Book
Advances in Neural Information Processing Systems 26 (NIPS 2013)
Edited by:
C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger
- Inferring neural population dynamics from multiple partial recordings of the same neural circuit Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob H. Macke
- Approximate Gaussian process inference for the drift function in stochastic differential equations Andreas Ruttor, Philipp Batz, Manfred Opper
- Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections Matthew Lawlor, Steven W. Zucker
- Transportability from Multiple Environments with Limited Experiments Elias Bareinboim, Sanghack Lee, Vasant Honavar, Judea Pearl
- On model selection consistency of penalized M-estimators: a geometric theory Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
- Robust Bloom Filters for Large MultiLabel Classification Tasks Moustapha M. Cisse, Nicolas Usunier, Thierry Artières, Patrick Gallinari
- On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation Harikrishna Narasimhan, Shivani Agarwal
- Sequential Transfer in Multi-armed Bandit with Finite Set of Models Mohammad Gheshlaghi azar, Alessandro Lazaric, Emma Brunskill
- A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles Jinwoo Shin, Andrew E. Gelfand, Misha Chertkov
- A Kernel Test for Three-Variable Interactions Dino Sejdinovic, Arthur Gretton, Wicher Bergsma
- Accelerated Mini-Batch Stochastic Dual Coordinate Ascent Shai Shalev-Shwartz, Tong Zhang
- A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks Junming Yin, Qirong Ho, Eric P. Xing
- Multi-Prediction Deep Boltzmann Machines Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio
- Learning and using language via recursive pragmatic reasoning about other agents Nathaniel J. Smith, Noah Goodman, Michael Frank
- Reinforcement Learning in Robust Markov Decision Processes Shiau Hong Lim, Huan Xu, Shie Mannor
- Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel Tai Qin, Karl Rohe
- A Novel Two-Step Method for Cross Language Representation Learning Min Xiao, Yuhong Guo
- Graphical Models for Inference with Missing Data Karthika Mohan, Judea Pearl, Jin Tian
- Convex Tensor Decomposition via Structured Schatten Norm Regularization Ryota Tomioka, Taiji Suzuki
- Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression Michalis Titsias RC AUEB, Miguel Lazaro-Gredilla
- Efficient Online Inference for Bayesian Nonparametric Relational Models Dae Il Kim, Prem K. Gopalan, David Blei, Erik Sudderth
- Convergence of Monte Carlo Tree Search in Simultaneous Move Games Viliam Lisy, Vojta Kovarik, Marc Lanctot, Branislav Bosansky
- Learning to Pass Expectation Propagation Messages Nicolas Heess, Daniel Tarlow, John Winn
- Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits Ben Shababo, Brooks Paige, Ari Pakman, Liam Paninski
- Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization Nataliya Shapovalova, Michalis Raptis, Leonid Sigal, Greg Mori
- Integrated Non-Factorized Variational Inference Shaobo Han, Xuejun Liao, Lawrence Carin
- A Gang of Bandits Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella
- Multiclass Total Variation Clustering Xavier Bresson, Thomas Laurent, David Uminsky, James von Brecht
- Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors Xiaoqin Zhang, Di Wang, Zhengyuan Zhou, Yi Ma
- BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep K. Ravikumar, Russell Poldrack
- Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching Marcelo Fiori, Pablo Sprechmann, Joshua Vogelstein, Pablo Muse, Guillermo Sapiro
- Optimal integration of visual speed across different spatiotemporal frequency channels Matjaz Jogan, Alan A. Stocker
- Translating Embeddings for Modeling Multi-relational Data Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
- Synthesizing Robust Plans under Incomplete Domain Models Tuan A. Nguyen, Subbarao Kambhampati, Minh Do
- Learning Gaussian Graphical Models with Observed or Latent FVSs Ying Liu, Alan Willsky
- Extracting regions of interest from biological images with convolutional sparse block coding Marius Pachitariu, Adam M. Packer, Noah Pettit, Henry Dalgleish, Michael Hausser, Maneesh Sahani
- Training and Analysing Deep Recurrent Neural Networks Michiel Hermans, Benjamin Schrauwen
- Low-Rank Matrix and Tensor Completion via Adaptive Sampling Akshay Krishnamurthy, Aarti Singh
- Fast Determinantal Point Process Sampling with Application to Clustering Byungkon Kang
- Matrix factorization with binary components Martin Slawski, Matthias Hein, Pavlo Lutsik
- Reshaping Visual Datasets for Domain Adaptation Boqing Gong, Kristen Grauman, Fei Sha
- Perfect Associative Learning with Spike-Timing-Dependent Plasticity Christian Albers, Maren Westkott, Klaus Pawelzik
- Tracking Time-varying Graphical Structure Erich Kummerfeld, David Danks
- Phase Retrieval using Alternating Minimization Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
- Unsupervised Structure Learning of Stochastic And-Or Grammars Kewei Tu, Maria Pavlovskaia, Song-Chun Zhu
- Learning Multi-level Sparse Representations Ferran Diego Andilla, Fred A. Hamprecht
- Estimation, Optimization, and Parallelism when Data is Sparse John Duchi, Michael I. Jordan, Brendan McMahan
- Predictive PAC Learning and Process Decompositions Cosma Shalizi, Aryeh Kontorovich
- Scalable Inference for Logistic-Normal Topic Models Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang
- A multi-agent control framework for co-adaptation in brain-computer interfaces Josh S. Merel, Roy Fox, Tony Jebara, Liam Paninski
- Conditional Random Fields via Univariate Exponential Families Eunho Yang, Pradeep K. Ravikumar, Genevera I. Allen, Zhandong Liu
- Adaptivity to Local Smoothness and Dimension in Kernel Regression Samory Kpotufe, Vikas Garg
- Online Learning with Costly Features and Labels Navid Zolghadr, Gabor Bartok, Russell Greiner, András György, Csaba Szepesvari
- An Approximate, Efficient LP Solver for LP Rounding Srikrishna Sridhar, Stephen Wright, Christopher Re, Ji Liu, Victor Bittorf, Ce Zhang
- Regression-tree Tuning in a Streaming Setting Samory Kpotufe, Francesco Orabona
- Estimating LASSO Risk and Noise Level Mohsen Bayati, Murat A. Erdogdu, Andrea Montanari
- Demixing odors - fast inference in olfaction Agnieszka Grabska-Barwinska, Jeff Beck, Alexandre Pouget, Peter Latham
- Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Ng
- Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result Paul Wagner
- Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl Edward Rasmussen
- Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex Sam Patterson, Yee Whye Teh
- When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade
- Sign Cauchy Projections and Chi-Square Kernel Ping Li, Gennady Samorodnitsk, John Hopcroft
- Transfer Learning in a Transductive Setting Marcus Rohrbach, Sandra Ebert, Bernt Schiele
- Solving inverse problem of Markov chain with partial observations Tetsuro Morimura, Takayuki Osogami, Tsuyoshi Ide
- Wavelets on Graphs via Deep Learning Raif Rustamov, Leonidas J. Guibas
- Stochastic Convex Optimization with Multiple Objectives Mehrdad Mahdavi, Tianbao Yang, Rong Jin
- Bayesian Hierarchical Community Discovery Charles Blundell, Yee Whye Teh
- Contrastive Learning Using Spectral Methods James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan P. Adams
- Deep Fisher Networks for Large-Scale Image Classification Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
- Linear Convergence with Condition Number Independent Access of Full Gradients Lijun Zhang, Mehrdad Mahdavi, Rong Jin
- Learning with Noisy Labels Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari
- Variational Policy Search via Trajectory Optimization Sergey Levine, Vladlen Koltun
- Dropout Training as Adaptive Regularization Stefan Wager, Sida Wang, Percy S. Liang
- Prior-free and prior-dependent regret bounds for Thompson Sampling Sebastien Bubeck, Che-Yu Liu
- Geometric optimisation on positive definite matrices for elliptically contoured distributions Suvrit Sra, Reshad Hosseini
- Capacity of strong attractor patterns to model behavioural and cognitive prototypes Abbas Edalat
- Manifold-based Similarity Adaptation for Label Propagation Masayuki Karasuyama, Hiroshi Mamitsuka
- New Subsampling Algorithms for Fast Least Squares Regression Paramveer Dhillon, Yichao Lu, Dean P. Foster, Lyle Ungar
- A message-passing algorithm for multi-agent trajectory planning José Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia
- Solving the multi-way matching problem by permutation synchronization Deepti Pachauri, Risi Kondor, Vikas Singh
- Auditing: Active Learning with Outcome-Dependent Query Costs Sivan Sabato, Anand D. Sarwate, Nati Srebro
- Restricting exchangeable nonparametric distributions Sinead A. Williamson, Steve N. MacEachern, Eric P. Xing
- On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization Ke Hou, Zirui Zhou, Anthony Man-Cho So, Zhi-Quan Luo
- Eluder Dimension and the Sample Complexity of Optimistic Exploration Daniel Russo, Benjamin Van Roy
- Efficient Algorithm for Privately Releasing Smooth Queries Ziteng Wang, Kai Fan, Jiaqi Zhang, Liwei Wang
- Buy-in-Bulk Active Learning Liu Yang, Jaime Carbonell
- On Poisson Graphical Models Eunho Yang, Pradeep K. Ravikumar, Genevera I. Allen, Zhandong Liu
- On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations Tamir Hazan, Subhransu Maji, Tommi Jaakkola
- Factorized Asymptotic Bayesian Inference for Latent Feature Models Kohei Hayashi, Ryohei Fujimaki
- Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation Martin Azizyan, Aarti Singh, Larry Wasserman
- Efficient Optimization for Sparse Gaussian Process Regression Yanshuai Cao, Marcus A. Brubaker, David J. Fleet, Aaron Hertzmann
- Robust learning of low-dimensional dynamics from large neural ensembles David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski
- Causal Inference on Time Series using Restricted Structural Equation Models Jonas Peters, Dominik Janzing, Bernhard Schölkopf
- Better Approximation and Faster Algorithm Using the Proximal Average Yao-Liang Yu
- Robust Low Rank Kernel Embeddings of Multivariate Distributions Le Song, Bo Dai
- Learning the Local Statistics of Optical Flow Dan Rosenbaum, Daniel Zoran, Yair Weiss
- Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis James R. Voss, Luis Rademacher, Mikhail Belkin
- Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization Julien Mairal
- Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions Yasin Abbasi Yadkori, Peter L. Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvari
- Improved and Generalized Upper Bounds on the Complexity of Policy Iteration Bruno Scherrer
- Approximate Inference in Continuous Determinantal Processes Raja Hafiz Affandi, Emily Fox, Ben Taskar
- Streaming Variational Bayes Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan
- One-shot learning by inverting a compositional causal process Brenden M. Lake, Russ R. Salakhutdinov, Josh Tenenbaum
- Large Scale Distributed Sparse Precision Estimation Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
- Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking Ryan D. Turner, Steven Bottone, Clay J. Stanek
- RNADE: The real-valued neural autoregressive density-estimator Benigno Uria, Iain Murray, Hugo Larochelle
- Estimating the Unseen: Improved Estimators for Entropy and other Properties Paul Valiant, Gregory Valiant
- Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin
- Parametric Task Learning Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima
- Generalized Denoising Auto-Encoders as Generative Models Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent
- Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation John Duchi, Martin J. Wainwright, Michael I. Jordan
- Reward Mapping for Transfer in Long-Lived Agents Xiaoxiao Guo, Satinder Singh, Richard L. Lewis
- Distributed Exploration in Multi-Armed Bandits Eshcar Hillel, Zohar S. Karnin, Tomer Koren, Ronny Lempel, Oren Somekh
- It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, Oliver Stegle
- Projecting Ising Model Parameters for Fast Mixing Justin Domke, Xianghang Liu
- Low-rank matrix reconstruction and clustering via approximate message passing Ryosuke Matsushita, Toshiyuki Tanaka
- Inverse Density as an Inverse Problem: the Fredholm Equation Approach Qichao Que, Mikhail Belkin
- Modeling Overlapping Communities with Node Popularities Prem K. Gopalan, Chong Wang, David Blei
- Reflection methods for user-friendly submodular optimization Stefanie Jegelka, Francis Bach, Suvrit Sra
- Compressive Feature Learning Hristo S. Paskov, Robert West, John C. Mitchell, Trevor Hastie
- Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions Eftychios A. Pnevmatikakis, Liam Paninski
- Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms Adrien Todeschini, François Caron, Marie Chavent
- Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi
- Reservoir Boosting : Between Online and Offline Ensemble Learning Leonidas Lefakis, François Fleuret
- Faster Ridge Regression via the Subsampled Randomized Hadamard Transform Yichao Lu, Paramveer Dhillon, Dean P. Foster, Lyle Ungar
- Convex Relaxations for Permutation Problems Fajwel Fogel, Rodolphe Jenatton, Francis Bach, Alexandre D'Aspremont
- Online Learning of Dynamic Parameters in Social Networks Shahin Shahrampour, Sasha Rakhlin, Ali Jadbabaie
- Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests Yacine Jernite, Yonatan Halpern, David Sontag
- Gaussian Process Conditional Copulas with Applications to Financial Time Series José Miguel Hernández-Lobato, James R. Lloyd, Daniel Hernández-Lobato
- Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty Haichao Zhang, David Wipf
- Online learning in episodic Markovian decision processes by relative entropy policy search Alexander Zimin, Gergely Neu
- Bayesian inference for low rank spatiotemporal neural receptive fields Mijung Park, Jonathan W. Pillow
- Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation Bogdan Savchynskyy, Jörg Hendrik Kappes, Paul Swoboda, Christoph Schnörr
- Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion Franz Kiraly, Louis Theran
- Decision Jungles: Compact and Rich Models for Classification Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi
- Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models Jie Liu, David Page
- (More) Efficient Reinforcement Learning via Posterior Sampling Ian Osband, Daniel Russo, Benjamin Van Roy
- Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting Shunan Zhang, Angela J. Yu
- Stochastic Optimization of PCA with Capped MSG Raman Arora, Andy Cotter, Nati Srebro
- Embed and Project: Discrete Sampling with Universal Hashing Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
- Optimal Neural Population Codes for High-dimensional Stimulus Variables Zhuo Wang, Alan A. Stocker, Daniel D. Lee
- Near-Optimal Entrywise Sampling for Data Matrices Dimitris Achlioptas, Zohar S. Karnin, Edo Liberty
- A Comparative Framework for Preconditioned Lasso Algorithms Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan
- Universal models for binary spike patterns using centered Dirichlet processes Il Memming Park, Evan W. Archer, Kenneth Latimer, Jonathan W. Pillow
- What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach Zhenwen Dai, Georgios Exarchakis, Jörg Lücke
- Correlations strike back (again): the case of associative memory retrieval Cristina Savin, Peter Dayan, Mate Lengyel
- Understanding Dropout Pierre Baldi, Peter J. Sadowski
- Supervised Sparse Analysis and Synthesis Operators Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M. Bronstein, Guillermo Sapiro
- The Pareto Regret Frontier Wouter M. Koolen
- Approximate Dynamic Programming Finally Performs Well in the Game of Tetris Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer
- Learning Feature Selection Dependencies in Multi-task Learning Daniel Hernández-Lobato, José Miguel Hernández-Lobato
- Dimension-Free Exponentiated Gradient Francesco Orabona
- Memory Limited, Streaming PCA Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain
- Σ-Optimality for Active Learning on Gaussian Random Fields Yifei Ma, Roman Garnett, Jeff Schneider
- Recurrent linear models of simultaneously-recorded neural populations Marius Pachitariu, Biljana Petreska, Maneesh Sahani
- On the Complexity and Approximation of Binary Evidence in Lifted Inference Guy Van den Broeck, Adnan Darwiche
- Pass-efficient unsupervised feature selection Crystal Maung, Haim Schweitzer
- Adaptive dropout for training deep neural networks Jimmy Ba, Brendan Frey
- On the Representational Efficiency of Restricted Boltzmann Machines James Martens, Arkadev Chattopadhya, Toni Pitassi, Richard Zemel
- Robust Spatial Filtering with Beta Divergence Wojciech Samek, Duncan Blythe, Klaus-Robert Müller, Motoaki Kawanabe
- DeViSE: A Deep Visual-Semantic Embedding Model Andrea Frome, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, Tomas Mikolov
- Symbolic Opportunistic Policy Iteration for Factored-Action MDPs Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli
- Least Informative Dimensions Fabian Sinz, Anna Stockl, Jan Grewe, Jan Benda
- A memory frontier for complex synapses Subhaneil Lahiri, Surya Ganguli
- Data-driven Distributionally Robust Polynomial Optimization Martin Mevissen, Emanuele Ragnoli, Jia Yuan Yu
- Learning Stochastic Inverses Andreas Stuhlmüller, Jacob Taylor, Noah Goodman
- Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs Yann Dauphin, Yoshua Bengio
- Distributed $k$-means and $k$-median Clustering on General Topologies Maria-Florina F. Balcan, Steven Ehrlich, Yingyu Liang
- Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) Francis Bach, Eric Moulines
- Predicting Parameters in Deep Learning Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas
- Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising Min Xu, Tao Qin, Tie-Yan Liu
- Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi Jaakkola
- q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions Assaf Glazer, Michael Lindenbaum, Shaul Markovitch
- Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA Vincent Q. Vu, Juhee Cho, Jing Lei, Karl Rohe
- Fast Template Evaluation with Vector Quantization Mohammad Amin Sadeghi, David Forsyth
- Sparse Additive Text Models with Low Rank Background Lei Shi
- Correlated random features for fast semi-supervised learning Brian McWilliams, David Balduzzi, Joachim M. Buhmann
- Variational Planning for Graph-based MDPs Qiang Cheng, Qiang Liu, Feng Chen, Alexander T. Ihler
- Adaptive Anonymity via $b$-Matching Krzysztof M. Choromanski, Tony Jebara, Kui Tang
- Statistical Active Learning Algorithms Maria-Florina F. Balcan, Vitaly Feldman
- The Power of Asymmetry in Binary Hashing Behnam Neyshabur, Nati Srebro, Russ R. Salakhutdinov, Yury Makarychev, Payman Yadollahpour
- Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search Aijun Bai, Feng Wu, Xiaoping Chen
- Distributed Submodular Maximization: Identifying Representative Elements in Massive Data Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause
- Analyzing the Harmonic Structure in Graph-Based Learning Xiao-Ming Wu, Zhenguo Li, Shih-Fu Chang
- Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic James L. Sharpnack, Akshay Krishnamurthy, Aarti Singh
- Bellman Error Based Feature Generation using Random Projections on Sparse Spaces Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand, Joelle Pineau, Doina Precup
- Similarity Component Analysis Soravit Changpinyo, Kuan Liu, Fei Sha
- Matrix Completion From any Given Set of Observations Troy Lee, Adi Shraibman
- A Deep Architecture for Matching Short Texts Zhengdong Lu, Hang Li
- Sensor Selection in High-Dimensional Gaussian Trees with Nuisances Daniel S. Levine, Jonathan P. How
- The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram
- Lasso Screening Rules via Dual Polytope Projection Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye
- Multiscale Dictionary Learning for Estimating Conditional Distributions Francesca Petralia, Joshua T. Vogelstein, David B. Dunson
- Dirty Statistical Models Eunho Yang, Pradeep K. Ravikumar
- Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation Dahua Lin
- Exact and Stable Recovery of Pairwise Interaction Tensors Shouyuan Chen, Michael R. Lyu, Irwin King, Zenglin Xu
- A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables Jing Xiang, Seyoung Kim
- High-Dimensional Gaussian Process Bandits Josip Djolonga, Andreas Krause, Volkan Cevher
- Generalizing Analytic Shrinkage for Arbitrary Covariance Structures Daniel Bartz, Klaus-Robert Müller
- Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation Vibhav Vineet, Carsten Rother, Philip Torr
- Online Robust PCA via Stochastic Optimization Jiashi Feng, Huan Xu, Shuicheng Yan
- Compete to Compute Rupesh K. Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Jürgen Schmidhuber
- Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms Yu Zhang
- Scalable Influence Estimation in Continuous-Time Diffusion Networks Nan Du, Le Song, Manuel Gomez Rodriguez, Hongyuan Zha
- More data speeds up training time in learning halfspaces over sparse vectors Amit Daniely, Nati Linial, Shai Shalev-Shwartz
- Top-Down Regularization of Deep Belief Networks Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-Hwee Lim
- Polar Operators for Structured Sparse Estimation Xinhua Zhang, Yao-Liang Yu, Dale Schuurmans
- Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space Xinhua Zhang, Wee Sun Lee, Yee Whye Teh
- Real-Time Inference for a Gamma Process Model of Neural Spiking David E. Carlson, Vinayak Rao, Joshua T. Vogelstein, Lawrence Carin
- Direct 0-1 Loss Minimization and Margin Maximization with Boosting Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang
- Marginals-to-Models Reducibility Tim Roughgarden, Michael Kearns
- Sketching Structured Matrices for Faster Nonlinear Regression Haim Avron, Vikas Sindhwani, David Woodruff
- Variance Reduction for Stochastic Gradient Optimization Chong Wang, Xi Chen, Alexander J. Smola, Eric P. Xing
- On Decomposing the Proximal Map Yao-Liang Yu
- Documents as multiple overlapping windows into grids of counts Alessandro Perina, Nebojsa Jojic, Manuele Bicego, Andrzej Truski
- Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model Fang Han, Han Liu
- Optimizing Instructional Policies Robert V. Lindsey, Michael C. Mozer, William J. Huggins, Harold Pashler
- Adaptive Market Making via Online Learning Jacob Abernethy, Satyen Kale
- Learning Prices for Repeated Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed
- Multilinear Dynamical Systems for Tensor Time Series Mark Rogers, Lei Li, Stuart J. Russell
- Computing the Stationary Distribution Locally Christina E. Lee, Asuman Ozdaglar, Devavrat Shah
- Latent Maximum Margin Clustering Guang-Tong Zhou, Tian Lan, Arash Vahdat, Greg Mori
- Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream Daniel L. Yamins, Ha Hong, Charles Cadieu, James J. DiCarlo
- Online PCA for Contaminated Data Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
- Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, Jeff Dean
- Learning Multiple Models via Regularized Weighting Daniel Vainsencher, Shie Mannor, Huan Xu
- Discriminative Transfer Learning with Tree-based Priors Nitish Srivastava, Russ R. Salakhutdinov
- Machine Teaching for Bayesian Learners in the Exponential Family Jerry Zhu
- Online Learning with Switching Costs and Other Adaptive Adversaries Nicolò Cesa-Bianchi, Ofer Dekel, Ohad Shamir
- From Bandits to Experts: A Tale of Domination and Independence Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour
- Which Space Partitioning Tree to Use for Search? Parikshit Ram, Alexander Gray
- Small-Variance Asymptotics for Hidden Markov Models Anirban Roychowdhury, Ke Jiang, Brian Kulis
- Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis Nikhil Rao, Christopher Cox, Rob Nowak, Timothy T. Rogers
- Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints Rishabh K. Iyer, Jeff A. Bilmes
- Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition Adel Javanmard, Andrea Montanari
- Scalable kernels for graphs with continuous attributes Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt
- Bayesian optimization explains human active search Ali Borji, Laurent Itti
- B-test: A Non-parametric, Low Variance Kernel Two-sample Test Wojciech Zaremba, Arthur Gretton, Matthew Blaschko
- Moment-based Uniform Deviation Bounds for $k$-means and Friends Matus J. Telgarsky, Sanjoy Dasgupta
- Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari
- Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions Ari Pakman, Liam Paninski
- Spectral methods for neural characterization using generalized quadratic models Il Memming Park, Evan W. Archer, Nicholas Priebe, Jonathan W. Pillow
- A Latent Source Model for Nonparametric Time Series Classification George H. Chen, Stanislav Nikolov, Devavrat Shah
- PAC-Bayes-Empirical-Bernstein Inequality Ilya O. Tolstikhin, Yevgeny Seldin
- Convex Two-Layer Modeling Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans
- The Randomized Dependence Coefficient David Lopez-Paz, Philipp Hennig, Bernhard Schölkopf
- Sparse Inverse Covariance Estimation with Calibration Tuo Zhao, Han Liu
- Thompson Sampling for 1-Dimensional Exponential Family Bandits Nathaniel Korda, Emilie Kaufmann, Remi Munos
- Accelerating Stochastic Gradient Descent using Predictive Variance Reduction Rie Johnson, Tong Zhang
- Multisensory Encoding, Decoding, and Identification Aurel A. Lazar, Yevgeniy Slutskiy
- Learning invariant representations and applications to face verification Qianli Liao, Joel Z. Leibo, Tomaso Poggio
- Optimistic Concurrency Control for Distributed Unsupervised Learning Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan
- Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. Michel Besserve, Nikos K. Logothetis, Bernhard Schölkopf
- Sinkhorn Distances: Lightspeed Computation of Optimal Transport Marco Cuturi
- Nonparametric Multi-group Membership Model for Dynamic Networks Myunghwan Kim, Jure Leskovec
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