JMLR 2022

353 papers

DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python

Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler

LinCDE: Conditional Density Estimation via Lindsey's Method

Zijun Gao, Trevor Hastie

Toolbox for Multimodal Learn (scikit-multimodallearn)

Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi

Analytically Tractable Hidden-States Inference in Bayesian Neural Networks

Luong-Ha Nguyen, James-A. Goulet

Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection

Xinyi Wang, Lang Tong

Overparameterization of Deep ResNet: Zero Loss and Mean-field Analysis

ZHIYAN DING, Shi Chen, Qin Li, Stephen J. Wright

Cascaded Diffusion Models for High Fidelity Image Generation

Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi

Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent

Wanrong Zhu, Zhipeng Lou, Wei Biao Wu

Optimal Transport for Stationary Markov Chains via Policy Iteration

Kevin O'Connor, Kevin McGoff, Andrew B. Nobel

PAC Guarantees and Effective Algorithms for Detecting Novel Categories

Si Liu, Risheek Garrepalli, Dan Hendrycks, Alan Fern, Debashis Mondal

Sampling Permutations for Shapley Value Estimation

Rory Mitchell, Joshua Cooper, Eibe Frank, Geoffrey Holmes

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks

Zhong Li, Jiequn Han, Weinan E, Qianxiao Li

The Correlation-assisted Missing Data Estimator

Timothy I. Cannings, Yingying Fan

Structure-adaptive Manifold Estimation

Nikita Puchkin, Vladimir Spokoiny

(f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics

Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet

Score Matched Neural Exponential Families for Likelihood-Free Inference

Lorenzo Pacchiardi, Ritabrata Dutta

Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric

Matteo Pegoraro, Mario Beraha

Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization

Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang

Optimality and Stability in Non-Convex Smooth Games

Guojun Zhang, Pascal Poupart, Yaoliang Yu

SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks

Weijing Tang, Jiaqi Ma, Qiaozhu Mei, Ji Zhu

Model Averaging Is Asymptotically Better Than Model Selection For Prediction

Tri M. Le, Bertrand S. Clarke

Active Learning for Nonlinear System Identification with Guarantees

Horia Mania, Michael I. Jordan, Benjamin Recht

An Improper Estimator with Optimal Excess Risk in Misspecified Density Estimation and Logistic Regression

Jaouad Mourtada, Stephane Gaiffas

A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One

Augusto Fasano, Daniele Durante

Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning

Kaiyi Ji, Junjie Yang, Yingbin Liang

Novel Min-Max Reformulations of Linear Inverse Problems

Mohammed Rayyan Sheriff, Debasish Chatterjee

Data-Derived Weak Universal Consistency

Narayana Santhanam, Venkatachalam Anantharam, Wojciech Szpankowski

MurTree: Optimal Decision Trees via Dynamic Programming and Search

Emir Demirović, Anna Lukina, Emmanuel Hebrard, Jeffrey Chan, James Bailey

Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting

Maxime Vono, Daniel Paulin, Arnaud Doucet

On Biased Stochastic Gradient Estimation

Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb

Fast and Robust Rank Aggregation against Model Misspecification

Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama

LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data

Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney

Evolutionary Variational Optimization of Generative Models

Jakob Drefs, Enrico Guiraud, Jörg Lücke

Supervised Dimensionality Reduction and Visualization using Centroid-Encoder

Tomojit Ghosh, Michael Kirby

Universal Approximation in Dropout Neural Networks

Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies, Jaron Sanders, Albert Senen-Cerda

Decimated Framelet System on Graphs and Fast G-Framelet Transforms

Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang

Spatial Multivariate Trees for Big Data Bayesian Regression

Michele Peruzzi, David B. Dunson

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

A Stochastic Bundle Method for Interpolation

Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar

On Generalizations of Some Distance Based Classifiers for HDLSS Data

Sarbojit Roy, Soham Sarkar, Subhajit Dutta, Anil K. Ghosh

Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality

Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet

Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems

Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan

Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes

Ali Kara, Serdar Yüksel

Interpolating Predictors in High-Dimensional Factor Regression

Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp

Scaling Laws from the Data Manifold Dimension

Utkarsh Sharma, Jared Kaplan

Deep Learning in Target Space

Michael Fairbank, Spyridon Samothrakis, Luca Citi

Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes

Justin D. Silverman, Kimberly Roche, Zachary C. Holmes, Lawrence A. David, Sayan Mukherjee

XAI Beyond Classification: Interpretable Neural Clustering

Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv

Empirical Risk Minimization under Random Censorship

Guillaume Ausset, Stéphan Clémençon, François Portier

Exploiting locality in high-dimensional Factorial hidden Markov models

Lorenzo Rimella, Nick Whiteley

Recovering shared structure from multiple networks with unknown edge distributions

Keith Levin, Asad Lodhia, Elizaveta Levina

Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions

shaogao lv, Heng Lian

Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models

Subhabrata Majumdar, George Michailidis