JMLR 2022

335 papers

(f,Gamma)-Divergences: Interpolating Between F-Divergences and Integral Probability Metrics Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet

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A Bregman Learning Framework for Sparse Neural Networks Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger

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A Class of Conjugate Priors for Multinomial Probit Models Which Includes the Multivariate Normal One Augusto Fasano, Daniele Durante

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A Closer Look at Embedding Propagation for Manifold Smoothing Diego Velazquez, Pau Rodriguez, Josep M. Gonfaus, F. Xavier Roca, Jordi Gonzalez

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A Computationally Efficient Framework for Vector Representation of Persistence Diagrams Kit C Chan, Umar Islambekov, Alexey Luchinsky, Rebecca Sanders

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A Distribution Free Conditional Independence Test with Applications to Causal Discovery Zhanrui Cai, Runze Li, Yaowu Zhang

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A Forward Approach for Sufficient Dimension Reduction in Binary Classification Jongkyeong Kang, Seung Jun Shin

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A Generalized Projected Bellman Error for Off-Policy Value Estimation in Reinforcement Learning Andrew Patterson, Adam White, Martha White

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A Kernel Two-Sample Test for Functional Data George Wynne, Andrew B. Duncan

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A Momentumized, Adaptive, Dual Averaged Gradient Method Aaron Defazio, Samy Jelassi

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A Nonconvex Framework for Structured Dynamic Covariance Recovery Katherine Tsai, Mladen Kolar, Oluwasanmi Koyejo

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A Perturbation-Based Kernel Approximation Framework Roy Mitz, Yoel Shkolnisky

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A Primer for Neural Arithmetic Logic Modules Bhumika Mistry, Katayoun Farrahi, Jonathon Hare

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A Proof of Convergence for the Gradient Descent Optimization Method with Random Initializations in the Training of Neural Networks with ReLU Activation for Piecewise Linear Target Functions Arnulf Jentzen, Adrian Riekert

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A Random Matrix Perspective on Random Tensors José Henrique de M. Goulart, Romain Couillet, Pierre Comon

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A Spectral-Based Analysis of the Separation Between Two-Layer Neural Networks and Linear Methods Lei Wu, Jihao Long

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A Statistical Approach for Optimal Topic Model Identification Craig M. Lewis, Francesco Grossetti

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A Stochastic Bundle Method for Interpolation Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar

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A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review Michael Pearce, Elena A. Erosheva

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A Unifying Framework for Variance-Reduced Algorithms for Findings Zeroes of Monotone Operators Xun Zhang, William B. Haskell, Zhisheng Ye

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A Universally Consistent Learning Rule with a Universally Monotone Error Vladimir Pestov

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A Wasserstein Distance Approach for Concentration of Empirical Risk Estimates Prashanth L.A., Sanjay P. Bhat

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A Worst Case Analysis of Calibrated Label Ranking Multi-Label Classification Method Lucas Henrique Sousa Mello, Flávio Miguel Varejão, Alexandre Loureiros Rodrigues

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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

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Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling Xi Chen, Bo Jiang, Tianyi Lin, Shuzhong Zhang

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Active Learning for Nonlinear System Identification with Guarantees Horia Mania, Michael I. Jordan, Benjamin Recht

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Active Structure Learning of Bayesian Networks in an Observational Setting Noa Ben-David, Sivan Sabato

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Adaptive Greedy Algorithm for Moderately Large Dimensions in Kernel Conditional Density Estimation Minh-Lien Jeanne Nguyen, Claire Lacour, Vincent Rivoirard

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Additive Nonlinear Quantile Regression in Ultra-High Dimension Ben Sherwood, Adam Maidman

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Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces Masaaki Imaizumi, Kenji Fukumizu

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Adversarial Classification: Necessary Conditions and Geometric Flows Nicolás García Trillos, Ryan Murray

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Adversarial Robustness Guarantees for Gaussian Processes Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen Roberts, Marta Kwiatkowska

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All You Need Is a Good Functional Prior for Bayesian Deep Learning Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Maurizio Filippone

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ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network Xing Fan, Marianna Pensky, Feng Yu, Teng Zhang

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An Efficient Sampling Algorithm for Non-Smooth Composite Potentials Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett

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An Error Analysis of Generative Adversarial Networks for Learning Distributions Jian Huang, Yuling Jiao, Zhen Li, Shiao Liu, Yang Wang, Yunfei Yang

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An Improper Estimator with Optimal Excess Risk in Misspecified Density Estimation and Logistic Regression Jaouad Mourtada, Stéphane Gaïffas

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An Optimization-Centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas

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Analytically Tractable Hidden-States Inference in Bayesian Neural Networks Luong-Ha Nguyen, James-A. Goulet

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Approximate Bayesian Computation via Classification Yuexi Wang, Tetsuya Kaji, Veronika Rockova

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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

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Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks Zhong Li, Jiequn Han, Weinan E, Qianxiao Li

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Are All Layers Created Equal? Chiyuan Zhang, Samy Bengio, Yoram Singer

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Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms Ping Ma, Yongkai Chen, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney

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Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method Alex Olshevsky

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Asymptotic Study of Stochastic Adaptive Algorithms in Non-Convex Landscape Sébastien Gadat, Ioana Gavra

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Attraction-Repulsion Spectrum in Neighbor Embeddings Jan Niklas Böhm, Philipp Berens, Dmitry Kobak

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Auto-Sklearn 2.0: Hands-Free AutoML via Meta-Learning Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter

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Batch Normalization Preconditioning for Neural Network Training Susanna Lange, Kyle Helfrich, Qiang Ye

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Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure Yang Ni, Francesco C. Stingo, Veerabhadran Baladandayuthapani

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Bayesian Multinomial Logistic Normal Models Through Marginally Latent Matrix-T Processes Justin D. Silverman, Kimberly Roche, Zachary C. Holmes, Lawrence A. David, Sayan Mukherjee

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Bayesian Pseudo Posterior Mechanism Under Asymptotic Differential Privacy Terrance D. Savitsky, Matthew R.Williams, Jingchen Hu

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Bayesian Subset Selection and Variable Importance for Interpretable Prediction and Classification Daniel R. Kowal

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Behavior Priors for Efficient Reinforcement Learning Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

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Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent Wanrong Zhu, Zhipeng Lou, Wei Biao Wu

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Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution Yichen Zhou, Giles Hooker

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Bounding the Error of Discretized Langevin Algorithms for Non-Strongly Log-Concave Targets Arnak S. Dalalyan, Avetik Karagulyan, Lionel Riou-Durand

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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

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Cauchy–Schwarz Regularized Autoencoder Linh Tran, Maja Pantic, Marc Peter Deisenroth

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Causal Aggregation: Estimation and Inference of Causal Effects by Constraint-Based Data Fusion Jaime Roquero Gimenez, Dominik Rothenhäusler

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Causal Classification: Treatment Effect Estimation vs. Outcome Prediction Carlos Fernández-Loría, Foster Provost

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CD-Split and HPD-Split: Efficient Conformal Regions in High Dimensions Rafael Izbicki, Gilson Shimizu, Rafael B. Stern

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Change Point Localization in Dependent Dynamic Nonparametric Random Dot Product Graphs Oscar Hernan Madrid Padilla, Yi Yu, Carey E. Priebe

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Clustering with Semidefinite Programming and Fixed Point Iteration Pedro Felzenszwalb, Caroline Klivans, Alice Paul

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Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guarantees Kean Ming Tan, Heather Battey, Wen-Xin Zhou

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Community Detection in Sparse Latent Space Models Fengnan Gao, Zongming Ma, Hongsong Yuan

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Conditions and Assumptions for Constraint-Based Causal Structure Learning Kayvan Sadeghi, Terry Soo

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Constraint Reasoning Embedded Structured Prediction Nan Jiang, Maosen Zhang, Willem-Jan van Hoeve, Yexiang Xue

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Contraction Rates for Sparse Variational Approximations in Gaussian Process Regression Dennis Nieman, Botond Szabo, Harry van Zanten

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Convergence Guarantees for the Good-Turing Estimator Amichai Painsky

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Convergence Rates for Gaussian Mixtures of Experts Nhat Ho, Chiao-Yu Yang, Michael I. Jordan

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D-GCCA: Decomposition-Based Generalized Canonical Correlation Analysis for Multi-View High-Dimensional Data Hai Shu, Zhe Qu, Hongtu Zhu

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Data-Derived Weak Universal Consistency Narayana Santhanam, Venkatachalam Anantharam, Wojciech Szpankowski

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De-Sequentialized Monte Carlo: A Parallel-in-Time Particle Smoother Adrien Corenflos, Nicolas Chopin, Simo Särkkä

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Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions Shaogao Lv, Heng Lian

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Decimated Framelet System on Graphs and Fast G-Framelet Transforms Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang

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Deep Learning in Target Space Michael Fairbank, Spyridon Samothrakis, Luca Citi

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Deep Limits and a Cut-Off Phenomenon for Neural Networks Benny Avelin, Anders Karlsson

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Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons Shijun Zhang, Zuowei Shen, Haizhao Yang

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Dependent Randomized Rounding for Clustering and Partition Systems with Knapsack Constraints David G. Harris, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh

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Depth Separation Beyond Radial Functions Luca Venturi, Samy Jelassi, Tristan Ozuch, Joan Bruna

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Detecting Latent Communities in Network Formation Models Shujie Ma, Liangjun Su, Yichong Zhang

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Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior Rajarshi Guhaniyogi, Cheng Li, Terrance D. Savitsky, Sanvesh Srivastava

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Distributed Bootstrap for Simultaneous Inference Under High Dimensionality Yang Yu, Shih-Kang Chao, Guang Cheng

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Distributed Learning of Finite Gaussian Mixtures Qiong Zhang, Jiahua Chen

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Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima Brian Swenson, Ryan Murray, H. Vincent Poor, Soummya Kar

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Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression Domagoj Cevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen

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Double Spike Dirichlet Priors for Structured Weighting Huiming Lin, Meng Li

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Early Stopping for Iterative Regularization with General Loss Functions Ting Hu, Yunwen Lei

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Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, Julien Seznec

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Efficient Inference for Dynamic Flexible Interactions of Neural Populations Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu

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Efficient Least Squares for Estimating Total Effects Under Linearity and Causal Sufficiency F. Richard Guo, Emilija Perković

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Efficient MCMC Sampling with Dimension-Free Convergence Rate Using ADMM-Type Splitting Maxime Vono, Daniel Paulin, Arnaud Doucet

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EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation Jun Ho Yoon, Seyoung Kim

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Empirical Risk Minimization Under Random Censorship Guillaume Ausset, Stephan Clémençon, François Portier

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Estimating Causal Effects Under Network Interference with Bayesian Generalized Propensity Scores Laura Forastiere, Fabrizia Mealli, Albert Wu, Edoardo M. Airoldi

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Estimating Density Models with Truncation Boundaries Using Score Matching Song Liu, Takafumi Kanamori, Daniel J. Williams

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Estimation and Inference on High-Dimensional Individualized Treatment Rule in Observational Data Using Split-and-Pooled De-Correlated Score Muxuan Liang, Young-Geun Choi, Yang Ning, Maureen A Smith, Ying-Qi Zhao

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EV-GAN: Simulation of Extreme Events with ReLU Neural Networks Michaël Allouche, Stéphane Girard, Emmanuel Gobet

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Evolutionary Variational Optimization of Generative Models Jakob Drefs, Enrico Guiraud, Jörg Lücke

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Exact Partitioning of High-Order Models with a Novel Convex Tensor Cone Relaxation Chuyang Ke, Jean Honorio

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Exact Simulation of Diffusion First Exit Times: Algorithm Acceleration Samuel Herrmann, Cristina Zucca

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Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks El Mehdi Achour, François Malgouyres, Franck Mamalet

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Expected Regret and Pseudo-Regret Are Equivalent When the Optimal Arm Is Unique Daron Anderson, Douglas J. Leith

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Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods Dachao Lin, Haishan Ye, Zhihua Zhang

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Exploiting Locality in High-Dimensional Factorial Hidden Markov Models Lorenzo Rimella, Nick Whiteley

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Extensions to the Proximal Distance Method of Constrained Optimization Alfonso Landeros, Oscar Hernan Madrid Padilla, Hua Zhou, Kenneth Lange

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Fairness-Aware PAC Learning from Corrupted Data Nikola Konstantinov, Christoph H. Lampert

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Fast and Robust Rank Aggregation Against Model Misspecification Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama

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Fast Stagewise Sparse Factor Regression Kun Chen, Ruipeng Dong, Wanwan Xu, Zemin Zheng

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Faster Randomized Interior Point Methods for Tall/Wide Linear Programs Agniva Chowdhury, Gregory Dexter, Palma London, Haim Avron, Petros Drineas

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Foolish Crowds Support Benign Overfitting Niladri S. Chatterji, Philip M. Long

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FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting Boxin Zhao, Y. Samuel Wang, Mladen Kolar

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Fully General Online Imitation Learning Michael K. Cohen, Marcus Hutter, Neel Nanda

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Functional Linear Regression with Mixed Predictors Daren Wang, Zifeng Zhao, Yi Yu, Rebecca Willett

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Fundamental Limits and Tradeoffs in Invariant Representation Learning Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar

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Gauss-Legendre Features for Gaussian Process Regression Paz Fink Shustin, Haim Avron

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Gaussian Process Boosting Fabio Sigrist

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Gaussian Process Parameter Estimation Using Mini-Batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti

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Gaussian Process Regression: Optimality, Robustness, and Relationship with Kernel Ridge Regression Wenjia Wang, Bing-Yi Jing

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Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag

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Generalized Ambiguity Decomposition for Ranking Ensemble Learning Hongzhi Liu, Yingpeng Du, Zhonghai Wu

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Generalized Matrix Factorization: Efficient Algorithms for Fitting Generalized Linear Latent Variable Models to Large Data Arrays Lukasz Kidzinski, Francis K.C. Hui, David I. Warton, Trevor J. Hastie

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Generalized Resubstitution for Classification Error Estimation Parisa Ghane, Ulisses Braga-Neto

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Generalized Sparse Additive Models Asad Haris, Noah Simon, Ali Shojaie

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Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance Lucas Mentch, Siyu Zhou

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Global Optimality and Finite Sample Analysis of SoftMax Off-Policy Actor Critic Under State Distribution Mismatch Shangtong Zhang, Remi Tachet des Combes, Romain Laroche

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Globally Injective ReLU Networks Michael Puthawala, Konik Kothari, Matti Lassas, Ivan Dokmanić, Maarten de Hoop

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Graph Partitioning and Sparse Matrix Ordering Using Reinforcement Learning and Graph Neural Networks Alice Gatti, Zhixiong Hu, Tess Smidt, Esmond G. Ng, Pieter Ghysels

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Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White

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Hamilton-Jacobi Equations on Graphs with Applications to Semi-Supervised Learning and Data Depth Jeff Calder, Mahmood Ettehad

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Handling Hard Affine SDP Shape Constraints in RKHSs Pierre-Cyril Aubin-Frankowski, Zoltan Szabo

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IALE: Imitating Active Learner Ensembles Christoffer Löffler, Christopher Mutschler

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Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon

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Improved Classification Rates for Localized SVMs Ingrid Blaschzyk, Ingo Steinwart

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Improved Generalization Bounds for Adversarially Robust Learning Idan Attias, Aryeh Kontorovich, Yishay Mansour

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Improving Bayesian Network Structure Learning in the Presence of Measurement Error Yang Liu, Anthony C. Constantinou, Zhigao Guo

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Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning Haiyun He, Hanshu Yan, Vincent Y. F. Tan

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Information-Theoretic Classification Accuracy: A Criterion That Guides Data-Driven Combination of Ambiguous Outcome Labels in Multi-Class Classification Chihao Zhang, Yiling Elaine Chen, Shihua Zhang, Jingyi Jessica Li

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Inherent Tradeoffs in Learning Fair Representations Han Zhao, Geoffrey J. Gordon

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Innovations Autoencoder and Its Application in One-Class Anomalous Sequence Detection Xinyi Wang, Lang Tong

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Integral Autoencoder Network for Discretization-Invariant Learning Yong Zheng Ong, Zuowei Shen, Haizhao Yang

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Interlocking Backpropagation: Improving Depthwise Model-Parallelism Aidan N. Gomez, Oscar Key, Kuba Perlin, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal

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Interpolating Predictors in High-Dimensional Factor Regression Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp

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Interpretable Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings Zeda Li, Scott A. Bruce, Tian Cai

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Interval-Censored Hawkes Processes Marian-Andrei Rizoiu, Alexander Soen, Shidi Li, Pio Calderon, Leanne J. Dong, Aditya Krishna Menon, Lexing Xie

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Intrinsic Dimension Estimation Using Wasserstein Distance Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin

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Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning Sébastien Forestier, Rémy Portelas, Yoan Mollard, Pierre-Yves Oudeyer

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Joint Continuous and Discrete Model Selection via Submodularity Jonathan Bunton, Paulo Tabuada

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Joint Estimation and Inference for Data Integration Problems Based on Multiple Multi-Layered Gaussian Graphical Models Subhabrata Majumdar, George Michailidis

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Joint Inference of Multiple Graphs from Matrix Polynomials Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra

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Jump Gaussian Process Model for Estimating Piecewise Continuous Regression Functions Chiwoo Park

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Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence Mattes Mollenhauer, Stefan Klus, Christof Schütte, Péter Koltai

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Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations Haoyuan Chen, Liang Ding, Rui Tuo

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Kernel Partial Correlation Coefficient --- a Measure of Conditional Dependence Zhen Huang, Nabarun Deb, Bodhisattva Sen

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KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints Aurélien Garivier, Hédi Hadiji, Pierre Ménard, Gilles Stoltz

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KoPA: Automated Kronecker Product Approximation Chencheng Cai, Rong Chen, Han Xiao

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Learning from Noisy Pairwise Similarity and Unlabeled Data Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama

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Learning Green's Functions Associated with Time-Dependent Partial Differential Equations Nicolas Boullé, Seick Kim, Tianyi Shi, Alex Townsend

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Learning Linear Non-Gaussian Directed Acyclic Graph with Diverging Number of Nodes Ruixuan Zhao, Xin He, Junhui Wang

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Learning Operators with Coupled Attention Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris

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Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training Diego Granziol, Stefan Zohren, Stephen Roberts

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Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models Shiwei Lan

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Learning to Optimize: A Primer and a Benchmark Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, Wotao Yin

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Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence Julie Nutini, Issam Laradji, Mark Schmidt

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LinCDE: Conditional Density Estimation via Lindsey's Method Zijun Gao, Trevor Hastie

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Linearization and Identification of Multiple-Attractor Dynamical Systems Through Laplacian Eigenmaps Bernardo Fichera, Aude Billard

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Logarithmic Regret for Episodic Continuous-Time Linear-Quadratic Reinforcement Learning over a Finite-Time Horizon Matteo Basei, Xin Guo, Anran Hu, Yufei Zhang

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Low-Rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok

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LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney

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Machine Learning on Graphs: A Model and Comprehensive Taxonomy Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy

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MALTS: Matching After Learning to Stretch Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

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Manifold Coordinates with Physical Meaning Samson J. Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen

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Mappings for Marginal Probabilities with Applications to Models in Statistical Physics Mehdi Molkaraie

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Matrix Completion with Covariate Information and Informative Missingness Huaqing Jin, Yanyuan Ma, Fei Jiang

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Maximum Sampled Conditional Likelihood for Informative Subsampling HaiYing Wang, Jae Kwang Kim

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Mean-Field Analysis of Piecewise Linear Solutions for Wide ReLU Networks Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli

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Meta-Analysis of Heterogeneous Data: Integrative Sparse Regression in High-Dimensions Subha Maity, Yuekai Sun, Moulinath Banerjee

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Metrics of Calibration for Probabilistic Predictions Imanol Arrieta-Ibarra, Paman Gujral, Jonathan Tannen, Mark Tygert, Cherie Xu

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Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling Keru Wu, Scott Schmidler, Yuansi Chen

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Minimax Optimal Approaches to the Label Shift Problem in Non-Parametric Settings Subha Maity, Yuekai Sun, Moulinath Banerjee

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Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez

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Model Averaging Is Asymptotically Better than Model Selection for Prediction Tri M. Le, Bertrand S. Clarke

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More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming Vo Nguyen Le Duy, Ichiro Takeuchi

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Multi-Agent Multi-Armed Bandits with Limited Communication Mridul Agarwal, Vaneet Aggarwal, Kamyar Azizzadenesheli

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Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos

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Multi-Task Dynamical Systems Alex Bird, Christopher K. I. Williams, Christopher Hawthorne

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Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach Kweku Abraham, Ismaël Castillo, Elisabeth Gassiat

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Multiple-Splitting Projection Test for High-Dimensional Mean Vectors Wanjun Liu, Xiufan Yu, Runze Li

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Multivariate Boosted Trees and Applications to Forecasting and Control Lorenzo Nespoli, Vasco Medici

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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

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Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling Gábor Melis, András György, Phil Blunsom

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Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes Ali Kara, Serdar Yuksel

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Network Regression with Graph Laplacians Yidong Zhou, Hans-Georg Müller

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Neural Estimation of Statistical Divergences Sreejith Sreekumar, Ziv Goldfeld

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New Insights for the Multivariate Square-Root Lasso Aaron J. Molstad

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No Weighted-Regret Learning in Adversarial Bandits with Delays Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet

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Non-Asymptotic and Accurate Learning of Nonlinear Dynamical Systems Yahya Sattar, Samet Oymak

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Non-Asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials Daiqi Gao, Yufeng Liu, Donglin Zeng

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Nonconvex Matrix Completion with Linearly Parameterized Factors Ji Chen, Xiaodong Li, Zongming Ma

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Nonparametric Adaptive Control and Prediction: Theory and Randomized Algorithms Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine

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Nonparametric Neighborhood Selection in Graphical Models Hao Dong, Yuedong Wang

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Nonparametric Principal Subspace Regression Yang Zhou, Mark Koudstaal, Dengdeng Yu, Dehan Kong, Fang Yao

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Nonstochastic Bandits with Composite Anonymous Feedback Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Claudio Gentile, Yishay Mansour

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Novel Min-Max Reformulations of Linear Inverse Problems Mohammed Rayyan Sheriff, Debasish Chatterjee

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Nystrom Regularization for Time Series Forecasting Zirui Sun, Mingwei Dai, Yao Wang, Shao-Bo Lin

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On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping Qiang Zhou, Sinno Jialin Pan

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On Biased Stochastic Gradient Estimation Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb

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On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems Michael Muehlebach, Michael I. Jordan

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On Generalizations of Some Distance Based Classifiers for HDLSS Data Sarbojit Roy, Soham Sarkar, Subhajit Dutta, Anil K. Ghosh

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On Instrumental Variable Regression for Deep Offline Policy Evaluation Yutian Chen, Liyuan Xu, Caglar Gulcehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet

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On Low-Rank Trace Regression Under General Sampling Distribution Nima Hamidi, Mohsen Bayati

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On Mixup Regularization Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert

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On Regularized Square-Root Regression Problems: Distributionally Robust Interpretation and Fast Computations Hong T.M. Chu, Kim-Chuan Toh, Yangjing Zhang

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On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) Using Mean Field Control (MFC) Washim Uddin Mondal, Mridul Agarwal, Vaneet Aggarwal, Satish V. Ukkusuri

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On the Complexity of Approximating Multimarginal Optimal Transport Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan

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On the Convergence Rates of Policy Gradient Methods Lin Xiao

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On the Efficiency of Entropic Regularized Algorithms for Optimal Transport Tianyi Lin, Nhat Ho, Michael I. Jordan

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On the Robustness to Misspecification of Α-Posteriors and Their Variational Approximations Marco Avella Medina, José Luis Montiel Olea, Cynthia Rush, Amilcar Velez

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Online Mirror Descent and Dual Averaging: Keeping Pace in the Dynamic Case Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander

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Online Nonnegative CP-Dictionary Learning for Markovian Data Hanbaek Lyu, Christopher Strohmeier, Deanna Needell

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Optimal Transport for Stationary Markov Chains via Policy Iteration Kevin O'Connor, Kevin McGoff, Andrew B. Nobel

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Optimality and Stability in Non-Convex Smooth Games Guojun Zhang, Pascal Poupart, Yaoliang Yu

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Oracle Complexity in Nonsmooth Nonconvex Optimization Guy Kornowski, Ohad Shamir

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Overparameterization of Deep ResNet: Zero Loss and Mean-Field Analysis Zhiyan Ding, Shi Chen, Qin Li, Stephen J. Wright

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OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems Chelsea Sidrane, Amir Maleki, Ahmed Irfan, Mykel J. Kochenderfer

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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

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Pathfinder: Parallel Quasi-Newton Variational Inference Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari

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PECOS: Prediction for Enormous and Correlated Output Spaces Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon

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Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach Yanwei Jia, Xun Yu Zhou

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Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms Yanwei Jia, Xun Yu Zhou

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Posterior Asymptotics for Boosted Hierarchical Dirichlet Process Mixtures Marta Catalano, Pierpaolo De Blasi, Antonio Lijoi, Igor Pruenster

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Power Iteration for Tensor PCA Jiaoyang Huang, Daniel Z. Huang, Qing Yang, Guang Cheng

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Principal Components Bias in Over-Parameterized Linear Models, and Its Manifestation in Deep Neural Networks Guy Hacohen, Daphna Weinshall

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Prior Adaptive Semi-Supervised Learning with Application to EHR Phenotyping Yichi Zhang, Molei Liu, Matey Neykov, Tianxi Cai

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Project and Forget: Solving Large-Scale Metric Constrained Problems Rishi Sonthalia, Anna C. Gilbert

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Projected Robust PCA with Application to Smooth Image Recovery Long Feng, Junhui Wang

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Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric Matteo Pegoraro, Mario Beraha

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Projection-Free Distributed Online Learning with Sublinear Communication Complexity Yuanyu Wan, Guanghui Wang, Wei-Wei Tu, Lijun Zhang

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Provable Tensor-Train Format Tensor Completion by Riemannian Optimization Jian-Feng Cai, Jingyang Li, Dong Xia

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Quantile Regression with ReLU Networks: Estimators and Minimax Rates Oscar Hernan Madrid Padilla, Wesley Tansey, Yanzhen Chen

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Ranking and Tuning Pre-Trained Models: A New Paradigm for Exploiting Model Hubs Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long

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Recovering Shared Structure from Multiple Networks with Unknown Edge Distributions Keith Levin, Asad Lodhia, Elizaveta Levina

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Recovery and Generalization in Over-Realized Dictionary Learning Jeremias Sulam, Chong You, Zhihui Zhu

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ReduNet: A White-Box Deep Network from the Principle of Maximizing Rate Reduction Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma

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Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data Davoud Ataee Tarzanagh, George Michailidis

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Regularized K-Means Through Hard-Thresholding Jakob Raymaekers, Ruben H. Zamar

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Representation Learning for Maximization of MI, Nonlinear ICA and Nonlinear Subspaces with Robust Density Ratio Estimation Hiroaki Sasaki, Takashi Takenouchi

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Rethinking Nonlinear Instrumental Variable Models Through Prediction Validity Chunxiao Li, Cynthia Rudin, Tyler H. McCormick

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Reverse-Mode Differentiation in Arbitrary Tensor Network Format: With Application to Supervised Learning Alex A. Gorodetsky, Cosmin Safta, John D. Jakeman

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Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold Bokun Wang, Shiqian Ma, Lingzhou Xue

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Robust and Scalable Manifold Learning via Landmark Diffusion for Long-Term Medical Signal Processing Chao Shen, Yu-Ting Lin, Hau-Tieng Wu

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Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks Alireza Fallah, Mert Gürbüzbalaban, Asuman Ozdaglar, Umut Şimşekli, Lingjiong Zhu

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Sampling Permutations for Shapley Value Estimation Rory Mitchell, Joshua Cooper, Eibe Frank, Geoffrey Holmes

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Scalable and Efficient Hypothesis Testing with Random Forests Tim Coleman, Wei Peng, Lucas Mentch

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Scalable Gaussian-Process Regression and Variable Selection Using Vecchia Approximations Jian Cao, Joseph Guinness, Marc G. Genton, Matthias Katzfuss

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Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin Tripp, Yuejie Chi

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Scaling Laws from the Data Manifold Dimension Utkarsh Sharma, Jared Kaplan

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Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters Wei Zhu, Qiang Qiu, Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng

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Score Matched Neural Exponential Families for Likelihood-Free Inference Lorenzo Pacchiardi, Ritabrata Dutta

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Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable Baoluo Sun, Yifan Cui, Eric Tchetgen Tchetgen

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Self-Healing Robust Neural Networks via Closed-Loop Control Zhuotong Chen, Qianxiao Li, Zheng Zhang

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Semiparametric Inference for Causal Effects in Graphical Models with Hidden Variables Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser

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SGD with Coordinate Sampling: Theory and Practice Rémi Leluc, François Portier

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Signature Moments to Characterize Laws of Stochastic Processes Ilya Chevyrev, Harald Oberhauser

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Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States Shi Dong, Benjamin Van Roy, Zhengyuan Zhou

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Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization Zhize Li, Jian Li

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Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee Bo Shen, Weijun Xie, Zhenyu Kong

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SODEN: A Scalable Continuous-Time Survival Model Through Ordinary Differential Equation Networks Weijing Tang, Jiaqi Ma, Qiaozhu Mei, Ji Zhu

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Solving L1-Regularized SVMs and Related Linear Programs: Revisiting the Effectiveness of Column and Constraint Generation Antoine Dedieu, Rahul Mazumder, Haoyue Wang

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Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet

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Sparse Additive Gaussian Process Regression Hengrui Luo, Giovanni Nattino, Matthew T. Pratola

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Sparse Continuous Distributions and Fenchel-Young Losses André F. T. Martins, Marcos Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae

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Spatial Multivariate Trees for Big Data Bayesian Regression Michele Peruzzi, David B. Dunson

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Stable Classification Dimitris Bertsimas, Jack Dunn, Ivan Paskov

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Stacking for Non-Mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors Yuling Yao, Aki Vehtari, Andrew Gelman

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Statistical Optimality and Computational Efficiency of Nystrom Kernel PCA Nicholas Sterge, Bharath K. Sriperumbudur

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Statistical Optimality and Stability of Tangent Transform Algorithms in Logit Models Indrajit Ghosh, Anirban Bhattacharya, Debdeep Pati

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Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification Yingying Zhang, Yan-Yong Zhao, Heng Lian

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Stochastic DCA with Variance Reduction and Applications in Machine Learning Hoai An Le Thi, Hoang Phuc Hau Luu, Hoai Minh Le, Tao Pham Dinh

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Stochastic Subgradient for Composite Convex Optimization with Functional Constraints Ion Necoara, Nitesh Kumar Singh

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Stochastic Zeroth-Order Optimization Under Nonstationarity and Nonconvexity Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra

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Structural Agnostic Modeling: Adversarial Learning of Causal Graphs Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag

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Structure Learning for Directed Trees Martin E. Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters

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Structure-Adaptive Manifold Estimation Nikita Puchkin, Vladimir Spokoiny

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Sufficient Reductions in Regression with Mixed Predictors Efstathia Bura, Liliana Forzani, Rodrigo Garcia Arancibia, Pamela Llop, Diego Tomassi

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Sum of Ranked Range Loss for Supervised Learning Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu

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Supervised Dimensionality Reduction and Visualization Using Centroid-Encoder Tomojit Ghosh, Michael Kirby

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Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity William Fedus, Barret Zoph, Noam Shazeer

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Testing Whether a Learning Procedure Is Calibrated Jon Cockayne, Matthew M. Graham, Chris J. Oates, T. J. Sullivan, Onur Teymur

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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

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The AIM and EM Algorithms for Learning from Coarse Data Manfred Jaeger

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The Correlation-Assisted Missing Data Estimator Timothy I. Cannings, Yingying Fan

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The EM Algorithm Is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures Nir Weinberger, Guy Bresler

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The Geometry of Uniqueness, Sparsity and Clustering in Penalized Estimation Ulrike Schneider, Patrick Tardivel

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The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference Across Multiple Networks Konstantinos Pantazis, Avanti Athreya, Jesus Arroyo, William N Frost, Evan S Hill, Vince Lyzinski

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The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett

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The Separation Capacity of Random Neural Networks Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk

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The Two-Sided Game of Googol José Correa, Andrés Cristi, Boris Epstein, José Soto

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The Weighted Generalised Covariance Measure Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann

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Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning Kaiyi Ji, Junjie Yang, Yingbin Liang

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Theoretical Foundations of T-SNE for Visualizing High-Dimensional Clustered Data T. Tony Cai, Rong Ma

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Three Rates of Convergence or Separation via U-Statistics in a Dependent Framework Quentin Duchemin, Yohann De Castro, Claire Lacour

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Tntorch: Tensor Network Learning with PyTorch Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler

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Topologically Penalized Regression on Manifolds Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Clément Levrard, Wolfgang Polonik

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Total Stability of SVMs and Localized SVMs Hannes Köhler, Andreas Christmann

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Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective Tenghui Li, Guoxu Zhou, Yuning Qiu, Qibin Zhao

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Towards an Efficient Approach for the Nonconvex Lp Ball Projection: Algorithm and Analysis Xiangyu Yang, Jiashan Wang, Hao Wang

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Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration Congliang Chen, Li Shen, Fangyu Zou, Wei Liu

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Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Mårten Björkman

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Training Two-Layer ReLU Networks with Gradient Descent Is Inconsistent David Holzmüller, Ingo Steinwart

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Transfer Learning in Information Criteria-Based Feature Selection Shaohan Chen, Nikolaos V. Sahinidis, Chuanhou Gao

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Tree-Based Models for Correlated Data Assaf Rabinowicz, Saharon Rosset

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Tree-Based Node Aggregation in Sparse Graphical Models Ines Wilms, Jacob Bien

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Tree-Values: Selective Inference for Regression Trees Anna C. Neufeld, Lucy L. Gao, Daniela M. Witten

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Truncated Emphatic Temporal Difference Methods for Prediction and Control Shangtong Zhang, Shimon Whiteson

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Two-Mode Networks: Inference with as Many Parameters as Actors and Differential Privacy Qiuping Wang, Ting Yan, Binyan Jiang, Chenlei Leng

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Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh

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Unbiased Estimators for Random Design Regression Michał Dereziński, Manfred K. Warmuth, Daniel Hsu

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Under-Bagging Nearest Neighbors for Imbalanced Classification Hanyuan Hang, Yuchao Cai, Hanfang Yang, Zhouchen Lin

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Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley

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Uniform Deconvolution for Poisson Point Processes Anna Bonnet, Claire Lacour, Franck Picard, Vincent Rivoirard

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Universal Approximation in Dropout Neural Networks Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies, Jaron Sanders, Albert Senen-Cerda

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Universal Approximation of Functions on Sets Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Michael A. Osborne, Ingmar Posner

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Universal Approximation Theorems for Differentiable Geometric Deep Learning Anastasis Kratsios, Léonie Papon

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Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective Daniel Sanz-Alonso, Ruiyi Yang

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Using Active Queries to Infer Symmetric Node Functions of Graph Dynamical Systems Abhijin Adiga, Chris J. Kuhlman, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz, Richard E. Stearns

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Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features Lars H. B. Olsen, Ingrid K. Glad, Martin Jullum, Kjersti Aas

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Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization Huan Li, Zhouchen Lin, Yongchun Fang

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Variational Inference in High-Dimensional Linear Regression Sumit Mukherjee, Subhabrata Sen

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Vector-Valued Least-Squares Regression Under Output Regularity Assumptions Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc

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Weakly Supervised Disentangled Generative Causal Representation Learning Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang

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When Hardness of Approximation Meets Hardness of Learning Eran Malach, Shai Shalev-Shwartz

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When Is the Convergence Time of Langevin Algorithms Dimension Independent? a Composite Optimization Viewpoint Yoav Freund, Yi-An Ma, Tong Zhang

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XAI Beyond Classification: Interpretable Neural Clustering Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou

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