Book
Advances in Neural Information Processing Systems 29 (NIPS 2016)
Edited by:
D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett
- Eliciting Categorical Data for Optimal Aggregation Chien-Ju Ho, Rafael Frongillo, Yiling Chen
- A Locally Adaptive Normal Distribution Georgios Arvanitidis, Lars K. Hansen, Søren Hauberg
- Tagger: Deep Unsupervised Perceptual Grouping Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hao, Harri Valpola, Jürgen Schmidhuber
- Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics Wei-Shou Hsu, Pascal Poupart
- Conditional Generative Moment-Matching Networks Yong Ren, Jun Zhu, Jialian Li, Yucen Luo
- Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks Hao Wang, Xingjian SHI, Dit-Yan Yeung
- Bayesian Intermittent Demand Forecasting for Large Inventories Matthias W. Seeger, David Salinas, Valentin Flunkert
- Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks Tianfan Xue, Jiajun Wu, Katherine Bouman, Bill Freeman
- Achieving budget-optimality with adaptive schemes in crowdsourcing Ashish Khetan, Sewoong Oh
- Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël RICHARD
- Generating Videos with Scene Dynamics Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
- Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods Andrej Risteski, Yuanzhi Li
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst
- Fast Distributed Submodular Cover: Public-Private Data Summarization Baharan Mirzasoleiman, Morteza Zadimoghaddam, Amin Karbasi
- Exponential Family Embeddings Maja Rudolph, Francisco Ruiz, Stephan Mandt, David Blei
- A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics William Hoiles, Mihaela van der Schaar
- Integrated perception with recurrent multi-task neural networks Hakan Bilen, Andrea Vedaldi
- Dialog-based Language Learning Jason E. Weston
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Yarin Gal, Zoubin Ghahramani
- Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks Noah Apthorpe, Alexander Riordan, Robert Aguilar, Jan Homann, Yi Gu, David Tank, H. Sebastian Seung
- Convolutional Neural Fabrics Shreyas Saxena, Jakob Verbeek
- Budgeted stream-based active learning via adaptive submodular maximization Kaito Fujii, Hisashi Kashima
- An equivalence between high dimensional Bayes optimal inference and M-estimation Madhu Advani, Surya Ganguli
- A Sparse Interactive Model for Matrix Completion with Side Information Jin Lu, Guannan Liang, Jiangwen Sun, Jinbo Bi
- Bi-Objective Online Matching and Submodular Allocations Hossein Esfandiari, Nitish Korula, Vahab Mirrokni
- Interpretable Distribution Features with Maximum Testing Power Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton
- Finding significant combinations of features in the presence of categorical covariates Laetitia Papaxanthos, Felipe Llinares-López, Dean Bodenham, Karsten Borgwardt
- A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing Ming Lin, Jieping Ye
- Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction Jacob Steinhardt, Gregory Valiant, Moses Charikar
- Threshold Bandits, With and Without Censored Feedback Jacob D. Abernethy, Kareem Amin, Ruihao Zhu
- Variational Bayes on Monte Carlo Steroids Aditya Grover, Stefano Ermon
- Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models Juho Lee, Lancelot F. James, Seungjin Choi
- Maximal Sparsity with Deep Networks? Bo Xin, Yizhou Wang, Wen Gao, David Wipf, Baoyuan Wang
- Single-Image Depth Perception in the Wild Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng
- Single Pass PCA of Matrix Products Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis
- Optimal Sparse Linear Encoders and Sparse PCA Malik Magdon-Ismail, Christos Boutsidis
- Measuring the reliability of MCMC inference with bidirectional Monte Carlo Roger B. Grosse, Siddharth Ancha, Daniel M. Roy
- Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections Xiaojiao Mao, Chunhua Shen, Yu-Bin Yang
- On Valid Optimal Assignment Kernels and Applications to Graph Classification Nils M. Kriege, Pierre-Louis Giscard, Richard Wilson
- Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models Tomoharu Iwata, Makoto Yamada
- Optimal Architectures in a Solvable Model of Deep Networks Jonathan Kadmon, Haim Sompolinsky
- Efficient state-space modularization for planning: theory, behavioral and neural signatures Daniel McNamee, Daniel M. Wolpert, Mate Lengyel
- A Communication-Efficient Parallel Algorithm for Decision Tree Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu
- Supervised Word Mover's Distance Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger
- Fast and accurate spike sorting of high-channel count probes with KiloSort Marius Pachitariu, Nicholas A. Steinmetz, Shabnam N. Kadir, Matteo Carandini, Kenneth D. Harris
- Learning brain regions via large-scale online structured sparse dictionary learning Elvis DOHMATOB, Arthur Mensch, Gael Varoquaux, Bertrand Thirion
- Improving PAC Exploration Using the Median Of Means Jason Pazis, Ronald E. Parr, Jonathan P. How
- Active Nearest-Neighbor Learning in Metric Spaces Aryeh Kontorovich, Sivan Sabato, Ruth Urner
- Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs Yu-Xiong Wang, Martial Hebert
- Learning Bayesian networks with ancestral constraints Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi, Adnan Darwiche
- Exponential expressivity in deep neural networks through transient chaos Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli
- MetaGrad: Multiple Learning Rates in Online Learning Tim van Erven, Wouter M. Koolen
- Learning under uncertainty: a comparison between R-W and Bayesian approach He Huang, Martin Paulus
- End-to-End Goal-Driven Web Navigation Rodrigo Nogueira, Kyunghyun Cho
- Higher-Order Factorization Machines Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata
- Efficient Second Order Online Learning by Sketching Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford
- Professor Forcing: A New Algorithm for Training Recurrent Networks Alex M. Lamb, Anirudh Goyal ALIAS PARTH GOYAL, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio
- Deep ADMM-Net for Compressive Sensing MRI yan yang, Jian Sun, Huibin Li, Zongben Xu
- Adaptive Averaging in Accelerated Descent Dynamics Walid Krichene, Alexandre Bayen, Peter L. Bartlett
- Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis Yoshinobu Kawahara
- Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani
- Hardness of Online Sleeping Combinatorial Optimization Problems Satyen Kale, Chansoo Lee, David Pal
- Density Estimation via Discrepancy Based Adaptive Sequential Partition Dangna Li, Kun Yang, Wing Hung Wong
- Quantized Random Projections and Non-Linear Estimation of Cosine Similarity Ping Li, Michael Mitzenmacher, Martin Slawski
- Algorithms and matching lower bounds for approximately-convex optimization Andrej Risteski, Yuanzhi Li
- The Parallel Knowledge Gradient Method for Batch Bayesian Optimization Jian Wu, Peter Frazier
- Edge-exchangeable graphs and sparsity Diana Cai, Trevor Campbell, Tamara Broderick
- Stochastic Variance Reduction Methods for Saddle-Point Problems Balamurugan Palaniappan, Francis Bach
- A Probabilistic Model of Social Decision Making based on Reward Maximization Koosha Khalvati, Seongmin A. Park, Jean-Claude Dreher, Rajesh PN Rao
- Bootstrap Model Aggregation for Distributed Statistical Learning JUN HAN, Qiang Liu
- Unsupervised Learning of 3D Structure from Images Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess
- beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data Valentina Zantedeschi, Rémi Emonet, Marc Sebban
- Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods Lev Bogolubsky, Pavel Dvurechenskii, Alexander Gasnikov, Gleb Gusev, Yurii Nesterov, Andrei M. Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii
- Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods Antoine Gautier, Quynh N. Nguyen, Matthias Hein
- Optimal Black-Box Reductions Between Optimization Objectives Zeyuan Allen-Zhu, Elad Hazan
- Sequential Neural Models with Stochastic Layers Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther
- Iterative Refinement of the Approximate Posterior for Directed Belief Networks Devon Hjelm, Russ R. Salakhutdinov, Kyunghyun Cho, Nebojsa Jojic, Vince Calhoun, Junyoung Chung
- Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles Stefan Lee, Senthil Purushwalkam Shiva Prakash, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra
- Learning shape correspondence with anisotropic convolutional neural networks Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael Bronstein
- Learning Tree Structured Potential Games Vikas Garg, Tommi Jaakkola
- RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, Walter Stewart
- PAC Reinforcement Learning with Rich Observations Akshay Krishnamurthy, Alekh Agarwal, John Langford
- Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data Xinghua Lou, Ken Kansky, Wolfgang Lehrach, CC Laan, Bhaskara Marthi, D. Phoenix, Dileep George
- Probabilistic Linear Multistep Methods Onur Teymur, Kostas Zygalakis, Ben Calderhead
- Computational and Statistical Tradeoffs in Learning to Rank Ashish Khetan, Sewoong Oh
- Split LBI: An Iterative Regularization Path with Structural Sparsity Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao
- Incremental Variational Sparse Gaussian Process Regression Ching-An Cheng, Byron Boots
- Sublinear Time Orthogonal Tensor Decomposition Zhao Song, David Woodruff, Huan Zhang
- Mapping Estimation for Discrete Optimal Transport Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard
- Greedy Feature Construction Dino Oglic, Thomas Gärtner
- Dynamic Network Surgery for Efficient DNNs Yiwen Guo, Anbang Yao, Yurong Chen
- Graph Clustering: Block-models and model free results Yali Wan, Marina Meila
- CMA-ES with Optimal Covariance Update and Storage Complexity Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel
- Feature selection in functional data classification with recursive maxima hunting José L. Torrecilla, Alberto Suárez
- Cyclades: Conflict-free Asynchronous Machine Learning Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré
- Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alexander J. Smola
- Spectral Learning of Dynamic Systems from Nonequilibrium Data Hao Wu, Frank Noe
- Dimension-Free Iteration Complexity of Finite Sum Optimization Problems Yossi Arjevani, Ohad Shamir
- Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition Seyed Hamidreza Kasaei, Ana Maria Tomé, Luís Seabra Lopes
- Active Learning with Oracle Epiphany Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Jerry Zhu
- Stochastic Optimization for Large-scale Optimal Transport Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis Bach
- The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM Damek Davis, Brent Edmunds, Madeleine Udell
- Coresets for Scalable Bayesian Logistic Regression Jonathan Huggins, Trevor Campbell, Tamara Broderick
- Sorting out typicality with the inverse moment matrix SOS polynomial Edouard Pauwels, Jean B. Lasserre
- The Multi-fidelity Multi-armed Bandit Kirthevasan Kandasamy, Gautam Dasarathy, Barnabas Poczos, Jeff Schneider
- Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition Theodore Bluche
- k*-Nearest Neighbors: From Global to Local Oren Anava, Kfir Levy
- Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images Vladimir Golkov, Marcin J. Skwark, Antonij Golkov, Alexey Dosovitskiy, Thomas Brox, Jens Meiler, Daniel Cremers
- Learnable Visual Markers Oleg Grinchuk, Vadim Lebedev, Victor Lempitsky
- Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators Shashank Singh, Barnabas Poczos
- Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution Christopher Lynn, Daniel D. Lee
- Regret Bounds for Non-decomposable Metrics with Missing Labels Nagarajan Natarajan, Prateek Jain
- Adaptive Concentration Inequalities for Sequential Decision Problems Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
- Refined Lower Bounds for Adversarial Bandits Sébastien Gerchinovitz, Tor Lattimore
- Structure-Blind Signal Recovery Dmitry Ostrovsky, Zaid Harchaoui, Anatoli Juditsky, Arkadi S. Nemirovski
- Reward Augmented Maximum Likelihood for Neural Structured Prediction Mohammad Norouzi, Samy Bengio, zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans
- Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen
- An Online Sequence-to-Sequence Model Using Partial Conditioning Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio
- Interaction Networks for Learning about Objects, Relations and Physics Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, koray kavukcuoglu
- Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian Victor Picheny, Robert B. Gramacy, Stefan Wild, Sebastien Le Digabel
- Combinatorial Energy Learning for Image Segmentation Jeremy B. Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel
- Bayesian Optimization for Probabilistic Programs Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank Wood
- Coin Betting and Parameter-Free Online Learning Francesco Orabona, David Pal
- Learning Deep Embeddings with Histogram Loss Evgeniya Ustinova, Victor Lempitsky
- An Efficient Streaming Algorithm for the Submodular Cover Problem Ashkan Norouzi-Fard, Abbas Bazzi, Ilija Bogunovic, Marwa El Halabi, Ya-Ping Hsieh, Volkan Cevher
- Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing Farshad Lahouti, Babak Hassibi
- Beyond Exchangeability: The Chinese Voting Process Moontae Lee, Seok Hyun Jin, David Mimno
- Robust Spectral Detection of Global Structures in the Data by Learning a Regularization Pan Zhang
- Optimal spectral transportation with application to music transcription Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya
- MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild Gregory Rogez, Cordelia Schmid
- A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ Dennis Wei
- CNNpack: Packing Convolutional Neural Networks in the Frequency Domain Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, Chao Xu
- Feature-distributed sparse regression: a screen-and-clean approach Jiyan Yang, Michael W. Mahoney, Michael Saunders, Yuekai Sun
- Generating Images with Perceptual Similarity Metrics based on Deep Networks Alexey Dosovitskiy, Thomas Brox
- Residual Networks Behave Like Ensembles of Relatively Shallow Networks Andreas Veit, Michael J. Wilber, Serge Belongie
- Low-Rank Regression with Tensor Responses Guillaume Rabusseau, Hachem Kadri
- Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent Chi Jin, Sham M. Kakade, Praneeth Netrapalli
- Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
- Diffusion-Convolutional Neural Networks James Atwood, Don Towsley
- Completely random measures for modelling block-structured sparse networks Tue Herlau, Mikkel N. Schmidt, Morten Mørup
- Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph Wang, Venkatesh Saligrama
- Synthesis of MCMC and Belief Propagation Sung-Soo Ahn, Michael Chertkov, Jinwoo Shin
- Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics Travis Monk, Cristina Savin, Jörg Lücke
- Disease Trajectory Maps Peter Schulam, Raman Arora
- Bayesian optimization for automated model selection Gustavo Malkomes, Charles Schaff, Roman Garnett
- Designing smoothing functions for improved worst-case competitive ratio in online optimization Reza Eghbali, Maryam Fazel
- Towards Unifying Hamiltonian Monte Carlo and Slice Sampling Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin
- Multi-step learning and underlying structure in statistical models Maia Fraser
- The non-convex Burer-Monteiro approach works on smooth semidefinite programs Nicolas Boumal, Vlad Voroninski, Afonso Bandeira
- Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre Bayen
- Spatiotemporal Residual Networks for Video Action Recognition Christoph Feichtenhofer, Axel Pinz, Richard Wildes
- Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes Jack Rae, Jonathan J. Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Timothy Lillicrap
- Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah Goodman
- Reconstructing Parameters of Spreading Models from Partial Observations Andrey Lokhov
- Tracking the Best Expert in Non-stationary Stochastic Environments Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
- Statistical Inference for Pairwise Graphical Models Using Score Matching Ming Yu, Mladen Kolar, Varun Gupta
- Learning Structured Sparsity in Deep Neural Networks Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li
- Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis Weiran Wang, Jialei Wang, Dan Garber, Dan Garber, Nati Srebro
- How Deep is the Feature Analysis underlying Rapid Visual Categorization? Sven Eberhardt, Jonah G. Cader, Thomas Serre
- Regret of Queueing Bandits Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai
- Dual Space Gradient Descent for Online Learning Trung Le, Tu Nguyen, Vu Nguyen, Dinh Phung
- Asynchronous Parallel Greedy Coordinate Descent Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, James Demmel, Cho-Jui Hsieh
- Catching heuristics are optimal control policies Boris Belousov, Gerhard Neumann, Constantin A. Rothkopf, Jan R. Peters
- Online Pricing with Strategic and Patient Buyers Michal Feldman, Tomer Koren, Roi Livni, Yishay Mansour, Aviv Zohar
- Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, Josh Tenenbaum
- Optimistic Gittins Indices Eli Gutin, Vivek Farias
- Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences Hongseok Namkoong, John C. Duchi
- Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation Weihao Gao, Sewoong Oh, Pramod Viswanath
- Domain Separation Networks Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan
- A Probabilistic Programming Approach To Probabilistic Data Analysis Feras Saad, Vikash K. Mansinghka
- Assortment Optimization Under the Mallows model Antoine Desir, Vineet Goyal, Srikanth Jagabathula, Danny Segev
- An algorithm for L1 nearest neighbor search via monotonic embedding Xinan Wang, Sanjoy Dasgupta
- Multi-armed Bandits: Competing with Optimal Sequences Zohar S. Karnin, Oren Anava
- NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang
- Probing the Compositionality of Intuitive Functions Eric Schulz, Josh Tenenbaum, David K. Duvenaud, Maarten Speekenbrink, Samuel J. Gershman
- Identification and Overidentification of Linear Structural Equation Models Bryant Chen
- An Architecture for Deep, Hierarchical Generative Models Philip Bachman
- Towards Conceptual Compression Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra
- Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters Zeyuan Allen-Zhu, Yang Yuan, Karthik Sridharan
- Consistent Kernel Mean Estimation for Functions of Random Variables Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O. Tolstikhin, Bernhard Schölkopf
- Hierarchical Clustering via Spreading Metrics Aurko Roy, Sebastian Pokutta
- Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation Jianxu Chen, Lin Yang, Yizhe Zhang, Mark Alber, Danny Z. Chen
- SDP Relaxation with Randomized Rounding for Energy Disaggregation Kiarash Shaloudegi, András György, Csaba Szepesvari, Wilsun Xu
- Finite Sample Prediction and Recovery Bounds for Ordinal Embedding Lalit Jain, Kevin G. Jamieson, Rob Nowak
- Search Improves Label for Active Learning Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
- A Simple Practical Accelerated Method for Finite Sums Aaron Defazio
- Coupled Generative Adversarial Networks Ming-Yu Liu, Oncel Tuzel
- Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf
- Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model Zhen Xu, Wen Dong, Sargur N. Srihari
- Multiple-Play Bandits in the Position-Based Model Paul Lagrée, Claire Vernade, Olivier Cappe
- Learning values across many orders of magnitude Hado P. van Hasselt, Arthur Guez, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver
- Attend, Infer, Repeat: Fast Scene Understanding with Generative Models S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, koray kavukcuoglu, Geoffrey E. Hinton
- Supervised Learning with Tensor Networks Edwin Stoudenmire, David J. Schwab
- Structured Prediction Theory Based on Factor Graph Complexity Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang
- The Multiple Quantile Graphical Model Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
- Orthogonal Random Features Felix Xinnan X. Yu, Ananda Theertha Suresh, Krzysztof M. Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar
- Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions Yichen Wang, Nan Du, Rakshit Trivedi, Le Song
- Convex Two-Layer Modeling with Latent Structure Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen
- Online Convex Optimization with Unconstrained Domains and Losses Ashok Cutkosky, Kwabena A. Boahen
- GAP Safe Screening Rules for Sparse-Group Lasso Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
- Local Similarity-Aware Deep Feature Embedding Chen Huang, Chen Change Loy, Xiaoou Tang
- Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvari
- Learning Multiagent Communication with Backpropagation Sainbayar Sukhbaatar, arthur szlam, Rob Fergus
- Sub-sampled Newton Methods with Non-uniform Sampling Peng Xu, Jiyan Yang, Fred Roosta, Christopher Ré, Michael W. Mahoney
- Examples are not enough, learn to criticize! Criticism for Interpretability Been Kim, Rajiv Khanna, Oluwasanmi O. Koyejo
- R-FCN: Object Detection via Region-based Fully Convolutional Networks Jifeng Dai, Yi Li, Kaiming He, Jian Sun
- Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel
- A Powerful Generative Model Using Random Weights for the Deep Image Representation Kun He, Yan Wang, John Hopcroft
- Privacy Odometers and Filters: Pay-as-you-Go Composition Ryan M. Rogers, Aaron Roth, Jonathan Ullman, Salil Vadhan
- More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu
- Supervised learning through the lens of compression Ofir David, Shay Moran, Amir Yehudayoff
- Sparse Support Recovery with Non-smooth Loss Functions Kévin Degraux, Gabriel Peyré, Jalal Fadili, Laurent Jacques
- Tractable Operations for Arithmetic Circuits of Probabilistic Models Yujia Shen, Arthur Choi, Adnan Darwiche
- Dual Learning for Machine Translation Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma
- Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow Gang Wang, Georgios Giannakis
- Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences Daniel Neil, Michael Pfeiffer, Shih-Chii Liu
- Only H is left: Near-tight Episodic PAC RL
- Stochastic Three-Composite Convex Minimization Alp Yurtsever, Bang Cong Vu, Volkan Cevher
- Synthesizing the preferred inputs for neurons in neural networks via deep generator networks Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune
- Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach Remi Lam, Karen Willcox, David H. Wolpert
- Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff Schneider, Barnabas Poczos
- Learning Parametric Sparse Models for Image Super-Resolution Yongbo Li, Weisheng Dong, Xuemei Xie, GUANGMING Shi, Xin Li, Donglai Xu
- Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula jean barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborová
- Large Margin Discriminant Dimensionality Reduction in Prediction Space Mohammad Saberian, Jose Costa Pereira, Can Xu, Jian Yang, Nuno Nvasconcelos
- Fast learning rates with heavy-tailed losses Vu C. Dinh, Lam S. Ho, Binh Nguyen, Duy Nguyen
- Dynamic matrix recovery from incomplete observations under an exact low-rank constraint Liangbei Xu, Mark Davenport
- Tight Complexity Bounds for Optimizing Composite Objectives Blake E. Woodworth, Nati Srebro
- A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control Ivan Herreros, Xerxes Arsiwalla, Paul Verschure
- Verification Based Solution for Structured MAB Problems Zohar S. Karnin
- SURGE: Surface Regularized Geometry Estimation from a Single Image Peng Wang, Xiaohui Shen, Bryan Russell, Scott Cohen, Brian Price, Alan L. Yuille
- CliqueCNN: Deep Unsupervised Exemplar Learning Miguel A. Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, Bjorn Ommer
- Computing and maximizing influence in linear threshold and triggering models Justin T. Khim, Varun Jog, Po-Ling Loh
- Data Programming: Creating Large Training Sets, Quickly Alexander J. Ratner, Christopher M. De Sa, Sen Wu, Daniel Selsam, Christopher Ré
- Flexible Models for Microclustering with Application to Entity Resolution Brenda Betancourt, Giacomo Zanella, Jeffrey W. Miller, Hanna Wallach, Abbas Zaidi, Rebecca C. Steorts
- Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah
- An ensemble diversity approach to supervised binary hashing Miguel A. Carreira-Perpinan, Ramin Raziperchikolaei
- Learning Influence Functions from Incomplete Observations Xinran He, Ke Xu, David Kempe, Yan Liu
- Backprop KF: Learning Discriminative Deterministic State Estimators Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel
- On the Recursive Teaching Dimension of VC Classes Xi Chen, Xi Chen, Yu Cheng, Bo Tang
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