Papers
Learning general conditional independence structures via the neighbourhood lattice
Arash A. Amini, Bryon Aragam, Qing Zhou
Statistical guarantees for denoising reflected diffusion models
Asbjørn Holk, Claudia Strauch, Lukas Trottner
Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes
Tim Gyger, Reinhard Furrer, Fabio Sigrist
STDE++: Polynomial-Time Amortization for Linear Differential Operators
Zekun Shi, Zheyuan Hu, Min Lin et al.
The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler
Nawaf Bou-Rabee, Bob Carpenter, Tore Selland Kleppe et al.
Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation
Yuze Han, Xiang Li, Zhihua Zhang
Embedding Network Autoregression for Time Series Analysis and Causal Peer Effect Inference
Jae Ho Chang, Subhadeep Paul
Three Types of Calibration using Properties and their Semantic and Formal Relationships
Rabanus Derr, Jessie Finocchiaro, Robert C. Williamson
Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex Optimization
Siyuan Zhang, Nachuan Xiao, Xin Liu
A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance
Axel F. Wolter, Tobias Sutter
FLAGG: Flexible Autoregressive Graph Generation
Samuel Cognolato, Alessandro Sperduti, Luciano Serafini
Nested Subspace Learning with Flags
Tom Szwagier, Xavier Pennec
A Unified Approach to Analysis and Design of Denoising Markov Models
Yinuo Ren, Grant M. Rotskoff, Lexing Ying
Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks
Nikolaos Tsilivis, Eitan Gronich, Julia Kempe et al.
A Single-Loop Stochastic Proximal Quasi-Newton Method for Large-Scale Nonsmooth Convex Optimization
Yongcun Song, Zimeng Wang, Xiaoming Yuan et al.
Statistical Learning Theory for Neural Operators
Niklas Reinhardt, Sven Wang, Jakob Zech
Deconvolution in unlinked linear models
Fadoua Balabdaoui, Antonio Di Noia, Cécile Durot
Spectral Truncation Kernels: Noncommutativity in C*-algebraic Kernel Machines
Yuka Hashimoto, Ayoub Hafid, Masahiro Ikeda et al.
High-dimensional Parameter Transfer With Fused-Regularizer
Zelin He, Ying Sun, Jingyuan Liu et al.
Exogenous Randomness Empowering Random Forests
Tianxing Mei, Yingying Fan, Jinchi Lv
Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data
Luoyao Yu, Lixing Zhu, Ruoqing Zhu et al.
Deep Nonparametric Conditional Independence Tests for Images
Marco Simnacher, Xiangnan Xu, Hani Park et al.
Semi-supervised learning for linear extremile regression
Rong Jiang, Jiangfeng Wang, Keming Yu
Transfer Learning via Regularized Random-effects Linear Discriminant Analysis
Hongzhe Zhang, Arnab Auddy, Hongzhe Li
Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent
Henry Lam, Zitong Wang