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. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[webpage]
[code]
- 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. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[webpage]
[code]
- 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. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[webpage]
[code]
- 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 {M}arkov 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]
- Markov Blanket and Markov Boundary of Multiple Variables
- Xu-Qing Liu, Xin-Sheng Liu; (43):1−50, 2018.
[abs][pdf][bib]
- Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions
- Carl-Johann Simon-Gabriel, Bernhard Schölkopf; (44):1−29, 2018.
[abs][pdf][bib]
- Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem
- Timothy C. Au; (45):1−30, 2018.
[abs][pdf][bib]
- On Tight Bounds for the Lasso
- Sara van de Geer; (46):1−48, 2018.
[abs][pdf][bib]
- Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery
- Christian Kümmerle, Juliane Sigl; (47):1−49, 2018.
[abs][pdf][bib]
[github.com]
- On Generalized Bellman Equations and Temporal-Difference Learning
- Huizhen Yu, A. Rupam Mahmood, Richard S. Sutton; (48):1−49, 2018.
[abs][pdf][bib]
- Design and Analysis of the NIPS 2016 Review Process
- Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg; (49):1−34, 2018.
[abs][pdf][bib]
- Emergence of Invariance and Disentanglement in Deep Representations
- Alessandro Achille, Stefano Soatto; (50):1−34, 2018.
[abs][pdf][bib]
- Covariances, Robustness, and Variational Bayes
- Ryan Giordano, Tamara Broderick, Michael I. Jordan; (51):1−49, 2018.
[abs][pdf][bib]
- Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization
- Tomoyuki Obuchi, Yoshiyuki Kabashima; (52):1−30, 2018.
[abs][pdf][bib]
- Profile-Based Bandit with Unknown Profiles
- Sylvain Lamprier, Thibault Gisselbrecht, Patrick Gallinari; (53):1−40, 2018.
[abs][pdf][bib]
- How Deep Are Deep Gaussian Processes?
- Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart, Aretha L. Teckentrup; (54):1−46, 2018.
[abs][pdf][bib]
- Fast MCMC Sampling Algorithms on Polytopes
- Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright, Bin Yu; (55):1−86, 2018.
[abs][pdf][bib]
- Modular Proximal Optimization for Multidimensional Total-Variation Regularization
- Alvaro Barbero, Suvrit Sra; (56):1−82, 2018.
[abs][pdf][bib]
- On Semiparametric Exponential Family Graphical Models
- Zhuoran Yang, Yang Ning, Han Liu; (57):1−59, 2018.
[abs][pdf][bib]
- Theoretical Analysis of Cross-Validation for Estimating the Risk of the $k$-Nearest Neighbor Classifier
- Alain Celisse, Tristan Mary-Huard; (58):1−54, 2018.
[abs][pdf][bib]
- Maximum Selection and Sorting with Adversarial Comparators
- Jayadev Acharya, Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh; (59):1−31, 2018.
[abs][pdf][bib]
- A New and Flexible Approach to the Analysis of Paired Comparison Data
- Ivo F. D. Oliveira, Nir Ailon, Ori Davidov; (60):1−29, 2018.
[abs][pdf][bib]
- Simple Classification Using Binary Data
- Deanna Needell, Rayan Saab, Tina Woolf; (61):1−30, 2018.
[abs][pdf][bib]
- Hinge-Minimax Learner for the Ensemble of Hyperplanes
- Dolev Raviv, Tamir Hazan, Margarita Osadchy; (62):1−30, 2018.
[abs][pdf][bib]
- Short-term Sparse Portfolio Optimization Based on Alternating Direction Method of Multipliers
- Zhao-Rong Lai, Pei-Yi Yang, Liangda Fang, Xiaotian Wu; (63):1−28, 2018.
[abs][pdf][bib]
[code]
- Scaling up Data Augmentation MCMC via Calibration
- Leo L. Duan, James E. Johndrow, David B. Dunson; (64):1−34, 2018.
[abs][pdf][bib]
- Extrapolating Expected Accuracies for Large Multi-Class Problems
- Charles Zheng, Rakesh Achanta, Yuval Benjamini; (65):1−30, 2018.
[abs][pdf][bib]
- Inference via Low-Dimensional Couplings
- Alessio Spantini, Daniele Bigoni, Youssef Marzouk; (66):1−71, 2018.
[abs][pdf][bib]
- Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
- Christian Donner, Manfred Opper; (67):1−34, 2018.
[abs][pdf][bib]
- Multivariate Bayesian Structural Time Series Model
- Jinwen Qiu, S. Rao Jammalamadaka, Ning Ning; (68):1−33, 2018.
[abs][pdf][bib]
- Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling
- Adrian Šošić, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl; (69):1−45, 2018.
[abs][pdf][bib]
- The Implicit Bias of Gradient Descent on Separable Data
- Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro; (70):1−57, 2018.
[abs][pdf][bib]
- Optimal Quantum Sample Complexity of Learning Algorithms
- Srinivasan Arunachalam, Ronald de Wolf; (71):1−36, 2018.
[abs][pdf][bib]
- Scikit-Multiflow: A Multi-output Streaming Framework
- Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem; (72):1−5, 2018. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
- Optimal Bounds for Johnson-Lindenstrauss Transformations
- Michael Burr, Shuhong Gao, Fiona Knoll; (73):1−22, 2018.
[abs][pdf][bib]
- An efficient distributed learning algorithm based on effective local functional approximations
- Dhruv Mahajan, Nikunj Agrawal, S. Sathiya Keerthi, Sundararajan Sellamanickam, Leon Bottou; (74):1−37, 2018.
[abs][pdf][bib]
- Sparse Estimation in Ising Model via Penalized Monte Carlo Methods
- Blazej Miasojedow, Wojciech Rejchel; (75):1−26, 2018.
[abs][pdf][bib]
- Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations
- Kai-Yang Chiang, Inderjit S. Dhillon, Cho-Jui Hsieh; (76):1−35, 2018.
[abs][pdf][bib]
- A Note on Quickly Sampling a Sparse Matrix with Low Rank Expectation
- Karl Rohe, Jun Tao, Xintian Han, Norbert Binkiewicz; (77):1−13, 2018.
[abs][pdf][bib]
- Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator
- Yixin Fang, Jinfeng Xu, Lei Yang; (78):1−21, 2018.
[abs][pdf][bib]
- A Random Matrix Analysis and Improvement of Semi-Supervised Learning for Large Dimensional Data
- Xiaoyi Mai; (79):1−27, 2018.
[abs][pdf][bib]
- Robust PCA by Manifold Optimization
- Teng Zhang, Yi Yang; (80):1−39, 2018.
[abs][pdf][bib]
- Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods
- Remi Leblond, Fabian Pedregosa, Simon Lacoste-Julien; (81):1−68, 2018.
[abs][pdf][bib]
- Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling
- Mariano Tepper, Anirvan M. Sengupta, Dmitri Chklovskii; (82):1−30, 2018.
[abs][pdf][bib]
- Seglearn: A Python Package for Learning Sequences and Time Series
- David M. Burns, Cari M. Whyne; (83):1−7, 2018. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- DALEX: Explainers for Complex Predictive Models in R
- Przemyslaw Biecek; (84):1−5, 2018.
[abs][pdf][bib]
Mastodon
