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20182026
most citedDiffusion models for missing value imputation in tabular data

20 citations · 49 across the 13 of their papers we have counts for

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cs.LG2026

MLIP Detective: Active Failure Mode Discovery Beyond Benchmark Scores for Machine-Learning Interatomic Potentials

Ryuhei Okuno, Nontawat Charoenphakdee, Kaoru Hisama +1

Universal machine-learning interatomic potentials (u-MLIPs) aim to generalize across diverse configurations. Benchmarks enable reproducible evaluation but may not expose failures o…

cs.LG2026

Do Coding Agents Deceive Us? Detecting and Preventing Cheating via Capped Evaluation with Randomized Tests

Thanawat Lodkaew, Johannes Ackermann, Soichiro Nishimori +3

A growing failure mode in agent evaluation and training is that models can achieve high evaluation scores by exploiting shortcuts instead of solving the intended task, producing de…

cs.LG2026

Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows

Wenwen Li, Yuki Orimo, Nontawat Charoenphakdee

Developing machine learning interatomic potentials (MLIPs) for complex materials systems remains challenging because it requires expertise in atomistic simulations, machine learnin…

cs.LG2025

P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials

Shih-Peng Huang, Nontawat Charoenphakdee, Yuta Tsuboi +2

Ensemble method is considered the gold standard for uncertainty quantification (UQ) in machine learning interatomic potentials (MLIPs). However, their high computational cost can l…

cs.LG2023

Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics

Kenta Oono, Nontawat Charoenphakdee, Kotatsu Bito +14

Virtual Human Generative Model (VHGM) is a generative model that approximates the joint probability over more than 2000 human healthcare-related attributes. This paper presents the…

cs.LG2022★ 20 cited

Diffusion models for missing value imputation in tabular data

Shuhan Zheng, Nontawat Charoenphakdee

Missing value imputation in machine learning is the task of estimating the missing values in the dataset accurately using available information. In this task, several deep generati…