arXiv Machine Learning July 21, 2026 By Zitao Wang, Nian Si, Molei Liu

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework

Read the original on arXiv Machine Learning →

arXiv:2509. 09371v2 Announce Type: replace-cross Abstract: Distributionally robust optimization (DRO) protects statistical learning against distributional shifts by optimizing the worst-case performance over a set of perturbed distributions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

fine-tuning

arXiv Machine Learning

Jun 30

A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems

arXiv:2606. 30230v1 Announce Type: cross Abstract: Learned reconstruction operators for inverse problems are typically trained under a fixed noise model, and generalize poorly when the distribution during testing differs from the one assumed during training.

By Floor van Maarschalkerwaart, Subhadip Mukherjee, Christoph Brune, Marcello Carioni

safety

arXiv Machine Learning

Aug 4

Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts

arXiv:2608. 00701v1 Announce Type: cross Abstract: Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another.

By Ying Jin, Ying Jin, Dominik Rothenh\"ausler

safety

arXiv Machine Learning

5d ago

Statistical Properties of Robust Learning under Distributional Shifts

arXiv:2608. 13133v1 Announce Type: cross Abstract: Distributional shifts arise when the target deployment environment differs from the source environment that generated the training data.

By Zhiyi Li, Xiaojie Mao, Yunbei Xu, Ruohan Zhan

safety

arXiv Machine Learning

Jul 9

PeTeR: Post-Training Robustification of Probabilistic Circuits

arXiv:2607. 07671v1 Announce Type: new Abstract: Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries.

By Adrian Ciotinga, Yeming Dai, YooJung Choi

benchmarks safety

arXiv Machine Learning

5d ago

Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning

arXiv:2608. 13418v1 Announce Type: cross Abstract: Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution.

By Yikai Xu, Zhao Chen, Jian Huang

diffusion benchmarks

arXiv Machine Learning

Jun 25

Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation

arXiv:2606. 25927v1 Announce Type: cross Abstract: As machine learning models and datasets continue to grow, developing complex models has become increasingly computationally demanding.

By Luyang Fang, Haoran Lu, Yongkai Chen, Wenxuan Zhong, Ping Ma

efficiency

Related stories

A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems

Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts

Statistical Properties of Robust Learning under Distributional Shifts

PeTeR: Post-Training Robustification of Probabilistic Circuits

Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning

Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation