Book
Advances in Neural Information Processing Systems 28 (NIPS 2015)
Edited by:
C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett
- Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling Zheng Qu, Peter Richtarik, Tong Zhang
- Associative Memory via a Sparse Recovery Model Arya Mazumdar, Ankit Singh Rawat
- Policy Gradient for Coherent Risk Measures Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor
- A fast, universal algorithm to learn parametric nonlinear embeddings Miguel A. Carreira-Perpinan, Max Vladymyrov
- Stochastic Online Greedy Learning with Semi-bandit Feedbacks Tian Lin, Jian Li, Wei Chen
- SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals Qing Sun, Dhruv Batra
- Robust Portfolio Optimization Huitong Qiu, Fang Han, Han Liu, Brian Caffo
- Top-k Multiclass SVM Maksim Lapin, Matthias Hein, Bernt Schiele
- Less is More: Nyström Computational Regularization Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco
- Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models Akihiro Kishimoto, Radu Marinescu, Adi Botea
- Differentially private subspace clustering Yining Wang, Yu-Xiang Wang, Aarti Singh
- Matrix Completion with Noisy Side Information Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon
- Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, james m. robins
- Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Gael Varoquaux
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian SHI, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun WOO
- Infinite Factorial Dynamical Model Isabel Valera, Francisco Ruiz, Lennart Svensson, Fernando Perez-Cruz
- Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation Scott Linderman, Matthew J. Johnson, Ryan P. Adams
- Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
- Data Generation as Sequential Decision Making Philip Bachman, Doina Precup
- Online Gradient Boosting Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo
- Optimal Ridge Detection using Coverage Risk Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman
- A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding Yuval Harel, Ron Meir, Manfred Opper
- Barrier Frank-Wolfe for Marginal Inference Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag
- Combinatorial Bandits Revisited Richard Combes, Mohammad Sadegh Talebi Mazraeh Shahi, Alexandre Proutiere, marc lelarge
- Efficient and Parsimonious Agnostic Active Learning Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
- Policy Evaluation Using the Ω-Return Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris
- Bayesian Optimization with Exponential Convergence Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez
- Statistical Model Criticism using Kernel Two Sample Tests James R. Lloyd, Zoubin Ghahramani
- Attention-Based Models for Speech Recognition Jan K. Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, Yoshua Bengio
- Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis Jimei Yang, Scott E. Reed, Ming-Hsuan Yang, Honglak Lee
- Backpropagation for Energy-Efficient Neuromorphic Computing Steve K. Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, Dharmendra S. Modha
- Efficient and Robust Automated Machine Learning Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter
- Time-Sensitive Recommendation From Recurrent User Activities Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song
- Local Expectation Gradients for Black Box Variational Inference Michalis Titsias RC AUEB, Miguel Lázaro-Gredilla
- Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy Marylou Gabrie, Eric W. Tramel, Florent Krzakala
- High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu
- Learning Continuous Control Policies by Stochastic Value Gradients Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, Yuval Tassa
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
- Efficient Non-greedy Optimization of Decision Trees Mohammad Norouzi, Maxwell Collins, Matthew A. Johnson, David J. Fleet, Pushmeet Kohli
- Learning with Incremental Iterative Regularization Lorenzo Rosasco, Silvia Villa
- Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets Justin Domke
- Sampling from Probabilistic Submodular Models Alkis Gotovos, Hamed Hassani, Andreas Krause
- A class of network models recoverable by spectral clustering Yali Wan, Marina Meila
- Closed-form Estimators for High-dimensional Generalized Linear Models Eunho Yang, Aurelie C. Lozano, Pradeep K. Ravikumar
- Expressing an Image Stream with a Sequence of Natural Sentences Cesc C. Park, Gunhee Kim
- Learning spatiotemporal trajectories from manifold-valued longitudinal data Jean-Baptiste SCHIRATTI, Stéphanie ALLASSONNIERE, Olivier Colliot, Stanley DURRLEMAN
- Fast Classification Rates for High-dimensional Gaussian Generative Models Tianyang Li, Adarsh Prasad, Pradeep K. Ravikumar
- Adaptive Online Learning Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
- Robust Regression via Hard Thresholding Kush Bhatia, Prateek Jain, Purushottam Kar
- b-bit Marginal Regression Martin Slawski, Ping Li
- Spectral Norm Regularization of Orthonormal Representations for Graph Transduction Rakesh Shivanna, Bibaswan K. Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis Bach
- Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition Cameron Musco, Christopher Musco
- Optimal Testing for Properties of Distributions Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath
- Combinatorial Cascading Bandits Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari
- Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process Ye Wang, David B. Dunson
- Training Very Deep Networks Rupesh K. Srivastava, Klaus Greff, Jürgen Schmidhuber
- Fast and Memory Optimal Low-Rank Matrix Approximation Se-Young Yun, marc lelarge, Alexandre Proutiere
- Character-level Convolutional Networks for Text Classification Xiang Zhang, Junbo Zhao, Yann LeCun
- Interactive Control of Diverse Complex Characters with Neural Networks Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, Emanuel V. Todorov
- Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets Armand Joulin, Tomas Mikolov
- Grammar as a Foreign Language Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton
- Practical and Optimal LSH for Angular Distance Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt
- GP Kernels for Cross-Spectrum Analysis Kyle R. Ulrich, David E. Carlson, Kafui Dzirasa, Lawrence Carin
- A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure Peter Schulam, Suchi Saria
- Local Smoothness in Variance Reduced Optimization Daniel Vainsencher, Han Liu, Tong Zhang
- Unlocking neural population non-stationarities using hierarchical dynamics models Mijung Park, Gergo Bohner, Jakob H. Macke
- Pointer Networks Oriol Vinyals, Meire Fortunato, Navdeep Jaitly
- Fast and Accurate Inference of Plackett–Luce Models Lucas Maystre, Matthias Grossglauser
- Learning Bayesian Networks with Thousands of Variables Mauro Scanagatta, Cassio P. de Campos, Giorgio Corani, Marco Zaffalon
- Differentially Private Learning of Structured Discrete Distributions Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt
- Generative Image Modeling Using Spatial LSTMs Lucas Theis, Matthias Bethge
- Sparse PCA via Bipartite Matchings Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis
- Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents Mithun Chakraborty, Sanmay Das
- Lifted Inference Rules With Constraints Happy Mittal, Anuj Mahajan, Vibhav G. Gogate, Parag Singla
- LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements CHRISTOS THRAMPOULIDIS, Ehsan Abbasi, Babak Hassibi
- Natural Neural Networks Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, koray kavukcuoglu
- Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models Michael C. Hughes, William T. Stephenson, Erik Sudderth
- Inference for determinantal point processes without spectral knowledge Rémi Bardenet, Michalis Titsias RC AUEB
- A Bayesian Framework for Modeling Confidence in Perceptual Decision Making Koosha Khalvati, Rajesh PN Rao
- Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning Christoph Dann, Emma Brunskill
- Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits Huasen Wu, R. Srikant, Xin Liu, Chong Jiang
- Latent Bayesian melding for integrating individual and population models Mingjun Zhong, Nigel Goddard, Charles Sutton
- Regressive Virtual Metric Learning Michaël Perrot, Amaury Habrard
- Halting in Random Walk Kernels Mahito Sugiyama, Karsten Borgwardt
- Kullback-Leibler Proximal Variational Inference Mohammad Emtiyaz E. Khan, Pierre Baque, François Fleuret, Pascal Fua
- A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements Qinqing Zheng, John Lafferty
- On-the-Job Learning with Bayesian Decision Theory Keenon Werling, Arun Tejasvi Chaganty, Percy S. Liang, Christopher D. Manning
- Spatial Transformer Networks Max Jaderberg, Karen Simonyan, Andrew Zisserman, koray kavukcuoglu
- Precision-Recall-Gain Curves: PR Analysis Done Right Peter Flach, Meelis Kull
- Planar Ultrametrics for Image Segmentation Julian E. Yarkony, Charless Fowlkes
- Sparse Local Embeddings for Extreme Multi-label Classification Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain
- Super-Resolution Off the Grid Qingqing Huang, Sham M. Kakade
- Automatic Variational Inference in Stan Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei
- Extending Gossip Algorithms to Distributed Estimation of U-statistics Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon
- Model-Based Relative Entropy Stochastic Search Abbas Abdolmaleki, Rudolf Lioutikov, Jan R. Peters, Nuno Lau, Luis Pualo Reis, Gerhard Neumann
- Semi-supervised Learning with Ladder Networks Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, Tapani Raiko
- Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces Takashi Takenouchi, Takafumi Kanamori
- Enforcing balance allows local supervised learning in spiking recurrent networks Ralph Bourdoukan, Sophie Denève
- Online Learning for Adversaries with Memory: Price of Past Mistakes Oren Anava, Elad Hazan, Shie Mannor
- Streaming, Distributed Variational Inference for Bayesian Nonparametrics Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
- Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models Juho Lee, Seungjin Choi
- The Self-Normalized Estimator for Counterfactual Learning Adith Swaminathan, Thorsten Joachims
- Information-theoretic lower bounds for convex optimization with erroneous oracles Yaron Singer, Jan Vondrak
- A Nonconvex Optimization Framework for Low Rank Matrix Estimation Tuo Zhao, Zhaoran Wang, Han Liu
- Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction Kisuk Lee, Aleksandar Zlateski, Vishwanathan Ashwin, H. Sebastian Seung
- Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms Yunwen Lei, Urun Dogan, Alexander Binder, Marius Kloft
- Scalable Inference for Gaussian Process Models with Black-Box Likelihoods Amir Dezfouli, Edwin V. Bonilla
- M-Best-Diverse Labelings for Submodular Energies and Beyond Alexander Kirillov, Dmytro Shlezinger, Dmitry P. Vetrov, Carsten Rother, Bogdan Savchynskyy
- BinaryConnect: Training Deep Neural Networks with binary weights during propagations Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
- No-Regret Learning in Bayesian Games Jason Hartline, Vasilis Syrgkanis, Eva Tardos
- Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso Eunho Yang, Aurelie C. Lozano
- Parallelizing MCMC with Random Partition Trees Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson
- Convergence rates of sub-sampled Newton methods Murat A. Erdogdu, Andrea Montanari
- Learning Theory and Algorithms for Forecasting Non-stationary Time Series Vitaly Kuznetsov, Mehryar Mohri
- Equilibrated adaptive learning rates for non-convex optimization Yann Dauphin, Harm de Vries, Yoshua Bengio
- Optimal Linear Estimation under Unknown Nonlinear Transform Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu
- Analysis of Robust PCA via Local Incoherence Huishuai Zhang, Yi Zhou, Yingbin Liang
- Probabilistic Variational Bounds for Graphical Models Qiang Liu, John W. Fisher III, Alexander T. Ihler
- The Human Kernel Andrew G. Wilson, Christoph Dann, Chris Lucas, Eric P. Xing
- Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization Xiangru Lian, Yijun Huang, Yuncheng Li, Ji Liu
- Evaluating the statistical significance of biclusters Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
- Fast and Guaranteed Tensor Decomposition via Sketching Yining Wang, Hsiao-Yu Tung, Alexander J. Smola, Anima Anandkumar
- Inverse Reinforcement Learning with Locally Consistent Reward Functions Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
- A hybrid sampler for Poisson-Kingman mixture models Maria Lomeli, Stefano Favaro, Yee Whye Teh
- Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen, Aditya Menon, Robert C. Williamson
- Visalogy: Answering Visual Analogy Questions Fereshteh Sadeghi, C. Lawrence Zitnick, Ali Farhadi
- Cornering Stationary and Restless Mixing Bandits with Remix-UCB Julien Audiffren, Liva Ralaivola
- The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels Purnamrita Sarkar, Deepayan Chakrabarti, peter j. bickel
- On the Accuracy of Self-Normalized Log-Linear Models Jacob Andreas, Maxim Rabinovich, Michael I. Jordan, Dan Klein
- Learnability of Influence in Networks Harikrishna Narasimhan, David C. Parkes, Yaron Singer
- Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes Ryan J. Giordano, Tamara Broderick, Michael I. Jordan
- Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization Fredrik D. Johansson, Ankani Chattoraj, Chiranjib Bhattacharyya, Devdatt Dubhashi
- 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, Jianfeng Gao, Xinying Song, Li Deng
- Robust Spectral Inference for Joint Stochastic Matrix Factorization Moontae Lee, David Bindel, David Mimno
- Minimax Time Series Prediction Wouter M. Koolen, Alan Malek, Peter L. Bartlett, Yasin Abbasi Yadkori
- Learning to Segment Object Candidates Pedro O. O. Pinheiro, Ronan Collobert, Piotr Dollar
- A Theory of Decision Making Under Dynamic Context Michael Shvartsman, Vaibhav Srivastava, Jonathan D. Cohen
- Particle Gibbs for Infinite Hidden Markov Models Nilesh Tripuraneni, Shixiang (Shane) Gu, Hong Ge, Zoubin Ghahramani
- Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff Ofer Dekel, Ronen Eldan, Tomer Koren
- Compressive spectral embedding: sidestepping the SVD Dinesh Ramasamy, Upamanyu Madhow
- Winner-Take-All Autoencoders Alireza Makhzani, Brendan J. Frey
- Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen
- COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez, Shuang Li, Hongyuan Zha, Le Song
- Nearly Optimal Private LASSO Kunal Talwar, Abhradeep Guha Thakurta, Li Zhang
- Calibrated Structured Prediction Volodymyr Kuleshov, Percy S. Liang
- Spectral Representations for Convolutional Neural Networks Oren Rippel, Jasper Snoek, Ryan P. Adams
- On the consistency theory of high dimensional variable screening Xiangyu Wang, Chenlei Leng, David B. Dunson
- Revenue Optimization against Strategic Buyers Mehryar Mohri, Andres Munoz
- The Population Posterior and Bayesian Modeling on Streams James McInerney, Rajesh Ranganath, David Blei
- Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions Amar Shah, Zoubin Ghahramani
- The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors Dan Rosenbaum, Yair Weiss
- Fighting Bandits with a New Kind of Smoothness Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari
- Sparse and Low-Rank Tensor Decomposition Parikshit Shah, Nikhil Rao, Gongguo Tang
- Testing Closeness With Unequal Sized Samples Bhaswar Bhattacharya, Gregory Valiant
- Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach Yinlam Chow, Aviv Tamar, Shie Mannor, Marco Pavone
- Fast Lifted MAP Inference via Partitioning Somdeb Sarkhel, Parag Singla, Vibhav G. Gogate
- Algorithmic Stability and Uniform Generalization Ibrahim M. Alabdulmohsin
- Learning with Group Invariant Features: A Kernel Perspective. Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio
- Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number Janne H. Korhonen, Pekka Parviainen
- Convergence Analysis of Prediction Markets via Randomized Subspace Descent Rafael Frongillo, Mark D. Reid
- SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk Guillaume Papa, Stéphan Clémençon, Aurélien Bellet
- Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection Jie Wang, Jieping Ye
- From random walks to distances on unweighted graphs Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola
- Tensorizing Neural Networks Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, Dmitry P. Vetrov
- On some provably correct cases of variational inference for topic models Pranjal Awasthi, Andrej Risteski
- GAP Safe screening rules for sparse multi-task and multi-class models Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
- The Pareto Regret Frontier for Bandits Tor Lattimore
- Measuring Sample Quality with Stein's Method Jackson Gorham, Lester Mackey
- Predtron: A Family of Online Algorithms for General Prediction Problems Prateek Jain, Nagarajan Natarajan, Ambuj Tewari
- MCMC for Variationally Sparse Gaussian Processes James Hensman, Alexander G. Matthews, Maurizio Filippone, Zoubin Ghahramani
- Action-Conditional Video Prediction using Deep Networks in Atari Games Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder Singh
- Unified View of Matrix Completion under General Structural Constraints Suriya Gunasekar, Arindam Banerjee, Joydeep Ghosh
- When are Kalman-Filter Restless Bandits Indexable? Christopher R. Dance, Tomi Silander
- 3D Object Proposals for Accurate Object Class Detection Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G. Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun
- Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm Qinqing Zheng, Ryota Tomioka
- Biologically Inspired Dynamic Textures for Probing Motion Perception Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré
- Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos J. Storkey
- Semi-supervised Sequence Learning Andrew M. Dai, Quoc V. Le
- Non-convex Statistical Optimization for Sparse Tensor Graphical Model Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng
- Lifted Symmetry Detection and Breaking for MAP Inference Timothy Kopp, Parag Singla, Henry Kautz
- Private Graphon Estimation for Sparse Graphs Christian Borgs, Jennifer Chayes, Adam Smith
- Online Learning with Adversarial Delays Kent Quanrud, Daniel Khashabi
- Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems Yuxin Chen, Emmanuel Candes
- Statistical Topological Data Analysis - A Kernel Perspective Roland Kwitt, Stefan Huber, Marc Niethammer, Weili Lin, Ulrich Bauer
- A Structural Smoothing Framework For Robust Graph Comparison Pinar Yanardag, S.V.N. Vishwanathan
- Bandits with Unobserved Confounders: A Causal Approach Elias Bareinboim, Andrew Forney, Judea Pearl
- Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients Bo Xie, Yingyu Liang, Le Song
- A Market Framework for Eliciting Private Data Bo Waggoner, Rafael Frongillo, Jacob D. Abernethy
- A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice Tasuku Soma, Yuichi Yoshida
- Space-Time Local Embeddings Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet
- Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvari
- Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier
- Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier
- A Gaussian Process Model of Quasar Spectral Energy Distributions Andrew Miller, Albert Wu, Jeff Regier, Jon McAuliffe, Dustin Lang, Mr. Prabhat, David Schlegel, Ryan P. Adams
- Fast Convergence of Regularized Learning in Games Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
- Communication Complexity of Distributed Convex Learning and Optimization Yossi Arjevani, Ohad Shamir
- Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin
- Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci, Philipp Hennig
- Sample Complexity of Learning Mahalanobis Distance Metrics Nakul Verma, Kristin Branson
- Sample Efficient Path Integral Control under Uncertainty Yunpeng Pan, Evangelos Theodorou, Michail Kontitsis
- Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction Been Kim, Julie A. Shah, Finale Doshi-Velez
- Regularization Path of Cross-Validation Error Lower Bounds Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, Ichiro Takeuchi
- Reflection, Refraction, and Hamiltonian Monte Carlo Hadi Mohasel Afshar, Justin Domke
- Exploring Models and Data for Image Question Answering Mengye Ren, Ryan Kiros, Richard Zemel
- Learning structured densities via infinite dimensional exponential families Siqi Sun, Mladen Kolar, Jinbo Xu
- Streaming Min-max Hypergraph Partitioning Dan Alistarh, Jennifer Iglesias, Milan Vojnovic
- Principal Differences Analysis: Interpretable Characterization of Differences between Distributions Jonas W. Mueller, Tommi Jaakkola
- An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching Xiao Li, Kannan Ramchandran
- Efficient Thompson Sampling for Online Matrix-Factorization Recommendation Jaya Kawale, Hung H. Bui, Branislav Kveton, Long Tran-Thanh, Sanjay Chawla
- Structured Transforms for Small-Footprint Deep Learning Vikas Sindhwani, Tara Sainath, Sanjiv Kumar
- Linear Multi-Resource Allocation with Semi-Bandit Feedback Tor Lattimore, Koby Crammer, Csaba Szepesvari
- On the Optimality of Classifier Chain for Multi-label Classification Weiwei Liu, Ivor Tsang
- Consistent Multilabel Classification Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon
- A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks Cengiz Pehlevan, Dmitri Chklovskii
- Hidden Technical Debt in Machine Learning Systems D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison
- NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning Kevin G. Jamieson, Lalit Jain, Chris Fernandez, Nicholas J. Glattard, Rob Nowak
- A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA James R. Voss, Mikhail Belkin, Luis Rademacher
- Learning Structured Output Representation using Deep Conditional Generative Models Kihyuk Sohn, Honglak Lee, Xinchen Yan
- Estimating Mixture Models via Mixtures of Polynomials Sida Wang, Arun Tejasvi Chaganty, Percy S. Liang
- Online Learning with Gaussian Payoffs and Side Observations Yifan Wu, András György, Csaba Szepesvari
- Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltan Szabo, Arthur Gretton
- Approximating Sparse PCA from Incomplete Data ABHISEK KUNDU, Petros Drineas, Malik Magdon-Ismail
- Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices Martin Slawski, Ping Li, Matthias Hein
- Learning visual biases from human imagination Carl Vondrick, Hamed Pirsiavash, Aude Oliva, Antonio Torralba
- End-To-End Memory Networks Sainbayar Sukhbaatar, arthur szlam, Jason Weston, Rob Fergus
- Fast Distributed k-Center Clustering with Outliers on Massive Data Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley
- BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen
- Lifelong Learning with Non-i.i.d. Tasks Anastasia Pentina, Christoph H. Lampert
- Regularized EM Algorithms: A Unified Framework and Statistical Guarantees Xinyang Yi, Constantine Caramanis
- Beyond Convexity: Stochastic Quasi-Convex Optimization Elad Hazan, Kfir Levy, Shai Shalev-Shwartz
- Learning From Small Samples: An Analysis of Simple Decision Heuristics Özgür Şimşek, Marcus Buckmann
- Deep Temporal Sigmoid Belief Networks for Sequence Modeling Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin
- Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's Vitaly Feldman, Will Perkins, Santosh Vempala
- Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels Felipe Tobar, Thang D. Bui, Richard E. Turner
- Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems Ruoyu Sun, Mingyi Hong
- Community Detection via Measure Space Embedding Mark Kozdoba, Shie Mannor
- Color Constancy by Learning to Predict Chromaticity from Luminance Ayan Chakrabarti
- Sample Complexity Bounds for Iterative Stochastic Policy Optimization Marin Kobilarov
- Copeland Dueling Bandits Masrour Zoghi, Zohar S. Karnin, Shimon Whiteson, Maarten de Rijke
- Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms Christopher M. De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré, Christopher Ré
- High-dimensional neural spike train analysis with generalized count linear dynamical systems Yuanjun Gao, Lars Busing, Krishna V. Shenoy, John P. Cunningham
- Neural Adaptive Sequential Monte Carlo Shixiang (Shane) Gu, Zoubin Ghahramani, Richard E. Turner
- Supervised Learning for Dynamical System Learning Ahmed Hefny, Carlton Downey, Geoffrey J. Gordon
- A Complete Recipe for Stochastic Gradient MCMC Yi-An Ma, Tianqi Chen, Emily Fox
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- Rethinking LDA: Moment Matching for Discrete ICA Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien
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