arXiv Machine Learning

Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting

arXiv:2607. 26458v1 Announce Type: cross Abstract: Domain generalization (DG) aims to learn from multiple source domains and generalize to unseen target domains.

arXiv Machine Learning

Jun 25

Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts

arXiv:2606. 25665v1 Announce Type: new Abstract: Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target data during training.

By Tien-Hung Nguyen, Tien-Dat Tran, M. -Duong Nguyen, Kok-Seng Wong

benchmarks 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

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 AI

Jun 9

Mechanistic Data Attribution: Tracing the Training Origins of Interpretable LLM Units

arXiv:2601. 21996v2 Announce Type: replace-cross Abstract: While Mechanistic Interpretability has identified interpretable circuits in LLMs, their causal origins in training data remain elusive.

By Jianhui Chen, Yuzhang Luo, Liangming Pan

llms safety

arXiv Machine Learning

Jun 5

REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting

arXiv:2606. 05264v1 Announce Type: new Abstract: Training robust multivariate time series forecasting models requires large, diverse corpora, yet many real-world domains provide only a handful of observed sequences.

By Moulik Gupta (Birla AI Labs), Dhruv Kumar (Birla AI Labs, Birla Institute of Technology and Science, Pilani), Murari Mandal (Birla AI Labs, Kalinga Institute of Industrial Technology), Saurabh Deshpande (Birla AI Labs)

arXiv AI

Jun 30

Attribution Graphs and Causal Probing for Mechanistic Discovery and Bias Repair in Multimodal Generative Learning

arXiv:2510. 12957v4 Announce Type: replace-cross Abstract: We treat the internals of generative models as mechanistic objects rather than black boxes.

By Noor Islam S. Mohammad, Ulu\u{g} Bayaz{\i}t

fine-tuning multimodal safety

arXiv AI

Jul 14

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.

By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui

arXiv Machine Learning

Jun 3

Attribution via Distributional Paths for Information Revelation

arXiv:2606. 03885v1 Announce Type: new Abstract: Feature attribution methods explain predictions by assigning importance scores to input features.

By Kieran A. Murphy, Shameen Shrestha

arXiv AI

Jun 17

FoundCause: Causal Discovery with Latent Confounders from Observational Data

arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.

By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan

llms

arXiv AI

Aug 6

Can Post-Training Transform LLMs into Causal Reasoners?

arXiv:2602. 06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts.

By Junqi Chen, Sirui Chen, Chaochao Lu

llms reinforcement-learning fine-tuning benchmarks

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

Jul 14

Domain-Aware Scaling Laws Uncover Data Synergy

arXiv:2607. 11052v1 Announce Type: new Abstract: Machine learning progress is often attributed to scaling model size and dataset volume, yet the composition of data can be just as consequential.

By Kimia Hamidieh, Lester Mackey, David Alvarez-Melis

llms benchmarks

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