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ORCID21st AISTATS 2018: Playa Blanca, Lanzarote, Canary Islands, Spain
- Amos J. Storkey, Fernando Pérez-Cruz:
International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Spain. Proceedings of Machine Learning Research 84, PMLR 2018 - Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
The Geometry of Random Features. 1-9 - Sungsoo Ahn, Michael Chertkov, Jinwoo Shin, Adrian Weller:
Gauged Mini-Bucket Elimination for Approximate Inference. 10-19 - Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt:
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians. 20-28 - Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh:
An Analysis of Categorical Distributional Reinforcement Learning. 29-37 - Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers:
Combinatorial Preconditioners for Proximal Algorithms on Graphs. 38-47 - Guy Uziel, Ran El-Yaniv:
Growth-Optimal Portfolio Selection under CVaR Constraints. 48-57 - Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Accelerated Stochastic Power Iteration. 58-67 - Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song, Haesun Park:
Multi-scale Nystrom Method. 68-76 - Satoshi Hara, Kohei Hayashi:
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach. 77-85 - James R. Foulds:
Mixed Membership Word Embeddings for Computational Social Science. 86-95 - Emma Pierson, Sam Corbett-Davies, Sharad Goel:
Fast Threshold Tests for Detecting Discrimination. 96-105 - Juho Piironen, Aki Vehtari:
Iterative Supervised Principal Components. 106-114 - Chieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David R. Kaeli, Jennifer G. Dy:
Iterative Spectral Method for Alternative Clustering. 115-123 - Dennis Forster, Jörg Lücke:
Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means. 124-132 - Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Parallelised Bayesian Optimisation via Thompson Sampling. 133-142 - Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman:
On the challenges of learning with inference networks on sparse, high-dimensional data. 143-151 - Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi:
Post Selection Inference with Kernels. 152-160 - Joel Ratsaby:
On how complexity affects the stability of a predictor. 161-167 - Zhiqiang Xu, Xin Gao:
On Truly Block Eigensolvers via Riemannian Optimization. 168-177 - Heng Guo, Kaan Kara, Ce Zhang:
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond. 178-187 - Rajiv Khanna, Anastasios Kyrillidis:
IHT dies hard: Provable accelerated Iterative Hard Thresholding. 188-198 - Jinshan Zeng, Ke Ma, Yuan Yao:
Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction. 199-207 - Dehan Kong, Howard D. Bondell, Weining Shen:
Outlier Detection and Robust Estimation in Nonparametric Regression. 208-216 - Luca Ambrogioni, Eric Maris:
Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis. 217-225 - Gabriele Abbati, Alessandra Tosi, Michael A. Osborne
, Seth R. Flaxman:
AdaGeo: Adaptive Geometric Learning for Optimization and Sampling. 226-234 - Xiand Gao, Xiaobo Li, Shuzhong Zhang:
Online Learning with Non-Convex Losses and Non-Stationary Regret. 235-243 - Christophe Dupuy, Francis R. Bach:
Learning Determinantal Point Processes in Sublinear Time. 244-257 - Kaiqing Zhang, Zhuoran Yang, Zhaoran Wang:
Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding. 258-268 - Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra:
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis. 269-278 - Young Hun Jung, Ambuj Tewari:
Online Boosting Algorithms for Multi-label Ranking. 279-287 - Sijia Liu, Jie Chen, Pin-Yu Chen, Alfred O. Hero III:
Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications. 288-297 - Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher:
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups. 298-307 - Jan Kremer, Fei Sha, Christian Igel:
Robust Active Label Correction. 308-316 - Amit Gruber, Chen Yanover, Tal El-Hay, Anders Sönnerborg, Vanni Borghi, Francesca Incardona, Yaara Goldschmidt:
Factorial HMMs with Collapsed Gibbs Sampling for Optimizing Long-term HIV Therapy. 317-326 - Pankaj Pansari, Chris Russell, M. Pawan Kumar:
Optimal Submodular Extensions for Marginal Estimation. 327-335 - Nir Rosenfeld, Amir Globerson:
Semi-Supervised Learning with Competitive Infection Models. 336-346 - Nir Rosenfeld, Yishay Mansour, Elad Yom-Tov:
Discriminative Learning of Prediction Intervals. 347-355 - Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh, Lawrence Carin:
Topic Compositional Neural Language Model. 356-365 - Eric T. Nalisnick, Padhraic Smyth:
Learning Priors for Invariance. 366-375 - Scott Cheng-Hsin Yang, Yue Yu, Arash Givchi, Pei Wang, Wai Keen Vong, Patrick Shafto:
Optimal Cooperative Inference. 376-385 - David Liau, Zhao Song, Eric Price, Ger Yang:
Stochastic Multi-armed Bandits in Constant Space. 386-394 - Dehua Cheng, Natali Ruchansky, Yan Liu:
Matrix completability analysis via graph k-connectivity. 395-403 - Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney:
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. 404-414 - Riikka Huusari, Hachem Kadri, Cécile Capponi:
Multi-view Metric Learning in Vector-valued Kernel Spaces. 415-424 - William Herlands, Edward McFowland, Andrew Gordon Wilson, Daniel B. Neill:
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data. 425-434 - Jacopo Cavazza, Pietro Morerio, Benjamin D. Haeffele, Connor Lane, Vittorio Murino, René Vidal:
Dropout as a Low-Rank Regularizer for Matrix Factorization. 435-444 - Tianbao Yang, Zhe Li, Lijun Zhang:
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. 445-453 - Masaaki Takada, Taiji Suzuki, Hironori Fujisawa:
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. 454-463 - Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch:
Boosting Variational Inference: an Optimization Perspective. 464-472 - Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, Marc Tommasi:
Personalized and Private Peer-to-Peer Machine Learning. 473-481 - Rose Yu, Max Guangyu Li, Yan Liu:
Tensor Regression Meets Gaussian Processes. 482-490 - Emanuel Laude, Tao Wu, Daniel Cremers:
A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization. 491-499 - Vivek Kumar Bagaria, Govinda M. Kamath, Vasilis Ntranos, Martin J. Zhang, David Tse:
Medoids in Almost-Linear Time via Multi-Armed Bandits. 500-509 - Zhiyang Wang, Ruida Zhou, Cong Shen:
Regional Multi-Armed Bandits. 510-518 - Yang Cao, Liyan Xie, Yao Xie, Huan Xu:
Nearly second-order optimality of online joint detection and estimation via one-sample update schemes. 519-528 - Or Sharir, Amnon Shashua:
Sum-Product-Quotient Networks. 529-537 - Sunil Gupta, Alistair Shilton, Santu Rana, Svetha Venkatesh:
Exploiting Strategy-Space Diversity for Batch Bayesian Optimization. 538-547 - Stéphan Clémençon, François Portier:
Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods. 548-556 - Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing:
Group invariance principles for causal generative models. 557-565 - Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker:
A Provable Algorithm for Learning Interpretable Scoring Systems. 566-574 - Hyunjik Kim, Yee Whye Teh:
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes. 575-584 - Shinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato:
Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams. 585-594 - Hejia Zhang, Po-Hsuan Chen, Peter J. Ramadge:
Transfer Learning on fMRI Datasets. 595-603 - Chaofan Chen, Cynthia Rudin:
An Optimization Approach to Learning Falling Rule Lists. 604-612 - Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaïd Harchaoui:
Catalyst for Gradient-based Nonconvex Optimization. 613-622 - Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin:
Benefits from Superposed Hawkes Processes. 623-631 - Julian Katz-Samuels, Clayton Scott:
Nonparametric Preference Completion. 632-641 - Mathieu Sinn, Ambrish Rawat:
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training. 642-651 - Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton:
Efficient and principled score estimation with Nyström kernel exponential families. 652-660 - Liqun Chen, Shuyang Dai, Yunchen Pu, Erjin Zhou, Chunyuan Li, Qinliang Su, Changyou Chen, Lawrence Carin:
Symmetric Variational Autoencoder and Connections to Adversarial Learning. 661-669 - Sergey Bartunov, Dmitry P. Vetrov:
Few-shot Generative Modelling with Generative Matching Networks. 670-678 - Tianyu Li, Guillaume Rabusseau, Doina Precup:
Nonlinear Weighted Finite Automata. 679-688 - Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman:
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models. 689-697 - Iñigo Urteaga, Chris Wiggins:
Variational inference for the multi-armed contextual bandit. 698-706 - Robert M. Gower, Nicolas Le Roux, Francis R. Bach:
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. 707-715 - Michal Derezinski, Manfred K. Warmuth:
Subsampling for Ridge Regression via Regularized Volume Sampling. 716-725 - Pavel Izmailov, Alexander Novikov, Dmitry Kropotov:
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. 726-735 - Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer:
Batch-Expansion Training: An Efficient Optimization Framework. 736-744 - Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka:
Batched Large-scale Bayesian Optimization in High-dimensional Spaces. 745-754 - Feras Saad, Vikash Mansinghka:
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series. 755-764 - Renbo Zhao, Volkan Cevher:
Stochastic Three-Composite Convex Minimization with a Linear Operator. 765-774 - Cynthia Rudin, Yining Wang:
Direct Learning to Rank And Rerank. 775-783 - Olivier Bachem, Mario Lucic, Silvio Lattanzi:
One-shot Coresets: The Case of k-Clustering. 784-792 - Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock:
Random Warping Series: A Random Features Method for Time-Series Embedding. 793-802 - Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar:
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD. 803-812 - Futoshi Futami, Issei Sato, Masashi Sugiyama:
Variational Inference based on Robust Divergences. 813-822 - Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon:
Variational Rejection Sampling. 823-832 - 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:
Best arm identification in multi-armed bandits with delayed feedback. 833-842 - Liyuan Xu, Junya Honda, Masashi Sugiyama:
A fully adaptive algorithm for pure exploration in linear bandits. 843-851 - Rajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai:
Contextual Bandits with Stochastic Experts. 852-861 - Sven Schmit, Carlos Riquelme:
Human Interaction with Recommendation Systems. 862-870 - I (Eli) Chien, Chung-Yi Lin, I-Hsiang Wang:
Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms. 871-879 - Mathieu Blondel, Vivien Seguy, Antoine Rolet:
Smooth and Sparse Optimal Transport. 880-889 - Ilija Bogunovic, Junyao Zhao, Volkan Cevher:
Robust Maximization of Non-Submodular Objectives. 890-899 - Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Cause-Effect Inference by Comparing Regression Errors. 900-909 - Alexis Bellot, Mihaela van der Schaar:
Tree-based Bayesian Mixture Model for Competing Risks. 910-918 - Julien Pérolat, Bilal Piot, Olivier Pietquin:
Actor-Critic Fictitious Play in Simultaneous Move Multistage Games. 919-928 - Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts:
Random Subspace with Trees for Feature Selection Under Memory Constraints. 929-937 - Jakob Runge:
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. 938-947 - Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos:
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures. 948-957 - Achintya Kundu, Francis R. Bach, Chiranjib Bhattacharyya:
Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach. i - Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei:
Variational Sequential Monte Carlo. 968-977 - Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida:
Statistically Efficient Estimation for Non-Smooth Probability Densities. 978-987 - Xu Hu, Guillaume Obozinski:
SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning. 988-997 - Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression. 998-1007 - Atsushi Nitanda, Taiji Suzuki:
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. 1008-1016 - Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun:
Statistical Sparse Online Regression: A Diffusion Approximation Perspective. 1017-1026 - Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida:
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. 1027-1036 - Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas B. Schön:
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs. 1037-1046 - Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar:
Learning to Round for Discrete Labeling Problems. 1047-1056 - Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright:
Approximate ranking from pairwise comparisons. 1057-1066 - Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez:
Semi-Supervised Prediction-Constrained Topic Models. 1067-1076 - Yichen Wang, Evangelos A. Theodorou, Apurv Verma, Le Song:
A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop. 1077-1086 - Pan Xu, Tianhao Wang, Quanquan Gu:
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms. 1087-1096 - Xiao Zhang, Lingxiao Wang, Quanquan Gu:
A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. 1097-1107 - Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama:
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. 1108-1116 - Masayuki Karasuyama, Hiroshi Mamitsuka:
Factor Analysis on a Graph. 1117-1126 - Junxiang Chen, Yale Chang, Peter J. Castaldi, Michael H. Cho, Brian Hobbs, Jennifer G. Dy:
Crowdclustering with Partition Labels. 1127-1136 - Ruiyi Zhang, Chunyuan Li, Changyou Chen, Lawrence Carin:
Learning Structural Weight Uncertainty for Sequential Decision-Making. 1137-1146 - Jiahao Xie, Hui Qian, Zebang Shen, Chao Zhang:
Towards Memory-Friendly Deterministic Incremental Gradient Method. 1147-1156 - Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Optimality of Approximate Inference Algorithms on Stable Instances. 1157-1166 - Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman:
Bayesian Approaches to Distribution Regression. 1167-1176 - Marko Mitrovic, Moran Feldman, Andreas Krause, Amin Karbasi:
Submodularity on Hypergraphs: From Sets to Sequences. 1177-1184 - Bowei Yan, Purnamrita Sarkar, Xiuyuan Cheng:
Provable Estimation of the Number of Blocks in Block Models. 1185-1194 - Michael T. Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence:
Differentially Private Regression with Gaussian Processes. 1195-1203 - Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi:
Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. 1204-1213 - Jakub M. Tomczak, Max Welling:
VAE with a VampPrior. 1214-1223 - Avi Pfeffer, Brian E. Ruttenberg, William Kretschmer, Alison O'Connor:
Structured Factored Inference for Probabilistic Programming. 1224-1232