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ORCIDJournal of Machine Learning Research, Volume 19
Volume 19, 2018
- Weili Guo, Haikun Wei, Yew-Soon Ong, Jaime Rubio Hervas, Junsheng Zhao, Hai Wang, Kanjian Zhang:
Numerical Analysis near Singularities in RBF Networks. 1:1-1:39 - Chen Chen, Min Ren, Min Zhang, Dabao Zhang:
A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations. 2:1-2:34 - Abhimanyu Das, David Kempe:
Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection. 3:1-3:34 - Ahmed M. Alaa, Mihaela van der Schaar:
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference. 4:1-4:62 - Noureddine El Karoui, Elizabeth Purdom:
Can We Trust the Bootstrap in High-dimensions? The Case of Linear Models. 5:1-5:66 - Tianbao Yang, Qihang Lin:
RSG: Beating Subgradient Method without Smoothness and Strong Convexity. 6:1-6:33 - Chiwoo Park, Daniel W. Apley:
Patchwork Kriging for Large-scale Gaussian Process Regression. 7:1-7:43 - Sanvesh Srivastava, Cheng Li, David B. Dunson:
Scalable Bayes via Barycenter in Wasserstein Space. 8:1-8:35 - Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuska:
Experience Selection in Deep Reinforcement Learning for Control. 9:1-9:56 - Jian Huang, Yuling Jiao, Yanyan Liu, Xiliang Lu:
A Constructive Approach to $L_0$ Penalized Regression. 10:1-10:37 - Leland Bybee, Yves F. Atchadé:
Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points. 11:1-11:38 - Mathieu Carrière, Bertrand Michel, Steve Oudot:
Statistical Analysis and Parameter Selection for Mapper. 12:1-12:39 - Ruidi Chen, Ioannis Ch. Paschalidis:
A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization. 13:1-13:48 - Riad Akrour, Abbas Abdolmaleki, Hany Abdulsamad, Jan Peters, Gerhard Neumann:
Model-Free Trajectory-based Policy Optimization with Monotonic Improvement. 14:1-14:25 - Arnaud Dessein, Nicolas Papadakis, Jean-Luc Rouas:
Regularized Optimal Transport and the Rot Mover's Distance. 15:1-15:53 - Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski:
ELFI: Engine for Likelihood-Free Inference. 16:1-16:7 - Audrey Durand, Odalric-Ambrym Maillard, Joelle Pineau:
Streaming kernel regression with provably adaptive mean, variance, and regularization. 17:1-17:34 - Manolis C. Tsakiris, René Vidal:
Dual Principal Component Pursuit. 18:1-18:50 - Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing:
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. 19:1-19:32 - Tor Lattimore:
Refining the Confidence Level for Optimistic Bandit Strategies. 20:1-20:32 - Zeyi Wen, Jiashuai Shi, Qinbin Li
, Bingsheng He, Jian Chen:
ThunderSVM: A Fast SVM Library on GPUs and CPUs. 21:1-21:5 - Muhammad J. Amjad, Devavrat Shah, Dennis Shen:
Robust Synthetic Control. 22:1-22:51 - Michal Derezinski, Manfred K. Warmuth:
Reverse Iterative Volume Sampling for Linear Regression. 23:1-23:39 - Lyudmila Grigoryeva, Juan-Pablo Ortega:
Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems. 24:1-24:40 - Maziar Raissi:
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations. 25:1-25:24 - Tom Ronan, Shawn Anastasio, Zhijie Qi, Pedro Henrique S. Vieira Tavares, Roman Sloutsky, Kristen M. Naegle:
OpenEnsembles: A Python Resource for Ensemble Clustering. 26:1-26:6 - Dominik Csiba, Peter Richtárik:
Importance Sampling for Minibatches. 27:1-27:21 - Ashish Khetan, Sewoong Oh:
Generalized Rank-Breaking: Computational and Statistical Tradeoffs. 28:1-28:42 - Moritz Hardt, Tengyu Ma, Benjamin Recht:
Gradient Descent Learns Linear Dynamical Systems. 29:1-29:44 - Nicole Mücke, Gilles Blanchard:
Parallelizing Spectrally Regularized Kernel Algorithms. 30:1-30:29 - Binyan Jiang, Xiangyu Wang, Chenlei Leng:
A Direct Approach for Sparse Quadratic Discriminant Analysis. 31:1-31:37 - Ohad Shamir:
Distribution-Specific Hardness of Learning Neural Networks. 32:1-32:29 - Chao Gao:
Goodness-of-Fit Tests for Random Partitions via Symmetric Polynomials. 33:1-33:50 - Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer:
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms. 34:1-34:46 - Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A. K. Suykens:
Kernel Density Estimation for Dynamical Systems. 35:1-35:49 - Mateo Rojas-Carulla, Bernhard Schölkopf, Richard E. Turner, Jonas Peters:
Invariant Models for Causal Transfer Learning. 36:1-36:34 - Gian-Andrea Thanei, Nicolai Meinshausen, Rajen Dinesh Shah:
The xyz algorithm for fast interaction search in high-dimensional data. 37:1-37:42 - Niloofar Yousefi, Yunwen Lei, Marius Kloft, Mansooreh Mollaghasemi, Georgios C. Anagnostopoulos:
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning. 38:1-38:47 - Luc Lehéricy:
State-by-state Minimax Adaptive Estimation for Nonparametric Hidden Markov Models. 39:1-39:46 - Sahand Negahban, Sewoong Oh, Kiran Koshy Thekumparampil, Jiaming Xu:
Learning from Comparisons and Choices. 40:1-40:95 - Bin Dai, Yu Wang, John A. D. Aston, Gang Hua, David P. Wipf:
Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models. 41:1-41:42 - Julien Ah-Pine:
An Efficient and Effective Generic Agglomerative Hierarchical Clustering Approach. 42:1-42:43 - Xu-Qing Liu, Xin-sheng Liu:
Markov Blanket and Markov Boundary of Multiple Variables. 43:1-43:50 - Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions. 44:1-44:29 - Timothy C. Au:
Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem. 45:1-45:30 - Sara A. van de Geer:
On Tight Bounds for the Lasso. 46:1-46:48 - Christian Kümmerle, Juliane Sigl:
Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery. 47:1-47:49 - Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton:
On Generalized Bellman Equations and Temporal-Difference Learning. 48:1-48:49 - Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg:
Design and Analysis of the NIPS 2016 Review Process. 49:1-49:34 - Alessandro Achille, Stefano Soatto:
Emergence of Invariance and Disentanglement in Deep Representations. 50:1-50:34 - Ryan Giordano, Tamara Broderick, Michael I. Jordan:
Covariances, Robustness, and Variational Bayes. 51:1-51:49 - Tomoyuki Obuchi, Yoshiyuki Kabashima:
Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization. 52:1-52:30 - Sylvain Lamprier, Thibault Gisselbrecht, Patrick Gallinari:
Profile-Based Bandit with Unknown Profiles. 53:1-53:40 - Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart, Aretha L. Teckentrup:
How Deep Are Deep Gaussian Processes? 54:1-54:46 - Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright, Bin Yu:
Fast MCMC Sampling Algorithms on Polytopes. 55:1-55:86 - Álvaro Barbero Jiménez, Suvrit Sra:
Modular Proximal Optimization for Multidimensional Total-Variation Regularization. 56:1-56:82 - Zhuoran Yang, Yang Ning, Han Liu:
On Semiparametric Exponential Family Graphical Models. 57:1-57:59 - Alain Celisse, Tristan Mary-Huard:
Theoretical Analysis of Cross-Validation for Estimating the Risk of the $k$-Nearest Neighbor Classifier. 58:1-58:54 - Jayadev Acharya, Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh:
Maximum Selection and Sorting with Adversarial Comparators. 59:1-59:31 - Ivo F. D. Oliveira, Nir Ailon, Ori Davidov:
A New and Flexible Approach to the Analysis of Paired Comparison Data. 60:1-60:29 - Deanna Needell, Rayan Saab, Tina Woolf:
Simple Classification Using Binary Data. 61:1-61:30 - Dolev Raviv, Tamir Hazan, Margarita Osadchy:
Hinge-Minimax Learner for the Ensemble of Hyperplanes. 62:1-62:30 - Zhao-Rong Lai, Pei-Yi Yang, Liangda Fang, Xiaotian Wu:
Short-term Sparse Portfolio Optimization Based on Alternating Direction Method of Multipliers. 63:1-63:28 - Leo L. Duan, James E. Johndrow, David B. Dunson:
Scaling up Data Augmentation MCMC via Calibration. 64:1-64:34 - Charles Y. Zheng, Rakesh Achanta, Yuval Benjamini:
Extrapolating Expected Accuracies for Large Multi-Class Problems. 65:1-65:30 - Alessio Spantini, Daniele Bigoni, Youssef M. Marzouk:
Inference via Low-Dimensional Couplings. 66:1-66:71 - Christian Donner, Manfred Opper:
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes. 67:1-67:34 - Jinwen Qiu, S. Rao Jammalamadaka, Ning Ning:
Multivariate Bayesian Structural Time Series Model. 68:1-68:33 - Adrian Sosic, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl:
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling. 69:1-69:45 - Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro:
The Implicit Bias of Gradient Descent on Separable Data. 70:1-70:57 - Srinivasan Arunachalam, Ronald de Wolf:
Optimal Quantum Sample Complexity of Learning Algorithms. 71:1-71:36 - Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem:
Scikit-Multiflow: A Multi-output Streaming Framework. 72:1-72:5 - Michael A. Burr, Shuhong Gao, Fiona Knoll:
Optimal Bounds for Johnson-Lindenstrauss Transformations. 73:1-73:22 - Dhruv Mahajan, Nikunj Agrawal, S. Sathiya Keerthi, Sundararajan Sellamanickam, Léon Bottou:
An efficient distributed learning algorithm based on effective local functional approximations. 74:1-74:37 - Blazej Miasojedow, Wojciech Rejchel:
Sparse Estimation in Ising Model via Penalized Monte Carlo Methods. 75:1-75:26 - Kai-Yang Chiang, Inderjit S. Dhillon, Cho-Jui Hsieh:
Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations. 76:1-76:35 - Karl Rohe, Jun Tao, Xintian Han, Norbert Binkiewicz:
A Note on Quickly Sampling a Sparse Matrix with Low Rank Expectation. 77:1-77:13 - Yixin Fang, Jinfeng Xu, Lei Yang:
Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator. 78:1-78:21 - Xiaoyi Mai:
A Random Matrix Analysis and Improvement of Semi-Supervised Learning for Large Dimensional Data. 79:1-79:27 - Teng Zhang, Yi Yang:
Robust PCA by Manifold Optimization. 80:1-80:39 - Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien:
Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods. 81:1-81:68 - Mariano Tepper, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling. 82:1-82:30 - David M. Burns, Cari M. Whyne:
Seglearn: A Python Package for Learning Sequences and Time Series. 83:1-83:7 - Przemyslaw Biecek:
DALEX: Explainers for Complex Predictive Models in R. 84:1-84:5
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