:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Schreyer, W. Max, Anderson, Christopher, Thompson, Reid F.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2502.16329
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improving self-training under distribution shifts via anchored confidence with theoretical guarantees
by: Joo, Taejong, et al.
Published: (2024)

On the effects of similarity metrics in decentralized deep learning under distributional shift
by: Zec, Edvin Listo, et al.
Published: (2024)

Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation
by: Sattarov, Timur, et al.
Published: (2025)

FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation
by: Sattarov, Timur, et al.
Published: (2024)

Generalization analysis with deep ReLU networks for metric and similarity learning
by: Zhou, Junyu, et al.
Published: (2024)

A universal policy wrapper with guarantees
by: Bolychev, Anton, et al.
Published: (2025)

Marginal and training-conditional guarantees in one-shot federated conformal prediction
by: Humbert, Pierre, et al.
Published: (2024)

Tensor train completion: local recovery guarantees via Riemannian optimization
by: Budzinskiy, Stanislav, et al.
Published: (2021)

Universal generalization guarantees for Wasserstein distributionally robust models
by: Le, Tam, et al.
Published: (2024)

Latent space analysis and generalization to out-of-distribution data
by: Rainey, Katie, et al.
Published: (2025)

Latent-space metrics for Complex-Valued VAE out-of-distribution detection under radar clutter
by: Rouzoumka, Y. A., et al.
Published: (2025)

Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
by: Sattarov, Timur, et al.
Published: (2025)

GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs
by: Reynisson, Kristófer, et al.
Published: (2024)

Layer-wise LoRA fine-tuning: a similarity metric approach
by: Ogawa, Keith Ando, et al.
Published: (2026)

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis
by: Sattarov, Timur, et al.
Published: (2024)

Spectral Normalization and Voigt-Reuss net: A universal approach to microstructure-property forecasting with physical guarantees
by: Keshav, Sanath, et al.
Published: (2025)

Aggregating distribution forecasts from deep ensembles
by: Schulz, Benedikt, et al.
Published: (2022)

Analytical results for uncertainty propagation through trained machine learning regression models
by: Thompson, Andrew
Published: (2024)

Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method
by: Baluta, Teodora, et al.
Published: (2024)

Towards a theory of out-of-distribution learning
by: Dey, Jayanta, et al.
Published: (2021)

Neural Coherence : Find higher performance to out-of-distribution tasks from few samples
by: Guiroy, Simon, et al.
Published: (2025)

Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation
by: Sanyal, Amartya, et al.
Published: (2024)

Theoretical guarantees for neural estimators in parametric statistics
by: Rödder, Almut, et al.
Published: (2025)

Sharing Knowledge without Sharing Data: Stitches can improve ensembles of disjointly trained models
by: Guijt, Arthur, et al.
Published: (2025)

A binary PSO based ensemble under-sampling model for rebalancing imbalanced training data
by: Li, Jinyan, et al.
Published: (2025)

Geometry-aware similarity metrics for neural representations on Riemannian and statistical manifolds
by: Cayco-Gajic, N Alex, et al.
Published: (2026)

A theoretical guarantee for SyncRank
by: Rao, Yang
Published: (2025)

Explainable post-training bias mitigation with distribution-based fairness metrics
by: Franks, Ryan, et al.
Published: (2025)

Theoretical guarantees in KL for Diffusion Flow Matching
by: Silveri, Marta Gentiloni, et al.
Published: (2024)

Revisiting RIP guarantees for sketching operators on mixture models
by: Belhadji, Ayoub, et al.
Published: (2023)

Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data
by: Nguyen, Son, et al.
Published: (2023)

Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training
by: Usynin, Dmitrii, et al.
Published: (2023)

Dynamical-generative downscaling of climate model ensembles
by: Lopez-Gomez, Ignacio, et al.
Published: (2024)

Finite-sample guarantees for data-driven forward-backward operator methods
by: Fabiani, Filippo, et al.
Published: (2025)

Causal rule ensemble approach for multi-arm data
by: Wan, Ke, et al.
Published: (2025)

An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
by: Tran, Hoang, et al.
Published: (2026)

Statistical guarantees for denoising reflected diffusion models
by: Holk, Asbjørn, et al.
Published: (2024)

Proximal basin hopping: global optimization with guarantees
by: Lauga, Guillaume, et al.
Published: (2026)

Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
by: Oliveira, Rafael
Published: (2026)

Leak Proof CMap; a framework for training and evaluation of cell line agnostic L1000 similarity methods
by: Shave, Steven, et al.
Published: (2024)