Journal of Machine Learning Research
The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.
JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing.
Latest papers
- Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions
- HanQin Cai, Keaton Hamm, Longxiu Huang, Deanna Needell, 2021.
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- mlr3pipelines - Flexible Machine Learning Pipelines in R
- Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl, 2021. (Machine Learning Open Source Software Paper)
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- Benchmarking Unsupervised Object Representations for Video Sequences
- Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker, 2021.
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- A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning
- Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen, 2021. (Machine Learning Open Source Software Paper)
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- Alibi Explain: Algorithms for Explaining Machine Learning Models
- Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca, 2021. (Machine Learning Open Source Software Paper)
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- Improved Shrinkage Prediction under a Spiked Covariance Structure
- Trambak Banerjee, Gourab Mukherjee, Debashis Paul, 2021.
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- A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration
- Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang, 2021.
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- Conditional independences and causal relations implied by sets of equations
- Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij, 2021.
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- Prediction Under Latent Factor Regression: Adaptive PCR, Interpolating Predictors and Beyond
- Xin Bing, Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp, 2021.
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- Doubly infinite residual neural networks: a diffusion process approach
- Stefano Peluchetti, Stefano Favaro, 2021.
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- Achieving Fairness in the Stochastic Multi-Armed Bandit Problem
- Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari, 2021.
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- Unlinked Monotone Regression
- Fadoua Balabdaoui, Charles R. Doss, Cécile Durot, 2021.
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- Optimal Rates of Distributed Regression with Imperfect Kernels
- Hongwei Sun, Qiang Wu, 2021.
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- Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Lower Query Complexity
- Bin Gu, Xiyuan Wei, Shangqian Gao, Ziran Xiong, Cheng Deng, Heng Huang, 2021.
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- First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems
- Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang, 2021.
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- Asymptotic Normality, Concentration, and Coverage of Generalized Posteriors
- Jeffrey W. Miller, 2021.
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- Estimation and Optimization of Composite Outcomes
- Daniel J. Luckett, Eric B. Laber, Siyeon Kim, Michael R. Kosorok, 2021.
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- The ensmallen library for flexible numerical optimization
- Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson, 2021. (Machine Learning Open Source Software Paper)
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- Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning
- Charles H. Martin, Michael W. Mahoney, 2021.
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- Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program)
- Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Lariviere, Alina Beygelzimer, Florence d'Alche-Buc, Emily Fox, Hugo Larochelle, 2021.
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- PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
- Ivan Stelmakh, Nihar Shah, Aarti Singh, 2021.
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- Counterfactual Mean Embeddings
- Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat, 2021.
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- MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
- Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven, 2021.
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- Are We Forgetting about Compositional Optimisers in Bayesian Optimisation?
- Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar, 2021.
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- When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks?
- Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett, 2021.
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- Information criteria for non-normalized models
- Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen, 2021.
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- The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
- Takuo Matsubara, Chris J. Oates, François-Xavier Briol, 2021.
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- A Greedy Algorithm for Quantizing Neural Networks
- Eric Lybrand, Rayan Saab, 2021.
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- What Causes the Test Error? Going Beyond Bias-Variance via ANOVA
- Licong Lin, Edgar Dobriban, 2021.
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- Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data
- Yikun Zhang, Yen-Chi Chen, 2021.
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- Factorization Machines with Regularization for Sparse Feature Interactions
- Kyohei Atarashi, Satoshi Oyama, Masahito Kurihara, 2021.
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- Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models
- Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda, 2021.
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- Universal consistency and rates of convergence of multiclass prototype algorithms in metric spaces
- László Györfi, Roi Weiss, 2021.
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- Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
- Tian Tong, Cong Ma, Yuejie Chi, 2021.
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- Hyperparameter Optimization via Sequential Uniform Designs
- Zebin Yang, Aijun Zhang, 2021.
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- Statistical guarantees for local graph clustering
- Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney, 2021.
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- Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model
- Meiling Hao, Lianqiang Qu, Dehan Kong, Liuquan Sun, Hongtu Zhu, 2021.
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- Nonparametric Modeling of Higher-Order Interactions via Hypergraphons
- Krishnakumar Balasubramanian, 2021.
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- On efficient multilevel Clustering via Wasserstein distances
- Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Phung, 2021.
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- Non-attracting Regions of Local Minima in Deep and Wide Neural Networks
- Henning Petzka, Cristian Sminchisescu, 2021.
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- Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace
- Jesús Arroyo, Avanti Athreya, Joshua Cape, Guodong Chen, Carey E. Priebe, Joshua T. Vogelstein, 2021.
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- Pseudo-Marginal Hamiltonian Monte Carlo
- Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten, 2021.
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- Generalization Properties of hyper-RKHS and its Applications
- Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A.K. Suykens, 2021.
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- Hoeffding's Inequality for General Markov Chains and Its Applications to Statistical Learning
- Jianqing Fan, Bai Jiang, Qiang Sun, 2021.
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- An algorithmic view of L2 regularization and some path-following algorithms
- Yunzhang Zhu, Renxiong Liu, 2021.
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- Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model
- Tong Wang, Qihang Lin, 2021.
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- Implicit Langevin Algorithms for Sampling From Log-concave Densities
- Liam Hodgkinson, Robert Salomone, Fred Roosta, 2021.
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- Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
- Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder, 2021.
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- An Inertial Newton Algorithm for Deep Learning
- Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels, 2021.
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- A Contextual Bandit Bake-off
- Alberto Bietti, Alekh Agarwal, John Langford, 2021.
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- Locally Differentially-Private Randomized Response for Discrete Distribution Learning
- Adriano Pastore, Michael Gastpar, 2021.
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- MushroomRL: Simplifying Reinforcement Learning Research
- Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters, 2021. (Machine Learning Open Source Software Paper)
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- Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes
- Steve Hanneke, 2021.
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- Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
- Niladri S. Chatterji, Philip M. Long, 2021.
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- Optimal Bounds between f-Divergences and Integral Probability Metrics
- Rohit Agrawal, Thibaut Horel, 2021.
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- LassoNet: A Neural Network with Feature Sparsity
- Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani, 2021.
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- Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints
- Molei Liu, Yin Xia, Kelly Cho, Tianxi Cai, 2021.
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- Bandit Convex Optimization in Non-stationary Environments
- Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou, 2021.
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- A flexible model-free prediction-based framework for feature ranking
- Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong, 2021.
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- Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
- George Wynne, François-Xavier Briol, Mark Girolami, 2021.
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- Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
- Kyriakos Axiotis, Maxim Sviridenko, 2021.
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- Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms
- Vikram Krishnamurthy, George Yin, 2021.
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- Some Theoretical Insights into Wasserstein GANs
- Gérard Biau, Maxime Sangnier, Ugo Tanielian, 2021.
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- A General Framework for Adversarial Label Learning
- Chidubem Arachie, Bert Huang, 2021.
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- Strong Consistency, Graph Laplacians, and the Stochastic Block Model
- Shaofeng Deng, Shuyang Ling, Thomas Strohmer, 2021.
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- An Importance Weighted Feature Selection Stability Measure
- Victor Hamer, Pierre Dupont, 2021.
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- Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization
- Michael R. Metel, Akiko Takeda, 2021.
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- NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
- Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy, 2021.
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- A Lyapunov Analysis of Accelerated Methods in Optimization
- Ashia C. Wilson, Ben Recht, Michael I. Jordan, 2021.
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- L-SVRG and L-Katyusha with Arbitrary Sampling
- Xun Qian, Zheng Qu, Peter Richtárik, 2021.
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- Non-parametric Quantile Regression via the K-NN Fused Lasso
- Steven Siwei Ye, Oscar Hernan Madrid Padilla, 2021.
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- River: machine learning for streaming data in Python
- Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet, 2021. (Machine Learning Open Source Software Paper)
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- mvlearn: Multiview Machine Learning in Python
- Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein, 2021. (Machine Learning Open Source Software Paper)
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- Towards a Unified Analysis of Random Fourier Features
- Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic, 2021.
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- Beyond English-Centric Multilingual Machine Translation
- Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Michael Auli, Armand Joulin, 2021.
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- Online stochastic gradient descent on non-convex losses from high-dimensional inference
- Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath, 2021.
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- Pathwise Conditioning of Gaussian Processes
- James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, 2021.
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- Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
- Joseph D. Janizek, Pascal Sturmfels, Su-In Lee, 2021.
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- A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints
- Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger Grosse, 2021.
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- Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression
- Gunwoong Park, Sang Jun Moon, Sion Park, Jong-June Jeon, 2021.
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- LocalGAN: Modeling Local Distributions for Adversarial Response Generation
- Baoxun Wang, Zhen Xu, Huan Zhang, Kexin Qiu, Deyuan Zhang, Chengjie Sun, 2021.
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- OpenML-Python: an extensible Python API for OpenML
- Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter, 2021. (Machine Learning Open Source Software Paper)
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- Adaptive estimation of nonparametric functionals
- Lin Liu, Rajarshi Mukherjee, James M. Robins, Eric Tchetgen Tchetgen, 2021.
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- On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
- Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan, 2021.
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- Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach
- Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli, 2021.
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- Guided Visual Exploration of Relations in Data Sets
- Kai Puolamäki, Emilia Oikarinen, Andreas Henelius, 2021.
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- Histogram Transform Ensembles for Large-scale Regression
- Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu, Hongwei Wen, 2021.
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- Consistent Semi-Supervised Graph Regularization for High Dimensional Data
- Xiaoyi Mai, Romain Couillet, 2021.
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- Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage
- Zhengrong Xing, Peter Carbonetto, Matthew Stephens, 2021.
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- NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation
- Anastasis Kratsios, Cody Hyndman, 2021.
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- Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler
- Tingting Zhao, Alexandre Bouchard-Côté, 2021.
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- Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs
- Tomer Galanti, Sagie Benaim, Lior Wolf, 2021.
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- Bayesian Text Classification and Summarization via A Class-Specified Topic Model
- Feifei Wang, Junni L. Zhang, Yichao Li, Ke Deng, Jun S. Liu, 2021.
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- On Solving Probabilistic Linear Diophantine Equations
- Patrick Kreitzberg, Oliver Serang, 2021.
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- Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
- Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek, 2021.
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- Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
- Jiyuan Tu, Weidong Liu, Xiaojun Mao, Xi Chen, 2021.
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- PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
- Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann, 2021.
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- Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction
- Maxime Cauchois, Suyash Gupta, John C. Duchi, 2021.
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- Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA
- Zengfeng Huang, Xuemin Lin, Wenjie Zhang, Ying Zhang, 2021.
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- Is SGD a Bayesian sampler? Well, almost
- Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis, 2021.
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- POT: Python Optimal Transport
- Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer, 2021. (Machine Learning Open Source Software Paper)
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- ChainerRL: A Deep Reinforcement Learning Library
- Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa, 2021. (Machine Learning Open Source Software Paper)
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- Analyzing the discrepancy principle for kernelized spectral filter learning algorithms
- Alain Celisse, Martin Wahl, 2021.
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- Kernel Operations on the GPU, with Autodiff, without Memory Overflows
- Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès, François-David Collin, Ghislain Durif, 2021. (Machine Learning Open Source Software Paper)
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- Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives
- Michael Muehlebach, Michael I. Jordan, 2021.
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- Prediction against a limited adversary
- Erhan Bayraktar, Ibrahim Ekren, Xin Zhang, 2021.
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- Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit
- Tao Luo, Zhi-Qin John Xu, Zheng Ma, Yaoyu Zhang, 2021.
[abs][pdf][bib] [code]
- Testing Conditional Independence via Quantile Regression Based Partial Copulas
- Lasse Petersen, Niels Richard Hansen, 2021.
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- Determining the Number of Communities in Degree-corrected Stochastic Block Models
- Shujie Ma, Liangjun Su, Yichong Zhang, 2021.
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- Path Length Bounds for Gradient Descent and Flow
- Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas, 2021.
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- A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family
- Trambak Banerjee, Qiang Liu, Gourab Mukherjee, Wengunag Sun, 2021.
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- Dynamic Tensor Recommender Systems
- Yanqing Zhang, Xuan Bi, Niansheng Tang, Annie Qu, 2021.
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- Sparse Tensor Additive Regression
- Botao Hao, Boxiang Wang, Pengyuan Wang, Jingfei Zhang, Jian Yang, Will Wei Sun, 2021.
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- Geometric structure of graph Laplacian embeddings
- Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini, 2021.
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- How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information
- Mariusz Kubkowski, Jan Mielniczuk, Paweł Teisseyre, 2021.
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- A Distributed Method for Fitting Laplacian Regularized Stratified Models
- Jonathan Tuck, Shane Barratt, Stephen Boyd, 2021.
[abs][pdf][bib] [code]
- Predictive Learning on Hidden Tree-Structured Ising Models
- Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate, 2021.
[abs][pdf][bib] [code]
- Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach
- Zhe Fei, Yi Li, 2021.
[abs][pdf][bib] [code]
- Normalizing Flows for Probabilistic Modeling and Inference
- George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan, 2021.
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- Incorporating Unlabeled Data into Distributionally Robust Learning
- Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon, 2021.
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- Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
- Minjie Wang, Genevera I. Allen, 2021.
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- GemBag: Group Estimation of Multiple Bayesian Graphical Models
- Xinming Yang, Lingrui Gan, Naveen N. Narisetty, Feng Liang, 2021.
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- Subspace Clustering through Sub-Clusters
- Weiwei Li, Jan Hannig, Sayan Mukherjee, 2021.
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- Sparse and Smooth Signal Estimation: Convexification of L0-Formulations
- Alper Atamturk, Andres Gomez, Shaoning Han, 2021.
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- Projection-free Decentralized Online Learning for Submodular Maximization over Time-Varying Networks
- Junlong Zhu, Qingtao Wu, Mingchuan Zhang, Ruijuan Zheng, Keqin Li, 2021.
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- Structure Learning of Undirected Graphical Models for Count Data
- Nguyen Thi Kim Hue, Monica Chiogna, 2021.
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- From Low Probability to High Confidence in Stochastic Convex Optimization
- Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang, 2021.
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- Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression
- Behzad Azmi, Dante Kalise, Karl Kunisch, 2021.
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- Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory
- Soon Hoe Lim, 2021.
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- Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates
- Tony Cai, Hongzhe Li, Rong Ma, 2021.
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- Wasserstein barycenters can be computed in polynomial time in fixed dimension
- Jason M Altschuler, Enric Boix-Adsera, 2021.
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- Banach Space Representer Theorems for Neural Networks and Ridge Splines
- Rahul Parhi, Robert D. Nowak, 2021.
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- High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
- Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan, 2021.
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- From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction
- Henning Lange, Steven L. Brunton, J. Nathan Kutz, 2021.
[abs][pdf][bib] [code]
- Residual Energy-Based Models for Text
- Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'Aurelio Ranzato, Arthur Szlam, 2021.
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- giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration
- Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, Kathryn Hess, 2021. (Machine Learning Open Source Software Paper)
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- Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures
- Umit Köse, Andrzej Ruszczyński, 2021.
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- A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables
- Zhao Tang Luo, Huiyan Sang, Bani Mallick, 2021.
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- Multi-class Gaussian Process Classification with Noisy Inputs
- Carlos Villacampa-Calvo, Bryan Zaldívar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, 2021.
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- Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation
- Melkior Ornik, Ufuk Topcu, 2021.
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- Neighborhood Structure Assisted Non-negative Matrix Factorization and Its Application in Unsupervised Point-wise Anomaly Detection
- Imtiaz Ahmed, Xia Ben Hu, Mithun P. Acharya, Yu Ding, 2021.
[abs][pdf][bib] [code]
- Asynchronous Online Testing of Multiple Hypotheses
- Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan, 2021.
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- Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
- Fei Lu, Mauro Maggioni, Sui Tang, 2021.
[abs][pdf][bib] [code]
- FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
- Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky, 2021.
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- A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
- Oliver Kroemer, Scott Niekum, George Konidaris, 2021.
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- Single and Multiple Change-Point Detection with Differential Privacy
- Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings, 2021.
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- Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
- Julian Zimmert, Yevgeny Seldin, 2021.
[abs][pdf][bib]
- Inference In High-dimensional Single-Index Models Under Symmetric Designs
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- Finite Time LTI System Identification
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- Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions
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- A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems
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- When random initializations help: a study of variational inference for community detection
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- A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters
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- Ranking and synchronization from pairwise measurements via SVD
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- A Unified Sample Selection Framework for Output Noise Filtering: An Error-Bound Perspective
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- Continuous Time Analysis of Momentum Methods
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- On Multi-Armed Bandit Designs for Dose-Finding Trials
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- Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors
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- Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit
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- Unfolding-Model-Based Visualization: Theory, Method and Applications
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- Mixing Time of Metropolis-Hastings for Bayesian Community Detection
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- Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
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- A Unified Framework for Random Forest Prediction Error Estimation
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- Consistent estimation of small masses in feature sampling
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- The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models
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- An Empirical Study of Bayesian Optimization: Acquisition Versus Partition
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- Regulating Greed Over Time in Multi-Armed Bandits
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- Domain Generalization by Marginal Transfer Learning
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- On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
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