| 2018 | A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians. Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt |
| 2018 | A Generic Approach for Escaping Saddle points. Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola |
| 2018 | A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization. Emanuel Laude, Tao Wu, Daniel Cremers |
| 2018 | A Provable Algorithm for Learning Interpretable Scoring Systems. Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker |
| 2018 | A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. Tianbao Yang, Zhe Li, Lijun Zhang |
| 2018 | A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop. Yichen Wang, Evangelos A. Theodorou, Apurv Verma, Le Song |
| 2018 | A Unified Dynamic Approach to Sparse Model Selection. Chendi Huang, Yuan Yao |
| 2018 | A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. Xiao Zhang, Lingxiao Wang, Quanquan Gu |
| 2018 | A fully adaptive algorithm for pure exploration in linear bandits. Liyuan Xu, Junya Honda, Masashi Sugiyama |
| 2018 | Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms. Pan Xu, Tianhao Wang, Quanquan Gu |
| 2018 | Accelerated Stochastic Power Iteration. Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré |
| 2018 | Achieving the time of 1-NN, but the accuracy of k-NN. Lirong Xue, Samory Kpotufe |
| 2018 | Actor-Critic Fictitious Play in Simultaneous Move Multistage Games. Julien Pérolat, Bilal Piot, Olivier Pietquin |
| 2018 | AdaGeo: Adaptive Geometric Learning for Optimization and Sampling. Gabriele Abbati, Alessandra Tosi, Michael A. Osborne, Seth R. Flaxman |
| 2018 | Adaptive Sampling for Coarse Ranking. Sumeet Katariya, Lalit K. Jain, Nandana Sengupta, James Evans, Robert Nowak |
| 2018 | Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi |
| 2018 | An Analysis of Categorical Distributional Reinforcement Learning. Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh |
| 2018 | An Optimization Approach to Learning Falling Rule Lists. Chaofan Chen, Cynthia Rudin |
| 2018 | Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy. Bai Jiang |
| 2018 | Approximate ranking from pairwise comparisons. Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright |
| 2018 | Asynchronous Doubly Stochastic Group Regularized Learning. Bin Gu, Zhouyuan Huo, Heng Huang |
| 2018 | Batch-Expansion Training: An Efficient Optimization Framework. Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer |
| 2018 | Batched Large-scale Bayesian Optimization in High-dimensional Spaces. Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka |
| 2018 | Bayesian Approaches to Distribution Regression. Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman |
| 2018 | Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences. He Zhao, Piyush Rai, Lan Du, Wray L. Buntine |
| 2018 | Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama |
| 2018 | Bayesian Structure Learning for Dynamic Brain Connectivity. Michael Riis Andersen, Ole Winther, Lars Kai Hansen, Russell A. Poldrack, Oluwasanmi Koyejo |
| 2018 | Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods. Stéphan Clémençon, François Portier |
| 2018 | Benefits from Superposed Hawkes Processes. Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin |
| 2018 | Best arm identification in multi-armed bandits with delayed feedback. Aditya Grover, Todor M. Markov, Peter M. Attia, Norman Jin, Nicolas Perkins, Bryan Cheong, Michael H. Chen, Zi Yang, Stephen J. Harris, William C. Chueh, Stefano Ermon |
| 2018 | Boosting Variational Inference: an Optimization Perspective. Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch |
| 2018 | Bootstrapping EM via Power EM and Convergence in the Naive Bayes Model. Costis Daskalakis, Christos Tzamos, Manolis Zampetakis |
| 2018 | Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means. Dennis Forster, Jörg Lücke |
| 2018 | Catalyst for Gradient-based Nonconvex Optimization. Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaïd Harchaoui |
| 2018 | Cause-Effect Inference by Comparing Regression Errors. Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf |
| 2018 | Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data Streams. Chris Hickey, Graham Cormode |
| 2018 | Combinatorial Penalties: Which structures are preserved by convex relaxations? Marwa El Halabi, Francis R. Bach, Volkan Cevher |
| 2018 | Combinatorial Preconditioners for Proximal Algorithms on Graphs. Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers |
| 2018 | Combinatorial Semi-Bandits with Knapsacks. Karthik Abinav Sankararaman, Aleksandrs Slivkins |
| 2018 | Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation. Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydin Buluç, Dmitriy Morozov, Leonid Oliker, Katherine A. Yelick, Sang-Yun Oh |
| 2018 | Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms. I (Eli) Chien, Chung-Yi Lin, I-Hsiang Wang |
| 2018 | Comparison Based Learning from Weak Oracles. Ehsan Kazemi, Lin Chen, Sanjoy Dasgupta, Amin Karbasi |
| 2018 | Competing with Automata-based Expert Sequences. Mehryar Mohri, Scott Yang |
| 2018 | Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap. Aryan Mokhtari, Hamed Hassani, Amin Karbasi |
| 2018 | Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. Jakob Runge |
| 2018 | Contextual Bandits with Stochastic Experts. Rajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai |
| 2018 | Convergence diagnostics for stochastic gradient descent with constant learning rate. Jerry Chee, Panos Toulis |
| 2018 | Convergence of Value Aggregation for Imitation Learning. Ching-An Cheng, Byron Boots |
| 2018 | Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach. Achintya Kundu, Francis R. Bach, Chiranjib Bhattacharyya |
| 2018 | Crowdclustering with Partition Labels. Junxiang Chen, Yale Chang, Peter J. Castaldi, Michael H. Cho, Brian D. Hobbs, Jennifer G. Dy |
| 2018 | Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control. Sanket Kamthe, Marc Peter Deisenroth |
| 2018 | Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs. Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas B. Schön |
| 2018 | Derivative Free Optimization Via Repeated Classification. Tatsunori Hashimoto, Steve Yadlowsky, John C. Duchi |
| 2018 | Differentially Private Regression with Gaussian Processes. Michael T. Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence |
| 2018 | Dimensionality Reduced $\ell^{0}$-Sparse Subspace Clustering. Yingzhen Yang |
| 2018 | Direct Learning to Rank And Rerank. Cynthia Rudin, Yining Wang |
| 2018 | Discriminative Learning of Prediction Intervals. Nir Rosenfeld, Yishay Mansour, Elad Yom-Tov |
| 2018 | Dropout as a Low-Rank Regularizer for Matrix Factorization. Jacopo Cavazza, Pietro Morerio, Benjamin D. Haeffele, Connor Lane, Vittorio Murino, René Vidal |
| 2018 | Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams. Shinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato |
| 2018 | Efficient Bayesian Methods for Counting Processes in Partially Observable Environments. Ferdian Jovan, Jeremy L. Wyatt, Nick Hawes |
| 2018 | Efficient Weight Learning in High-Dimensional Untied MLNs. Khan Mohammad Al Farabi, Somdeb Sarkhel, Deepak Venugopal |
| 2018 | Efficient and principled score estimation with Nyström kernel exponential families. Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton |
| 2018 | Exploiting Strategy-Space Diversity for Batch Bayesian Optimization. Sunil Gupta, Alistair Shilton, Santu Rana, Svetha Venkatesh |
| 2018 | FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney |
| 2018 | Factor Analysis on a Graph. Masayuki Karasuyama, Hiroshi Mamitsuka |
| 2018 | Factorial HMMs with Collapsed Gibbs Sampling for Optimizing Long-term HIV Therapy. Amit Gruber, Chen Yanover, Tal El-Hay, Anders Sönnerborg, Vanni Borghi, Francesca Incardona, Yaara Goldschmidt |
| 2018 | Factorized Recurrent Neural Architectures for Longer Range Dependence. Francois Belletti, Alex Beutel, Sagar Jain, Ed Huai-hsin Chi |
| 2018 | Fast Threshold Tests for Detecting Discrimination. Emma Pierson, Sam Corbett-Davies, Sharad Goel |
| 2018 | Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure. Beilun Wang, Arshdeep Sekhon, Yanjun Qi |
| 2018 | Fast generalization error bound of deep learning from a kernel perspective. Taiji Suzuki |
| 2018 | Few-shot Generative Modelling with Generative Matching Networks. Sergey Bartunov, Dmitry P. Vetrov |
| 2018 | Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction. Jinshan Zeng, Ke Ma, Yuan Yao |
| 2018 | Frank-Wolfe Splitting via Augmented Lagrangian Method. Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien |
| 2018 | Gauged Mini-Bucket Elimination for Approximate Inference. Sungsoo Ahn, Michael Chertkov, Jinwoo Shin, Adrian Weller |
| 2018 | Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data. William Herlands, Edward McFowland, Andrew Gordon Wilson, Daniel B. Neill |
| 2018 | Generalized Binary Search For Split-Neighborly Problems. Stephen Mussmann, Percy Liang |
| 2018 | Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression. Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon |
| 2018 | Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett |
| 2018 | Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. Atsushi Nitanda, Taiji Suzuki |
| 2018 | Graphical Models for Non-Negative Data Using Generalized Score Matching. Shiqing Yu, Mathias Drton, Ali Shojaie |
| 2018 | Group invariance principles for causal generative models. Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing |
| 2018 | Growth-Optimal Portfolio Selection under CVaR Constraints. Guy Uziel, Ran El-Yaniv |
| 2018 | Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida |
| 2018 | HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step. Yuting Ye, Lihua Lei, Cheng Ju |
| 2018 | High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups. Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher |
| 2018 | Human Interaction with Recommendation Systems. Sven Schmit, Carlos Riquelme |
| 2018 | IHT dies hard: Provable accelerated Iterative Hard Thresholding. Rajiv Khanna, Anastasios Kyrillidis |
| 2018 | Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. Masaaki Takada, Taiji Suzuki, Hironori Fujisawa |
| 2018 | Inference in Sparse Graphs with Pairwise Measurements and Side Information. Dylan J. Foster, Karthik Sridharan, Daniel Reichman |
| 2018 | Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis. Luca Ambrogioni, Eric Maris |
| 2018 | International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Spain Amos J. Storkey, Fernando Pérez-Cruz |
| 2018 | Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth. Jussi Viinikka, Ralf Eggeling, Mikko Koivisto |
| 2018 | Iterative Spectral Method for Alternative Clustering. Chieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David R. Kaeli, Jennifer G. Dy |
| 2018 | Iterative Supervised Principal Components. Juho Piironen, Aki Vehtari |
| 2018 | Kernel Conditional Exponential Family. Michael Arbel, Arthur Gretton |
| 2018 | Labeled Graph Clustering via Projected Gradient Descent. Shiau Hong Lim, Gregory Calvez |
| 2018 | Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method. Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro |
| 2018 | Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond. Heng Guo, Kaan Kara, Ce Zhang |
| 2018 | Learning Determinantal Point Processes in Sublinear Time. Christophe Dupuy, Francis R. Bach |
| 2018 | Learning Generative Models with Sinkhorn Divergences. Aude Genevay, Gabriel Peyré, Marco Cuturi |
| 2018 | Learning Hidden Quantum Markov Models. Siddarth Srinivasan, Geoffrey J. Gordon, Byron Boots |
| 2018 | Learning Priors for Invariance. Eric T. Nalisnick, Padhraic Smyth |
| 2018 | Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity. Asish Ghoshal, Jean Honorio |
| 2018 | Learning Structural Weight Uncertainty for Sequential Decision-Making. Ruiyi Zhang, Chunyuan Li, Changyou Chen, Lawrence Carin |
| 2018 | Learning linear structural equation models in polynomial time and sample complexity. Asish Ghoshal, Jean Honorio |
| 2018 | Learning to Round for Discrete Labeling Problems. Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar |
| 2018 | Learning with Complex Loss Functions and Constraints. Harikrishna Narasimhan |
| 2018 | Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go? Chandrashekar Lakshminarayanan, Csaba Szepesvári |
| 2018 | Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach. Satoshi Hara, Kohei Hayashi |
| 2018 | Matrix completability analysis via graph k-connectivity. Dehua Cheng, Natali Ruchansky, Yan Liu |
| 2018 | Matrix-normal models for fMRI analysis. Michael Shvartsman, Narayanan Sundaram, Mikio Aoi, Adam Charles, Theodore L. Willke, Jonathan D. Cohen |
| 2018 | Medoids in Almost-Linear Time via Multi-Armed Bandits. Vivek Kumar Bagaria, Govinda M. Kamath, Vasilis Ntranos, Martin J. Zhang, David Tse |
| 2018 | Metrics for Deep Generative Models. Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt |
| 2018 | Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning. Shashank Singh, Barnabás Póczos, Jian Ma |
| 2018 | Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems. Jason Ge, Zhaoran Wang, Mengdi Wang, Han Liu |
| 2018 | Mixed Membership Word Embeddings for Computational Social Science. James R. Foulds |
| 2018 | Multi-objective Contextual Bandit Problem with Similarity Information. Eralp Turgay, Doruk Öner, Cem Tekin |
| 2018 | Multi-scale Nystrom Method. Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song, Haesun Park |
| 2018 | Multi-view Metric Learning in Vector-valued Kernel Spaces. Riikka Huusari, Hachem Kadri, Cécile Capponi |
| 2018 | Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams |
| 2018 | Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations. Alan Lazarus, Dirk Husmeier, Theodore Papamarkou |
| 2018 | Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models. Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman |
| 2018 | Near-Optimal Machine Teaching via Explanatory Teaching Sets. Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, Yisong Yue |
| 2018 | Nearly second-order optimality of online joint detection and estimation via one-sample update schemes. Yang Cao, Liyan Xie, Yao Xie, Huan Xu |
| 2018 | Nested CRP with Hawkes-Gaussian Processes. Xi Tan, Vinayak A. Rao, Jennifer Neville |
| 2018 | Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training. Mathieu Sinn, Ambrish Rawat |
| 2018 | Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding. Kaiqing Zhang, Zhuoran Yang, Zhaoran Wang |
| 2018 | Nonlinear Weighted Finite Automata. Tianyu Li, Guillaume Rabusseau, Doina Precup |
| 2018 | Nonparametric Bayesian sparse graph linear dynamical systems. Rahi Kalantari, Joydeep Ghosh, Mingyuan Zhou |
| 2018 | Nonparametric Preference Completion. Julian Katz-Samuels, Clayton Scott |
| 2018 | Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization. Seung-Jean Kim, Johan Lim, Joong-Ho Won |
| 2018 | On Statistical Optimality of Variational Bayes. Debdeep Pati, Anirban Bhattacharya, Yun Yang |
| 2018 | On Truly Block Eigensolvers via Riemannian Optimization. Zhiqiang Xu, Xin Gao |
| 2018 | On denoising modulo 1 samples of a function. Mihai Cucuringu, Hemant Tyagi |
| 2018 | On how complexity affects the stability of a predictor. Joel Ratsaby |
| 2018 | On the Statistical Efficiency of Compositional Nonparametric Prediction. Yixi Xu, Jean Honorio, Xiao Wang |
| 2018 | On the challenges of learning with inference networks on sparse, high-dimensional data. Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman |
| 2018 | One-shot Coresets: The Case of k-Clustering. Olivier Bachem, Mario Lucic, Silvio Lattanzi |
| 2018 | Online Boosting Algorithms for Multi-label Ranking. Young Hun Jung, Ambuj Tewari |
| 2018 | Online Continuous Submodular Maximization. Lin Chen, Hamed Hassani, Amin Karbasi |
| 2018 | Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments. Yanning Shen, Tianyi Chen, Georgios B. Giannakis |
| 2018 | Online Learning with Non-Convex Losses and Non-Stationary Regret. Xiand Gao, Xiaobo Li, Shuzhong Zhang |
| 2018 | Online Regression with Partial Information: Generalization and Linear Projection. Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi |
| 2018 | Optimal Cooperative Inference. Scott Cheng-Hsin Yang, Yue Yu, Arash Givchi, Pei Wang, Wai Keen Vong, Patrick Shafto |
| 2018 | Optimal Submodular Extensions for Marginal Estimation. Pankaj Pansari, Chris Russell, M. Pawan Kumar |
| 2018 | Optimality of Approximate Inference Algorithms on Stable Instances. Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan |
| 2018 | Outlier Detection and Robust Estimation in Nonparametric Regression. Dehan Kong, Howard D. Bondell, Weining Shen |
| 2018 | Parallel and Distributed MCMC via Shepherding Distributions. Arkabandhu Chowdhury, Christopher M. Jermaine |
| 2018 | Parallelised Bayesian Optimisation via Thompson Sampling. Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos |
| 2018 | Personalized and Private Peer-to-Peer Machine Learning. Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, Marc Tommasi |
| 2018 | Plug-in Estimators for Conditional Expectations and Probabilities. Steffen Grünewälder |
| 2018 | Policy Evaluation and Optimization with Continuous Treatments. Nathan Kallus, Angela Zhou |
| 2018 | Post Selection Inference with Kernels. Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi |
| 2018 | Practical Bayesian optimization in the presence of outliers. Ruben Martinez-Cantin, Kevin Tee, Michael McCourt |
| 2018 | Probability-Revealing Samples. Krzysztof Onak, Xiaorui Sun |
| 2018 | Product Kernel Interpolation for Scalable Gaussian Processes. Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson |
| 2018 | Provable Estimation of the Number of Blocks in Block Models. Bowei Yan, Purnamrita Sarkar, Xiuyuan Cheng |
| 2018 | Proximity Variational Inference. Jaan Altosaar, Rajesh Ranganath, David M. Blei |
| 2018 | Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures. Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos |
| 2018 | Random Subspace with Trees for Feature Selection Under Memory Constraints. Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts |
| 2018 | Random Warping Series: A Random Features Method for Time-Series Embedding. Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock |
| 2018 | Reducing Crowdsourcing to Graphon Estimation, Statistically. Devavrat Shah, Christina E. Lee |
| 2018 | Regional Multi-Armed Bandits. Zhiyang Wang, Ruida Zhou, Cong Shen |
| 2018 | Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham |
| 2018 | Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis. Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra |
| 2018 | Robust Active Label Correction. Jan Kremer, Fei Sha, Christian Igel |
| 2018 | Robust Locally-Linear Controllable Embedding. Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi |
| 2018 | Robust Maximization of Non-Submodular Objectives. Ilija Bogunovic, Junyao Zhao, Volkan Cevher |
| 2018 | Robust Vertex Enumeration for Convex Hulls in High Dimensions. Pranjal Awasthi, Bahman Kalantari, Yikai Zhang |
| 2018 | Robustness of classifiers to uniform $\ell_p$ and Gaussian noise. Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi |
| 2018 | SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning. Xu Hu, Guillaume Obozinski |
| 2018 | Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. Pavel Izmailov, Alexander Novikov, Dmitry Kropotov |
| 2018 | Scalable Generalized Dynamic Topic Models. Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt |
| 2018 | Scalable Hash-Based Estimation of Divergence Measures. Morteza Noshad, Alfred O. Hero III |
| 2018 | Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes. Hyunjik Kim, Yee Whye Teh |
| 2018 | Semi-Supervised Learning with Competitive Infection Models. Nir Rosenfeld, Amir Globerson |
| 2018 | Semi-Supervised Prediction-Constrained Topic Models. Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez |
| 2018 | Sketching for Kronecker Product Regression and P-splines. Huaian Diao, Zhao Song, Wen Sun, David P. Woodruff |
| 2018 | Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD. Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar |
| 2018 | Smooth and Sparse Optimal Transport. Mathieu Blondel, Vivien Seguy, Antoine Rolet |
| 2018 | Solving lp-norm regularization with tensor kernels. Saverio Salzo, Lorenzo Rosasco, Johan A. K. Suykens |
| 2018 | Sparse Linear Isotonic Models. Sheng Chen, Arindam Banerjee |
| 2018 | Spectral Algorithms for Computing Fair Support Vector Machines. Matt Olfat, Anil Aswani |
| 2018 | Statistical Sparse Online Regression: A Diffusion Approximation Perspective. Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun |
| 2018 | Statistically Efficient Estimation for Non-Smooth Probability Densities. Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida |
| 2018 | Stochastic Multi-armed Bandits in Constant Space. David Liau, Zhao Song, Eric Price, Ger Yang |
| 2018 | Stochastic Three-Composite Convex Minimization with a Linear Operator. Renbo Zhao, Volkan Cevher |
| 2018 | Stochastic Zeroth-order Optimization in High Dimensions. Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh |
| 2018 | Stochastic algorithms for entropy-regularized optimal transport problems. Brahim Khalil Abid, Robert M. Gower |
| 2018 | Structured Factored Inference for Probabilistic Programming. Avi Pfeffer, Brian E. Ruttenberg, William Kretschmer, Alison O'Connor |
| 2018 | Structured Optimal Transport. David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka |
| 2018 | Submodularity on Hypergraphs: From Sets to Sequences. Marko Mitrovic, Moran Feldman, Andreas Krause, Amin Karbasi |
| 2018 | Subsampling for Ridge Regression via Regularized Volume Sampling. Michal Derezinski, Manfred K. Warmuth |
| 2018 | Sum-Product-Quotient Networks. Or Sharir, Amnon Shashua |
| 2018 | Symmetric Variational Autoencoder and Connections to Adversarial Learning. Liqun Chen, Shuyang Dai, Yunchen Pu, Erjin Zhou, Chunyuan Li, Qinliang Su, Changyou Chen, Lawrence Carin |
| 2018 | Teacher Improves Learning by Selecting a Training Subset. Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu |
| 2018 | Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series. Feras Saad, Vikash Mansinghka |
| 2018 | Tensor Regression Meets Gaussian Processes. Rose Yu, Max Guangyu Li, Yan Liu |
| 2018 | The Binary Space Partitioning-Tree Process. Xuhui Fan, Bin Li, Scott A. Sisson |
| 2018 | The Geometry of Random Features. Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller |
| 2018 | The Power Mean Laplacian for Multilayer Graph Clustering. Pedro Mercado, Antoine Gautier, Francesco Tudisco, Matthias Hein |
| 2018 | The emergence of spectral universality in deep networks. Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli |
| 2018 | Topic Compositional Neural Language Model. Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh, Lawrence Carin |
| 2018 | Towards Memory-Friendly Deterministic Incremental Gradient Method. Jiahao Xie, Hui Qian, Zebang Shen, Chao Zhang |
| 2018 | Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation. Mohammadreza Soltani, Chinmay Hegde |
| 2018 | Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. Robert M. Gower, Nicolas Le Roux, Francis R. Bach |
| 2018 | Transfer Learning on fMRI Datasets. Hejia Zhang, Po-Hsuan Chen, Peter J. Ramadge |
| 2018 | Tree-based Bayesian Mixture Model for Competing Risks. Alexis Bellot, Mihaela van der Schaar |
| 2018 | Turing: Composable inference for probabilistic programming. Hong Ge, Kai Xu, Zoubin Ghahramani |
| 2018 | VAE with a VampPrior. Jakub M. Tomczak, Max Welling |
| 2018 | Variational Inference based on Robust Divergences. Futoshi Futami, Issei Sato, Masashi Sugiyama |
| 2018 | Variational Rejection Sampling. Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon |
| 2018 | Variational Sequential Monte Carlo. Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei |
| 2018 | Variational inference for the multi-armed contextual bandit. Iñigo Urteaga, Chris Wiggins |
| 2018 | Weighted Tensor Decomposition for Learning Latent Variables with Partial Data. Omer Gottesman, Weiwei Pan, Finale Doshi-Velez |
| 2018 | Why Adaptively Collected Data Have Negative Bias and How to Correct for It. Xinkun Nie, Xiaoying Tian, Jonathan Taylor, James Zou |
| 2018 | Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications. Sijia Liu, Jie Chen, Pin-Yu Chen, Alfred O. Hero III |