| 2019 | $β^3$-IRT: A New Item Response Model and its Applications. Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach |
| 2019 | A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure. Juho Lee, Lancelot F. James, Seungjin Choi, Francois Caron |
| 2019 | A Continuous-Time View of Early Stopping for Least Squares Regression. Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani |
| 2019 | A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions. Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka |
| 2019 | A Fast Sampling Algorithm for Maximum Inner Product Search. Qin Ding, Hsiang-Fu Yu, Cho-Jui Hsieh |
| 2019 | A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes. Jialin Song, Yuxin Chen, Yisong Yue |
| 2019 | A Geometric Perspective on the Transferability of Adversarial Directions. Zachary Charles, Harrison Rosenberg, Dimitris S. Papailiopoulos |
| 2019 | A Higher-Order Kolmogorov-Smirnov Test. Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani |
| 2019 | A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems. Rishabh K. Iyer, Jeffrey A. Bilmes |
| 2019 | A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects. Daniel Malinsky, Ilya Shpitser, Thomas S. Richardson |
| 2019 | A Robust Zero-Sum Game Framework for Pool-based Active Learning. Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang |
| 2019 | A Stein-Papangelou Goodness-of-Fit Test for Point Processes. Jiasen Yang, Vinayak A. Rao, Jennifer Neville |
| 2019 | A Swiss Army Infinitesimal Jackknife. Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick |
| 2019 | A Thompson Sampling Algorithm for Cascading Bandits. Wang Chi Cheung, Vincent Y. F. Tan, Zixin Zhong |
| 2019 | A Topological Regularizer for Classifiers via Persistent Homology. Chao Chen, Xiuyan Ni, Qinxun Bai, Yusu Wang |
| 2019 | A Unified Weight Learning Paradigm for Multi-view Learning. Lai Tian, Feiping Nie, Xuelong Li |
| 2019 | A maximum-mean-discrepancy goodness-of-fit test for censored data. Tamara Fernandez, Arthur Gretton |
| 2019 | A new evaluation framework for topic modeling algorithms based on synthetic corpora. Hanyu Shi, Martin Gerlach, Isabel Diersen, Doug Downey, Luis A. Nunes Amaral |
| 2019 | A recurrent Markov state-space generative model for sequences. Anand Ramachandran, Steven S. Lumetta, Eric W. Klee, Deming Chen |
| 2019 | ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery. Raj Agrawal, Chandler Squires, Karren D. Yang, Karthikeyan Shanmugam, Caroline Uhler |
| 2019 | Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches. Filip Hanzely, Peter Richtárik |
| 2019 | Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives. Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié |
| 2019 | Accelerating Imitation Learning with Predictive Models. Ching-An Cheng, Xinyan Yan, Evangelos A. Theodorou, Byron Boots |
| 2019 | Active Exploration in Markov Decision Processes. Jean Tarbouriech, Alessandro Lazaric |
| 2019 | Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization. Filip de Roos, Philipp Hennig |
| 2019 | Active Ranking with Subset-wise Preferences. Aadirupa Saha, Aditya Gopalan |
| 2019 | Active multiple matrix completion with adaptive confidence sets. Andrea Locatelli, Alexandra Carpentier, Michal Valko |
| 2019 | Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models. Randy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian |
| 2019 | Adaptive Ensemble Prediction for Deep Neural Networks based on Confidence Level. Hiroshi Inoue |
| 2019 | Adaptive Estimation for Approximate Daniel LeJeune, Reinhard Heckel, Richard G. Baraniuk |
| 2019 | Adaptive Gaussian Copula ABC. Yanzhi Chen, Michael U. Gutmann |
| 2019 | Adaptive MCMC via Combining Local Samplers. Kiárash Shaloudegi, András György |
| 2019 | Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity. Kohei Miyaguchi, Kenji Yamanishi |
| 2019 | Adaptive Rao-Blackwellisation in Gibbs Sampling for Probabilistic Graphical Models. Craig Kelly, Somdeb Sarkhel, Deepak Venugopal |
| 2019 | Adversarial Discrete Sequence Generation without Explicit NeuralNetworks as Discriminators. Zhongliang Li, Tian Xia, Xingyu Lou, Kaihe Xu, Shaojun Wang, Jing Xiao |
| 2019 | Adversarial Learning of a Sampler Based on an Unnormalized Distribution. Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin |
| 2019 | Adversarial Variational Optimization of Non-Differentiable Simulators. Gilles Louppe, Joeri Hermans, Kyle Cranmer |
| 2019 | Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes. Linfeng Liu, Liping Liu |
| 2019 | An Online Algorithm for Smoothed Regression and LQR Control. Gautam Goel, Adam Wierman |
| 2019 | An Optimal Algorithm for Stochastic Three-Composite Optimization. Renbo Zhao, William B. Haskell, Vincent Y. F. Tan |
| 2019 | An Optimal Algorithm for Stochastic and Adversarial Bandits. Julian Zimmert, Yevgeny Seldin |
| 2019 | An Optimal Control Approach to Sequential Machine Teaching. Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu |
| 2019 | Analysis of Network Lasso for Semi-Supervised Regression. Alexander Jung, Natalia Vesselinova |
| 2019 | Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms. Alihan Hüyük, Cem Tekin |
| 2019 | Are we there yet? Manifold identification of gradient-related proximal methods. Yifan Sun, Halyun Jeong, Julie Nutini, Mark Schmidt |
| 2019 | Attenuating Bias in Word vectors. Sunipa Dev, Jeff M. Phillips |
| 2019 | Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models. Kaspar Märtens, Michalis K. Titsias, Christopher Yau |
| 2019 | Auto-Encoding Total Correlation Explanation. Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan |
| 2019 | AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI. Chen Yu, Bojan Karlas, Jie Zhong, Ce Zhang, Ji Liu |
| 2019 | Autoencoding any Data through Kernel Autoencoders. Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc |
| 2019 | Avoiding Latent Variable Collapse with Generative Skip Models. Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei |
| 2019 | Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman |
| 2019 | Bandit Online Learning with Unknown Delays. Bingcong Li, Tianyi Chen, Georgios B. Giannakis |
| 2019 | Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design. Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue |
| 2019 | Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference. Kelvin Hsu, Fabio Ramos |
| 2019 | Bayesian Learning of Neural Network Architectures. Georgi Dikov, Justin Bayer |
| 2019 | Bayesian optimisation under uncertain inputs. Rafael Oliveira, Lionel Ott, Fabio Ramos |
| 2019 | Bernoulli Race Particle Filters. Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis |
| 2019 | Best of many worlds: Robust model selection for online supervised learning. Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett |
| 2019 | Binary Space Partitioning Forest. Xuhui Fan, Bin Li, Scott A. Sisson |
| 2019 | Black Box Quantiles for Kernel Learning. Anthony Tompkins, Ransalu Senanayake, Philippe Morere, Fabio Ramos |
| 2019 | Blind Demixing via Wirtinger Flow with Random Initialization. Jialin Dong, Yuanming Shi |
| 2019 | Block Stability for MAP Inference. Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan |
| 2019 | Boosting Transfer Learning with Survival Data from Heterogeneous Domains. Alexis Bellot, Mihaela van der Schaar |
| 2019 | Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature. Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause |
| 2019 | Bridging the gap between regret minimization and best arm identification, with application to A/B tests. Rémy Degenne, Thomas Nedelec, Clément Calauzènes, Vianney Perchet |
| 2019 | Calibrating Deep Convolutional Gaussian Processes. Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone |
| 2019 | Can You Trust This Prediction? Auditing Pointwise Reliability After Learning. Peter Schulam, Suchi Saria |
| 2019 | Causal Discovery in the Presence of Missing Data. Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang |
| 2019 | Classification using margin pursuit. Matthew J. Holland |
| 2019 | Classifying Signals on Irregular Domains via Convolutional Cluster Pooling. Angelo Porrello, Davide Abati, Simone Calderara, Rita Cucchiara |
| 2019 | Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. Alexander Lin, Yingzhuo Zhang, Jeremy Heng, Stephen A. Allsop, Kay M. Tye, Pierre E. Jacob, Demba E. Ba |
| 2019 | Complexities in Projection-Free Stochastic Non-convex Minimization. Zebang Shen, Cong Fang, Peilin Zhao, Junzhou Huang, Hui Qian |
| 2019 | Computation Efficient Coded Linear Transform. Sinong Wang, Jiashang Liu, Ness B. Shroff, Pengyu Yang |
| 2019 | Conditional Sparse $L_p$-norm Regression With Optimal Probability. John Hainline, Brendan Juba, Hai S. Le, David P. Woodruff |
| 2019 | Conditionally Independent Multiresolution Gaussian Processes. Jalil Taghia, Thomas B. Schön |
| 2019 | Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes. Tongfei Chen, Jirí Navrátil, Vijay S. Iyengar, Karthikeyan Shanmugam |
| 2019 | Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha P. Talukdar |
| 2019 | Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning. Guillaume Rabusseau, Tianyu Li, Doina Precup |
| 2019 | Conservative Exploration using Interleaving. Sumeet Katariya, Branislav Kveton, Zheng Wen, Vamsi K. Potluru |
| 2019 | Consistent Online Optimization: Convex and Submodular. Mohammad Reza Karimi Jaghargh, Andreas Krause, Silvio Lattanzi, Sergei Vassilvitskii |
| 2019 | Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. Anirudh Vemula, Wen Sun, J. Andrew Bagnell |
| 2019 | Convergence of Gradient Descent on Separable Data. Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry |
| 2019 | Correcting the bias in least squares regression with volume-rescaled sampling. Michal Derezinski, Manfred K. Warmuth, Daniel Hsu |
| 2019 | Correspondence Analysis Using Neural Networks. Hsiang Hsu, Salman Salamatian, Flávio P. Calmon |
| 2019 | Cost aware Inference for IoT Devices. Pengkai Zhu, Durmus Alp Emre Acar, Nan Feng, Prateek Jain, Venkatesh Saligrama |
| 2019 | Credit Assignment Techniques in Stochastic Computation Graphs. Théophane Weber, Nicolas Heess, Lars Buesing, David Silver |
| 2019 | Data-Driven Approach to Multiple-Source Domain Adaptation. Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang |
| 2019 | Data-dependent compression of random features for large-scale kernel approximation. Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick |
| 2019 | Database Alignment with Gaussian Features. Osman Emre Dai, Daniel Cullina, Negar Kiyavash |
| 2019 | Decentralized Gradient Tracking for Continuous DR-Submodular Maximization. Jiahao Xie, Chao Zhang, Zebang Shen, Chao Mi, Hui Qian |
| 2019 | Deep Neural Networks Learn Non-Smooth Functions Effectively. Masaaki Imaizumi, Kenji Fukumizu |
| 2019 | Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex. Hongyang Zhang, Junru Shao, Ruslan Salakhutdinov |
| 2019 | Deep Switch Networks for Generating Discrete Data and Language. Payam Delgosha, Naveen Goela |
| 2019 | Deep Topic Models for Multi-label Learning. Rajat Panda, Ankit Pensia, Nikhil Mehta, Mingyuan Zhou, Piyush Rai |
| 2019 | Deep learning with differential Gaussian process flows. Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski |
| 2019 | Defending against Whitebox Adversarial Attacks via Randomized Discretization. Yuchen Zhang, Percy Liang |
| 2019 | Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems. Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright |
| 2019 | Designing Optimal Binary Rating Systems. Nikhil Garg, Ramesh Johari |
| 2019 | Detection of Planted Solutions for Flat Satisfiability Problems. Quentin Berthet, Jordan S. Ellenberg |
| 2019 | Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. Mike Wu, Noah D. Goodman, Stefano Ermon |
| 2019 | Differentially Private Online Submodular Minimization. Adrian Rivera Cardoso, Rachel Cummings |
| 2019 | Direct Acceleration of SAGA using Sampled Negative Momentum. Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhi-Quan Luo |
| 2019 | Distilling Policy Distillation. Wojciech M. Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, Max Jaderberg |
| 2019 | Distributed Inexact Newton-type Pursuit for Non-convex Sparse Learning. Bo Liu, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu, Junzhou Huang, Dimitris N. Metaxas |
| 2019 | Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets. Mehrdad Ghadiri, Mark Schmidt |
| 2019 | Distributional reinforcement learning with linear function approximation. Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra |
| 2019 | Distributionally Robust Submodular Maximization. Matthew Staib, Bryan Wilder, Stefanie Jegelka |
| 2019 | Does data interpolation contradict statistical optimality? Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov |
| 2019 | Domain-Size Aware Markov Logic Networks. Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla |
| 2019 | Doubly Semi-Implicit Variational Inference. Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry P. Vetrov |
| 2019 | Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function. Wojciech Tarnowski, Piotr Warchol, Stanislaw Jastrzebski, Jacek Tabor, Maciej A. Nowak |
| 2019 | Efficient Bayes Risk Estimation for Cost-Sensitive Classification. Daniel Andrade, Yuzuru Okajima |
| 2019 | Efficient Bayesian Experimental Design for Implicit Models. Steven Kleinegesse, Michael U. Gutmann |
| 2019 | Efficient Bayesian Optimization for Target Vector Estimation. Anders Kirk Uhrenholt, Bjørn Sand Jensen |
| 2019 | Efficient Greedy Coordinate Descent for Composite Problems. Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi |
| 2019 | Efficient Inference in Multi-task Cox Process Models. Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla |
| 2019 | Efficient Linear Bandits through Matrix Sketching. Ilja Kuzborskij, Leonardo Cella, Nicolò Cesa-Bianchi |
| 2019 | Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods. Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie |
| 2019 | Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz |
| 2019 | Error bounds for sparse classifiers in high-dimensions. Antoine Dedieu |
| 2019 | Estimating Network Structure from Incomplete Event Data. Benjamin Mark, Garvesh Raskutti, Rebecca Willett |
| 2019 | Estimation of Non-Normalized Mixture Models. Takeru Matsuda, Aapo Hyvärinen |
| 2019 | Evaluating model calibration in classification. Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön |
| 2019 | Exploring Wenbo Ren, Jia Liu, Ness B. Shroff |
| 2019 | Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks. Yue Yu, Jiaxiang Wu, Junzhou Huang |
| 2019 | Exponential Weights on the Hypercube in Polynomial Time. Sudeep Raja Putta, Abhishek Shetty |
| 2019 | Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr |
| 2019 | Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models. Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon |
| 2019 | Fast Algorithms for Sparse Reduced-Rank Regression. Benjamin Dubois, Jean-François Delmas, Guillaume Obozinski |
| 2019 | Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs. Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause, Joachim M. Buhmann |
| 2019 | Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems. Dan Garber, Atara Kaplan |
| 2019 | Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. Sharan Vaswani, Francis R. Bach, Mark Schmidt |
| 2019 | Fast and Robust Shortest Paths on Manifolds Learned from Data. Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober |
| 2019 | Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds. Pan Zhou, Xiao-Tong Yuan, Jiashi Feng |
| 2019 | Feature subset selection for the multinomial logit model via mixed-integer optimization. Shunsuke Kamiya, Ryuhei Miyashiro, Yuichi Takano |
| 2019 | Finding the bandit in a graph: Sequential search-and-stop. Pierre Perrault, Vianney Perchet, Michal Valko |
| 2019 | Fisher Information and Natural Gradient Learning in Random Deep Networks. Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi |
| 2019 | Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes |
| 2019 | Fixing Mini-batch Sequences with Hierarchical Robust Partitioning. Shengjie Wang, Wenruo Bai, Chandrashekhar Lavania, Jeff A. Bilmes |
| 2019 | Forward Amortized Inference for Likelihood-Free Variational Marginalization. Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva W. P. van den Borne, Yagmur Güçlütürk, Max Hinne, Eric Maris, Marcel van Gerven |
| 2019 | Foundations of Sequence-to-Sequence Modeling for Time Series. Zelda Mariet, Vitaly Kuznetsov |
| 2019 | From Cost-Sensitive to Tight F-measure Bounds. Kevin Bascol, Rémi Emonet, Élisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban |
| 2019 | Gain estimation of linear dynamical systems using Thompson Sampling. Matias I. Müller, Cristian R. Rojas |
| 2019 | Gaussian Process Latent Variable Alignment Learning. Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell |
| 2019 | Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. Andrés F. López-Lopera, S. T. John, Nicolas Durrande |
| 2019 | Gaussian Regression with Convex Constraints. Matey Neykov |
| 2019 | Generalized Boltzmann Machine with Deep Neural Structure. Yingru Liu, Dongliang Xie, Xin Wang |
| 2019 | Generalizing the theory of cooperative inference. Pei Wang, Pushpi Paranamana, Patrick Shafto |
| 2019 | Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems. Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar |
| 2019 | Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability. Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira |
| 2019 | Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation. Mingming Sun, Ping Li |
| 2019 | Greedy and IHT Algorithms for Non-convex Optimization with Monotone Costs of Non-zeros. Shinsaku Sakaue |
| 2019 | HS I (Eli) Chien, Huozhi Zhou, Pan Li |
| 2019 | Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication. Jayadev Acharya, Ziteng Sun, Huanyu Zhang |
| 2019 | Harmonizable mixture kernels with variational Fourier features. Zheyang Shen, Markus Heinonen, Samuel Kaski |
| 2019 | Hierarchical Clustering for Euclidean Data. Moses Charikar, Vaggos Chatziafratis, Rad Niazadeh, Grigory Yaroslavtsev |
| 2019 | High Dimensional Inference in Partially Linear Models. Ying Zhu, Zhuqing Yu, Guang Cheng |
| 2019 | High-dimensional Mixed Graphical Model with Ordinal Data: Parameter Estimation and Statistical Inference. Huijie Feng, Yang Ning |
| 2019 | Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical Models. Gunwoong Park, Hyewon Park |
| 2019 | Imitation-Regularized Offline Learning. Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy |
| 2019 | Implicit Kernel Learning. Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos |
| 2019 | Improved Semi-Supervised Learning with Multiple Graphs. Krishnamurthy Viswanathan, Sushant Sachdeva, Andrew Tomkins, Sujith Ravi |
| 2019 | Improving Quadrature for Constrained Integrands. Henry R. Chai, Roman Garnett |
| 2019 | Improving the Stability of the Knockoff Procedure: Multiple Simultaneous Knockoffs and Entropy Maximization. Jaime Roquero Gimenez, James Y. Zou |
| 2019 | Inferring Multidimensional Rates of Aging from Cross-Sectional Data. Emma Pierson, Pang Wei Koh, Tatsunori B. Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson, Percy Liang |
| 2019 | Infinite Task Learning in RKHSs. Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc |
| 2019 | Interaction Detection with Bayesian Decision Tree Ensembles. Junliang Du, Antonio R. Linero |
| 2019 | Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. Tengyuan Liang, James Stokes |
| 2019 | Interpolating between Optimal Transport and MMD using Sinkhorn Divergences. Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouvé, Gabriel Peyré |
| 2019 | Interpretable Almost-Exact Matching for Causal Inference. Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky |
| 2019 | Interpretable Cascade Classifiers with Abstention. Matthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar |
| 2019 | Interpreting Black Box Predictions using Fisher Kernels. Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo |
| 2019 | Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. Nathan Kallus, Xiaojie Mao, Angela Zhou |
| 2019 | Inverting Supervised Representations with Autoregressive Neural Density Models. Charlie Nash, Nate Kushman, Christopher K. I. Williams |
| 2019 | Iterative Bayesian Learning for Crowdsourced Regression. Jungseul Ok, Sewoong Oh, Yunhun Jang, Jinwoo Shin, Yung Yi |
| 2019 | KAMA-NNs: Low-dimensional Rotation Based Neural Networks. Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang |
| 2019 | Kernel Exponential Family Estimation via Doubly Dual Embedding. Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He |
| 2019 | Knockoffs for the Mass: New Feature Importance Statistics with False Discovery Guarantees. Jaime Roquero Gimenez, Amirata Ghorbani, James Y. Zou |
| 2019 | Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features. Arno Solin, Manon Kok |
| 2019 | LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood |
| 2019 | Lagrange Coded Computing: Optimal Design for Resiliency, Security, and Privacy. Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, Amir Salman Avestimehr |
| 2019 | Large-Margin Classification in Hyperbolic Space. Hyunghoon Cho, Benjamin Demeo, Jian Peng, Bonnie Berger |
| 2019 | Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. Mathieu Blondel, André F. T. Martins, Vlad Niculae |
| 2019 | Learning Controllable Fair Representations. Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon |
| 2019 | Learning Determinantal Point Processes by Corrective Negative Sampling. Zelda Mariet, Mike Gartrell, Suvrit Sra |
| 2019 | Learning Influence-Receptivity Network Structure with Guarantee. Ming Yu, Varun Gupta, Mladen Kolar |
| 2019 | Learning Invariant Representations with Kernel Warping. Yingyi Ma, Vignesh Ganapathiraman, Xinhua Zhang |
| 2019 | Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm. Nikos Kargas, Nicholas D. Sidiropoulos |
| 2019 | Learning Natural Programs from a Few Examples in Real-Time. Nagarajan Natarajan, Danny Simmons, Naren Datha, Prateek Jain, Sumit Gulwani |
| 2019 | Learning One-hidden-layer Neural Networks under General Input Distributions. Weihao Gao, Ashok Vardhan Makkuva, Sewoong Oh, Pramod Viswanath |
| 2019 | Learning One-hidden-layer ReLU Networks via Gradient Descent. Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu |
| 2019 | Learning Rules-First Classifiers. Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan |
| 2019 | Learning Tree Structures from Noisy Data. Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate |
| 2019 | Learning the Structure of a Nonstationary Vector Autoregression. Daniel Malinsky, Peter Spirtes |
| 2019 | Learning to Optimize under Non-Stationarity. Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu |
| 2019 | Least Squares Estimation of Weakly Convex Functions. Sun Sun, Yaoliang Yu |
| 2019 | Lifelong Optimization with Low Regret. Yi-Shan Wu, Po-An Wang, Chi-Jen Lu |
| 2019 | Lifted Weight Learning of Markov Logic Networks Revisited. Ondrej Kuzelka, Vyacheslav Kungurtsev |
| 2019 | Lifting high-dimensional non-linear models with Gaussian regressors. Christos Thrampoulidis, Ankit Singh Rawat |
| 2019 | Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity. Simon S. Du, Wei Hu |
| 2019 | Linear Queries Estimation with Local Differential Privacy. Raef Bassily |
| 2019 | Local Saddle Point Optimization: A Curvature Exploitation Approach. Leonard Adolphs, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann |
| 2019 | Locally Private Mean Estimation: $Z$-test and Tight Confidence Intervals. Marco Gaboardi, Ryan Rogers, Or Sheffet |
| 2019 | Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity. Dan Garber |
| 2019 | Lovasz Convolutional Networks. Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Pratim Talukdar |
| 2019 | Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang |
| 2019 | Low-Precision Random Fourier Features for Memory-constrained Kernel Approximation. Jian Zhang, Avner May, Tri Dao, Christopher Ré |
| 2019 | Markov Properties of Discrete Determinantal Point Processes. Kayvan Sadeghi, Alessandro Rinaldo |
| 2019 | Matroids, Matchings, and Fairness. Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii |
| 2019 | MaxHedge: Maximizing a Maximum Online. Stephen Pasteris, Fabio Vitale, Kevin S. Chan, Shiqiang Wang, Mark Herbster |
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