48 citations · 60 across the 25 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…
Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing
Shoma Shimizu, Takayuki Nishio, Shota Saito +3
This paper proposes a neural architecture search (NAS) method for split computing. Split computing is an emerging machine-learning inference technique that addresses the privacy an…