JMLR Volume 23
- Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
- Subhabrata Majumdar, George Michailidis; (1):1−53, 2022.
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[code]
- Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions
- Shaogao Lv, Heng Lian; (2):1−32, 2022.
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- Recovering shared structure from multiple networks with unknown edge distributions
- Keith Levin, Asad Lodhia, Elizaveta Levina; (3):1−48, 2022.
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- Exploiting locality in high-dimensional Factorial hidden Markov models
- Lorenzo Rimella, Nick Whiteley; (4):1−34, 2022.
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[code]
- Empirical Risk Minimization under Random Censorship
- Guillaume Ausset, Stephan Clémençon, François Portier; (5):1−59, 2022.
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- XAI Beyond Classification: Interpretable Neural Clustering
- Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou; (6):1−28, 2022.
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[code]
- Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes
- Justin D. Silverman, Kimberly Roche, Zachary C. Holmes, Lawrence A. David, Sayan Mukherjee; (7):1−42, 2022.
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[code]
- Deep Learning in Target Space
- Michael Fairbank, Spyridon Samothrakis, Luca Citi; (8):1−46, 2022.
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[code]
- Scaling Laws from the Data Manifold Dimension
- Utkarsh Sharma, Jared Kaplan; (9):1−34, 2022.
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[code]
- Interpolating Predictors in High-Dimensional Factor Regression
- Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp; (10):1−60, 2022.
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- Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes
- Ali Kara, Serdar Yuksel; (11):1−46, 2022.
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- Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems
- Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan; (12):1−83, 2022.
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[code]
- Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
- Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet; (13):1−35, 2022.
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[code]
- On Generalizations of Some Distance Based Classifiers for HDLSS Data
- Sarbojit Roy, Soham Sarkar, Subhajit Dutta, Anil K. Ghosh; (14):1−41, 2022.
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- A Stochastic Bundle Method for Interpolation
- Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar; (15):1−57, 2022.
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[code]
- TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems
- Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Hua Huang, Carola-Bibiane Schönlieb; (16):1−48, 2022.
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[code]
- Spatial Multivariate Trees for Big Data Bayesian Regression
- Michele Peruzzi, David B. Dunson; (17):1−40, 2022.
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[code]
- Decimated Framelet System on Graphs and Fast G-Framelet Transforms
- Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang; (18):1−68, 2022.
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[code]
- Universal Approximation in Dropout Neural Networks
- Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies, Jaron Sanders, Albert Senen-Cerda; (19):1−46, 2022.
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- Supervised Dimensionality Reduction and Visualization using Centroid-Encoder
- Tomojit Ghosh, Michael Kirby; (20):1−34, 2022.
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[code]
- Evolutionary Variational Optimization of Generative Models
- Jakob Drefs, Enrico Guiraud, Jörg Lücke; (21):1−51, 2022.
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[code]
- LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data
- Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney; (22):1−36, 2022.
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- Fast and Robust Rank Aggregation against Model Misspecification
- Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama; (23):1−35, 2022.
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- On Biased Stochastic Gradient Estimation
- Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb; (24):1−43, 2022.
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- Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
- Maxime Vono, Daniel Paulin, Arnaud Doucet; (25):1−69, 2022.
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- MurTree: Optimal Decision Trees via Dynamic Programming and Search
- Emir Demirović, Anna Lukina, Emmanuel Hebrard, Jeffrey Chan, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Peter J. Stuckey; (26):1−47, 2022.
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[code]
- Data-Derived Weak Universal Consistency
- Narayana Santhanam, Venkatachalam Anantharam, Wojciech Szpankowski; (27):1−55, 2022.
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- Novel Min-Max Reformulations of Linear Inverse Problems
- Mohammed Rayyan Sheriff, Debasish Chatterjee; (28):1−46, 2022.
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- Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
- Kaiyi Ji, Junjie Yang, Yingbin Liang; (29):1−41, 2022.
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- A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
- Augusto Fasano, Daniele Durante; (30):1−26, 2022.
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- An improper estimator with optimal excess risk in misspecified density estimation and logistic regression
- Jaouad Mourtada, Stéphane Gaïffas; (31):1−49, 2022.
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- Active Learning for Nonlinear System Identification with Guarantees
- Horia Mania, Michael I. Jordan, Benjamin Recht; (32):1−30, 2022.
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- Model Averaging Is Asymptotically Better Than Model Selection For Prediction
- Tri M. Le, Bertrand S. Clarke; (33):1−53, 2022.
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- SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
- Weijing Tang, Jiaqi Ma, Qiaozhu Mei, Ji Zhu; (34):1−29, 2022.
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[code]
- Optimality and Stability in Non-Convex Smooth Games
- Guojun Zhang, Pascal Poupart, Yaoliang Yu; (35):1−71, 2022.
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- Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization
- Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang; (36):1−70, 2022.
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- Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
- Matteo Pegoraro, Mario Beraha; (37):1−59, 2022.
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[code]
- Score Matched Neural Exponential Families for Likelihood-Free Inference
- Lorenzo Pacchiardi, Ritabrata Dutta; (38):1−71, 2022.
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[code]
- (f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics
- Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet; (39):1−70, 2022.
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- Structure-adaptive Manifold Estimation
- Nikita Puchkin, Vladimir Spokoiny; (40):1−62, 2022.
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- The correlation-assisted missing data estimator
- Timothy I. Cannings, Yingying Fan; (41):1−49, 2022.
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- Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
- Zhong Li, Jiequn Han, Weinan E, Qianxiao Li; (42):1−85, 2022.
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- Sampling Permutations for Shapley Value Estimation
- Rory Mitchell, Joshua Cooper, Eibe Frank, Geoffrey Holmes; (43):1−46, 2022.
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- PAC Guarantees and Effective Algorithms for Detecting Novel Categories
- Si Liu, Risheek Garrepalli, Dan Hendrycks, Alan Fern, Debashis Mondal, Thomas G. Dietterich; (44):1−47, 2022.
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[code]
- Optimal Transport for Stationary Markov Chains via Policy Iteration
- Kevin O'Connor, Kevin McGoff, Andrew B. Nobel; (45):1−52, 2022.
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[code]
- Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent
- Wanrong Zhu, Zhipeng Lou, Wei Biao Wu; (46):1−22, 2022.
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- Cascaded Diffusion Models for High Fidelity Image Generation
- Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans; (47):1−33, 2022.
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[code]
- Overparameterization of Deep ResNet: Zero Loss and Mean-field Analysis
- Zhiyan Ding, Shi Chen, Qin Li, Stephen J. Wright; (48):1−65, 2022.
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- Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection
- Xinyi Wang, Lang Tong; (49):1−27, 2022.
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- Analytically Tractable Hidden-States Inference in Bayesian Neural Networks
- Luong-Ha Nguyen, James-A. Goulet; (50):1−33, 2022.
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- Toolbox for Multimodal Learn (scikit-multimodallearn)
- Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette; (51):1−7, 2022. (Machine Learning Open Source Software Paper)
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[code]
- LinCDE: Conditional Density Estimation via Lindsey's Method
- Zijun Gao, Trevor Hastie; (52):1−55, 2022.
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- DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python
- Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler; (53):1−6, 2022. (Machine Learning Open Source Software Paper)
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[code]
- SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
- Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter; (54):1−9, 2022. (Machine Learning Open Source Software Paper)
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[code]
- Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy
- Terrance D. Savitsky, Matthew R.Williams, Jingchen Hu; (55):1−37, 2022.
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- solo-learn: A Library of Self-supervised Methods for Visual Representation Learning
- Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci; (56):1−6, 2022. (Machine Learning Open Source Software Paper)
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[code]
- Inherent Tradeoffs in Learning Fair Representations
- Han Zhao, Geoffrey J. Gordon; (57):1−26, 2022.
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