arXiv Machine Learning

Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies

arXiv:2603. 15158v2 Announce Type: replace Abstract: Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes.

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

Jun 18

Anti-causal domain generalization: Leveraging unlabeled data

arXiv:2602. 17187v2 Announce Type: replace-cross Abstract: The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments.

By Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller, Jonas Peters, Nicolai Meinshausen, Christina Heinze-Deml

arXiv Machine Learning

Jun 19

Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction

arXiv:2601. 02322v2 Announce Type: replace-cross Abstract: A common approach to out-of-distribution prediction restricts models to causal or invariant covariates to avoid spurious associations that may change across environments.

By Shuozhi Zuo, Yixin Wang

arXiv Machine Learning

Jul 1

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

arXiv:2507. 14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled target data.

By Wooseok Ha, Yuansi Chen

fine-tuning

arXiv Machine Learning

Jun 18

Shrinkage priors for Bayesian Substitute Confounders

arXiv:2606. 18535v1 Announce Type: cross Abstract: Multi-cause observational studies contain information about unmeasured confounding through the dependence structure among causes.

By Yordan P. Raykov, Hengrui Luo, Justin D. Strait, Wasiur R. KhudaBukhsh

arXiv Machine Learning

Aug 4

Learning the Pareto Frontier of Predictive Models under Distribution Shift

arXiv:2608. 00632v1 Announce Type: new Abstract: Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks.

By Yiming Dong, Jiwei Zhao, Yang Young Lu

fine-tuning

arXiv Machine Learning

Jun 25

Efficient Analytic Uncertainty Quantification for Multi-Modal Regression

arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.

By Kun Jin, James Harrison, Jiawei Li, Sihan Liu, Jiayi Liu, Randolph Linderman, Yuening Li, Arnab Bhadury, Sourabh Prakash Bansod, Liang Liu, Jasper Snoek

multimodal benchmarks

arXiv AI

Jul 7

A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning

arXiv:2607. 03600v1 Announce Type: cross Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean source and adversarially perturbed target domains.

By Sushant Dagaji Desale, Rahul Mishra, Ashutosh Kumar Sinha

reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Aug 12

Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Images

arXiv:2606. 21806v2 Announce Type: replace Abstract: Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains.

By Jingyuan Chen, Kangrui Ruan, Junzhe Zhang

rag benchmarks

arXiv Machine Learning

Jul 10

Prediction-Powered Active Testing

arXiv:2607. 08347v1 Announce Type: cross Abstract: Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled.

By Kianoosh Ashouritaklimi, Valentin Kilian, Daolang Huang, Tom Rainforth, Fran\c{c}ois Caron

arXiv Machine Learning

Jun 26

Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding

arXiv:2606. 27114v1 Announce Type: new Abstract: Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and debiasing under unobserved confounding scenarios.

By Haoran Zhang, Chuanpu Li, Yuxin Fu, Bin Tong, Guan Wang, Bo Zheng, Feng Zhou

rag benchmarks safety

arXiv Machine Learning

Jul 7

Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation

arXiv:2303. 08777v3 Announce Type: replace-cross Abstract: Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theoretical properties when embedded in a learning procedure remain insufficiently understood.

By Diego Marcondes, Cl\'audia Peixoto

safety

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