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