TagsArticle Source
- Title: Volume 48: Proceedings of The 33rd International Conference on Machine Learning
- Editors: Maria Florina Balcan, Kilian Q. Weinberger
Proceedings of The 33rd International Conference on Machine Learning
Editors: Maria Florina Balcan, Kilian Q. Weinberger
Accepted Papers
No Oops, You Won’t Do It Again: Mechanisms for Self-correction in Crowdsourcing
Nihar Shah, Dengyong Zhou
[abs] [pdf] [supplementary]
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues
Nihar Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin Wainwright
[abs] [pdf] [supplementary]
Uprooting and Rerooting Graphical Models
Adrian Weller
[abs] [pdf] [supplementary]
A Deep Learning Approach to Unsupervised Ensemble Learning
Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph Chang, Yuval Kluger
[abs] [pdf] [supplementary]
Revisiting Semi-Supervised Learning with Graph Embeddings
Zhilin Yang, William Cohen, Ruslan Salakhudinov
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Chelsea Finn, Sergey Levine, Pieter Abbeel
[abs] [pdf] [supplementary]
Diversity-Promoting Bayesian Learning of Latent Variable Models
Pengtao Xie, Jun Zhu, Eric Xing
[abs] [pdf] [supplementary]
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
Kirthevasan Kandasamy, Yaoliang Yu
[abs] [pdf] [supplementary]
Hawkes Processes with Stochastic Excitations
Young Lee, Kar Wai Lim, Cheng Soon Ong
[abs] [pdf] [supplementary]
Data-driven Rank Breaking for Efficient Rank Aggregation
Ashish Khetan, Sewoong Oh
[abs] [pdf] [supplementary]
Dropout distillation
Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
[abs] [pdf] [supplementary]
Metadata-conscious anonymous messaging
Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath
[abs] [pdf] [supplementary]
The Teaching Dimension of Linear Learners
Ji Liu, Xiaojin Zhu, Hrag Ohannessian
[abs] [pdf] [supplementary]
Truthful Univariate Estimators
Ioannis Caragiannis, Ariel Procaccia, Nisarg Shah
[abs] [pdf] [supplementary]
Why Regularized Auto-Encoders learn Sparse Representation?
Devansh Arpit, Yingbo Zhou, Hung Ngo, Venu Govindaraju
[abs] [pdf] [supplementary]
k-variates++: more pluses in the k-means++
Richard Nock, Raphael Canyasse, Roksana Boreli, Frank Nielsen
[abs] [pdf] [supplementary]
Multi-Player Bandits – a Musical Chairs Approach
Jonathan Rosenski, Ohad Shamir, Liran Szlak
[abs] [pdf] [supplementary]
The Information Sieve
Greg Ver Steeg, Aram Galstyan
[abs] [pdf] [supplementary]
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin
Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, JingDong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse Engel, Linxi Fan, Christopher Fougner, Awni Hannun, Billy Jun, Tony Han, Patrick LeGresley, Xiangang Li, Libby Lin, Sharan Narang, Andrew Ng, Sherjil Ozair, Ryan Prenger, Sheng Qian, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Chong Wang, Yi Wang, Zhiqian Wang, Bo Xiao, Yan Xie, Dani Yogatama, Jun Zhan, Zhenyao Zhu
On the Consistency of Feature Selection With Lasso for Non-linear Targets
Yue Zhang, Weihong Guo, Soumya Ray
Minimum Regret Search for Single- and Multi-Task Optimization
Jan Hendrik Metzen
[abs] [pdf] [supplementary]
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, John Wernsing
The Variational Nystrom method for large-scale spectral problems
Max Vladymyrov, Miguel Carreira-Perpinan
[abs] [pdf] [supplementary]
Multi-Bias Non-linear Activation in Deep Neural Networks
Hongyang Li, Wanli Ouyang, Xiaogang Wang
Asymmetric Multi-task Learning Based on Task Relatedness and Loss
Giwoong Lee, Eunho Yang, Sung ju Hwang
Accurate Robust and Efficient Error Estimation for Decision Trees
Lixin Fan
Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity
Ohad Shamir
[abs] [pdf] [supplementary]
Convergence of Stochastic Gradient Descent for PCA
Ohad Shamir
[abs] [pdf] [supplementary]
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data
Andrew Lan, Tom Goldstein, Richard Baraniuk, Christoph Studer
A Kernelized Stein Discrepancy for Goodness-of-fit Tests
Qiang Liu, Jason Lee, Michael Jordan
[abs] [pdf] [supplementary]
Variable Elimination in the Fourier Domain
Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla, Bart Selman
[abs] [pdf] [supplementary]
Low-Rank Matrix Approximation with Stability
Dongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang, Stephen Chu
Linking losses for density ratio and class-probability estimation
Aditya Menon, Cheng Soon Ong
[abs] [pdf] [supplementary]
Stochastic Variance Reduction for Nonconvex Optimization
Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alex Smola
[abs] [pdf] [supplementary]
Hierarchical Variational Models
Rajesh Ranganath, Dustin Tran, David Blei
[abs] [pdf] [supplementary]
Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams
Roy Adams, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, Benjamin Marlin
Binary embeddings with structured hashed projections
Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun
[abs] [pdf] [supplementary]
A Variational Analysis of Stochastic Gradient Algorithms
Stephan Mandt, Matthew Hoffman, David Blei
[abs] [pdf] [supplementary]
Adaptive Sampling for SGD by Exploiting Side Information
Siddharth Gopal
Learning from Multiway Data: Simple and Efficient Tensor Regression
Rose Yu, Yan Liu
A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models
Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low
[abs] [pdf] [supplementary]
Online Stochastic Linear Optimization under One-bit Feedback
Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-hua Zhou
[abs] [pdf] [supplementary]
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
Rodolphe Jenatton, Jim Huang, Cedric Archambeau
Actively Learning Hemimetrics with Applications to Eliciting User Preferences
Adish Singla, Sebastian Tschiatschek, Andreas Krause
Learning Simple Algorithms from Examples
Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus
Learning Physical Intuition of Block Towers by Example
Adam Lerer, Sam Gross, Rob Fergus
Structure Learning of Partitioned Markov Networks
Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu
[abs] [pdf] [supplementary]
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient
Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi
[abs] [pdf] [supplementary]
Beyond CCA: Moment Matching for Multi-View Models
Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien
[abs] [pdf] [supplementary]
Fast methods for estimating the Numerical rank of large matrices
Shashanka Ubaru, Yousef Saad
[abs] [pdf] [supplementary]
Unsupervised Deep Embedding for Clustering Analysis
Junyuan Xie, Ross Girshick, Ali Farhadi
Efficient Private Empirical Risk Minimization for High-dimensional Learning
Shiva Prasad Kasiviswanathan, Hongxia Jin
[abs] [pdf] [supplementary]
Parameter Estimation for Generalized Thurstone Choice Models
Milan Vojnovic, Seyoung Yun
[abs] [pdf] [supplementary]
Large-Margin Softmax Loss for Convolutional Neural Networks
Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang
A Random Matrix Approach to Echo-State Neural Networks
Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings
Rie Johnson, Tong Zhang
Optimality of Belief Propagation for Crowdsourced Classification
Jungseul Ok, Sewoong Oh, Jinwoo Shin, Yung Yi
[abs] [pdf] [supplementary]
Stability of Controllers for Gaussian Process Forward Models
Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Anne Romer, Henner Schmidt, Jan Peters
Learning privately from multiparty data
Jihun Hamm, Yingjun Cao, Mikhail Belkin
[abs] [pdf] [supplementary]
Network Morphism
Tao Wei, Changhu Wang, Yong Rui, Chang Wen Chen
A Kronecker-factored approximate Fisher matrix for convolution layers
Roger Grosse, James Martens
[abs] [pdf] [supplementary]
Experimental Design on a Budget for Sparse Linear Models and Applications
Sathya Narayanan Ravi, Vamsi Ithapu, Sterling Johnson, Vikas Singh
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Dokania, Simon Lacoste-Julien
[abs] [pdf] [supplementary]
Exact Exponent in Optimal Rates for Crowdsourcing
Chao Gao, Yu Lu, Dengyong Zhou
[abs] [pdf] [supplementary]
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification
Yuting Zhang, Kibok Lee, Honglak Lee
[abs] [pdf] [supplementary]
Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit
Jie Shen, Ping Li, Huan Xu
[abs] [pdf] [supplementary]
A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization
Frank Curtis
[abs] [pdf] [supplementary]
Stochastic Quasi-Newton Langevin Monte Carlo
Umut Simsekli, Roland Badeau, Taylan Cemgil, Gaël Richard
[abs] [pdf] [supplementary]
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li
[abs] [pdf] [supplementary]
Fast Rate Analysis of Some Stochastic Optimization Algorithms
Chao Qu, Huan Xu, Chong jin Ong
[abs] [pdf] [supplementary]
Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing
Ke Li, Jitendra Malik
[abs] [pdf] [supplementary]
Smooth Imitation Learning for Online Sequence Prediction
Hoang Le, Andrew Kang, Yisong Yue, Peter Carr
[abs] [pdf] [supplementary]
Community Recovery in Graphs with Locality
Yuxin Chen, Govinda Kamath, Changho Suh, David Tse
Variance Reduction for Faster Non-Convex Optimization
Zeyuan Allen-Zhu, Elad Hazan
Loss factorization, weakly supervised learning and label noise robustness
Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni
[abs] [pdf] [supplementary]
Analysis of Deep Neural Networks with Extended Data Jacobian Matrix
Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff Bilmes, Matthai Plilipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, Ozlem Aslan
Doubly Decomposing Nonparametric Tensor Regression
Masaaki Imaizumi, Kohei Hayashi
[abs] [pdf] [supplementary]
Hyperparameter optimization with approximate gradient
Fabian Pedregosa
[abs] [pdf] [supplementary]
SDCA without Duality, Regularization, and Individual Convexity
Shai Shalev-Shwartz
Heteroscedastic Sequences: Beyond Gaussianity
Oren Anava, Shie Mannor
[abs] [pdf] [supplementary]
A Neural Autoregressive Approach to Collaborative Filtering
Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou
[abs] [pdf] [supplementary]
On the Quality of the Initial Basin in Overspecified Neural Networks
Itay Safran, Ohad Shamir
[abs] [pdf] [supplementary]
Primal-Dual Rates and Certificates
Celestine Dünner, Simone Forte, Martin Takac, Martin Jaggi
[abs] [pdf] [supplementary]
Minimizing the Maximal Loss: How and Why
Shai Shalev-Shwartz, Yonatan Wexler
[abs] [pdf] [supplementary]
The Information-Theoretic Requirements of Subspace Clustering with Missing Data
Daniel Pimentel-Alarcon, Robert Nowak
Online Learning with Feedback Graphs Without the Graphs
Alon Cohen, Tamir Hazan, Tomer Koren
PAC learning of Probabilistic Automaton based on the Method of Moments
Hadrien Glaude, Olivier Pietquin
[abs] [pdf] [supplementary]
Estimating Structured Vector Autoregressive Models
Igor Melnyk, Arindam Banerjee
[abs] [pdf] [supplementary]
Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends
Christopher Tosh
[abs] [pdf] [supplementary]
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda
[abs] [pdf] [supplementary]
A New PAC-Bayesian Perspective on Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
[abs] [pdf] [supplementary]
Correlation Clustering and Biclustering with Locally Bounded Errors
Gregory Puleo, Olgica Milenkovic
PAC Lower Bounds and Efficient Algorithms for The Max \(K\)-Armed Bandit Problem
Yahel David, Nahum Shimkin
[abs] [pdf] [supplementary]
A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation
Mohamed Elhoseiny, Tarek El-Gaaly, Amr Bakry, Ahmed Elgammal
[abs] [pdf] [supplementary]
BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces
Shane Carr, Roman Garnett, Cynthia Lo
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms
Yossi Arjevani, Ohad Shamir
[abs] [pdf] [supplementary]
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning
Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis Haupt
[abs] [pdf] [supplementary]
Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation
David Wipf
Fast k-means with accurate bounds
James Newling, Francois Fleuret
[abs] [pdf] [supplementary]
Boolean Matrix Factorization and Noisy Completion via Message Passing
Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner
[abs] [pdf] [supplementary]
Convolutional Rectifier Networks as Generalized Tensor Decompositions
Nadav Cohen, Amnon Shashua
[abs] [pdf] [supplementary]
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow
Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht
Anytime Exploration for Multi-armed Bandits using Confidence Information
Kwang-Sung Jun, Robert Nowak
[abs] [pdf] [supplementary]
Structured Prediction Energy Networks
David Belanger, Andrew McCallum
[abs] [pdf] [supplementary]
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time
Yuchen Zhang, Jason D. Lee, Michael I. Jordan
Compressive Spectral Clustering
Nicolas Tremblay, Gilles Puy, Remi Gribonval, Pierre Vandergheynst
[abs] [pdf] [supplementary]
Low-rank tensor completion: a Riemannian manifold preconditioning approach
Hiroyuki Kasai, Bamdev Mishra
[abs] [pdf] [supplementary]
Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow
Huishuai Zhang, Yuejie Chi, Yingbin Liang
[abs] [pdf] [supplementary]
Estimating Maximum Expected Value through Gaussian Approximation
Carlo D’Eramo, Marcello Restelli, Alessandro Nuara
[abs] [pdf] [supplementary]
Representational Similarity Learning with Application to Brain Networks
Urvashi Oswal, Christopher Cox, Matthew Lambon-Ralph, Timothy Rogers, Robert Nowak
[abs] [pdf] [supplementary]
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal, Zoubin Ghahramani
[abs] [pdf] [supplementary]
Generative Adversarial Text to Image Synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee
[abs] [pdf] [supplementary]
Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data
Sandhya Prabhakaran, Elham Azizi, Ambrose Carr, Dana Pe’er
[abs] [pdf] [supplementary]
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
Zeyuan Allen-Zhu, Yang Yuan
Sparse Parameter Recovery from Aggregated Data
Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo
[abs] [pdf] [supplementary]
Deep Structured Energy Based Models for Anomaly Detection
Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling
Zeyuan Allen-Zhu, Zheng Qu, Peter Richtarik, Yang Yuan
Unitary Evolution Recurrent Neural Networks
Martin Arjovsky, Amar Shah, Yoshua Bengio
Markov Latent Feature Models
Aonan Zhang, John Paisley
The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks
Yingfei Wang, Chu Wang, Warren Powell
[abs] [pdf] [supplementary]
A Simple and Provable Algorithm for Sparse Diagonal CCA
Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell Poldrack
[abs] [pdf] [supplementary]
Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods
Huikang Liu, Weijie Wu, Anthony Man-Cho So
[abs] [pdf] [supplementary]
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks
Devansh Arpit, Yingbo Zhou, Bhargava Kota, Venu Govindaraju
[abs] [pdf] [supplementary]
Learning to Generate with Memory
Chongxuan Li, Jun Zhu, Bo Zhang
[abs] [pdf] [supplementary]
Learning End-to-end Video Classification with Rank-Pooling
Basura Fernando, Stephen Gould
Learning to Filter with Predictive State Inference Machines
Wen Sun, Arun Venkatraman, Byron Boots, J.Andrew Bagnell
[abs] [pdf] [supplementary]
A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling
Mostafa Rahmani, Geroge Atia
DCM Bandits: Learning to Rank with Multiple Clicks
Sumeet Katariya, Branislav Kveton, Csaba Szepesvari, Zheng Wen
[abs] [pdf] [supplementary]
Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, Yoram Singer
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm
Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
[abs] [pdf] [supplementary]
Contextual Combinatorial Cascading Bandits
Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen
[abs] [pdf] [supplementary]
Conservative Bandits
Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvari
[abs] [pdf] [supplementary]
Variance-Reduced and Projection-Free Stochastic Optimization
Elad Hazan, Haipeng Luo
[abs] [pdf] [supplementary]
Factored Temporal Sigmoid Belief Networks for Sequence Learning
Jiaming Song, Zhe Gan, Lawrence Carin
[abs] [pdf] [supplementary]
False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking
QianQian Xu, Jiechao Xiong, Xiaochun Cao, Yuan Yao
[abs] [pdf] [supplementary]
Strongly-Typed Recurrent Neural Networks
David Balduzzi, Muhammad Ghifary
Distributed Clustering of Linear Bandits in Peer to Peer Networks
Nathan Korda, Balazs Szorenyi, Shuai Li
[abs] [pdf] [supplementary]
Collapsed Variational Inference for Sum-Product Networks
Han Zhao, Tameem Adel, Geoff Gordon, Brandon Amos
[abs] [pdf] [supplementary]
On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search
Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone
Benchmarking Deep Reinforcement Learning for Continuous Control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel
[abs] [pdf] [supplementary]
\(K\)-Means Clustering with Distributed Dimensions
Hu Ding, Yu Liu, Lingxiao Huang, Jian Li
[abs] [pdf] [supplementary]
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Dmitry Ulyanov, Vadim Lebedev, Andrea, Victor Lempitsky
[abs] [pdf] [supplementary]
Fast Constrained Submodular Maximization: Personalized Data Summarization
Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi
[abs] [pdf] [supplementary]
On the Statistical Limits of Convex Relaxations
Zhaoran Wang, Quanquan Gu, Han Liu
[abs] [pdf] [supplementary]
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher
[abs] [pdf] [supplementary]
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
Igor Colin, Aurelien Bellet, Joseph Salmon, Stéphan Clémençon
[abs] [pdf] [supplementary]
Solving Ridge Regression using Sketched Preconditioned SVRG
Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz
[abs] [pdf] [supplementary]
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
Prashanth L.A., Cheng Jie, Michael Fu, Steve Marcus, Csaba Szepesvari
Estimating Accuracy from Unlabeled Data: A Bayesian Approach
Emmanouil Antonios Platanios, Avinava Dubey, Tom Mitchell
[abs] [pdf] [supplementary]
Non-negative Matrix Factorization under Heavy Noise
Chiranjib Bhattacharya, Navin Goyal, Ravindran Kannan, Jagdeep Pani
[abs] [pdf] [supplementary]
Extreme F-measure Maximization using Sparse Probability Estimates
Kalina Jasinska, Krzysztof Dembczynski, Robert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hullermeier
[abs] [pdf] [supplementary]
Auxiliary Deep Generative Models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther
Importance Sampling Tree for Large-scale Empirical Expectation
Olivier Canevet, Cijo Jose, Francois Fleuret
[abs] [pdf] [supplementary]
Starting Small - Learning with Adaptive Sample Sizes
Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann
[abs] [pdf] [supplementary]
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Thang Bui, Daniel Hernandez-Lobato, Jose miguel Hernandez-Lobato, Yingzhen Li, Richard Turner
[abs] [pdf] [supplementary]
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Jovana Mitrovic, Dino Sejdinovic, Yee-Whye Teh
Predictive Entropy Search for Multi-objective Bayesian Optimization
Daniel Hernandez-Lobato, Jose miguel Hernandez-Lobato, Amar Shah, Ryan Adams
[abs] [pdf] [supplementary]
Rich Component Analysis
Rong Ge, James Zou
[abs] [pdf] [supplementary]
Black-Box Alpha Divergence Minimization
Jose miguel Hernandez-Lobato, Yingzhen Li, Mark Rowland, Thang Bui, Daniel Hernandez-Lobato, Richard Turner
[abs] [pdf] [supplementary]
One-Shot Generalization in Deep Generative Models
Danilo Rezende, Shakir, Ivo Danihelka, Karol Gregor, Daan Wierstra
Optimal Classification with Multivariate Losses
Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit Dhillon
[abs] [pdf] [supplementary]
A ranking approach to global optimization
Cedric Malherbe, Emile Contal, Nicolas Vayatis
[abs] [pdf] [supplementary]
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms
Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric Xing
[abs] [pdf] [supplementary]
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling
Christopher De Sa, Chris Re, Kunle Olukotun
[abs] [pdf] [supplementary]
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi
[abs] [pdf] [supplementary]
Anytime optimal algorithms in stochastic multi-armed bandits
Rémy Degenne, Vianney Perchet
[abs] [pdf] [supplementary]
Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design
William Hoiles, Mihaela van der Schaar
[abs] [pdf] [supplementary]
On collapsed representation of hierarchical Completely Random Measures
Gaurav Pandey, Ambedkar Dukkipati
[abs] [pdf] [supplementary]
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
Andre Martins, Ramon Astudillo
[abs] [pdf] [supplementary]
Black-box Optimization with a Politician
Sebastien Bubeck, Yin Tat Lee
Gaussian process nonparametric tensor estimator and its minimax optimality
Heishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami
[abs] [pdf] [supplementary]
No-Regret Algorithms for Heavy-Tailed Linear Bandits
Andres Munoz Medina, Scott Yang
Extended and Unscented Kitchen Sinks
Edwin Bonilla, Daniel Steinberg, Alistair Reid
[abs] [pdf] [supplementary]
Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization
Zhiqiang Xu, Peilin Zhao, Jianneng Cao, Xiaoli Li
[abs] [pdf] [supplementary]
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims
[abs] [pdf] [supplementary]
ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission
Jinsung Yoon, Ahmed Alaa, Scott Hu, Mihaela van der Schaar
[abs] [pdf] [supplementary]
An optimal algorithm for the Thresholding Bandit Problem
Andrea Locatelli, Maurilio Gutzeit, Alexandra Carpentier
[abs] [pdf] [supplementary]
Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching
Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
Christos Louizos, Max Welling
[abs] [pdf] [supplementary]
Learning Granger Causality for Hawkes Processes
Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha
[abs] [pdf] [supplementary]
Neural Variational Inference for Text Processing
Yishu Miao, Lei Yu, Phil Blunsom
[abs] [pdf] [supplementary]
Dictionary Learning for Massive Matrix Factorization
Arthur Mensch, Julien Mairal, Bertrand Thirion, Gael Varoquaux
Pixel Recurrent Neural Networks
Aaron Van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu
Why Most Decisions Are Easy in Tetris—And Perhaps in Other Sequential Decision Problems, As Well
Ozgur Simsek, Simon Algorta, Amit Kothiyal
Gaussian quadrature for matrix inverse forms with applications
Chengtao Li, Suvrit Sra, Stefanie Jegelka
[abs] [pdf] [supplementary]
Train and Test Tightness of LP Relaxations in Structured Prediction
Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag
[abs] [pdf] [supplementary]
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares
Raman Arora, Poorya Mianjy, Teodor Marinov
[abs] [pdf] [supplementary]
Hierarchical Compound Poisson Factorization
Mehmet Basbug, Barbara Engelhardt
[abs] [pdf] [supplementary]
Opponent Modeling in Deep Reinforcement Learning
He He, Jordan Boyd-Graber, Kevin Kwok, Hal Daumé III
No penalty no tears: Least squares in high-dimensional linear models
Xiangyu Wang, David Dunson, Chenlei Leng
[abs] [pdf] [supplementary]
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Zheng Qu, Peter Richtarik, Martin Takac, Olivier Fercoq
[abs] [pdf] [supplementary]
On Graduated Optimization for Stochastic Non-Convex Problems
Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz
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Meta-Learning with Memory-Augmented Neural Networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap
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The knockoff filter for FDR control in group-sparse and multitask regression
Ran Dai, Rina Barber
Softened Approximate Policy Iteration for Markov Games
Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin
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Stochastic Block BFGS: Squeezing More Curvature out of Data
Robert Gower, Donald Goldfarb, Peter Richtarik
Differential Geometric Regularization for Supervised Learning of Classifiers
Qinxun Bai, Steven Rosenberg, Zheng Wu, Stan Sclaroff
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Exploiting Cyclic Symmetry in Convolutional Neural Networks
Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu
Graying the black box: Understanding DQNs
Tom Zahavy, Nir Ben-Zrihem, Shie Mannor
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The Sum-Product Theorem: A Foundation for Learning Tractable Models
Abram Friesen, Pedro Domingos
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Pareto Frontier Learning with Expensive Correlated Objectives
Amar Shah, Zoubin Ghahramani
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Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
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A Simple and Strongly-Local Flow-Based Method for Cut Improvement
Nate Veldt, David Gleich, Michael Mahoney
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Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin
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Barron and Cover’s Theory in Supervised Learning and its Application to Lasso
Masanori Kawakita, Jun’ichi Takeuchi
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Nonparametric Canonical Correlation Analysis
Tomer Michaeli, Weiran Wang, Karen Livescu
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BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits
Alexander Rakhlin, Karthik Sridharan
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Associative Long Short-Term Memory
Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves
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Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
Persistence weighted Gaussian kernel for topological data analysis
Genki Kusano, Yasuaki Hiraoka, Kenji Fukumizu
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Learning Convolutional Neural Networks for Graphs
Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov
Persistent RNNs: Stashing Recurrent Weights On-Chip
Greg Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh
Recurrent Orthogonal Networks and Long-Memory Tasks
Mikael Henaff, Arthur Szlam, Yann LeCun
The Arrow of Time in Multivariate Time Series
Stefan Bauer, Bernhard Schölkopf, Jonas Peters
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Mixture Proportion Estimation via Kernel Embeddings of Distributions
Harish Ramaswamy, Clayton Scott, Ambuj Tewari
[abs] [pdf] [supplementary]
Fast DPP Sampling for Nystrom with Application to Kernel Methods
Chengtao Li, Stefanie Jegelka, Suvrit Sra
[abs] [pdf] [supplementary]
Complex Embeddings for Simple Link Prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, Guillaume Bouchard
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Interactive Bayesian Hierarchical Clustering
Sharad Vikram, Sanjoy Dasgupta
[abs] [pdf] [supplementary]
A Convolutional Attention Network for Extreme Summarization of Source Code
Miltiadis Allamanis, Hao Peng, Charles Sutton
How to Fake Multiply by a Gaussian Matrix
Michael Kapralov, Vamsi Potluru, David Woodruff
[abs] [pdf] [supplementary]
Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing
Marco Gaboardi, Hyun Lim, Ryan Rogers, Salil Vadhan
[abs] [pdf] [supplementary]
Pliable Rejection Sampling
Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric Maillard
[abs] [pdf] [supplementary]
Differentially Private Policy Evaluation
Borja Balle, Maziar Gomrokchi, Doina Precup
[abs] [pdf] [supplementary]
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
Philip Thomas, Emma Brunskill
[abs] [pdf] [supplementary]
Discrete Deep Feature Extraction: A Theory and New Architectures
Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Boelcskei
[abs] [pdf] [supplementary]
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert Schapire
[abs] [pdf] [supplementary]
Training Deep Neural Networks via Direct Loss Minimization
Yang Song, Alexander Schwing, Richard, Raquel Urtasun
[abs] [pdf] [supplementary]
Sequence to Sequence Training of CTC-RNNs with Partial Windowing
Kyuyeon Hwang, Wonyong Sung
[abs] [pdf] [supplementary]
Variational Inference for Monte Carlo Objectives
Andriy Mnih, Danilo Rezende
[abs] [pdf] [supplementary]
Hierarchical Decision Making In Electricity Grid Management
Gal Dalal, Elad Gilboa, Shie Mannor
Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization
Eric Balkanski, Baharan Mirzasoleiman, Andreas Krause, Yaron Singer
[abs] [pdf] [supplementary]
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee
Isotonic Hawkes Processes
Yichen Wang, Bo Xie, Nan Du, Le Song
[abs] [pdf] [supplementary]
Cross-Graph Learning of Multi-Relational Associations
Hanxiao Liu, Yiming Yang
Markov-modulated Marked Poisson Processes for Check-in Data
Jiangwei Pan, Vinayak Rao, Pankaj Agarwal, Alan Gelfand
Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference
Tudor Achim, Ashish Sabharwal, Stefano Ermon
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On the Power and Limits of Distance-Based Learning
Periklis Papakonstantinou, Jia Xu, Guang Yang
[abs] [pdf] [supplementary]
A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery
Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit Dhillon
[abs] [pdf] [supplementary]
Generalized Direct Change Estimation in Ising Model Structure
Farideh Fazayeli, Arindam Banerjee
Robust Principal Component Analysis with Side Information
Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit Dhillon
[abs] [pdf] [supplementary]
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation
Huan Gui, Jiawei Han, Quanquan Gu
[abs] [pdf] [supplementary]
Early and Reliable Event Detection Using Proximity Space Representation
Maxime Sangnier, Jerome Gauthier, Alain Rakotomamonjy
Stratified Sampling Meets Machine Learning
Edo Liberty, Kevin Lang, Konstantin Shmakov
Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model
Xinze Guan, Raviv Raich, Weng-Keen Wong
[abs] [pdf] [supplementary]
Generalization Properties and Implicit Regularization for Multiple Passes SGM
Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco
[abs] [pdf] [supplementary]
Principal Component Projection Without Principal Component Analysis
Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford
[abs] [pdf] [supplementary]
Recovery guarantee of weighted low-rank approximation via alternating minimization
Yuanzhi Li, Yingyu Liang, Andrej Risteski
[abs] [pdf] [supplementary]
Deconstructing the Ladder Network Architecture
Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua Bengio
[abs] [pdf] [supplementary]
Generalization and Exploration via Randomized Value Functions
Ian Osband, Benjamin Van Roy, Zheng Wen
[abs] [pdf] [supplementary]
Evasion and Hardening of Tree Ensemble Classifiers
Alex Kantchelian, J. D. Tygar, Anthony Joseph
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Dynamic Memory Networks for Visual and Textual Question Answering
Caiming Xiong, Stephen Merity, Richard Socher
Estimating Cosmological Parameters from the Dark Matter Distribution
Siamak Ravanbakhsh, Junier Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff Schneider, Barnabas Poczos
[abs] [pdf] [supplementary]
Learning Population-Level Diffusions with Generative RNNs
Tatsunori Hashimoto, David Gifford, Tommi Jaakkola
[abs] [pdf] [supplementary]
Expressiveness of Rectifier Networks
Xingyuan Pan, Vivek Srikumar
[abs] [pdf] [supplementary]
Discrete Distribution Estimation under Local Privacy
Peter Kairouz, Keith Bonawitz, Daniel Ramage
[abs] [pdf] [supplementary]
Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies
David Inouye, Pradeep Ravikumar, Inderjit Dhillon
[abs] [pdf] [supplementary]
A Box-Constrained Approach for Hard Permutation Problems
Cong Han Lim, Steve Wright
[abs] [pdf] [supplementary]
Geometric Mean Metric Learning
Pourya Zadeh, Reshad Hosseini, Suvrit Sra
Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity
Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina Eldar, Tong Zhang
[abs] [pdf] [supplementary]
Conditional Bernoulli Mixtures for Multi-label Classification
Cheng Li, Bingyu Wang, Virgil Pavlu, Javed Aslam
[abs] [pdf] [supplementary]
Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Yutian Chen, Zoubin Ghahramani
[abs] [pdf] [supplementary]
Recycling Randomness with Structure for Sublinear time Kernel Expansions
Krzysztof Choromanski, Vikas Sindhwani
[abs] [pdf] [supplementary]
Bidirectional Helmholtz Machines
Jorg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier
Jacob Abernethy, Elad Hazan
[abs] [pdf] [supplementary]
Preconditioning Kernel Matrices
Kurt Cutajar, Michael Osborne, John Cunningham, Maurizio Filippone
[abs] [pdf] [supplementary]
Greedy Column Subset Selection: New Bounds and Distributed Algorithms
Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh, Morteza Zadimoghaddam
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Dynamic Capacity Networks
Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville
Pricing a Low-regret Seller
Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod
Estimation from Indirect Supervision with Linear Moments
Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang
[abs] [pdf] [supplementary]
Speeding up k-means by approximating Euclidean distances via block vectors
Thomas Bottesch, Thomas Bühler, Markus Kächele
Learning and Inference via Maximum Inner Product Search
Stephen Mussmann, Stefano Ermon
[abs] [pdf] [supplementary]
A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums
Anton Rodomanov, Dmitry Kropotov
[abs] [pdf] [supplementary]
A Kernel Test of Goodness of Fit
Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton
[abs] [pdf] [supplementary]
Interacting Particle Markov Chain Monte Carlo
Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank Wood
[abs] [pdf] [supplementary]
Faster Eigenvector Computation via Shift-and-Invert Preconditioning
Dan Garber, Elad Hazan, Chi Jin, Sham, Cameron Musco, Praneeth Netrapalli, Aaron Sidford
[abs] [pdf] [supplementary]
A Theory of Generative ConvNet
Jianwen Xie, Yang Lu, Song-Chun Zhu, Yingnian Wu
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
Quanming Yao, James Kwok
Computationally Efficient Nyström Approximation using Fast Transforms
Si Si, Cho-Jui Hsieh, Inderjit Dhillon
[abs] [pdf] [supplementary]
Gromov-Wasserstein Averaging of Kernel and Distance Matrices
Gabriel Peyré, Marco Cuturi, Justin Solomon
Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics
Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy
[abs] [pdf] [supplementary]
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams
[abs] [pdf] [supplementary]
Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning
Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov
Discriminative Embeddings of Latent Variable Models for Structured Data
Hanjun Dai, Bo Dai, Le Song
[abs] [pdf] [supplementary]
Robust Random Cut Forest Based Anomaly Detection on Streams
Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers
[abs] [pdf] [supplementary]
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein
Clustering High Dimensional Categorical Data via Topographical Features
Chao Chen, Novi Quadrianto
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis
Rong Ge, Chi Jin, Sham, Praneeth Netrapalli, Aaron Sidford
[abs] [pdf] [supplementary]
Algorithms for Optimizing the Ratio of Submodular Functions
Wenruo Bai, Rishabh Iyer, Kai Wei, Jeff Bilmes
Model-Free Imitation Learning with Policy Optimization
Jonathan Ho, Jayesh Gupta, Stefano Ermon
[abs] [pdf] [supplementary]
ADIOS: Architectures Deep In Output Space
Moustapha Cisse, Maruan Al-Shedivat, Samy Bengio
Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
Control of Memory, Active Perception, and Action in Minecraft
Junhyuk Oh, Valliappa Chockalingam, Satinder, Honglak Lee
[abs] [pdf] [supplementary]
The Label Complexity of Mixed-Initiative Classifier Training
Jina Suh, Xiaojin Zhu, Saleema Amershi
[abs] [pdf] [supplementary]
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
Aaron Schein, Mingyuan Zhou, David Blei, Hanna Wallach
[abs] [pdf] [supplementary]
Tensor Decomposition via Joint Matrix Schur Decomposition
Nicolo Colombo, Nikos Vlassis
Continuous Deep Q-Learning with Model-based Acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine
[abs] [pdf] [supplementary]
Domain Adaptation with Conditional Transferable Components
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf
[abs] [pdf] [supplementary]
Fixed Point Quantization of Deep Convolutional Networks
Darryl Lin, Sachin Talathi, Sreekanth Annapureddy
Provable Algorithms for Inference in Topic Models
Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra
Epigraph projections for fast general convex programming
Po-Wei Wang, Matt Wytock, Zico Kolter
[abs] [pdf] [supplementary]
Fast Algorithms for Segmented Regression
Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
[abs] [pdf] [supplementary]
Energetic Natural Gradient Descent
Philip Thomas, Bruno Castro da Silva, Christoph Dann, Emma Brunskill
[abs] [pdf] [supplementary]
Partition Functions from Rao-Blackwellized Tempered Sampling
David Carlson, Patrick Stinson, Ari Pakman, Liam Paninski
[abs] [pdf] [supplementary]
Learning Mixtures of Plackett-Luce Models
Zhibing Zhao, Peter Piech, Lirong Xia
[abs] [pdf] [supplementary]
Near Optimal Behavior via Approximate State Abstraction
David Abel, David Hershkowitz, Michael Littman
Power of Ordered Hypothesis Testing
Lihua Lei, William Fithian
[abs] [pdf] [supplementary]
PHOG: Probabilistic Model for Code
Pavol Bielik, Veselin Raychev, Martin Vechev
[abs] [pdf] [supplementary]
Shifting Regret, Mirror Descent, and Matrices
Andras Gyorgy, Csaba Szepesvari
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Jelena Luketina, Tapani Raiko, Mathias Berglund, Klaus Greff
Model-Free Trajectory Optimization for Reinforcement Learning
Riad Akrour, Gerhard Neumann, Hany Abdulsamad, Abbas Abdolmaleki
[abs] [pdf] [supplementary]
Controlling the distance to a Kemeny consensus without computing it
Yunlong Jiao, Anna Korba, Eric Sibony
[abs] [pdf] [supplementary]
Horizontally Scalable Submodular Maximization
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause
[abs] [pdf] [supplementary]
Group Equivariant Convolutional Networks
Taco Cohen, Max Welling
[abs] [pdf] [supplementary]
Stochastic Discrete Clenshaw-Curtis Quadrature
Nico Piatkowski, Katharina Morik
Correcting Forecasts with Multifactor Neural Attention
Matthew Riemer, Aditya Vempaty, Flavio Calmon, Fenno Heath, Richard Hull, Elham Khabiri
Learning Representations for Counterfactual Inference
Fredrik Johansson, Uri Shalit, David Sontag
[abs] [pdf] [supplementary]
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series
Yunseong Hwang, Anh Tong, Jaesik Choi
[abs] [pdf] [supplementary]
Inference Networks for Sequential Monte Carlo in Graphical Models
Brooks Paige, Frank Wood
[abs] [pdf] [supplementary]
Slice Sampling on Hamiltonian Trajectories
Benjamin Bloem-Reddy, John Cunningham
[abs] [pdf] [supplementary]
Noisy Activation Functions
Caglar Gulcehre, Marcin Moczulski, Misha Denil, Yoshua Bengio
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification
Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit Dhillon
[abs] [pdf] [supplementary]