Google
Google Scholar
MS Academic
CiteSeerX
CORE
Semantic ScholarJournal of Machine Learning Research, Volume 20
Volume 20, 2019
Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina:
Adaptation Based on Generalized Discrepancy. 1:1-1:30Sho Sonoda, Noboru Murata:
Transport Analysis of Infinitely Deep Neural Network. 2:1-2:52Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space. 3:1-3:44Clément Bouttier, Ioana Gavra:
Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations. 4:1-4:45Han Chen, Garvesh Raskutti, Ming Yuan:
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression. 5:1-5:37Piotr Szymanski, Tomasz Kajdanowicz:
scikit-multilearn: A Python library for Multi-Label Classification. 6:1-6:22Murat A. Erdogdu, Mohsen Bayati, Lee H. Dicker:
Scalable Approximations for Generalized Linear Problems. 7:1-7:45Giorgos Borboudakis, Ioannis Tsamardinos:
Forward-Backward Selection with Early Dropping. 8:1-8:39Adel Javanmard, Hamid Nazerzadeh:
Dynamic Pricing in High-dimensions. 9:1-9:49Salar Fattahi, Somayeh Sojoudi:
Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions. 10:1-10:44Mehmet Eren Ahsen, Mathukumalli Vidyasagar:
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory. 11:1-11:23Shusen Wang, Alex Gittens, Michael W. Mahoney:
Scalable Kernel K-Means Clustering with Nystr\"om Approximation: Relative-Error Bounds. 12:1-12:49Ofer Meshi, Ben London, Adrian Weller, David Sontag:
Train and Test Tightness of LP Relaxations in Structured Prediction. 13:1-13:34Steffen Grünewälder, Azadeh Khaleghi:
Approximations of the Restless Bandit Problem. 14:1-14:37Trevor Campbell, Tamara Broderick:
Automated Scalable Bayesian Inference via Hilbert Coresets. 15:1-15:38Ben Dai, Junhui Wang, Xiaotong Shen, Annie Qu:
Smooth neighborhood recommender systems. 16:1-16:24Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour:
Delay and Cooperation in Nonstochastic Bandits. 17:1-17:38María Luz Gámiz Pérez, María Dolores Martínez Miranda, Jens Perch Nielsen:
Multiplicative local linear hazard estimation and best one-sided cross-validation. 18:1-18:29Enrique G. Rodrigo, Juan A. Aledo, José A. Gámez:
spark-crowd: A Spark Package for Learning from Crowdsourced Big Data. 19:1-19:5HanQin Cai, Jian-Feng Cai, Ke Wei:
Accelerated Alternating Projections for Robust Principal Component Analysis. 20:1-20:33Ashish Khetan, Sewoong Oh:
Spectrum Estimation from a Few Entries. 21:1-21:55Yanning Shen, Tianyi Chen, Georgios B. Giannakis:
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics. 22:1-22:36Chengchun Shi, Wenbin Lu, Rui Song:
Determining the Number of Latent Factors in Statistical Multi-Relational Learning. 23:1-23:38Luciana Ferrer, Mitchell McLaren:
Joint PLDA for Simultaneous Modeling of Two Factors. 24:1-24:29Alberto Bietti, Julien Mairal:
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations. 25:1-25:49Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic:
TensorLy: Tensor Learning in Python. 26:1-26:6Veit Elser, Dan Schmidt, Jonathan S. Yedidia:
Monotone Learning with Rectified Wire Networks. 27:1-27:42Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, Noah D. Goodman:
Pyro: Deep Universal Probabilistic Programming. 28:1-28:6Bernard Chazelle, Chu Wang:
Iterated Learning in Dynamic Social Networks. 29:1-29:28Aleksis Pirinen, Brendan Ames:
Exact Clustering of Weighted Graphs via Semidefinite Programming. 30:1-30:34Franz J. Király, Harald Oberhauser:
Kernels for Sequentially Ordered Data. 31:1-31:45Jan Kralj, Marko Robnik-Sikonja, Nada Lavrac:
NetSDM: Semantic Data Mining with Network Analysis. 32:1-32:50Roei Gelbhart, Ran El-Yaniv:
The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient. 33:1-33:38Zahra S. Razaee, Arash A. Amini, Jingyi Jessica Li:
Matched Bipartite Block Model with Covariates. 34:1-34:44Christopher R. Dance, Tomi Silander:
Optimal Policies for Observing Time Series and Related Restless Bandit Problems. 35:1-35:93Patrick Rebeschini, Sekhar Tatikonda:
A New Approach to Laplacian Solvers and Flow Problems. 36:1-36:37Tyler Maunu, Teng Zhang, Gilad Lerman:
A Well-Tempered Landscape for Non-convex Robust Subspace Recovery. 37:1-37:59Yichen Chen, Yinyu Ye, Mengdi Wang:
Approximation Hardness for A Class of Sparse Optimization Problems. 38:1-38:27Miles E. Lopes, Shusen Wang, Michael W. Mahoney:
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication. 39:1-39:40Qianxiao Li, Cheng Tai, Weinan E:
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. 40:1-40:47Julian Katz-Samuels, Gilles Blanchard, Clayton Scott:
Decontamination of Mutual Contamination Models. 41:1-41:57Jialei Wang, Tong Zhang:
Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations. 42:1-42:56Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen:
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization. 43:1-43:58Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao:
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python. 44:1-44:5Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang:
Robust Frequent Directions with Application in Online Learning. 45:1-45:41Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou:
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. 46:1-46:36Zhixin Zhou, Arash A. Amini:
Analysis of spectral clustering algorithms for community detection: the general bipartite setting. 47:1-47:47Ying-Qi Zhao, Eric B. Laber, Yang Ning, Sumona Saha, Bruce E. Sands:
Efficient augmentation and relaxation learning for individualized treatment rules using observational data. 48:1-48:23Akshara Rai, Rika Antonova, Franziska Meier, Christopher G. Atkeson:
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots. 49:1-49:24Felix Berkenkamp, Angela P. Schoellig, Andreas Krause:
No-Regret Bayesian Optimization with Unknown Hyperparameters. 50:1-50:24Gregor Pirs, Erik Strumbelj:
Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures. 51:1-51:18Sondre Glimsdal, Ole-Christoffer Granmo:
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems. 52:1-52:24Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl:
Tunability: Importance of Hyperparameters of Machine Learning Algorithms. 53:1-53:32Maximilian Hüttenrauch, Adrian Sosic, Gerhard Neumann:
Deep Reinforcement Learning for Swarm Systems. 54:1-54:31Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Neural Architecture Search: A Survey. 55:1-55:21Zengfeng Huang:
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices. 56:1-56:23Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan:
Multi-class Heterogeneous Domain Adaptation. 57:1-57:31Po-Wei Wang, Ching-Pei Lee, Chih-Jen Lin:
The Common-directions Method for Regularized Empirical Risk Minimization. 58:1-58:49Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei Sha:
Kernel Approximation Methods for Speech Recognition. 59:1-59:36Wenwu Wang, Ping Yu, Lu Lin, Tiejun Tong:
Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression. 60:1-60:49Dong Xia, Fan Zhou:
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising. 61:1-61:42Sébastien Bubeck, Nikhil R. Devanur, Zhiyi Huang, Rad Niazadeh:
Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing. 62:1-62:37Peter L. Bartlett, Nick Harvey, Christopher Liaw, Abbas Mehrabian:
Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks. 63:1-63:17Bastian Bohn, Christian Rieger, Michael Griebel:
A Representer Theorem for Deep Kernel Learning. 64:1-64:32Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. 65:1-65:50Kevin L. Keys, Hua Zhou, Kenneth Lange:
Proximal Distance Algorithms: Theory and Practice. 66:1-66:38Hubie Chen, Matthew Valeriote:
Learnability of Solutions to Conjunctive Queries. 67:1-67:28John C. Duchi, Hongseok Namkoong:
Variance-based Regularization with Convex Objectives. 68:1-68:55Vince Lyzinski, Keith Levin, Carey E. Priebe:
On Consistent Vertex Nomination Schemes. 69:1-69:39Tomoyuki Obuchi, Yoshiyuki Kabashima:
Semi-Analytic Resampling in Lasso. 70:1-70:33Gábor Braun, Sebastian Pokutta, Daniel Zink:
Lazifying Conditional Gradient Algorithms. 71:1-71:42Can Karakus, Yifan Sun, Suhas N. Diggavi, Wotao Yin:
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning. 72:1-72:47Alain Durmus, Szymon Majewski, Blazej Miasojedow:
Analysis of Langevin Monte Carlo via Convex Optimization. 73:1-73:46Sebastian Becker, Patrick Cheridito, Arnulf Jentzen:
Deep Optimal Stopping. 74:1-74:25Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi:
Fairness Constraints: A Flexible Approach for Fair Classification. 75:1-75:42Shiqing Yu, Mathias Drton, Ali Shojaie:
Generalized Score Matching for Non-Negative Data. 76:1-76:70Yingying Fan, Emre Demirkaya, Jinchi Lv:
Nonuniformity of P-values Can Occur Early in Diverging Dimensions. 77:1-77:33Anindya Bhadra, Jyotishka Datta, Yunfan Li, Nicholas G. Polson, Brandon T. Willard:
Prediction Risk for the Horseshoe Regression. 78:1-78:39Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern:
Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions. 79:1-79:33Ruilin Li, Xiaojing Ye, Haomin Zhou, Hongyuan Zha:
Learning to Match via Inverse Optimal Transport. 80:1-80:37Mónika Csikós, Nabil H. Mustafa, Andrey Kupavskii:
Tight Lower Bounds on the VC-dimension of Geometric Set Systems. 81:1-81:8Rob Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards, Emily Hadley, Peter Baumgartner:
SMART: An Open Source Data Labeling Platform for Supervised Learning. 82:1-82:5Jaouad Mourtada, Stéphane Gaïffas:
On the optimality of the Hedge algorithm in the stochastic regime. 83:1-83:28Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
Differentiable Game Mechanics. 84:1-84:40Leonardo Vilela Teixeira, Renato M. Assunção, Rosangela Helena Loschi:
Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees. 85:1-85:35Armin Eftekhari, Gregory Ongie, Laura Balzano, Michael B. Wakin:
Streaming Principal Component Analysis From Incomplete Data. 86:1-86:62Botond Szabó, Harry van Zanten:
An asymptotic analysis of distributed nonparametric methods. 87:1-87:30Merlin Mpoudeu, Bertrand Clarke:
Model Selection via the VC Dimension. 88:1-88:26Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow:
Dependent relevance determination for smooth and structured sparse regression. 89:1-89:43Muhammad A. Masood, Finale Doshi-Velez:
A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization. 90:1-90:56Jason Altschuler, Victor-Emmanuel Brunel, Alan Malek:
Best Arm Identification for Contaminated Bandits. 91:1-91:39Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad:
AffectiveTweets: a Weka Package for Analyzing Affect in Tweets. 92:1-92:6Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans:
iNNvestigate Neural Networks! 93:1-93:8Mark Bun, Kobbi Nissim, Uri Stemmer:
Simultaneous Private Learning of Multiple Concepts. 94:1-94:34Gunwoong Park, Sion Park:
High-Dimensional Poisson Structural Equation Model Learning via $\ell_1$-Regularized Regression. 95:1-95:41Yue Zhao, Zain Nasrullah, Zheng Li:
PyOD: A Python Toolbox for Scalable Outlier Detection. 96:1-96:7
last updated on 2019-10-14 22:55 CEST by the dblp team
data released under the ODC-BY 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
the dblp computer science bibliography is funded by:
