5 citations · 9 across the 10 of their papers we have counts for
5 papers · 1 filter
Learning Stability of Replay-Based Co-Optimization for Transmission Expansion under Strategic Bidding
Tomonari Kanazawa, Hikaru Hoshino, Eiko Furutani
This paper investigates the behavior of learning-based co-optimization for transmission expansion under strategic bidding in electricity markets. In this framework, transmission ca…
Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration
Ruochen Jin, Zhanliang Wang, Zongyu Dai +2
Preference alignment often makes large language models (LLMs) overconfident and poorly calibrated. Traditional post-hoc temperature scaling is inherently domain-dependent: a temper…
A Semantic-Sampling Framework for Evaluating Calibration in Open-Ended Question Answering
Zhanliang Wang, Jiancong Xiao, Ruochen Jin +3
Calibration measures whether a model's predicted confidence aligns with its empirical accuracy, and is central to the reliable deployment of large language models (LLMs) in high-st…
Beyond Logit Adjustment: A Residual Decomposition Framework for Long-Tailed Reranking
Zhanliang Wang, Hongzhuo Chen, Quan Minh Nguyen +2
Long-tailed classification, where a small number of frequent classes dominate many rare ones, remains challenging because models systematically favor frequent classes at inference…
Autonomous Agent-Orchestrated Digital Twins (AADT): Leveraging the OpenClaw Framework for State Synchronization in Rare Genetic Disorders
Hongzhuo Chen, Zhanliang Wang, Quan M. Nguyen +3
Background: Medical Digital Twins (MDTs) are computational representations of individual patients that integrate clinical, genomic, and physiological data to support diagnosis, tre…