7 papers
Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling
Kaito Baba, Evripidis Bampis, Giorgos Mitropoulos
Recently, Antoniadis et al. (ICLR 2025) proposed a framework for incorporating predictions to approximate NP-hard selection problems. Despite its simplicity, this approach tightly…
Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation
Kaito Baba, Risa Kishikawa, Satoshi Kodera
We propose MARL-Rad, a multi-modal multi-agent reinforcement learning framework for radiology report generation that trains the entire agentic system on policy within its deployed…
Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces
Kaito Baba, Yoshihiko Ozaki, Shuhei Watanabe
We propose conditional PED-ANOVA (condPED-ANOVA), a principled framework for estimating hyperparameter importance (HPI) in conditional search spaces, where the presence or domain o…
Prover Agent: An Agent-Based Framework for Formal Mathematical Proofs
Kaito Baba, Chaoran Liu, Shuhei Kurita +1
We present Prover Agent, a novel AI agent for automated theorem proving that integrates large language models (LLMs) with a formal proof assistant, Lean. Prover Agent coordinates a…
Application of Contrastive Learning on ECG Data: Evaluating Performance in Japanese and Classification with Around 100 Labels
Junichiro Takahashi, JingChuan Guan, Masataka Sato +5
The electrocardiogram (ECG) is a fundamental tool in cardiovascular diagnostics due to its powerful and non-invasive nature. One of the most critical usages is to determine whether…
JRadiEvo: A Japanese Radiology Report Generation Model Enhanced by Evolutionary Optimization of Model Merging
Kaito Baba, Ryota Yagi, Junichiro Takahashi +2
With the rapid advancement of large language models (LLMs), foundational models (FMs) have seen significant advancements. Healthcare is one of the most crucial application areas fo…
The T05 System for The VoiceMOS Challenge 2024: Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic Speech
Kaito Baba, Wataru Nakata, Yuki Saito +1
We present our system (denoted as T05) for the VoiceMOS Challenge (VMC) 2024. Our system was designed for the VMC 2024 Track 1, which focused on the accurate prediction of naturaln…