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
- Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra
- Vardan Papyan, 2020.
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- Online matrix factorization for Markovian data and applications to Network Dictionary Learning
- Hanbaek Lyu, Deanna Needell, Laura Balzano, 2020.
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- Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
- Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau, 2020.
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- Adaptive Rates for Total Variation Image Denoising
- Francesco Ortelli, Sara van de Geer, 2020.
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- On Efficient Adjustment in Causal Graphs
- Janine Witte, Leonard Henckel, Marloes H. Maathuis, Vanessa Didelez, 2020.
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- A Group-Theoretic Framework for Data Augmentation
- Shuxiao Chen, Edgar Dobriban, Jane H. Lee, 2020.
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- Rank-based Lasso - efficient methods for high-dimensional robust model selection
- Wojciech Rejchel, Małgorzata Bogdan, 2020.
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- Best Practices for Scientific Research on Neural Architecture Search
- Marius Lindauer, Frank Hutter, 2020.
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- Fair Data Adaptation with Quantile Preservation
- Drago Plečko, Nicolai Meinshausen, 2020.
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- Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables
- Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen, 2020.
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- Risk Bounds for Reservoir Computing
- Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega, 2020.
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- Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection
- Joonas Hämäläinen, Alisson S. C. Alencar, Tommi Kärkkäinen, César L. C. Mattos, Amauri H. Souza Júnior, João P. P. Gomes, 2020.
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- algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
- Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui, 2020. (Machine Learning Open Source Software Paper)
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- The Error-Feedback framework: SGD with Delayed Gradients
- Sebastian U. Stich, Sai Praneeth Karimireddy, 2020.
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- Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information
- Leighton Pate Barnes, Yanjun Han, Ayfer Ozgur, 2020.
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- Convex Programming for Estimation in Nonlinear Recurrent Models
- Sohail Bahmani, Justin Romberg, 2020.
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- Dual Extrapolation for Sparse GLMs
- Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon, 2020.
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- Robust high dimensional learning for Lipschitz and convex losses
- Chinot Geoffrey, Lecué Guillaume, Lerasle Matthieu, 2020.
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- Spectral Deconfounding via Perturbed Sparse Linear Models
- Domagoj Ćevid, Peter Bühlmann, Nicolai Meinshausen, 2020.
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- Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion
- Dimitris Bertsimas, Michael Lingzhi Li, 2020.
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- Stable Regression: On the Power of Optimization over Randomization
- Dimitris Bertsimas, Ivan Paskov, 2020.
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- Nonparametric graphical model for counts
- Arkaprava Roy, David B Dunson, 2020.
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- Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications
- Frederik Heber, Žofia Trst’anová, Benedict Leimkuhler, 2020.
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- A Sparse Semismooth Newton Based Proximal Majorization-Minimization Algorithm for Nonconvex Square-Root-Loss Regression Problems
- Peipei Tang, Chengjing Wang, Defeng Sun, Kim-Chuan Toh, 2020.
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- Recovery of a Mixture of Gaussians by Sum-of-Norms Clustering
- Tao Jiang, Stephen Vavasis, Chen Wen Zhai, 2020.
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- Ultra-High Dimensional Single-Index Quantile Regression
- Yuankun Zhang, Heng Lian, Yan Yu, 2020.
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- Geomstats: A Python Package for Riemannian Geometry in Machine Learning
- Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec, 2020. (Machine Learning Open Source Software Paper)
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- Theory of Curriculum Learning, with Convex Loss Functions
- Daphna Weinshall, Dan Amir, 2020.
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- Learning Sums of Independent Random Variables with Sparse Collective Support
- Anindya De, Philip M. Long, Rocco A. Servedio, 2020.
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- AdaGrad stepsizes: Sharp convergence over nonconvex landscapes
- Rachel Ward, Xiaoxia Wu, Leon Bottou, 2020.
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- Spectral bandits
- Tomáš Kocák, Rémi Munos, Branislav Kveton, Shipra Agrawal, Michal Valko, 2020.
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- On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach
- Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique Del Castillo, 2020.
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- Dynamic Assortment Optimization with Changing Contextual Information
- Xi Chen, Yining Wang, Yuan Zhou, 2020.
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- Mining Topological Structure in Graphs through Forest Representations
- Robin Vandaele, Yvan Saeys, Tijl De Bie, 2020.
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- Provable Convex Co-clustering of Tensors
- Eric C. Chi, Brian J. Gaines, Will Wei Sun, Hua Zhou, Jian Yang, 2020.
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- Multiclass Anomaly Detector: the CS++ Support Vector Machine
- Alistair Shilton, Sutharshan Rajasegarar, Marimuthu Palaniswami, 2020.
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- scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn
- Sebastian Pölsterl, 2020. (Machine Learning Open Source Software Paper)
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- Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images
- Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang, 2020.
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- ProtoAttend: Attention-Based Prototypical Learning
- Sercan O. Arik, Tomas Pfister, 2020.
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- A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
- Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem, 2020.
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- Learning Data-adaptive Non-parametric Kernels
- Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li, 2020.
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- Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging
- Baihua He, Yanyan Liu, Yuanshan Wu, Guosheng Yin, Xingqiu Zhao, 2020.
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- On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond
- Xiao-Tong Yuan, Ping Li, 2020.
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- Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms
- Simon Fischer, Ingo Steinwart, 2020.
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- Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data
- Martin Slawski, Emanuel Ben-David, Ping Li, 2020.
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- Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance
- Dalit Engelhardt, 2020.
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- A Numerical Measure of the Instability of Mapper-Type Algorithms
- Francisco Belchi, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan, 2020.
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- Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models
- Reza Mohammadi, Matthew Pratola, Maurits Kaptein, 2020.
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- Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
- Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu, 2020.
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- Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms
- Yazhen Wang, Shang Wu, 2020.
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- Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach
- Haoran Wang, Thaleia Zariphopoulou, Xun Yu Zhou, 2020.
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- A determinantal point process for column subset selection
- Ayoub Belhadji, Rémi Bardenet, Pierre Chainais, 2020.
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- Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning
- Lucas Lehnert, Michael L. Littman, 2020.
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- Conic Optimization for Quadratic Regression Under Sparse Noise
- Igor Molybog, Ramtin Madani, Javad Lavaei, 2020.
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- Contextual Explanation Networks
- Maruan Al-Shedivat, Avinava Dubey, Eric Xing, 2020.
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- Learning and Interpreting Multi-Multi-Instance Learning Networks
- Alessandro Tibo, Manfred Jaeger, Paolo Frasconi, 2020.
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- Semi-parametric Learning of Structured Temporal Point Processes
- Ganggang Xu, Ming Wang, Jiangze Bian, Hui Huang, Timothy R. Burch, Sandro C. Andrade, Jingfei Zhang, Yongtao Guan, 2020.
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- Adaptive Smoothing for Path Integral Control
- Dominik Thalmeier, Hilbert J. Kappen, Simone Totaro, Vicenç Gómez, 2020.
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- A Unified q-Memorization Framework for Asynchronous Stochastic Optimization
- Bin Gu, Wenhan Xian, Zhouyuan Huo, Cheng Deng, Heng Huang, 2020.
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- Beyond Trees: Classification with Sparse Pairwise Dependencies
- Yaniv Tenzer, Amit Moscovich, Mary Frances Dorn, Boaz Nadler, Clifford Spiegelman, 2020.
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- Efficient Adjustment Sets for Population Average Causal Treatment Effect Estimation in Graphical Models
- Andrea Rotnitzky, Ezequiel Smucler, 2020.
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- Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs
- Rui Tuo, Wenjia Wang, 2020.
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- Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods
- Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M Stuart, 2020.
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- Scikit-network: Graph Analysis in Python
- Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier, 2020. (Machine Learning Open Source Software Paper)
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- Topology of Deep Neural Networks
- Gregory Naitzat, Andrey Zhitnikov, Lek-Heng Lim, 2020.
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- Near-optimal Individualized Treatment Recommendations
- Haomiao Meng, Ying-Qi Zhao, Haoda Fu, Xingye Qiao, 2020.
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- Distributed High-dimensional Regression Under a Quantile Loss Function
- Xi Chen, Weidong Liu, Xiaojun Mao, Zhuoyi Yang, 2020.
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- Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
- Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone, 2020.
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- Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
- Boyue Li, Shicong Cen, Yuxin Chen, Yuejie Chi, 2020.
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- Variational Inference for Computational Imaging Inverse Problems
- Francesco Tonolini, Jack Radford, Alex Turpin, Daniele Faccio, Roderick Murray-Smith, 2020.
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- Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
- Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson, 2020.
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- Optimal Estimation of Sparse Topic Models
- Xin Bing, Florentina Bunea, Marten Wegkamp, 2020.
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- Breaking the Curse of Nonregularity with Subagging --- Inference of the Mean Outcome under Optimal Treatment Regimes
- Chengchun Shi, Wenbin Lu, Rui Song, 2020.
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- Wide Neural Networks with Bottlenecks are Deep Gaussian Processes
- Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle, 2020.
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- Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality
- Ryumei Nakada, Masaaki Imaizumi, 2020.
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- Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes
- Emily C. Hector, Peter X.-K. Song, 2020.
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- Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise
- Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara, 2020.
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- Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success
- Lucas Mentch, Siyu Zhou, 2020.
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- Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior
- William Hoiles, Vikram Krishnamurthy, Kunal Pattanayak, 2020.
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- The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization
- Dmitry Kobak, Jonathan Lomond, Benoit Sanchez, 2020.
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- Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels
- Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber, 2020.
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- Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes
- Nathan Kallus, Masatoshi Uehara, 2020.
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- High Dimensional Forecasting via Interpretable Vector Autoregression
- William B. Nicholson, Ines Wilms, Jacob Bien, David S. Matteson, 2020.
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- Complete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group
- Yuexiang Zhai, Zitong Yang, Zhenyu Liao, John Wright, Yi Ma, 2020.
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- Cramer-Wold Auto-Encoder
- Szymon Knop, Przemysław Spurek, Jacek Tabor, Igor Podolak, Marcin Mazur, Stanisław Jastrzębski, 2020.
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- Trust-Region Variational Inference with Gaussian Mixture Models
- Oleg Arenz, Mingjun Zhong, Gerhard Neumann, 2020.
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- Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
- Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski, 2020.
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- apricot: Submodular selection for data summarization in Python
- Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble, 2020. (Machine Learning Open Source Software Paper)
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- Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective
- Chao Gao, Yuan Yao, Weizhi Zhu, 2020.
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- Generating Weighted MAX-2-SAT Instances with Frustrated Loops: an RBM Case Study
- Yan Ru Pei, Haik Manukian, Massimiliano Di Ventra, 2020.
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- Learning Big Gaussian Bayesian Networks: Partition, Estimation and Fusion
- Jiaying Gu, Qing Zhou, 2020.
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- Streamlined Variational Inference with Higher Level Random Effects
- Tui H. Nolan, Marianne Menictas, Matt P. Wand, 2020.
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- Asymptotic Consistency of $\alpha$-{R}\'enyi-Approximate Posteriors
- Prateek Jaiswal, Vinayak Rao, Harsha Honnappa, 2020.
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- Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise
- Andrei Kulunchakov, Julien Mairal, 2020.
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- Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality
- Miaoyan Wang, Lexin Li, 2020.
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- Spectral Algorithms for Community Detection in Directed Networks
- Zhe Wang, Yingbin Liang, Pengsheng Ji, 2020.
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- Dual Iterative Hard Thresholding
- Xiao-Tong Yuan, Bo Liu, Lezi Wang, Qingshan Liu, Dimitris N. Metaxas, 2020.
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- Robust Reinforcement Learning with Bayesian Optimisation and Quadrature
- Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael A. Osborne, Shimon Whiteson, 2020.
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- The Kalai-Smorodinsky solution for many-objective Bayesian optimization
- Mickael Binois, Victor Picheny, Patrick Taillandier, Abderrahmane Habbal, 2020.
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- Distributionally Ambiguous Optimization for Batch Bayesian Optimization
- Nikitas Rontsis, Michael A. Osborne, Paul J. Goulart, 2020.
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- Local Causal Network Learning for Finding Pairs of Total and Direct Effects
- Yue Liu, Zhuangyan Fang, Yangbo He, Zhi Geng, Chunchen Liu, 2020.
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- Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
- Junhong Lin, Volkan Cevher, 2020.
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- Empirical Priors for Prediction in Sparse High-dimensional Linear Regression
- Ryan Martin, Yiqi Tang, 2020.
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- A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints
- Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang, 2020.
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- Nesterov's Acceleration for Approximate Newton
- Haishan Ye, Luo Luo, Zhihua Zhang, 2020.
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- Importance Sampling Techniques for Policy Optimization
- Alberto Maria Metelli, Matteo Papini, Nico Montali, Marcello Restelli, 2020.
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- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu, 2020.
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- Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks
- Amir R. Asadi, Emmanuel Abbe, 2020.
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- metric-learn: Metric Learning Algorithms in Python
- William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet, 2020. (Machine Learning Open Source Software Paper)
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- Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
- Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang, 2020.
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- Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions
- Benjamin Fehrman, Benjamin Gess, Arnulf Jentzen, 2020.
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- A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks
- Owen Marschall, Kyunghyun Cho, Cristina Savin, 2020.
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- Probabilistic Learning on Graphs via Contextual Architectures
- Davide Bacciu, Federico Errica, Alessio Micheli, 2020. (Machine Learning Open Source Software Paper)
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- Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
- Yao Ma, Alex Olshevsky, Csaba Szepesvari, Venkatesh Saligrama, 2020.
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- Monte Carlo Gradient Estimation in Machine Learning
- Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih, 2020.
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- Convergence of Sparse Variational Inference in Gaussian Processes Regression
- David R. Burt, Carl Edward Rasmussen, Mark van der Wilk, 2020.
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- AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models
- Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang, 2020. (Machine Learning Open Source Software Paper)
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- A General System of Differential Equations to Model First-Order Adaptive Algorithms
- Andre Belotto da Silva, Maxime Gazeau, 2020.
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- A Regularization-Based Adaptive Test for High-Dimensional GLMs
- Chong Wu, Gongjun Xu, Xiaotong Shen, Wei Pan, 2020.
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- Apache Mahout: Machine Learning on Distributed Dataflow Systems
- Robin Anil, Gokhan Capan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman, Trevor Grant, Shannon Quinn, Paritosh Ranjan, Sebastian Schelter, Özgür Yılmazel, 2020. (Machine Learning Open Source Software Paper)
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- Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
- Rad Niazadeh, Tim Roughgarden, Joshua R. Wang, 2020.
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- Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination
- Hang Yu, Songwei Wu, Luyin Xin, Justin Dauwels, 2020.
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- Tensor Regression Networks
- Jean Kossaifi, Zachary C. Lipton, Arinbjorn Kolbeinsson, Aran Khanna, Tommaso Furlanello, Anima Anandkumar, 2020.
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- Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering
- Israel A. Almodóvar-Rivera, Ranjan Maitra, 2020.
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- A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
- Aryan Mokhtari, Alec Koppel, Martin Takac, Alejandro Ribeiro, 2020.
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- Bayesian Closed Surface Fitting Through Tensor Products
- Olivier Binette, Debdeep Pati, David B. Dunson, 2020.
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- Tslearn, A Machine Learning Toolkit for Time Series Data
- Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, Eli Woods, 2020. (Machine Learning Open Source Software Paper)
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- Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models
- Jiahe Lin, George Michailidis, 2020.
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- GluonTS: Probabilistic and Neural Time Series Modeling in Python
- Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang, 2020. (Machine Learning Open Source Software Paper)
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- Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space
- Elisabeth Gassiat, Sylvain Le Corff, Luc Lehéricy, 2020.
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- NEVAE: A Deep Generative Model for Molecular Graphs
- Bidisha Samanta, Abir De, Gourhari Jana, Vicenç Gómez, Pratim Chattaraj, Niloy Ganguly, Manuel Gomez-Rodriguez, 2020.
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- Prediction regions through Inverse Regression
- Emilie Devijver, Emeline Perthame, 2020.
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- High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model
- Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini, 2020.
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- MFE: Towards reproducible meta-feature extraction
- Edesio Alcobaça, Felipe Siqueira, Adriano Rivolli, Luís P. F. Garcia, Jefferson T. Oliva, André C. P. L. F. de Carvalho, 2020. (Machine Learning Open Source Software Paper)
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- ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
- Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh, 2020.
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- Bayesian Model Selection with Graph Structured Sparsity
- Youngseok Kim, Chao Gao, 2020.
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- ThunderGBM: Fast GBDTs and Random Forests on GPUs
- Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen, 2020. (Machine Learning Open Source Software Paper)
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- Change Point Estimation in a Dynamic Stochastic Block Model
- Monika Bhattacharjee, Moulinath Banerjee, George Michailidis, 2020.
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- Quadratic Decomposable Submodular Function Minimization: Theory and Practice
- Pan Li, Niao He, Olgica Milenkovic, 2020.
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- Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
- Aryan Mokhtari, Hamed Hassani, Amin Karbasi, 2020.
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- Sparse Projection Oblique Randomer Forests
- Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Carey E. Priebe, Jason Yim, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein, 2020.
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- Stochastic Nested Variance Reduction for Nonconvex Optimization
- Dongruo Zhou, Pan Xu, Quanquan Gu, 2020.
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- AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings)
- Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé, 2020. (Machine Learning Open Source Software Paper)
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- Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size
- Francois Kamper, Sarel J. Steel, Johan A. du Preez, 2020.
[abs][pdf][bib]
- General Latent Feature Models for Heterogeneous Datasets
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- Joint Causal Inference from Multiple Contexts
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- Loss Control with Rank-one Covariance Estimate for Short-term Portfolio Optimization
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- GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
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- Representation Learning for Dynamic Graphs: A Survey
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- On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
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- The weight function in the subtree kernel is decisive
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- WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions
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- pyts: A Python Package for Time Series Classification
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- Learning with Fenchel-Young losses
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- On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent
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- Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
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- Generalized Nonbacktracking Bounds on the Influence
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- Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes
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- GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
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- High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix
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- Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data
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- Practical Locally Private Heavy Hitters
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- Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning
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- On lp-Support Vector Machines and Multidimensional Kernels
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- Generalized probabilistic principal component analysis of correlated data
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- Neyman-Pearson classification: parametrics and sample size requirement
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