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ORCIDJournal of Machine Learning Research, Volume 20
Volume 20, 2019
- Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina:
Adaptation Based on Generalized Discrepancy. 1:1-1:30 - Sho Sonoda, Noboru Murata:
Transport Analysis of Infinitely Deep Neural Network. 2:1-2:52 - Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space. 3:1-3:44 - Clément Bouttier, Ioana Gavra:
Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations. 4:1-4:45 - Han Chen, Garvesh Raskutti, Ming Yuan:
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression. 5:1-5:37 - Piotr Szymanski, Tomasz Kajdanowicz:
scikit-multilearn: A Python library for Multi-Label Classification. 6:1-6:22 - Murat A. Erdogdu, Mohsen Bayati, Lee H. Dicker:
Scalable Approximations for Generalized Linear Problems. 7:1-7:45 - Giorgos Borboudakis, Ioannis Tsamardinos:
Forward-Backward Selection with Early Dropping. 8:1-8:39 - Adel Javanmard, Hamid Nazerzadeh:
Dynamic Pricing in High-dimensions. 9:1-9:49 - Salar Fattahi, Somayeh Sojoudi:
Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions. 10:1-10:44 - Mehmet Eren Ahsen, Mathukumalli Vidyasagar:
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory. 11:1-11:23 - Shusen Wang, Alex Gittens, Michael W. Mahoney:
Scalable Kernel K-Means Clustering with Nystr\"om Approximation: Relative-Error Bounds. 12:1-12:49 - Ofer Meshi, Ben London, Adrian Weller, David A. Sontag:
Train and Test Tightness of LP Relaxations in Structured Prediction. 13:1-13:34 - Steffen Grünewälder, Azadeh Khaleghi:
Approximations of the Restless Bandit Problem. 14:1-14:37 - Trevor Campbell, Tamara Broderick:
Automated Scalable Bayesian Inference via Hilbert Coresets. 15:1-15:38 - Ben Dai, Junhui Wang, Xiaotong Shen, Annie Qu:
Smooth neighborhood recommender systems. 16:1-16:24 - Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour:
Delay and Cooperation in Nonstochastic Bandits. 17:1-17:38 - María Luz Gámiz, María Dolores Martínez Miranda, Jens Perch Nielsen:
Multiplicative local linear hazard estimation and best one-sided cross-validation. 18:1-18:29 - Enrique González Rodrigo, Juan A. Aledo, José A. Gámez:
spark-crowd: A Spark Package for Learning from Crowdsourced Big Data. 19:1-19:5 - HanQin Cai, Jian-Feng Cai, Ke Wei:
Accelerated Alternating Projections for Robust Principal Component Analysis. 20:1-20:33 - Ashish Khetan, Sewoong Oh:
Spectrum Estimation from a Few Entries. 21:1-21:55 - Yanning Shen, Tianyi Chen, Georgios B. Giannakis:
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics. 22:1-22:36 - Chengchun Shi, Wenbin Lu, Rui Song:
Determining the Number of Latent Factors in Statistical Multi-Relational Learning. 23:1-23:38 - Luciana Ferrer, Mitchell McLaren:
Joint PLDA for Simultaneous Modeling of Two Factors. 24:1-24:29 - Alberto Bietti, Julien Mairal:
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations. 25:1-25:49 - Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic:
TensorLy: Tensor Learning in Python. 26:1-26:6 - Veit Elser, Dan Schmidt, Jonathan S. Yedidia:
Monotone Learning with Rectified Wire Networks. 27:1-27:42 - Eli 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. 28:1-28:6 - Bernard Chazelle, Chu Wang:
Iterated Learning in Dynamic Social Networks. 29:1-29:28 - Aleksis Pirinen, Brendan P. W. Ames
:
Exact Clustering of Weighted Graphs via Semidefinite Programming. 30:1-30:34 - Franz J. Király, Harald Oberhauser:
Kernels for Sequentially Ordered Data. 31:1-31:45 - Jan Kralj, Marko Robnik-Sikonja, Nada Lavrac:
NetSDM: Semantic Data Mining with Network Analysis. 32:1-32:50 - Roei Gelbhart, Ran El-Yaniv:
The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient. 33:1-33:38 - Zahra S. Razaee, Arash A. Amini, Jingyi Jessica Li:
Matched Bipartite Block Model with Covariates. 34:1-34:44 - Christopher R. Dance, Tomi Silander:
Optimal Policies for Observing Time Series and Related Restless Bandit Problems. 35:1-35:93 - Patrick Rebeschini, Sekhar Tatikonda:
A New Approach to Laplacian Solvers and Flow Problems. 36:1-36:37 - Tyler Maunu, Teng Zhang, Gilad Lerman:
A Well-Tempered Landscape for Non-convex Robust Subspace Recovery. 37:1-37:59 - Yichen Chen, Yinyu Ye, Mengdi Wang:
Approximation Hardness for A Class of Sparse Optimization Problems. 38:1-38:27 - Miles E. Lopes, Shusen Wang, Michael W. Mahoney:
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication. 39:1-39:40 - Qianxiao Li, Cheng Tai, Weinan E:
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. 40:1-40:47 - Julian Katz-Samuels, Gilles Blanchard, Clayton Scott:
Decontamination of Mutual Contamination Models. 41:1-41:57 - Jialei Wang, Tong Zhang:
Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations. 42:1-42:56 - Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen:
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization. 43:1-43:58 - Jason 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. 44:1-44:5 - Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang:
Robust Frequent Directions with Application in Online Learning. 45:1-45:41 - Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou:
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. 46:1-46:36 - Zhixin Zhou, Arash A. Amini:
Analysis of spectral clustering algorithms for community detection: the general bipartite setting. 47:1-47:47 - Ying-Qi Zhao, Eric B. Laber, Yang Ning, Sumona Saha, Bruce E. Sands:
Efficient augmentation and relaxation learning for individualized treatment rules using observational data. 48:1-48:23 - Akshara Rai, Rika Antonova, Franziska Meier, Christopher G. Atkeson:
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots. 49:1-49:24 - Felix Berkenkamp, Angela P. Schoellig, Andreas Krause:
No-Regret Bayesian Optimization with Unknown Hyperparameters. 50:1-50:24 - Gregor Pirs, Erik Strumbelj:
Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures. 51:1-51:18 - Sondre Glimsdal, Ole-Christoffer Granmo:
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems. 52:1-52:24 - Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl:
Tunability: Importance of Hyperparameters of Machine Learning Algorithms. 53:1-53:32 - Maximilian Hüttenrauch, Adrian Sosic, Gerhard Neumann:
Deep Reinforcement Learning for Swarm Systems. 54:1-54:31 - Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Neural Architecture Search: A Survey. 55:1-55:21 - Zengfeng Huang:
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices. 56:1-56:23 - Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan:
Multi-class Heterogeneous Domain Adaptation. 57:1-57:31 - Po-Wei Wang, Ching-Pei Lee, Chih-Jen Lin:
The Common-directions Method for Regularized Empirical Risk Minimization. 58:1-58:49 - Avner 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. 59:1-59:36 - Wenwu Wang, Ping Yu, Lu Lin, Tiejun Tong:
Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression. 60:1-60:49 - Dong Xia, Fan Zhou:
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising. 61:1-61:42 - Sébastien Bubeck, Nikhil R. Devanur, Zhiyi Huang, Rad Niazadeh:
Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing. 62:1-62:37 - Peter L. Bartlett, Nick Harvey, Christopher Liaw, Abbas Mehrabian:
Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks. 63:1-63:17 - Bastian Bohn, Christian Rieger, Michael Griebel:
A Representer Theorem for Deep Kernel Learning. 64:1-64:32 - Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. 65:1-65:50 - Kevin L. Keys, Hua Zhou, Kenneth Lange:
Proximal Distance Algorithms: Theory and Practice. 66:1-66:38 - Hubie Chen, Matthew Valeriote:
Learnability of Solutions to Conjunctive Queries. 67:1-67:28 - John C. Duchi, Hongseok Namkoong:
Variance-based Regularization with Convex Objectives. 68:1-68:55 - Vince Lyzinski, Keith D. Levin, Carey E. Priebe:
On Consistent Vertex Nomination Schemes. 69:1-69:39 - Tomoyuki Obuchi, Yoshiyuki Kabashima:
Semi-Analytic Resampling in Lasso. 70:1-70:33 - Gábor Braun, Sebastian Pokutta, Daniel Zink:
Lazifying Conditional Gradient Algorithms. 71:1-71:42 - Can Karakus, Yifan Sun, Suhas N. Diggavi, Wotao Yin:
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning. 72:1-72:47 - Alain Durmus, Szymon Majewski, Blazej Miasojedow:
Analysis of Langevin Monte Carlo via Convex Optimization. 73:1-73:46 - Sebastian Becker, Patrick Cheridito, Arnulf Jentzen:
Deep Optimal Stopping. 74:1-74:25 - Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi:
Fairness Constraints: A Flexible Approach for Fair Classification. 75:1-75:42 - Shiqing Yu, Mathias Drton, Ali Shojaie:
Generalized Score Matching for Non-Negative Data. 76:1-76:70 - Yingying Fan, Emre Demirkaya, Jinchi Lv:
Nonuniformity of P-values Can Occur Early in Diverging Dimensions. 77:1-77:33 - Anindya Bhadra, Jyotishka Datta, Yunfan Li, Nicholas G. Polson, Brandon T. Willard:
Prediction Risk for the Horseshoe Regression. 78:1-78:39 - Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern:
Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions. 79:1-79:33 - Ruilin Li, Xiaojing Ye, Haomin Zhou, Hongyuan Zha:
Learning to Match via Inverse Optimal Transport. 80:1-80:37 - Mónika Csikós, Nabil H. Mustafa, Andrey Kupavskii:
Tight Lower Bounds on the VC-dimension of Geometric Set Systems. 81:1-81:8 - Rob Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards, Emily Hadley, Peter Baumgartner:
SMART: An Open Source Data Labeling Platform for Supervised Learning. 82:1-82:5 - Jaouad Mourtada, Stéphane Gaïffas:
On the optimality of the Hedge algorithm in the stochastic regime. 83:1-83:28 - Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
Differentiable Game Mechanics. 84:1-84:40 - Leonardo Vilela Teixeira, Renato M. Assunção, Rosangela Helena Loschi:
Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees. 85:1-85:35 - Armin Eftekhari, Gregory Ongie, Laura Balzano, Michael B. Wakin:
Streaming Principal Component Analysis From Incomplete Data. 86:1-86:62 - Botond Szabó, Harry van Zanten:
An asymptotic analysis of distributed nonparametric methods. 87:1-87:30 - Merlin Mpoudeu, Bertrand S. Clarke:
Model Selection via the VC Dimension. 88:1-88:26 - Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow:
Dependent relevance determination for smooth and structured sparse regression. 89:1-89:43 - Muhammad A. Masood, Finale Doshi-Velez:
A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization. 90:1-90:56 - Jason M. Altschuler, Victor-Emmanuel Brunel, Alan Malek:
Best Arm Identification for Contaminated Bandits. 91:1-91:39 - Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad:
AffectiveTweets: a Weka Package for Analyzing Affect in Tweets. 92:1-92:6 - Maximilian 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! 93:1-93:8 - Mark Bun, Kobbi Nissim, Uri Stemmer:
Simultaneous Private Learning of Multiple Concepts. 94:1-94:34 - Gunwoong Park, Sion Park:
High-Dimensional Poisson Structural Equation Model Learning via $\ell_1$-Regularized Regression. 95:1-95:41 - Yue Zhao, Zain Nasrullah, Zheng Li:
PyOD: A Python Toolbox for Scalable Outlier Detection. 96:1-96:7 - Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion. 97:1-97:22 - Wenjing Liao, Mauro Maggioni:
Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data. 98:1-98:63 - William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson:
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction. 99:1-99:51 - Khue-Dung Dang, Matias Quiroz, Robert Kohn, Minh-Ngoc Tran, Mattias Villani:
Hamiltonian Monte Carlo with Energy Conserving Subsampling. 100:1-100:31 - Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Low Permutation-rank Matrices: Structural Properties and Noisy Completion. 101:1-101:43 - Maria-Florina Balcan, Yingyu Liang, Zhao Song, David P. Woodruff, Hongyang Zhang:
Non-Convex Matrix Completion and Related Problems via Strong Duality. 102:1-102:56 - Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani:
Regularization via Mass Transportation. 103:1-103:68 - Salvatore Ruggieri:
Complete Search for Feature Selection in Decision Trees. 104:1-104:34 - Max Sommerfeld, Jörn Schrieber, Yoav Zemel, Axel Munk:
Optimal Transport: Fast Probabilistic Approximation with Exact Solvers. 105:1-105:23 - Ziyan Luo, Defeng Sun, Kim-Chuan Toh, Naihua Xiu:
Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method. 106:1-106:25 - Zemin Zheng, Mohammad Taha Bahadori, Yan Liu, Jinchi Lv:
Scalable Interpretable Multi-Response Regression via SEED. 107:1-107:34 - Omer Weissbrod, Shachar Kaufman, David Golan, Saharon Rosset:
Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling. 108:1-108:30 - De Wen Soh, Sekhar Tatikonda:
Learning Unfaithful $K$-separable Gaussian Graphical Models. 109:1-109:30 - Michael Unser:
A Representer Theorem for Deep Neural Networks. 110:1-110:30 - Abhishek Kaul, Venkata K. Jandhyala, Stergios B. Fotopoulos:
An Efficient Two Step Algorithm for High Dimensional Change Point Regression Models Without Grid Search. 111:1-111:40 - Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl:
Measuring the Effects of Data Parallelism on Neural Network Training. 112:1-112:49 - Xiaozhou Wang, Zhuoyi Yang, Xi Chen, Weidong Liu:
Distributed Inference for Linear Support Vector Machine. 113:1-113:41 - Richard Y. Zhang, Somayeh Sojoudi, Javad Lavaei:
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery. 114:1-114:34 - Yuqi Gu, Gongjun Xu:
Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models. 115:1-115:58 - Andreas Loukas:
Graph Reduction with Spectral and Cut Guarantees. 116:1-116:42 - Edwin V. Bonilla, Karl Krauth, Amir Dezfouli:
Generic Inference in Latent Gaussian Process Models. 117:1-117:63 - Mokhtar Z. Alaya, Simon Bussy, Stéphane Gaïffas, Agathe Guilloux:
Binarsity: a penalization for one-hot encoded features in linear supervised learning. 118:1-118:34 - Kean Ming Tan, Junwei Lu, Tong Zhang, Han Liu:
Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models. 119:1-119:38 - Stephen Page, Steffen Grünewälder:
Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces. 120:1-120:49 - Bin Hong, Weizhong Zhang, Wei Liu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang:
Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction. 121:1-121:39 - Dmitri S. Pavlichin, Jiantao Jiao, Tsachy Weissman:
Approximate Profile Maximum Likelihood. 122:1-122:55 - Setareh Ariafar, Jaume Coll-Font, Dana H. Brooks, Jennifer G. Dy:
ADMMBO: Bayesian Optimization with Unknown Constraints using ADMM. 123:1-123:26 - Ian Osband, Benjamin Van Roy, Daniel J. Russo, Zheng Wen:
Deep Exploration via Randomized Value Functions. 124:1-124:62 - Javier Sánchez-Monedero, Pedro Antonio Gutiérrez, María Pérez-Ortiz:
ORCA: A Matlab/Octave Toolbox for Ordinal Regression. 125:1-125:5 - Christoph D. Hofer, Roland Kwitt, Marc Niethammer:
Learning Representations of Persistence Barcodes. 126:1-126:45 - Steven M. Hill, Chris J. Oates, Duncan A. J. Blythe, Sach Mukherjee:
Causal Learning via Manifold Regularization. 127:1-127:32 - Edward Barker, Charl J. Ras:
Unsupervised Basis Function Adaptation for Reinforcement Learning. 128:1-128:73 - Håvard Kvamme, Ørnulf Borgan, Ida Scheel:
Time-to-Event Prediction with Neural Networks and Cox Regression. 129:1-129:30 - Ashwin Srinivasan, Lovekesh Vig, Michael Bain:
Logical Explanations for Deep Relational Machines Using Relevance Information. 130:1-130:47 - Sophie Burkhardt, Stefan Kramer:
Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model. 131:1-131:27 - HaiYing Wang:
More Efficient Estimation for Logistic Regression with Optimal Subsamples. 132:1-132:59 - Luca Venturi, Afonso S. Bandeira, Joan Bruna:
Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes. 133:1-133:34 - Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularization Methods. 134:1-134:47 - Christian Agrell:
Gaussian Processes with Linear Operator Inequality Constraints. 135:1-135:36 - Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang:
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis. 136:1-136:37 - Mohammad J. Saberian, Nuno Vasconcelos:
Multiclass Boosting: Margins, Codewords, Losses, and Algorithms. 137:1-137:68 - Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros:
Generalized Maximum Entropy Estimation. 138:1-138:29 - Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, Brian M. Sadler:
Decentralized Dictionary Learning Over Time-Varying Digraphs. 139:1-139:62 - Zuofeng Shang, Botao Hao, Guang Cheng:
Nonparametric Bayesian Aggregation for Massive Data. 140:1-140:81 - Thanh Van Nguyen, Raymond K. W. Wong, Chinmay Hegde:
Provably Accurate Double-Sparse Coding. 141:1-141:43 - Ji Chen, Xiaodong Li:
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA. 142:1-142:39 - Zhenyue Zhang, Yuqing Xia:
Minimal Sample Subspace Learning: Theory and Algorithms. 143:1-143:57 - Bin Li, Yik-Chung Wu:
Convergence of Gaussian Belief Propagation Under General Pairwise Factorization: Connecting Gaussian MRF with Pairwise Linear Gaussian Model. 144:1-144:30 - Benjamin Letham, Eytan Bakshy:
Bayesian Optimization for Policy Search via Online-Offline Experimentation. 145:1-145:30 - Amos Beimel, Kobbi Nissim, Uri Stemmer:
Characterizing the Sample Complexity of Pure Private Learners. 146:1-146:33 - Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf:
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise. 147:1-147:50 - Mokhtar Z. Alaya, Olga Klopp:
Collective Matrix Completion. 148:1-148:43 - Daniil Ryabko:
On Asymptotic and Finite-Time Optimality of Bayesian Predictors. 149:1-149:24 - Berk Ustun, Cynthia Rudin:
Learning Optimized Risk Scores. 150:1-150:75 - Vasileios Maroulas, Joshua L. Mike, Christopher Oballe:
Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams. 151:1-151:49 - Sen Na, Zhuoran Yang, Zhaoran Wang, Mladen Kolar:
High-dimensional Varying Index Coefficient Models via Stein's Identity. 152:1-152:44 - David G. Harris, Shi Li, Thomas W. Pensyl, Aravind Srinivasan, Khoa Trinh:
Approximation Algorithms for Stochastic Clustering. 153:1-153:33 - Koulik Khamaru, Martin J. Wainwright:
Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems. 154:1-154:52 - Theodore Vasiloudis, Gianmarco De Francisci Morales, Henrik Boström:
Quantifying Uncertainty in Online Regression Forests. 155:1-155:35 - Yuntao Chen, Chenxia Han, Yanghao Li, Zehao Huang, Yi Jiang, Naiyan Wang, Zhaoxiang Zhang:
SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition. 156:1-156:8 - Ali Ahmed, Alireza Aghasi, Paul Hand:
Simultaneous Phase Retrieval and Blind Deconvolution via Convex Programming. 157:1-157:28 - Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Hayden S. Helm, Joshua T. Vogelstein:
GraSPy: Graph Statistics in Python. 158:1-158:7 - Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié:
Optimal Convergence Rates for Convex Distributed Optimization in Networks. 159:1-159:31 - Mauro Maggioni, James M. Murphy:
Learning by Unsupervised Nonlinear Diffusion. 160:1-160:56 - Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A. K. Suykens:
Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization. 161:1-161:44 - Sylvain Arlot, Alain Celisse, Zaïd Harchaoui:
A Kernel Multiple Change-point Algorithm via Model Selection. 162:1-162:56 - Jie Wang, Zhanqiu Zhang, Jieping Ye:
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets. 163:1-163:42 - Arjun Sondhi, Ali Shojaie:
The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks. 164:1-164:31 - Francois Kamper, Sarel Steel, Johan A. du Preez:
On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size. 165:1-165:37 - Mehmet Eren Ahsen, Robert M. Vogel, Gustavo A. Stolovitzky:
Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions. 166:1-166:40 - Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang:
Stochastic Canonical Correlation Analysis. 167:1-167:46 - Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard:
Determinantal Point Processes for Coresets. 168:1-168:70 - Michael Minyi Zhang, Sinead A. Williamson:
Embarrassingly Parallel Inference for Gaussian Processes. 169:1-169:26 - Daren Wang, Xinyang Lu, Alessandro Rinaldo:
DBSCAN: Optimal Rates For Density-Based Cluster Estimation. 170:1-170:50 - Alexander M. Franks, Peter Hoff:
Shared Subspace Models for Multi-Group Covariance Estimation. 171:1-171:37 - Andrew Cotter, Heinrich Jiang, Maya R. Gupta, Serena Lutong Wang, Taman Narayan, Seungil You, Karthik Sridharan:
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. 172:1-172:59 - Yousra El-Bachir, Anthony C. Davison:
Fast Automatic Smoothing for Generalized Additive Models. 173:1-173:27 - Jesse A. Livezey, Alejandro F. Bujan, Friedrich T. Sommer:
Learning Overcomplete, Low Coherence Dictionaries with Linear Inference. 174:1-174:42 - Felix Bießmann, Tammo Rukat, Philipp Schmidt, Prathik Naidu, Sebastian Schelter, Andrey Taptunov, Dustin Lange, David Salinas:
DataWig: Missing Value Imputation for Tables. 175:1-175:6 - Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk:
New Convergence Aspects of Stochastic Gradient Algorithms. 176:1-176:49 - Aaron Fisher, Cynthia Rudin, Francesca Dominici:
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously. 177:1-177:81 - Daniel Coelho de Castro, Jeremy Tan, Bernhard Kainz, Ender Konukoglu, Ben Glocker:
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning. 178:1-178:29 - Lyudmila Grigoryeva, Juan-Pablo Ortega:
Differentiable reservoir computing. 179:1-179:62 - Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko:
DPPy: DPP Sampling with Python. 180:1-180:7 - Saeed Saremi, Aapo Hyvärinen:
Neural Empirical Bayes. 181:1-181:23 - Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez:
Model Selection in Bayesian Neural Networks via Horseshoe Priors. 182:1-182:46 - Raaz Dwivedi, Yuansi Chen, Martin J. Wainwright, Bin Yu:
Log-concave sampling: Metropolis-Hastings algorithms are fast. 183:1-183:42 - Aharon Azulay, Yair Weiss:
Why do deep convolutional networks generalize so poorly to small image transformations? 184:1-184:25
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