3 citations · 8 across the 10 of their papers we have counts for
10 papers
CoffeeBench: Benchmarking Long-Horizon LLM Agents in Heterogeneous Multi-Agent Economies
Issa Sugiura, Daichi Hattori, Kazuo Araragi +5
As LLM agents become capable of increasingly long-horizon tasks, evaluating their performance in economic systems is becoming increasingly important. Unlike existing benchmarks tha…
EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
Issa Sugiura, Takashi Ishida, Taro Makino +4
Large Language Models (LLMs) have made remarkable progress, surpassing human performance on several benchmarks in domains such as mathematics and coding. A key driver of this progr…
Supervised Contrastive Block Disentanglement
Taro Makino, Ji Won Park, Natasa Tagasovska +11
Real-world datasets often combine data collected under different experimental conditions. This yields larger datasets, but also introduces spurious correlations that make it diffic…
Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning
Divyam Madaan, Taro Makino, Sumit Chopra +1
Supervised multi-modal learning involves mapping multiple modalities to a target label. Previous studies in this field have concentrated on capturing in isolation either the inter-…
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout
Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of…
Generative multitask learning mitigates target-causing confounding
Taro Makino, Krzysztof J. Geras, Kyunghyun Cho
We propose generative multitask learning (GMTL), a simple and scalable approach to causal representation learning for multitask learning. Our approach makes a minor change to the c…
Differences between human and machine perception in medical diagnosis
Taro Makino, Stanislaw Jastrzebski, Witold Oleszkiewicz +18
Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since their performance can be severely degraded by dataset shifts to which h…
Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
Nan Wu, Zhe Huang, Yiqiu Shen +8
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…
Understanding the robustness of deep neural network classifiers for breast cancer screening
Witold Oleszkiewicz, Taro Makino, Stanisław Jastrzębski +5
Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented…