Draft – Accepted Papers

An Optimal Policy for Target Localization with Application to Electron Microscopy Raphael Sznitman, Aurelien Lucchi, Peter Frazier, Bruno Jedynak, Pascal Fua

Domain Generalization via Invariant Feature Representation Krikamol Muandet, David Balduzzi, Bernhard Schoelkopf

A Spectral Learning Approach to Range-Only SLAM Byron Boots, Geoff Gordon

Near-Optimal Bounds for Cross-Validation via Loss Stability Ravi Kumar, Daniel Lokshtanov, Sergei Vassilvitskii, Andrea Vattani

Sparsity-Based Generalization Bounds for Predictive Sparse Coding Nishant Mehta, Alexander Gray

Sparse Uncorrelated Linear Discriminant Analysis Xiaowei Zhang, Delin Chu

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher

Fast Probabilistic Optimization from Noisy Gradients Philipp Hennig

Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir, Tong Zhang abstract/pdf

Stochastic Alternating Direction Method of Multipliers Hua Ouyang, Niao he, Long Tran, Alexander Gray

Noisy Sparse Subspace Clustering Yu-Xiang Wang, Huan Xu

Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models Sinead Williamson, Avinava Dubey, Eric Xing

Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla

Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures James Bergstra, Daniel Yamins, David Cox

Gibbs Max-Margin Topic Models with Fast Sampling Algorithms Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang

Cost-Sensitive Tree of Classifiers Zhixiang Xu, Matt Kusner, Kilian Weinberger, Minmin Chen

Learning Hash Functions Using Column Generation Xi Li, Guosheng Lin, Chunhua Shen, Anton van den Hengel, Anthony Dick

Combinatorial Multi-Armed Bandit: General Framework, Results and Applications Wei Chen, Yajun Wang, Yang Yuan

Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization Yuxin Chen, Andreas Krause

Convex formulations of radius-margin based Support Vector Machines
Huyen Do, Alexandros Kalousis

Modelling Sparse Dynamical Systems with Compressed Predictive State Representations William Hamilton, Mahdi Milani Fard, Joelle Pineau

A Machine Learning Framework for Programming by Example Aditya Menon, Omer Tamuz, Sumit Gulwani, Butler Lampson, Adam Kalai

Discriminatively Activated Sparselets Ross Girshick, Hyun Oh Song, Trevor Darrell

The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification Ofir Pele, Ben Taskar, Amir Globerson, Michael Werman
abstract, pdf

Fixed-Point Model For Structured Labeling Quannan Li, Jingdong Wang, David Wipf, Zhuowen Tu a

Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation Boqing Gong, Kristen Grauman, Fei Sha

Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur a

Principal Component Analysis on non-Gaussian Dependent Data Fang Han, Han Liu

Learning Linear Bayesian Networks with Latent Variables Animashree Anandkumar, Daniel Hsu, Adel Javanmard, Sham Kakade

Multiple Identifications in Multi-Armed Bandits Sebastian Bubeck, Tengyao Wang, Nitin Viswanathan

Learning Optimally Sparse Support Vector Machines Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro

Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks Creighton Heaukulani, Ghahramani Zoubin

Efficient Sparse Group Feature Selection via Nonconvex Optimization Shuo Xiang, Xiaoshen Tong, Jieping Ye

Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model Min Xiao, Yuhong Guo

Maximum Variance Correction with Application to A* Search Wenlin Chen, Kilian Weinberger, Yixin Chen

Adaptive Sparsity in Gaussian Graphical Models Eleanor Wong, Suyash Awate, P. Thomas Fletcher

Average Reward Optimization Objective In Partially Observable Domains Yuri Grinberg, Doina Precup

Feature Selection in High-Dimensional Classification Mladen Kolar, Han Liu

Human Boosting Harsh Pareek, Pradeep Ravikumar

Efficient Dimensionality Reduction for Canonical Correlation Analysis Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias

Parsing epileptic events using a Markov switching process model for correlated time series Drausin Wulsin, Emily Fox, Brian Litt

Optimal rates for stochastic convex optimization under Tsybakov noise condition Aaditya Ramdas, Aarti Singh a

A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning Arash Afkanpour, Andras Gyorgy, Csaba Szepesvari, Michael Bowling abstract/pdf

Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery Yudong Chen, Constantine Caramanis

Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method Taiji Suzuki

A New Frontier of Kernel Design for Structured Data Kilho Shin

Learning with Marginalized Corrupted Features Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Weinberger

Approximation properties of DBNs with binary hidden units and real-valued visible units Oswin Krause, Asja Fischer, Tobias Glasmachers, Christian Igel abstract/pdf

Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization Martin Jaggi

General Functional Matrix Factorization Using Gradient Boosting Tianqi Chen, Hang Li, Qiang Yang, Yong Yu

Iterative Learning and Denoising in Convolutional Neural Associative Memories Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi abstract/pdf/supplementary

Scaling Multidimensional Gaussian Processes using Projected Additive Approximations Elad Gilboa, Yunus Saatci, John Cunningham abstract/pdf/supplementary

Active Learning for Multi-Objective Optimization Marcela Zuluaga, Guillaume Sergent, Andreas Krause, Markus Pueschel

A Generalized Kernel Approach to Structured Output Learning Hachem Kadri, Mohammad Ghavamzadeh, Philippe Preux

Efficient Active Learning of Halfspaces: an Aggressive Approach Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz

Enhanced statistical rankings via targeted data collection Braxton Osting, Christoph Brune, Stanley Osher

Online Feature Selection for Model-based Reinforcement Learning Trung Nguyen, Zhuoru Li, Tomi Silander, Tze Yun Leong

ELLA: An Efficient Lifelong Learning Algorithm Paul Ruvolo, Eric Eaton

A Structural SVM Based Approach for Optimizing Partial AUC Harikrishna Narasimhan, Shivani Agarwal

Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs Sesh Kumar K. S., Francis Bach

Adaptive Task Assignment for Crowdsourced Classification Chien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko

Better Mixing via Deep Representations Yoshua Bengio, Gregoire Mesnil, Yann Dauphin, Salah Rifai

Online Latent Dirichlet Allocation with Infinite Vocabulary Ke Zhai, Jordan Boyd-Graber

Characterizing the Representer Theorem Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvari

Dynamical Models and tracking regret in online convex programming Eric Hall, Rebecca Willett a

Large-Scale Bandit Problems and KWIK Learning Jacob Abernethy, Kareem Amin, Michael Kearns, Moez Draief

Vanishing Component Analysis Roi Livni, David Lehavi, Sagi Schein, Hila Nachliely, Shai Shalev-Shwartz, Amir Globerson

Learning an Internal Dynamics Model from Control Demonstration Matthew Golub, Steven Chase, Byron Yu

Robust Structural Metric Learning Daryl Lim, Gert Lanckriet, Brian McFee

Constrained fractional set programs and their application in local clustering and community detection Thomas Bühler, Shyam Sundar Rangapuram, Simon Setzer, Matthias Hein

Efficient Semi-supervised and Active Learning of Disjunctions Nina Balcan, Christopher Berlind, Steven Ehrlich, Yingyu Liang

Convex Adversarial Collective Classification Mohamad Ali Torkamani, Daniel Lowd

Rounding Methods for Discrete Linear Classification Yann Chevaleyre, Frederick Koriche, Jean-Daniel Zucker

Mixture of Mutually Exciting Processes for Viral Diffusion  Shuang-Hong Yang

Gaussian Process Vine Copulas for Multivariate Dependence  David Lopez-Paz, Jose Miguel Hernandez-Lobato, Ghahramani  Zoubin

Stochastic Simultaneous Optimistic Optimization  Michal Valko, Alexandra Carpentier, Remi Munos

Toward Optimal Stratification for Stratified Monte-Carlo Integration  Alexandra Carpentier, Remi Munos

A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems  Pinghua Gong, Jieping Ye, Changshui Zhang

Thurstonian Boltzmann Machines: Learning from Multiple Inequalities  Truyen Tran, Dinh Phung, Svetha Venkatesh

A Variational Approximation for Topic Modeling of Hierarchical Corpora  Do-kyum Kim, Geoffrey Voelker, Lawrence Saul

Forecastable Component Analysis (ForeCA) Georg Goerg

Ellipsoidal Multiple Instance Learning  Gabriel Krummenacher, Cheng Soon Ong, Joachim Buhmann

Local Low-Rank Matrix Approximation  Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer

Generic exploration and K-armed voting bandits  Tanguy Urvoy, Fabrice Clerot, Raphael Feraud, Sami Naamane

A unifying framework for vector-valued manifold regularization and multi-view learning  Minh Ha Quang, Loris Bazzani, Vittorio Murino

Learning Connections in Financial Time Series  Gartheeban Ganeshapillai, John Guttag, Andrew Lo

Fast dropout training  Sida Wang

Scalable Optimization for Neighbor Embedding for Visualization Zhirong Yang, Jaakko Peltonen, Samuel Kaski

Precision-recall space to correct external indices for biclustering  Blaise Hanczar, Mohamed Nadif

Monochromatic Bi-Clustering   Shai Ben-David, Sharon Wulff, Ruth Urner

Gated Autoencoders with Tied Input Weights  Alain Droniou, Olivier Sigaud

Strict Monotonicity of Sum of Squares Error  and Normalized Cut in the Lattice of Clusterings  Nicola Rebagliati

Transition Matrix Estimation in High Dimensional Vector Autoregressive Models  Fang Han, Han Liu

Label Partitioning For Sublinear Ranking  Jason Weston, Ameesh Makadia, Hector Yee

Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing  Huayan Wang, Koller Daphne

Collaborative hyperparameter tuning  Rémi Bardenet, Mátyás Brendel, Balazs Kegl, Michele Sebag

SADA: A General Framework to Support Robust Causation Discovery  Ruichu Cai, Zhenjie Zhang, Zhifeng Hao

Jointly Learning and Selecting Features via Conditional Point-wise Mixture RBMs Kihyuk Sohn, Honglak Lee

Sequential Bayesian Search  Zheng Wen, Branislav Kveton, Brian Eriksson, Sandilya Bhamidipati

Sparse projections onto the simplex Anastasios Kyrillidis, Stephen Becker, Volkan Cevher, Christoph Koch

Modeling Musical Influence with Topic Models  Uri Shalit, Daphna Weinshall, Gal Chechik

Subtle Topic Models and Discovering Subtly Manifested Software Concerns Automatically   Mrinal Das, Suparna Bhattacharya, Chiranjib Bhattacharyya, Gopinath Kanchi

Exploring the Mind: Integrating Questionnaires and fMRI  Esther Salazar, Ahmad Hariri, Lawrence Carin

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions  Quoc Tran Dinh, Anastasios Kyrillidis, Volkan Cevher

A Practical Algorithm for Topic Modeling with Provable Guarantees Sanjeev Arora, Rong Ge, Yonatan Halpern, David Mimno, Ankur Moitra, David Sontag, Michael Zhu

Distributed training of large-scale logistic models  Siddharth Gopal, Yiming Yang

An Adaptive Learning Rate for Stochastic Variational Inference  Rajesh Ranganath, Chong Wang, Blei  David, Eric Xing

Margins, Shrinkage, and Boosting Matus Telgarsky

Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment  Billy Chang, Uwe Kruger, Rafal Kustra

Large-Scale Learning with Less RAM via Randomization  D. Sculley, Daniel Golovin, Brendan McMahan, Michael Young

Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization  Stefano Ermon, Carla Gomes, Ashish Sabharwal, Bart Selman

Sparse coding for multitask and transfer learning  Andreas Maurer, Massi Pontil, Bernardino Romera-Paredes

Direct Modeling of Complex Invariances in Visual Object Features  Ka YU Hui

Learning biochemical kinetic models from single-molecule data with hierarchically-coupled hidden Markov models  Jan-Willem Van de Meent, Jonathan Bronson, Ruben Gonzalez, Chris Wiggins

Activized Learning with Uniform Classification Noise  Liu Yang, Steve Hanneke

Guided Policy Search Levine, Sergey

Squared-loss Mutual Information Regularization Niu, Gang

Gossip-based distributed stochastic bandit algorithms Busa-Fekete, Robert

The Sample-Complexity of General Reinforcement Learning Lattimore, Tor

Hierarchical Regularization Cascade for Joint Learning Zweig, Alon

Multi-Class Classification with Maximum Margin Multiple Kernel Rostamizadeh, Afshin

Bayesian Games for Adversarial Regression Problems Großhans, Michael

Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing Chen, Xi

Markov Network Estimation From Multi-attribute Data Kolar, Mladen

MILEAGE: Multiple Instance LEArning with Global Embedding Zhang, Dan

Sparse Recovery under Linear Transformation Liu, Ji

Learning mid-level representations of objects by harnessing the aperture problem Memisevic, Roland

Efficient Ranking from Pairwise Comparisons Wauthier, Fabian

Differentially Private Learning with Kernels Jain, Prateek

Thompson Sampling for Contextual Bandits with Linear Payoffs Agrawal, Shipra

Clustering and Learning Behaviors using a Sparse Latent Space Montesano, Lui

Inference algorithms for pattern-based CRFs on sequence data Takhanov, Rustem

One-Bit Compressed Sensing: Provable Support and Vector Recovery Jain, Prateek

Tensor Analyzers Tang, Yichuan

Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression Hocking, Toby

Learning from Human List Production Jun, Kwang-Sung

A Fast and Exact Energy Minimization Algorithm for Cycle MRFs Wang, Huayan

Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning Tarlow, Daniel

An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source Separation Bryan, Nicholas

Estimating Unknown Sparsity in Compressed Sensing Lopes, Miles

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes Broderick, Tamara

The Most Generative Maximum Margin Bayesian Networks Peharz, Robert

Fastfood – Computing Hilbert Space Expansions in loglinear time Smola, Alexander

Causal Estimation of Peer Influence Effects Kao, Edward

Joint Transfer and Batch-mode Active Learning Chattopadhyay, Rita

Relaxed expectation propagation based on $l_1$-penalized KL minimization Qi, Yuan

Mean Reversion with a Variance Threshold Cuturi, Marco

Top-down particle filtering for Bayesian decision trees Lakshminarayanan, Balaji

Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations Balasubramanian, Krishnakumar

Can We Recognize Tiger by Bus Images? — Robust and Discriminative Self-Taught Image Categorization Wang, Hua

Safe Policy Iteration Restelli, Marcello

Unfolding Latent Tree Structures using 4th Order Tensors Ishteva, Mariya

Learning Fair Representations Zemel, Rich

Hierarchical Tensor Decomposition of Latent Tree Graphical Models Song, Le

No more pesky learning rates Schaul, Tom

Multi-View Clustering and Feature Learning via Structured Sparsity Wang, Hua

Planning by Prioritized Sweeping with Small Backups van Seijen, Harm

Value Iteration with incremental representation learning for continuous POMDPs Brechtel, Sebastian

Learning Heteroscedastic Models by Convex Programming under Group Sparsity Dalalyan, Arnak

Covariate Shift in Hilber Space: A Solution Via Sorrogate Kernels Zhang, Kai

A Local Algorithm for Finding Well-Connected Clusters Zhu, Zeyuan Allen

Efficient Multi-label Classification with Many Labels Bi, Wei

Spectral Compressed Sensing via Structured Matrix Completion Chen, Yuxin

Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model Yang, Ming

Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted Images Cho, Kyunghyun

On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions Kar, Purushottam

Non-Linear Stationary Subspace Analysis with Application to Video Classification Baktashmotlagh, Mahsa

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy Honorio, Jean

That was fast! Speeding up NN search of high dimensional distributions. Coviello, Emanuele

Entropic Affinities: Properties and Efficient Numerical Computation Carreira-Perpinan, Miguel

Local Deep Kernel Learning for Efficient Non-linear SVM Prediction Varma, Manik

Temporal Difference Methods for the Variance of the Reward To Go Tamar, Aviv

$\propto$SVM for Learning with Label Proportions Yu, Felix

Parameter Learning and Convergent Inference for Dense Random Fields Kraehenbuehl, Philipp

Loss-Proportional Subsampling for Subsequent ERM Mineiro, Paul

Scalable Simple Random Sampling and Stratified Sampling Meng, Xiangrui

Riemannian Similarity Learning Cheng, Li

On Compact Codes for Spatially Pooled Features Jia, Yangqing

Dynamic Covariance Models for Multivariate Financial Time Series Wu, Yue

Revisiting the Nystrom method for improved large-scale machine learning Gittens, Alex

Infinite Positive Semidefinite Tensor Factorization with Application to Music Signal Analysis Yoshii, Kazuyoshi

A Unified Robust Regression Model for Lasso-like Algorithms Yang, Wenzhuo

Quickly Boosting Decision Trees — Pruning Underachieving Features Early Appel, Ron

On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance Agarwal, Shivani

Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment Chuang, Jason

Online Kernel Learning with a Near Optimal Sparsity Bound Zhang, Lijun

Spectral Learning of Hidden Markov Models from Dynamic and Static Data Huang, Tzu-Kuo

Analogy-preserving Semantic Embedding for Visual Object Categorization Hwang, Sung Ju

Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training Izbicki, Michael

Factorial Multi-Task Learning : A Bayesian Nonparametric Approach Gupta, Sunil

Modeling Information Propagation with Survival Theory Gomez-Rodriguez, Manuel

Better Rates for Any Adversarial Deterministic MDPs Hazan, Elad

ABC Reinforcement Learning Dimitrakakis, Christos

Sharp Generalization Error Bounds for Randomly-projected Classifiers Durrant, Robert

On learning parametric-output HMMs Kontorovich, Aryeh

Latent Dirichlet Allocation Topic Model with Soft Assignment of Descriptors to Words Weinshall, Daphna

On autoencoder scoring Kamyshanska, Hanna

Infinite Markov-Switching Maximum Entropy Discrimination Machines Chatzis, Sotirios

A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers Morvant, Emilie

Sparse PCA through Low-rank Approximations Papailiopoulos, Dimitris

Computation-Risk Tradeoffs for Covariance-Thresholded Regression Shender, Dinah

Exact Rule Learning via Boolean Compressed Sensing Varshney, Kush

Robust Sparse Regression under Adversarial Corruption Chen, Yudong

Optimization with First-Order Surrogate Functions Mairal, Julien

Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Forecasting Koppula, Hema

Consistency versus Realizable H-Consistency for Multiclass Classification Long, Phil

Feature Multi-Selection among Subjective Features Sabato, Sivan

Domain Adaptation under Target and Conditional Shift Zhang, Kun

Collective Stability and Structured Prediction: Generalization from One Example London, Ben

Stable Coactive Learning via Perturbation Raman, Karthik

Max-Margin Multiple-Instance Dictionary Learning Tu, Zhuowen

Fast Semidifferential-based Submodular Function Optimization Jegelka, Stefanie

Kernelized Bayesian Matrix Factorization Gönen, Mehmet

Learning the Structure of Sum-Product Networks Gens, Robert

Quantile Regression for Large-scale Applications Mahoney, Michael

Robust Regression on MapReduce Mahoney, Michael

Infinitesimal Annealing for Training Semi-Supervised Support Vector Machine Takeuchi, Ichiro

One-Pass AUC Optimization Gao, Wei

Learning Convex QP Relaxations for Structured Prediction Jancsary, Jeremy

Concurrent Reinforcement Learning from Customer Interaction Sequences Silver, David

Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner Sun, Peng

Stability and Hypothesis Transfer Learning Kuzborskij, Ilja

Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models Khan, Mohammad Emtiyaz

Modeling Temporal Evolution and Multiscale Structure in Networks Herlau, Tue

Dependent Normalized Random Measures Chen, Changyou

Fast Max-Margin Matrix Factorization with Data Augmentation Xu, Minjie

Natural Image Bases to Represent Neuroimaging Data Gupta, Ashish

Breaking the Small Cluster Barrier of Graph Clustering Ailon, Nir

Approximate Inference in Collective Graphical Models Sheldon, Daniel

Scaling the Indian Buffet Process via Submodular Maximization Reed, Colorado

Mini-Batch Primal and Dual Methods for SVMs Takac, Martin

The lasso, persistence, and leave-one-out cross-validation Homrighausen, Darren

Spectral Experts for Estimating Mixtures of Linear Regressions Chaganty, Arun Tejasvi

Distribution to Distribution Regression Oliva, Junier

Regularization of Neural Networks using DropConnect Wan, Li

Gaussian Process Kernels for Pattern Discovery and Extrapolation Wilson, Andrew

Anytime Representation Learning Xu, Zhixiang

Algorithms for Direct 0–1 Loss Optimization in Binary Classification Sanner, Scott

Top-k Selection based on Adaptive Sampling of Noisy Preferences Busa-Fekete, Robert

The Extended Parameter Filter Erol, Yusuf Bugra

Exploiting Ontology Structures and Unlabeled Data for Learning Blum, Avrim

O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions Zhang, Lijun

Optimizing the F-measure in Multi-label Classification: Plug-in Rule Approach versus Structured Loss Minimization Dembczynski, Krzysztof

On the importance of initialization and momentum in deep learning Sutskever, Ilya

Collaborative Filtering with Hybrid Restricted Boltzmann Machines Nakov, Preslav

Intersecting singularities for multi-structured estimation Richard, Emile

Structure Discovery in Nonparametric Regression through Compositional Kernel Search Lloyd, James

Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events Friedland, Lisa

Smooth Operators and an RKHS Integration Approach Grunewalder, Steffen

The Cross-Entropy Method Optimizes for Quantiles Goschin, Sergiu

Topic Discovery through Data Dependent and Random Projections Ding, Weicong

Bayesian Learning of Recursively Factored Environments Bellemare, Marc

Selective sampling algorithms for cost-sensitive multiclass prediction Agarwal, Alekh

The Bigraphical Lasso Kalaitzis, Alfredo

Almost Optimal Exploration in Multi-Armed Bandits Koren, Tomer

Deep Canonical Correlation Analysis Andrew, Galen

Consistency of Online Random Forests Denil, Misha

Sparse Gaussian Conditional Random Fields: Algorithms, Theory, and Application to Energy Forecasting Kolter, Zico

Fast Image Tagging Chen, Minmin

Expensive Function Optimization with Stochastic Binary Outcomes Tesch, Matthew

Multiple-Source Cross Validation Geras, Krzysztof

Learning Triggering Kernels for Multi-dimensional Hawkes Processes Zhou, Ke

On the difficulty of training Recurrent Neural Networks Pascanu, Razvan

Maxout Networks Goodfellow, Ian

Predictable Dual-View Hashing Rastegari, Mohammad

Deep learning with COTS HPC systems Coates, Adam

Nonparametric Mixture of Gaussian Processes with Constraints Ross, James

Scale Invariant Conditional Dependence Measures J Reddi, Sashank

Learning Policies for Contextual Submodular Prediction Ross, Stephane

Manifold Perserving Hierarchical Topic Models for Quantization and Approximation Kim, Minje

Safe Screening of Non-Support Vectors in Pathwise SVM Computation Takeuchi, Ichiro

Cost-sensitive Multiclass Classification Risk Bounds Pires, Bernardo

Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion Yi, Jinfeng

Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models Simsekli, Umut

Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration Kuleshov, Volodymyr

Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling Ahmed, Amr

Tree-Independent Dual-Tree Algorithms Curtin, Ryan

Multilinear Multitask Learning Romera-Paredes, Bernardino

Online Learning under Delayed Feedback Gyorgy, Andras

Adaptive Hamiltonian and Riemann Manifold Monte Carlo Wang, Ziyu

Coco-Q: Learning in Stochastic Games with Side Payments Hilliard, Elizabeth

On Nonlinear Generalization of Sparse Coding and Dictionary Learning Ho, Jeffrey