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JMLR Volume 19

Numerical Analysis near Singularities in RBF Networks
Weili Guo, Haikun Wei, Yew-Soon Ong, Jaime Rubio Hervas, Junsheng Zhao, Hai Wang, Kanjian Zhang; (1):1−39, 2018.
[abs][pdf][bib]

A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations
Chen Chen, Min Ren, Min Zhang, Dabao Zhang; (2):1−34, 2018.
[abs][pdf][bib]

Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection
Abhimanyu Das, David Kempe; (3):1−34, 2018.
[abs][pdf][bib]

A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
Ahmed M. Alaa, Mihaela van der Schaar; (4):1−62, 2018.
[abs][pdf][bib]

Can We Trust the Bootstrap in High-dimensions? The Case of Linear Models
Noureddine El Karoui, Elizabeth Purdom; (5):1−66, 2018.
[abs][pdf][bib]

RSG: Beating Subgradient Method without Smoothness and Strong Convexity
Tianbao Yang, Qihang Lin; (6):1−33, 2018.
[abs][pdf][bib]

Patchwork Kriging for Large-scale Gaussian Process Regression
Chiwoo Park, Daniel Apley; (7):1−43, 2018.
[abs][pdf][bib]

Scalable Bayes via Barycenter in Wasserstein Space
Sanvesh Srivastava, Cheng Li, David B. Dunson; (8):1−35, 2018.
[abs][pdf][bib]

Experience Selection in Deep Reinforcement Learning for Control
Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuška; (9):1−56, 2018.
[abs][pdf][bib]

A Constructive Approach to $L_0$ Penalized Regression
Jian Huang, Yuling Jiao, Yanyan Liu, Xiliang Lu; (10):1−37, 2018.
[abs][pdf][bib]

Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points
Leland Bybee, Yves Atchadé; (11):1−38, 2018.
[abs][pdf][bib]

Statistical Analysis and Parameter Selection for Mapper
Mathieu Carrière, Bertrand Michel, Steve Oudot; (12):1−39, 2018.
[abs][pdf][bib]

A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization
Ruidi Chen, Ioannis Ch. Paschalidis; (13):1−48, 2018.
[abs][pdf][bib]

Model-Free Trajectory-based Policy Optimization with Monotonic Improvement
Riad Akrour, Abbas Abdolmaleki, Hany Abdulsamad, Jan Peters, Gerhard Neumann; (14):1−25, 2018.
[abs][pdf][bib]

Regularized Optimal Transport and the Rot Mover's Distance
Arnaud Dessein, Nicolas Papadakis, Jean-Luc Rouas; (15):1−53, 2018.
[abs][pdf][bib]

ELFI: Engine for Likelihood-Free Inference
Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski; (16):1−7, 2018.
[abs][pdf][bib]      [readthedocs.io] [github.com]

Streaming kernel regression with provably adaptive mean, variance, and regularization
Audrey Durand, Odalric-Ambrym Maillard, Joelle Pineau; (17):1−34, 2018.
[abs][pdf][bib]

Dual Principal Component Pursuit
Manolis C. Tsakiris, René Vidal; (18):1−50, 2018.
[abs][pdf][bib]

Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters
Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing; (19):1−32, 2018.
[abs][pdf][bib]

Refining the Confidence Level for Optimistic Bandit Strategies
Tor Lattimore; (20):1−32, 2018.
[abs][pdf][bib]

ThunderSVM: A Fast SVM Library on GPUs and CPUs
Zeyi Wen, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (21):1−5, 2018.
[abs][pdf][bib]      [readthedocs.io] [github.com]

Robust Synthetic Control
Muhammad Amjad, Devavrat Shah, Dennis Shen; (22):1−51, 2018.
[abs][pdf][bib]

Reverse Iterative Volume Sampling for Linear Regression
Michał Dereziński, Manfred K. Warmuth; (23):1−39, 2018.
[abs][pdf][bib]

Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems
Lyudmila Grigoryeva, Juan-Pablo Ortega; (24):1−40, 2018.
[abs][pdf][bib]

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
Maziar Raissi; (25):1−24, 2018.
[abs][pdf][bib]

OpenEnsembles: A Python Resource for Ensemble Clustering
Tom Ronan, Shawn Anastasio, Zhijie Qi, Pedro Henrique S. Vieira Tavares, Roman Sloutsky, Kristen M. Naegle; (26):1−6, 2018.
[abs][pdf][bib]      [github.io] [github.com]

Importance Sampling for Minibatches
Dominik Csiba, Peter Richtárik; (27):1−21, 2018.
[abs][pdf][bib]

Generalized Rank-Breaking: Computational and Statistical Tradeoffs
Ashish Khetan, Sewoong Oh; (28):1−42, 2018.
[abs][pdf][bib]

Gradient Descent Learns Linear Dynamical Systems
Moritz Hardt, Tengyu Ma, Benjamin Recht; (29):1−44, 2018.
[abs][pdf][bib]

Parallelizing Spectrally Regularized Kernel Algorithms
Nicole Mücke, Gilles Blanchard; (30):1−29, 2018.
[abs][pdf][bib]

A Direct Approach for Sparse Quadratic Discriminant Analysis
Binyan Jiang, Xiangyu Wang, Chenlei Leng; (31):1−37, 2018.
[abs][pdf][bib]

Distribution-Specific Hardness of Learning Neural Networks
Ohad Shamir; (32):1−29, 2018.
[abs][pdf][bib]

Goodness-of-Fit Tests for Random Partitions via Symmetric Polynomials
Chao Gao; (33):1−50, 2018.
[abs][pdf][bib]

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer; (34):1−46, 2018.
[abs][pdf][bib]

Kernel Density Estimation for Dynamical Systems
Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A.K. Suykens; (35):1−49, 2018.
[abs][pdf][bib]

Invariant Models for Causal Transfer Learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters; (36):1−34, 2018.
[abs][pdf][bib]

The xyz algorithm for fast interaction search in high-dimensional data
Gian-Andrea Thanei, Nicolai Meinshausen, Rajen D. Shah; (37):1−42, 2018.
[abs][pdf][bib]

Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning
Niloofar Yousefi, Yunwen Lei, Marius Kloft, Mansooreh Mollaghasemi, Georgios C. Anagnostopoulos; (38):1−47, 2018.
[abs][pdf][bib]

State-by-state Minimax Adaptive Estimation for Nonparametric Hidden Markov Models
Luc Lehéricy; (39):1−46, 2018.
[abs][pdf][bib]

Learning from Comparisons and Choices
Sahand Negahban, Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu; (40):1−95, 2018.
[abs][pdf][bib]

Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models
Bin Dai, Yu Wang, John Aston, Gang Hua, David Wipf; (41):1−42, 2018.
[abs][pdf][bib]

An Efficient and Effective Generic Agglomerative Hierarchical Clustering Approach
Julien Ah-Pine; (42):1−43, 2018.
[abs][pdf][bib]

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