:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Banerjee, Sayantan, Wylomanska, Agnieszka, S, Sundar
Format:	Preprint
Published:	2025
Subjects:	Methodology Computation Machine Learning
Online Access:	https://arxiv.org/abs/2508.12970
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Empirical study of periodic autoregressive models with additive noise -- estimation and testing
by: Żuławiński, Wojciech, et al.
Published: (2023)

Data denoising with self consistency, variance maximization, and the Kantorovich dominance
by: Hiew, Joshua Zoen-Git, et al.
Published: (2025)

Statistical learning for constrained functional parameters in infinite-dimensional models
by: Nabi, Razieh, et al.
Published: (2024)

Assessing the overall and partial causal well-specification of nonlinear additive noise models
by: Schultheiss, Christoph, et al.
Published: (2023)

Fractional lower-order covariance-based measures for cyclostationary time series with heavy-tailed distributions: application to dependence testing and model order identification
by: Żuławiński, Wojciech, et al.
Published: (2026)

Two-sample comparison through additive tree models for density ratios
by: Awaya, Naoki, et al.
Published: (2025)

Robust variance estimators in application to segmentation of measurement data distorted by impulsive and non-Gaussian noise
by: Witulska, Justyna, et al.
Published: (2025)

A fast non-reversible sampler for Bayesian finite mixture models
by: Ascolani, Filippo, et al.
Published: (2025)

Semi-supervised learning for linear extremile regression
by: Jiang, Rong, et al.
Published: (2025)

A semi-supervised learning using over-parameterized regression
by: Hagiwara, Katsuyuki
Published: (2024)

A Bayesian approach to learning mixtures of nonparametric components
by: Zhang, Yilei, et al.
Published: (2025)

A new approach to locally adaptive polynomial regression
by: Chatterjee, Sabyasachi, et al.
Published: (2024)

Consistent model selection in the spiked Wigner model via AIC-type criteria
by: Mukherjee, Soumendu Sundar
Published: (2023)

Robust variable selection for spatial point processes observed with noise
by: Sturm, Dominik, et al.
Published: (2025)

Barycentric model aggregation in the Wasserstein space of distributions and a variational approach to consistency
by: Androulakis, Emmanouil, et al.
Published: (2025)

Gradient-based optimization for variational empirical Bayes multiple regression
by: Banerjee, Saikat, et al.
Published: (2024)

High-dimensional semi-supervised learning: in search for optimal inference of the mean
by: Zhang, Yuqian, et al.
Published: (2019)

Assessment of background noise properties in time and time-frequency domains in the context of vibration-based local damage detection in real environment
by: Skowronek, Katarzyna, et al.
Published: (2024)

AL-SPCE -- Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning
by: Pires, A., et al.
Published: (2025)

Three approaches to supervised learning for compositional data with pairwise logratios
by: Coenders, Germa, et al.
Published: (2021)

Group selection and shrinkage: Structured sparsity for semiparametric additive models
by: Thompson, Ryan, et al.
Published: (2021)

Sampling by averaging: A multiscale approach to score estimation
by: Cordero-Encinar, Paula, et al.
Published: (2025)

Misspecifying non-compensatory as compensatory IRT: analysis of estimated skills and variance
by: Tamano, Hiroshi, et al.
Published: (2025)

Classification by sparse generalized additive models
by: Abramovich, Felix
Published: (2022)

An integrated perspective of robustness in regression through the lens of the bias-variance trade-off
by: Okuno, Akifumi
Published: (2024)

Numerically robust Gaussian state estimation with singular observation noise
by: Krämer, Nicholas, et al.
Published: (2025)

A partial likelihood approach to tree-based density modeling and its application in Bayesian inference
by: Ma, Li, et al.
Published: (2024)

BARTSIMP: flexible spatial covariate modeling and prediction using Bayesian additive regression trees
by: Jiang, Alex Ziyu, et al.
Published: (2023)

A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC
by: Llorente, F., et al.
Published: (2021)

One-shot Conditional Sampling: MMD meets Nearest Neighbors
by: Chatterjee, Anirban, et al.
Published: (2025)

Learning under Latent Group Sparsity via Diffusion on Networks
by: Ghosh, Subhroshekhar, et al.
Published: (2025)

Leveraging heterogeneity for identifiability: Bayesian order-based learning of multiple DAGs
by: Chang, Hyunwoong, et al.
Published: (2026)

Markov chain Monte Carlo without evaluating the target: an auxiliary variable approach
by: Yuan, Wei, et al.
Published: (2024)

Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimization
by: Petit, Sébastien, et al.
Published: (2022)

Learning linear acyclic causal model including Gaussian noise using ancestral relationships
by: Cai, Ming, et al.
Published: (2024)

Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models
by: Do, Dat, et al.
Published: (2024)

Optimal Bias-variance Tradeoff in Matrix and Tensor Estimation
by: Kumar, Shivam, et al.
Published: (2025)

Censored Graphical Horseshoe: Bayesian sparse precision matrix estimation with censored and missing data
by: Mai, The Tien, et al.
Published: (2026)

Byzantine-tolerant distributed learning of finite mixture models
by: Zhang, Qiong, et al.
Published: (2024)

What is in the model? A Comparison of variable selection criteria and model search approaches
by: Xu, Shuangshuang, et al.
Published: (2025)