13 papers
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…
Weight Adaptation for Improving Parallel Performance of Adaptive Stochastic Natural Gradient
Yutaro Yamada, Kento Uchida, Shinichi Shirakawa
Probabilistic model-based evolutionary algorithms are promising for black-box optimization. Specifically, the adaptive stochastic natural gradient (ASNG) adaptively updates its lea…
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…
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…
BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems
Yutaro Yamada, Kei Hiroshima, Nozomu Yoshinari +2
Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise. Recent studies have therefore e…
Convergence Analysis of Evolution Strategies for Mixed-Integer Optimization
Ryoki Hamano, Kento Uchida, Shinichi Shirakawa
Mixed-integer extensions of evolution strategies (ES) that discretize selected coordinates of sampled continuous vectors often impose a lower bound on the standard deviation of int…