From the 1 of 34 linked papers with an AI index.
34 papers
When Does More Correct Data Hurt? Insertion-Stability and the Limits of Dimension-Based Theory
Joseph Sankoorikal Johny
Adding data known to be correct ought to be safe. Not always. Larsen, Pabbaraju and Shetty model the failure with a monotone adversary, which reads an i.i.d. training sample and ma…
Impact of Expert-Following Strategies in Financial Asset Recommendation
Ryuki Unno, Koshi Watanabe, Keigo Sakurai +3
The paper introduces an expert-following strategy that recommends assets by identifying top-performing investors and using their ROI-weighted purchase frequency, achieving simultan…
FD: A Dedicated Framework for Fine-Grained Dataset Distillation
Hongxu Ma, Guang Li, Shijie Wang +5
Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong results on general benchmarks. Decoup…
Dynamic Objective Selection with Safeguards and LLM Oversight for Financial Decision-Making
Keigo Sakurai, Takahiro Ogawa, Miki Haseyama +2
Financial decision-making tasks such as stock recommendation and portfolio allocation typically estimate future return and risk and then select trades or allocations for an investo…
Hierarchical Federated Learning with Dynamic Clustering and Adaptive Regularization for Robust Infrastructure Inspection
Yuhu Feng, Keisuke Maeda, Takahiro Ogawa +1
The deployment of data-driven computer vision models for structural health monitoring (SHM) is heavily constrained by the data silo dilemma due to stringent privacy and security re…
SAS: Semantic-aware Sampling for Generative Dataset Distillation
Mingzhuo Li, Guang Li, Linfeng Ye +4
Deep neural networks have achieved impressive performance across a wide range of tasks, but this success often comes with substantial computational and storage costs due to large-s…