50 citations · 60 across the 21 of their papers we have counts for
6 papers · 1 filter
SIGMA: SHAP-Guided Implicit-Trajectory Generation for Metadata-Free LLM-Based AutoFE
Xuan Zheng, Kento Uchida, Shinichi Shirakawa
Recent research has leveraged Large Language Models (LLMs) to enhance Automated Feature Engineering (AutoFE) through semantic descriptions and trajectory-based prompting. However,…
Neural Architecture Search of Sample Reweighting Networks for Complex Distribution Shift
Keisuke Sugawara, Kento Uchida, Shinichi Shirakawa
Sample reweighting is a major approach to addressing distribution shifts, such as label noise and class imbalance. Meta-Weight-Net (MW-Net) is a promising sample reweighting networ…
OnDeFog: Online Decision Transformer under Frame Dropping
Daiki Yotsufuji, Kenta Nishihara, Shoma Shimizu +2
In challenging real-world reinforcement learning applications, communication delays or sensor failures often cause frame dropping, in which the agent cannot receive the dropped sta…
Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning
Kei Hiroshima, Kento Uchida, Shinichi Shirakawa
Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned knowledge. Recent advances in large pre-t…
Surrogate Benchmarks for Model Merging Optimization
Rio Akizuki, Yuya Kudo, Nozomu Yoshinari +4
Model merging techniques aim to integrate the abilities of multiple models into a single model. Most model merging techniques have hyperparameters, and their setting affects the pe…
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari +3
High sensitivity of neural architecture search (NAS) methods against their input such as step-size (i.e., learning rate) and search space prevents practitioners from applying them…