works on

From the 9 of 408 papers with an AI index.

most citedThe Atacama Cosmology Telescope: A Measurement of the DR6 CMB Lensing Power Spectrum and its Implications for Structure Growth

266 citations

Showing cs.LGShow all

19 papers · 1 filter

cs.LG2026

An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks

Sheng Lun Christine Cao, Destenie Nock, Alex Davis

Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Machine learning can push the…

cs.LG2026

MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection

Xueying Ding, Simon Klüttermann, Haomin Wen +2

Quality benchmarks are essential for fairly and accurately tracking scientific progress and enabling practitioners to make informed methodological choices. Outlier detection (OD) o…

cs.LG2026

Muscle Synergy Priors Enhance Biomechanical Fidelity in Predictive Musculoskeletal Locomotion Simulation

Ilseung Park, Eunsik Choi, Jangwhan Ahn +1

Human locomotion emerges from high-dimensional neuromuscular control, making predictive musculoskeletal simulation challenging. We present a physiology-informed reinforcement-learn…

cs.LG2026

Causal methods for LLM development and evaluation

Dennis Frauen, Marie Brockschmidt, Konstantin Hess +10

Large language model (LLM) development is currently driven by large-scale empirical iteration over data mixtures, reward models, routing strategies, and evaluation pipelines. Here,…

cs.LG202611 cited

Trading off rewards and errors in multi-armed bandits

Akram Erraqabi, Alessandro Lazaric, Michal Valko +2

In multi-armed bandits, the most-explored arms are the most informative, while reward maximization typically pulls only the best arm. We study the tradeoff between identifying arm…

cs.LG2026

Unichain and Aperiodicity are Sufficient for Asymptotic Optimality of Average-Reward Restless Bandits

Yige Hong, Qiaomin Xie, Yudong Chen +1

We consider the infinite-horizon, average-reward restless bandit problem in discrete time. We propose a new class of policies that are designed to drive a progressively larger subs…