Volume 23, 2022
Inherent Tradeoffs in Learning Fair Representations.

Han Zhao Geoffrey J. Gordon

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

Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy.

Terrance D. Savitsky Matthew R. Williams Jingchen Hu

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

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 Hachem Kadri François Laviolette

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 Tim Salimans

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 Thomas G. Dietterich

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 G. 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 Stéphane Gaïffas

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 Demirovic Anna Lukina Emmanuel Hebrard Jeffrey Chan James Bailey Christopher Leckie Kotagiri Ramamohanarao Peter J. Stuckey

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 I. Avilés-Rivero Jingwei Liang Ying Fu Hua Huang Carola-Bibiane Schönlieb

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 Kumar 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 Devran Kara Serdar Yüksel

Interpolating Predictors in High-Dimensional Factor Regression.

Florentina Bunea Seth Strimas-Mackey Marten H. 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 Joey Tianyi Zhou

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