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NIPS · 24.8k rows

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6,100	Orthogonal Random Features Felix Xinnan Yu Ananda Theertha Suresh Krzysztof Choromanski Daniel Holtmann-Rice Sanjiv Kumar Google Research, New York {felixyu, theertha, kchoro, dhr, sanjivk}@google.com Abstract We present an intriguing discovery related to Random Fourier Features: in Gaussian kernel ...	2016	196
6,101	Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions Yichen Wang⇧, Nan Du⇤, Rakshit Trivedi⇧, Le Song⇧ ⇤Google Research ⇧College of Computing, Georgia Institute of Technology {yichen.wang, rstrivedi}@gatech.edu, dunan@google.com lsong@cc.gatech.edu Abstract Matching users t...	2016	197
6,102	Convex Two-Layer Modeling with Latent Structure Vignesh Ganapathiraman†, Xinhua Zhang†, Yaoliang Yu∗, Junfeng Wen♯ †University of Illinois at Chicago, Chicago, IL, USA ∗University of Waterloo, Waterloo, ON, Canada, ♯University of Alberta, Edmonton, AB, Canada {vganap2, zhangx}@uic.edu, yaoliang.yu@uwate...	2016	198
6,103	Online Convex Optimization with Unconstrained Domains and Losses Ashok Cutkosky Department of Computer Science Stanford University ashokc@cs.stanford.edu Kwabena Boahen Department of Bioengineering Stanford University boahen@stanford.edu Abstract We propose an online convex optimization algorithm ...	2016	199
6,104	A Locally Adaptive Normal Distribution Georgios Arvanitidis, Lars Kai Hansen and Søren Hauberg Technical University of Denmark, Lyngby, Denmark DTU Compute, Section for Cognitive Systems {gear,lkai,sohau}@dtu.dk Abstract The multivariate normal density is a monotonic function of the distance to the mean, ...	2016	2
6,105	Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks Noah J. Apthorpe1∗ Alexander J. Riordan2∗ Rob E. Aguilar1 Jan Homann2 Yi Gu2 David W. Tank2 H. Sebastian Seung12 1Computer Science Department 2Princeton Neuroscience Institute Princeton University {apthorpe, ariordan...	2016	20
6,106	GAP Safe Screening Rules for Sparse-Group Lasso Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon LTCI, CNRS, Télécom ParisTech Université Paris-Saclay 75013 Paris, France first.last@telecom-paristech.fr Abstract For statistical learning in high dimension, sparse regularizations have proven...	2016	200
6,107	Local Similarity-Aware Deep Feature Embedding Chen Huang Chen Change Loy Xiaoou Tang Department of Information Engineering, The Chinese University of Hong Kong {chuang,ccloy,xtang}@ie.cuhk.edu.hk Abstract Existing deep embedding methods in vision tasks are capable of learning a compact Euclidean space f...	2016	201
6,108	Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities Ruitong Huang Department of Computing Science University of Alberta, AB, Canada ruitong@ualberta.ca Tor Lattimore School of Informatics and Computing Indiana University, IN, USA tor.lattimore@gmai...	2016	202
6,109	Learning Multiagent Communication with Backpropagation Sainbayar Sukhbaatar Dept. of Computer Science Courant Institute, New York University sainbar@cs.nyu.edu Arthur Szlam Facebook AI Research New York aszlam@fb.com Rob Fergus Facebook AI Research New York robfergus@fb.com Abstract Many t...	2016	203
6,110	Sub-sampled Newton Methods with Non-uniform Sampling Peng Xu† Jiyan Yang† Farbod Roosta-Khorasani‡ Christopher Ré† Michael W. Mahoney‡ † Stanford University ‡ University of California at Berkeley pengxu@stanford.edu jiyan@stanford.edu farbod@icsi.berkeley.edu chrismre@cs.stanford.edu mmahoney@stat.berkeley....	2016	204
6,111	Examples are not Enough, Learn to Criticize! Criticism for Interpretability Been Kim⇤ Allen Institute for AI beenkim@csail.mit.edu Rajiv Khanna UT Austin rajivak@utexas.edu Oluwasanmi Koyejo UIUC sanmi@illinois.edu Abstract Example-based explanations are widely used in the effort to improve the ...	2016	205
6,112	R-FCN: Object Detection via Region-based Fully Convolutional Networks Jifeng Dai Microsoft Research Asia Yi Li∗ Tsinghua University Kaiming He Microsoft Research Jian Sun Microsoft Research Abstract We present region-based, fully convolutional networks for accurate and efﬁcient object detection...	2016	206
6,113	Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models Amin Jalali Department of Electrical Engineering University of Washington Seattle, WA 98195 amjalali@uw.edu Qiyang Han Department of Statistics University of Washington Seattle, WA 98195 royhan@uw.edu Ioana Dumitriu...	2016	207
6,114	A Powerful Generative Model Using Random Weights for the Deep Image Representation Kun He∗, Yan Wang † Department of Computer Science and Technology Huazhong University of Science and Technology, Wuhan 430074, China brooklet60@hust.edu.cn, yanwang@hust.edu.cn John Hopcroft Department of Computer Science...	2016	208
6,115	Privacy Odometers and Filters: Pay-as-you-Go Composition Ryan Rogers∗ Aaron Roth† Jonathan Ullman‡ Salil Vadhan§ Abstract In this paper we initiate the study of adaptive composition in differential privacy when the length of the composition, and the privacy parameters themselves can be chosen adaptive...	2016	209
6,116	Convolutional Neural Fabrics Shreyas Saxena Jakob Verbeek INRIA Grenoble – Laboratoire Jean Kuntzmann Abstract Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a “fabric” that emb...	2016	21
6,117	More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning Xinyang Yi†∗ Zhaoran Wang‡∗ Zhuoran Yang‡∗ Constantine Caramanis† Han Liu‡ †The University of Texas at Austin ‡Princeton University †{yixy,constantine}@utexas.edu ‡{zhaoran,zy6,hanliu}@princeton.e...	2016	210
6,118	On statistical learning via the lens of compression Oﬁr David Department of Mathematics Technion - Israel Institute of Technology ofirdav@tx.technion.ac.il Shay Moran Department of Computer Science Technion - Israel Institute of Technology shaymrn@cs.technion.ac.il Amir Yehudayoff Department of Math...	2016	211
6,119	Sparse Support Recovery with Non-smooth Loss Functions Kévin Degraux ISPGroup/ICTEAM, FNRS Université catholique de Louvain Louvain-la-Neuve, Belgium 1348 kevin.degraux@uclouvain.be Gabriel Peyré CNRS, DMA École Normale Supérieure Paris, France 75775 gabriel.peyre@ens.fr Jalal M. Fadili Norman...	2016	212
6,120	Tractable Operations for Arithmetic Circuits of Probabilistic Models Yujia Shen and Arthur Choi and Adnan Darwiche Computer Science Department University of California Los Angeles, CA 90095 {yujias,aychoi,darwiche}@cs.ucla.edu Abstract We consider tractable representations of probability distributions a...	2016	213
6,121	Dual Learning for Machine Translation Di He1,∗, Yingce Xia2,∗, Tao Qin3, Liwei Wang1, Nenghai Yu2, Tie-Yan Liu3, Wei-Ying Ma3 1Key Laboratory of Machine Perception (MOE), School of EECS, Peking University 2University of Science and Technology of China 3Microsoft Research 1{dih,wanglw}@cis.pku.edu.cn; 2xiaying...	2016	214
6,122	Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow Gang Wang∗,† and Georgios B. Giannakis† ∗ECE Dept. and Digital Tech. Center, Univ. of Minnesota, Mpls, MN 55455, USA † School of Automation, Beijing Institute of Technology, Beijing 100081, China {gangwang, georgios}@umn.edu...	2016	215
6,123	Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences Daniel Neil, Michael Pfeiffer, and Shih-Chii Liu Institute of Neuroinformatics University of Zurich and ETH Zurich Zurich, Switzerland 8057 {dneil, pfeiffer, shih}@ini.uzh.ch Abstract Recurrent Neural Networks (RNNs) ...	2016	216
6,124	Launch and Iterate: Reducing Prediction Churn Q. Cormier ENS Lyon 15 parvis René Descartes Lyon, France quentin.cormier@ens-lyon.fr M. Milani Fard, K. Canini, M. R. Gupta Google Inc. 1600 Amphitheatre Parkway Mountain View, CA 94043 {mmilanifard,canini,mayagupta}@google.com Abstract Practical ap...	2016	217
6,125	Stochastic Three-Composite Convex Minimization Alp Yurtsever, B`˘ang Công V˜u, and Volkan Cevher Laboratory for Information and Inference Systems (LIONS) École Polytechnique Fédérale de Lausanne, Switzerland alp.yurtsever@epﬂ.ch, bang.vu@epﬂ.ch, volkan.cevher@epﬂ.ch Abstract We propose a stochastic optimiza...	2016	218
6,126	Synthesizing the preferred inputs for neurons in neural networks via deep generator networks Anh Nguyen anguyen8@uwyo.edu Alexey Dosovitskiy dosovits@cs.uni-freiburg.de Jason Yosinski jason@geometric.ai Thomas Brox brox@cs.uni-freiburg.de Jeff Clune jeffclune@uwyo.edu Abstract Deep neural netw...	2016	219
6,127	Budgeted stream-based active learning via adaptive submodular maximization Kaito Fujii Kyoto University JST, ERATO, Kawarabayashi Large Graph Project fujii@ml.ist.i.kyoto-u.ac.jp Hisashi Kashima Kyoto University kashima@i.kyoto-u.ac.jp Abstract Active learning enables us to reduce the annotation cos...	2016	22
6,128	Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach Remi R. Lam Massachusetts Institute of Technology Cambridge, MA rlam@mit.edu Karen E. Willcox Massachusetts Institute of Technology Cambridge, MA kwillcox@mit.edu David H. Wolpert Santa Fe Institute Santa Fe,...	2016	220
6,129	Gaussian Process Bandit Optimisation with Multi-ﬁdelity Evaluations Kirthevasan Kandasamy ♮, Gautam Dasarathy ♦, Junier Oliva ♮, Jeff Schneider ♮, Barnabás Póczos ♮ ♮Carnegie Mellon University, ♦Rice University {kandasamy, joliva, schneide, bapoczos}@cs.cmu.edu, gautamd@rice.edu Abstract In many sci...	2016	221
6,130	Learning Parametric Sparse Models for Image Super-Resolution Yongbo Li, Weisheng Dong∗, Xuemei Xie, Guangming Shi1, Xin Li2, Donglai Xu3 State Key Lab. of ISN, School of Electronic Engineering, Xidian University, China 1Key Lab. of IPIU (Chinese Ministry of Education), Xidian University, China 2Lane Dep. of C...	2016	222
6,131	Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula Jean Barbier, Mohamad Dia and Nicolas Macris Laboratoire de Théorie des Communications, Faculté Informatique et Communications, Ecole Polytechnique Fédérale de Lausanne, 1015, Suisse. firstname.lastname@epfl.ch Flore...	2016	223
6,132	Large Margin Discriminant Dimensionality Reduction in Prediction Space Mohammad Saberian Netﬂix esaberian@netflix.com Jose Costa Pereira INESCTEC jose.c.pereira@inesctec.pt Can Xu Google canxu@google.com Jian Yang Yahoo Research jianyang@yahoo-inc.com Nuno Vasconcelos UC San Diego nvasco...	2016	224
6,133	Fast learning rates with heavy-tailed losses Vu Dinh1 Lam Si Tung Ho2 Duy Nguyen3 Binh T. Nguyen4 1Program in Computational Biology, Fred Hutchinson Cancer Research Center 2Department of Biostatistics, University of California, Los Angeles 3Department of Statistics, University of Wisconsin-Madison 4Depa...	2016	225
6,134	Dynamic matrix recovery from incomplete observations under an exact low-rank constraint Liangbei Xu Mark A. Davenport Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, GA 30318 lxu66@gatech.edu mdav@gatech.edu Abstract Low-rank matrix factorizations arise in ...	2016	226
6,135	Tight Complexity Bounds for Optimizing Composite Objectives Blake Woodworth Toyota Technological Institute at Chicago Chicago, IL, 60637 blake@ttic.edu Nathan Srebro Toyota Technological Institute at Chicago Chicago, IL, 60637 nati@ttic.edu Abstract We provide tight upper and lower bounds on the c...	2016	227
6,136	A Forward Model at Purkinje Cell Synapses Facilitates Cerebellar Anticipatory Control Ivan Herreros-Alonso SPECS lab Universitat Pompeu Fabra Barcelona, Spain ivan.herreros@upf.edu Xerxes D. Arsiwalla SPECS lab Universitat Pompeu Fabra Barcelona, Spain Paul F.M.J. Verschure SPECS, UPF Catalan ...	2016	228
6,137	Veriﬁcation Based Solution for Structured MAB Problems Zohar Karnin Yahoo Research New York, NY 10036 zkarnin@ymail.com Abstract We consider the problem of ﬁnding the best arm in a stochastic Multi-armed Bandit (MAB) game and propose a general framework based on veriﬁcation that applies to multiple we...	2016	229
6,138	An equivalence between high dimensional Bayes optimal inference and M-estimation Madhu Advani Surya Ganguli Department of Applied Physics, Stanford University msadvani@stanford.edu and sganguli@stanford.edu Abstract When recovering an unknown signal from noisy measurements, the computational difﬁcul...	2016	23
6,139	SURGE: Surface Regularized Geometry Estimation from a Single Image Peng Wang1 Xiaohui Shen2 Bryan Russell2 Scott Cohen2 Brian Price2 Alan Yuille3 1University of California, Los Angeles 2Adobe Research 3Johns Hopkins University Abstract This paper introduces an approach to regularize 2.5D surface normal an...	2016	230
6,140	CliqueCNN: Deep Unsupervised Exemplar Learning Miguel A. Bautista∗, Artsiom Sanakoyeu∗, Ekaterina Sutter, Björn Ommer Heidelberg Collaboratory for Image Processing IWR, Heidelberg University, Germany firstname.lastname@iwr.uni-heidelberg.de Abstract Exemplar learning is a powerful paradigm for discovering v...	2016	231
6,141	Computing and maximizing inﬂuence in linear threshold and triggering models Justin Khim Department of Statistics The Wharton School University of Pennsylvania Philadelphia, PA 19104 jkhim@wharton.upenn.edu Varun Jog Electrical & Computer Engineering Department University of Wisconsin - Madison Mad...	2016	232
6,142	Data Programming: Creating Large Training Sets, Quickly Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré Stanford University {ajratner,cdesa,senwu,dselsam,chrismre}@stanford.edu Abstract Large labeled training sets are the critical building blocks of supervised learning methods a...	2016	233
6,143	Flexible Models for Microclustering with Application to Entity Resolution Giacomo Zanella∗ Department of Decision Sciences Bocconi University giacomo.zanella@unibocconi.it Brenda Betancourt∗ Department of Statistical Science Duke University bb222@stat.duke.edu Hanna Wallach Microsoft Research ha...	2016	234
6,144	Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering Christina E. Lee Yihua Li Devavrat Shah Dogyoon Song Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology {celee,...	2016	235
6,145	An Ensemble Diversity Approach to Supervised Binary Hashing Miguel ´A. Carreira-Perpi˜n´an EECS, University of California, Merced mcarreira-perpinan@ucmerced.edu Ramin Raziperchikolaei EECS, University of California, Merced rraziperchikolaei@ucmerced.edu Abstract Binary hashing is a well-known approac...	2016	236
6,146	Learning Inﬂuence Functions from Incomplete Observations Xinran He Ke Xu David Kempe Yan Liu University of Southern California, Los Angeles, CA 90089 {xinranhe, xuk, dkempe, yanliu.cs}@usc.edu Abstract We study the problem of learning inﬂuence functions under incomplete observations of node activation...	2016	237
6,147	Backprop KF: Learning Discriminative Deterministic State Estimators Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel {haarnoja, anuragajay, svlevine, pabbeel}@berkeley.edu Department of Computer Science, University of California, Berkeley Abstract Generative state estimators based on probabilistic...	2016	238
6,148	On the Recursive Teaching Dimension of VC Classes Xi Chen Department of Computer Science Columbia University xichen@cs.columbia.edu Yu Cheng Department of Computer Science University of Southern California yu.cheng.1@usc.edu Bo Tang Department of Computer Science Oxford University tangbonk1@gm...	2016	239
6,149	A Sparse Interactive Model for Matrix Completion with Side Information Jin Lu Guannan Liang Jiangwen Sun Jinbo Bi University of Connecticut Storrs, CT 06269 {jin.lu, guannan.liang, jiangwen.sun, jinbo.bi}@uconn.edu Abstract Matrix completion methods can beneﬁt from side information besides the parti...	2016	24
6,150	Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain Timothy N. Rubin SurveyMonkey Oluwasanmi Koyejo Univ. of Illinois, Urbana-Champaign Michael N. Jones Indiana University Tal Yarkoni University of Texas at Austin Abstract This paper presents Generalized ...	2016	240
6,151	Fast ϵ-free Inference of Simulation Models with Bayesian Conditional Density Estimation George Papamakarios School of Informatics University of Edinburgh g.papamakarios@ed.ac.uk Iain Murray School of Informatics University of Edinburgh i.murray@ed.ac.uk Abstract Many statistical models can be simu...	2016	241
6,152	Ladder Variational Autoencoders Casper Kaae Sønderby⇤ casperkaae@gmail.com Tapani Raiko† tapani.raiko@aalto.fi Lars Maaløe‡ larsma@dtu.dk Søren Kaae Sønderby⇤ skaaesonderby@gmail.com Ole Winther⇤,‡ olwi@dtu.dk Abstract Variational autoencoders are powerful models for unsupervised learning. Howev...	2016	242
6,153	Improved Deep Metric Learning with Multi-class N-pair Loss Objective Kihyuk Sohn NEC Laboratories America, Inc. ksohn@nec-labs.com Abstract Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based ...	2016	243
6,154	Learning Sparse Gaussian Graphical Models with Overlapping Blocks Mohammad Javad Hosseini1 Su-In Lee1,2 1Department of Computer Science & Engineering, University of Washington, Seattle 2Department of Genome Sciences, University of Washington, Seattle {hosseini, suinlee}@cs.washington.edu Abstract We pre...	2016	244
6,155	Probabilistic Inference with Generating Functions for Poisson Latent Variable Models Kevin Winner1 and Daniel Sheldon1,2 {kwinner,sheldon}@cs.umass.edu 1 College of Information and Computer Sciences, University of Massachusetts Amherst 2 Department of Computer Science, Mount Holyoke College Abstract Graph...	2016	245
6,156	Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation Emmanuel Abbe Applied and Computational Mathematics and EE Dept. Princeton University eabbe@princeton.edu Colin Sandon Department of Mathematics Princeton University sandon@princeton.edu Abstr...	2016	246
6,157	A Uniﬁed Approach for Learning the Parameters of Sum-Product Networks Han Zhao Machine Learning Dept. Carnegie Mellon University han.zhao@cs.cmu.edu Pascal Poupart School of Computer Science University of Waterloo ppoupart@uwaterloo.ca Geoff Gordon Machine Learning Dept. Carnegie Mellon Universi...	2016	247
6,158	The Multiscale Laplacian Graph Kernel Risi Kondor Department of Computer Science Department of Statistics University of Chicago Chicago, IL 60637 risi@cs.uchicago.edu Horace Pan Department of Computer Science University of Chicago Chicago, IL 60637 hopan@uchicago.edu Abstract Many real world g...	2016	248
6,159	Learning the Number of Neurons in Deep Networks Jose M. Alvarez∗ Data61 @ CSIRO Canberra, ACT 2601, Australia jose.alvarez@data61.csiro.au Mathieu Salzmann CVLab, EPFL CH-1015 Lausanne, Switzerland mathieu.salzmann@epfl.ch Abstract Nowadays, the number of layers and of neurons in each layer of a dee...	2016	249
6,160	Bi-Objective Online Matching and Submodular Allocations Hossein Esfandiari University of Maryland College Park, MD 20740 hossein@cs.umd.edu Nitish Korula Google Research New York, NY 10011 nitish@google.com Vahab Mirrokni Google Research New York, NY 10011 mirrokni@google.com Abstract Onli...	2016	25
6,161	Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition Jinzhuo Wang, Wenmin Wang, Xiongtao Chen, Ronggang Wang, Wen Gao† School of Electronics and Computer Engineering, Peking University †School of Electronics Engineering and Computer Science, Peking University jzwang@...	2016	250
6,162	Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes Chris Junchi Li Zhaoran Wang Han Liu Department of Operations Research and Financial Engineering, Princeton University {junchil, zhaoran, hanliu}@princeton.edu Abstract Solving statistical learning problems often...	2016	251
6,163	Spatio–Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments Ransalu Senanayake University of Sydney rsen4557@uni.sydney.edu.au Lionel Ott University of Sydney lionel.ott@sydney.edu.au Simon O’Callaghan Data61/CSIRO, Australia simon.ocallaghan@data61.csiro.au Fabio ...	2016	252
6,164	CRF-CNN: Modeling Structured Information in Human Pose Estimation Xiao Chu The Chinese University of Hong Kong xchu@ee.cuhk.edu.hk Wanli Ouyang The Chinese University of Hong Kong wlouyang@ee.cuhk.edu.hk Hongsheng Li The Chinese University of Hong Kong hsli@ee.cuhk.edu.hk Xiaogang Wang The Chine...	2016	253
6,165	Bayesian latent structure discovery from multi-neuron recordings Scott W. Linderman Columbia University swl2133@columbia.edu Ryan P. Adams Harvard University and Twitter rpa@seas.harvard.edu Jonathan W. Pillow Princeton University pillow@princeton.edu Abstract Neural circuits contain heterogeneo...	2016	254
6,166	Latent Attention For If-Then Program Synthesis Xinyun Chen∗ Shanghai Jiao Tong University Chang Liu Richard Shin Dawn Song UC Berkeley Mingcheng Chen† UIUC Abstract Automatic translation from natural language descriptions into programs is a longstanding challenging problem. In this work, we consider...	2016	255
6,167	Understanding Probabilistic Sparse Gaussian Process Approximations Matthias Bauer†‡ Mark van der Wilk† Carl Edward Rasmussen† †Department of Engineering, University of Cambridge, Cambridge, UK ‡Max Planck Institute for Intelligent Systems, T¨ubingen, Germany {msb55, mv310, cer54}@cam.ac.uk Abstract Go...	2016	256
6,168	Wasserstein Training of Restricted Boltzmann Machines Grégoire Montavon Technische Universität Berlin gregoire.montavon@tu-berlin.de Klaus-Robert Müller∗ Technische Universität Berlin klaus-robert.mueller@tu-berlin.de Marco Cuturi CREST, ENSAE, Université Paris-Saclay marco.cuturi@ensae.fr Abstrac...	2016	257
6,169	A primal-dual method for conic constrained distributed optimization problems Necdet Serhat Aybat Department of Industrial Engineering Penn State University University Park, PA 16802 nsa10@psu.edu Erfan Yazdandoost Hamedani Department of Industrial Engineering Penn State University University Park, P...	2016	258
6,170	Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations Behnam Neyshabur∗ Toyota Technological Institute at Chicago bneyshabur@ttic.edu Yuhuai Wu∗ University of Toronto ywu@cs.toronto.edu Ruslan Salakhutdinov Carnegie Mellon University rsalakhu@cs.cmu.edu Nathan Srebro To...	2016	259
6,171	Interpretable Distribution Features with Maximum Testing Power Wittawat Jitkrittum, Zoltán Szabó, Kacper Chwialkowski, Arthur Gretton wittawatj@gmail.com zoltan.szabo.m@gmail.com kacper.chwialkowski@gmail.com arthur.gretton@gmail.com Gatsby Unit, University College London Abstract Two semimetric...	2016	26
6,172	Communication-Optimal Distributed Clustering∗ Jiecao Chen Indiana University Bloomington, IN 47401 jiecchen@indiana.edu He Sun University of Bristol Bristol, BS8 1UB, UK h.sun@bristol.ac.uk David P. Woodruff IBM Research Almaden San Jose, CA 95120 dpwoodru@us.ibm.com Qin Zhang Indiana Univer...	2016	260
6,173	Boosting with Abstention Corinna Cortes Google Research New York, NY 10011 corinna@google.com Giulia DeSalvo Courant Institute New York, NY 10012 desalvo@cims.nyu.edu Mehryar Mohri Courant Institute and Google New York, NY 10012 mohri@cims.nyu.edu Abstract We present a new boosting algorithm...	2016	261
6,174	Linear dynamical neural population models through nonlinear embeddings Yuanjun Gao⇤1 , Evan Archer⇤12, Liam Paninski12, John P. Cunningham12 Department of Statistics1 and Grossman Center2 Columbia University New York, NY, United States yg2312@columbia.edu, evan@stat.columbia.edu, liam@stat.columbia.edu, j...	2016	262
6,175	Rényi Divergence Variational Inference Yingzhen Li University of Cambridge Cambridge, CB2 1PZ, UK yl494@cam.ac.uk Richard E. Turner University of Cambridge Cambridge, CB2 1PZ, UK ret26@cam.ac.uk Abstract This paper introduces the variational Rényi bound (VR) that extends traditional variational infe...	2016	263
6,176	Stochastic Gradient Geodesic MCMC Methods Chang Liu†, Jun Zhu†, Yang Song‡∗ † Dept. of Comp. Sci. & Tech., TNList Lab; Center for Bio-Inspired Computing Research † State Key Lab for Intell. Tech. & Systems, Tsinghua University, Beijing, China ‡ Dept. of Physics, Tsinghua University, Beijing, China {chang-li14...	2016	264
6,177	A Posteriori Error Bounds for Joint Matrix Decomposition Problems Nicolò Colombo Department of Statistical Science University College London nicolo.colombo@ucl.ac.uk Nikos Vlassis Adobe Research San Jose, CA vlassis@adobe.com Abstract Joint matrix triangularization is often used for estimating the...	2016	265
6,178	Global Analysis of Expectation Maximization for Mixtures of Two Gaussians Ji Xu Columbia University jixu@cs.columbia.edu Daniel Hsu Columbia University djhsu@cs.columbia.edu Arian Maleki Columbia University arian@stat.columbia.edu Abstract Expectation Maximization (EM) is among the most popular ...	2016	266
6,179	Stochastic Structured Prediction under Bandit Feedback Artem Sokolov⋄,∗, Julia Kreutzer∗, Christopher Lo†,∗, Stefan Riezler‡,∗ ∗Computational Linguistics & ‡IWR, Heidelberg University, Germany {sokolov,kreutzer,riezler}@cl.uni-heidelberg.de †Department of Mathematics, Tufts University, Boston, MA, USA chris...	2016	267
6,180	Estimating the class prior and posterior from noisy positives and unlabeled data Shantanu Jain, Martha White, Predrag Radivojac Department of Computer Science Indiana University, Bloomington, Indiana, USA {shajain, martha, predrag}@indiana.edu Abstract We develop a classiﬁcation algorithm for estimating p...	2016	268
6,181	A Minimax Approach to Supervised Learning Farzan Farnia∗ farnia@stanford.edu David Tse∗ dntse@stanford.edu Abstract Given a task of predicting Y from X, a loss function L, and a set of probability distributions Γ on (X, Y ), what is the optimal decision rule minimizing the worstcase expected loss over Γ? ...	2016	269
6,182	Finding signiﬁcant combinations of features in the presence of categorical covariates Laetitia Papaxanthos∗, Felipe Llinares-López∗, Dean Bodenham, Karsten Borgwardt Machine Learning and Computational Biology Lab D-BSSE, ETH Zurich *Equally contributing authors. Abstract In high-dimensional settings, wher...	2016	27
6,183	Blazing the trails before beating the path: Sample-efﬁcient Monte-Carlo planning Jean-Bastien Grill Michal Valko SequeL team, INRIA Lille - Nord Europe, France jean-bastien.grill@inria.fr michal.valko@inria.fr Rémi Munos Google DeepMind, UK∗ munos@google.com Abstract You are a robot and you live i...	2016	270
6,184	Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages Yin Cheng Ng Dept. of Statistical Science University College London y.ng.12@ucl.ac.uk Pawel Chilinski Dept. of Computing Science University College London ucabchi@ucl.ac.uk Ricardo Silva Dept. of Statistical ...	2016	271
6,185	Improved Dropout for Shallow and Deep Learning Zhe Li1, Boqing Gong2, Tianbao Yang1 1The University of Iowa, Iowa city, IA 52245 2University of Central Florida, Orlando, FL 32816 {zhe-li-1,tianbao-yang}@uiowa.edu bgong@crcv.ucf.edu Abstract Dropout has been witnessed with great success in training deep ne...	2016	272
6,186	Clustering Signed Networks with the Geometric Mean of Laplacians Pedro Mercado1, Francesco Tudisco2 and Matthias Hein1 1Saarland University, Saarbrücken, Germany 2University of Padua, Padua, Italy Abstract Signed networks allow to model positive and negative relationships. We analyze existing extensions o...	2016	273
6,187	Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random Ilya Shpitser Department of Computer Science Johns Hopkins University ilyas@cs.jhu.edu Abstract Missing records are a perennial problem in analysis of complex data of all types, when the target of inference is some fu...	2016	274
6,188	Discriminative Gaifman Models Mathias Niepert NEC Labs Europe Heidelberg, Germany mathias.niepert@neclabs.eu Abstract We present discriminative Gaifman models, a novel family of relational machine learning models. Gaifman models learn feature representations bottom up from representations of locally con...	2016	275
6,189	Selective inference for group-sparse linear models Fan Yang Department of Statistics University of Chicago fyang1@uchicago.edu Rina Foygel Barber Department of Statistics University of Chicago rina@uchicago.edu Prateek Jain Microsoft Research India prajain@microsoft.com John Lafferty Depts. of...	2016	276
6,190	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen†‡, Yan Duan†‡, Rein Houthooft†‡, John Schulman†‡, Ilya Sutskever‡, Pieter Abbeel†‡ † UC Berkeley, Department of Electrical Engineering and Computer Sciences ‡ OpenAI Abstract This paper describes Inf...	2016	277
6,191	Automated scalable segmentation of neurons from multispectral images Uygar Sümbül Grossman Center for the Statistics of Mind and Dept. of Statistics, Columbia University Douglas Roossien Jr. University of Michigan Medical School Fei Chen MIT Media Lab and McGovern Institute Nicholas Barry MIT Media ...	2016	278
6,192	Robustness of classiﬁers: from adversarial to random noise Alhussein Fawzi∗, Seyed-Mohsen Moosavi-Dezfooli∗, Pascal Frossard École Polytechnique Fédérale de Lausanne Lausanne, Switzerland {alhussein.fawzi, seyed.moosavi, pascal.frossard} at epfl.ch Abstract Several recent works have shown that state-of-th...	2016	279
6,193	A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing Ming Lin University of Michigan linmin@umich.edu Jieping Ye University of Michigan jpye@umich.edu Abstract We develop an efﬁcient alternating framework for learning a generalized version of Factorizatio...	2016	28
6,194	Composing graphical models with neural networks for structured representations and fast inference Matthew James Johnson Harvard University mattjj@seas.harvard.edu David Duvenaud Harvard University dduvenaud@seas.harvard.edu Alexander B. Wiltschko Harvard University, Twitter awiltsch@fas.harvard.edu ...	2016	280
6,195	SoundNet: Learning Sound Representations from Unlabeled Video Yusuf Aytar∗ MIT yusuf@csail.mit.edu Carl Vondrick∗ MIT vondrick@mit.edu Antonio Torralba MIT torralba@mit.edu Abstract We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected i...	2016	281
6,196	Dual Decomposed Learning with Factorwise Oracles for Structural SVMs of Large Output Domain Ian E.H. Yen † Xiangru Huang ‡ Kai Zhong ‡ Ruohan Zhang ‡ Pradeep Ravikumar † Inderjit S. Dhillon ‡ † Carnegie Mellon University ‡ University of Texas at Austin Abstract Many applications of machine learnin...	2016	282
6,197	Deep Learning for Predicting Human Strategic Behavior Jason Hartford, James R. Wright, Kevin Leyton-Brown Department of Computer Science University of British Columbia {jasonhar, jrwright, kevinlb}@cs.ubc.ca Abstract Predicting the behavior of human participants in strategic settings is an important pro...	2016	283
6,198	Online and Differentially-Private Tensor Decomposition Yining Wang Machine Learning Department Carnegie Mellon University yiningwa@cs.cmu.edu Animashree Anandkumar Department of EECS University of California, Irvine a.anandkumar@uci.edu Abstract Tensor decomposition is an important tool for big data...	2016	284
6,199	Multivariate tests of association based on univariate tests Ruth Heller Department of Statistics and Operations Research Tel-Aviv University Tel-Aviv, Israel 6997801 ruheller@gmail.com Yair Heller heller.yair@gmail.com Abstract For testing two vector random variables for independence, we propose tes...	2016	285

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