korhonen, Janne H Korhonen

Learning with Group Invariant Features: A Kernel Perspective.

Youssef Mroueh, Stephen Voinea, Tomaso A Poggio

Algorithmic Stability and Uniform Generalization

Ibrahim M Alabdulmohsin

Fast Lifted MAP Inference via Partitioning

Somdeb Sarkhel, Parag Singla, Vibhav G Gogate

Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach

Yinlam Chow, Aviv Tamar, Shie Mannor, Marco Pavone

Testing Closeness With Unequal Sized Samples

Bhaswar Bhattacharya, Gregory Valiant

Sparse and Low-Rank Tensor Decomposition

Parikshit Shah, Nikhil Rao, Gongguo Tang

Fighting Bandits with a New Kind of Smoothness

Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari

The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors

Dan Rosenbaum, Yair Weiss

Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions

Amar Shah, Zoubin Ghahramani

The Population Posterior and Bayesian Modeling on Streams

James McInerney, Rajesh Ranganath, David Blei

Revenue Optimization against Strategic Buyers

Mehryar Mohri, Andres Munoz

On the consistency theory of high dimensional variable screening

Xiangyu Wang, Chenlei Leng, David B. Dunson, David B Dunson

Spectral Representations for Convolutional Neural Networks

Oren Rippel, Jasper Snoek, Ryan P. Adams

Calibrated Structured Prediction

Volodymyr Kuleshov, Percy Liang

Nearly Optimal Private LASSO

Kunal Talwar, Abhradeep Guha Thakurta, Li Zhang

COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez, Shuang Li, Hongyuan Zha

Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis

Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen

Winner-Take-All Autoencoders

Alireza Makhzani, Brendan J. Frey

Compressive spectral embedding: sidestepping the SVD

Dinesh Ramasamy, Upamanyu Madhow

Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff

Ofer Dekel, Ronen Eldan, Tomer Koren

Particle Gibbs for Infinite Hidden Markov Models

Nilesh Tripuraneni, Shixiang (Shane) Gu, Hong Ge, Zoubin Ghahramani

A Theory of Decision Making Under Dynamic Context

Michael Shvartsman, Vaibhav Srivastava, Jonathan D. Cohen

Learning to Segment Object Candidates

Pedro O O. Pinheiro, Ronan Collobert, Piotr Dollár

Minimax Time Series Prediction

Wouter M. Koolen, Alan Malek, Yasin Abbasi Yadkori, Peter L. Bartlett, Peter L Bartlett

Robust Spectral Inference for Joint Stochastic Matrix Factorization

Moontae Lee, David Bindel, David Mimno

End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture

Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He

Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization

Fredrik D Johansson, Ankani Chattoraj, Chiranjib Bhattacharyya, Devdatt Dubhashi

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes

Ryan J Giordano, Tamara Broderick, Michael I. Jordan, Michael I Jordan

Learnability of Influence in Networks

Harikrishna Narasimhan, David C. Parkes, Yaron Singer

On the Accuracy of Self-Normalized Log-Linear Models

Jacob Andreas, Maxim Rabinovich, Dan Klein, Michael I. Jordan, Michael I Jordan

The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels

Purnamrita Sarkar, Deepayan Chakrabarti, peter j bickel

Cornering Stationary and Restless Mixing Bandits with Remix-UCB

Julien Audiffren, Liva Ralaivola

Visalogy: Answering Visual Analogy Questions

Fereshteh Sadeghi, C. Lawrence Zitnick, Ali Farhadi

Learning with Symmetric Label Noise: The Importance of Being Unhinged

Brendan van Rooyen, Aditya Menon, Robert C. Williamson

A hybrid sampler for Poisson-Kingman mixture models

Maria Lomeli, Stefano Favaro, Yee Whye Teh

Inverse Reinforcement Learning with Locally Consistent Reward Functions

Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

Fast and Guaranteed Tensor Decomposition via Sketching

Yining Wang, Hsiao-Yu Tung, Alexander J Smola, Anima Anandkumar

Evaluating the statistical significance of biclusters

Jason Lee, Yuekai Sun, Jonathan E Taylor

Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization

Xiangru Lian, Yijun Huang, Yuncheng Li, Ji Liu

The Human Kernel

Andrew G Wilson, Christoph Dann, Chris Lucas, Eric P. Xing, Eric P Xing

Probabilistic Variational Bounds for Graphical Models

Qiang Liu, John W. Fisher III, Alex Ihler

Analysis of Robust PCA via Local Incoherence

Huishuai Zhang, Yi Zhou, Yingbin Liang

Optimal Linear Estimation under Unknown Nonlinear Transform

Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu

Equilibrated adaptive learning rates for non-convex optimization

Yann Dauphin, Harm de Vries, Yoshua Bengio

Learning Theory and Algorithms for Forecasting Non-stationary Time Series

Vitaly Kuznetsov, Mehryar Mohri

Convergence rates of sub-sampled Newton methods

Murat A Erdogdu, Andrea Montanari

Parallelizing MCMC with Random Partition Trees

Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson, David B Dunson

Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso

Eunho Yang, Aurelie C. Lozano

No-Regret Learning in Bayesian Games

Jason Hartline, Vasilis Syrgkanis, Eva Tardos

BinaryConnect: Training Deep Neural Networks with binary weights during propagations

Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David

M-Best-Diverse Labelings for Submodular Energies and Beyond

Alexander Kirillov, Dmytro Shlezinger, Dmitry P Vetrov, Carsten Rother, Bogdan Savchynskyy

Scalable Inference for Gaussian Process Models with Black-Box Likelihoods

Amir Dezfouli, Edwin V. Bonilla

Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms

Yunwen Lei, Urun Dogan, Alexander Binder, Marius Kloft

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction

Kisuk Lee, Aleksandar Zlateski, Vishwanathan Ashwin, H. Sebastian Seung

A Nonconvex Optimization Framework for Low Rank Matrix Estimation

Tuo Zhao, Zhaoran Wang, Han Liu