20 papers
On the Sharp Input-Output Analysis of Nonlinear Systems under Adversarial Attacks
Jihun Kim, Yuchen Fang, Javad Lavaei
This paper is concerned with learning the input-output mapping of general nonlinear dynamical systems. While the existing literature focuses on Gaussian inputs and benign disturban…
LLMs Should Express Uncertainty Explicitly
Junyu Guo, Shangding Gu, Ming Jin +2
Large language models (LLMs) often produce confident yet incorrect answers, which can lead to risky failures in real-world applications. We study whether post-training can make a m…
Structural Correspondence and Universal Approximation in Diagonal plus Low-Rank Neural Networks
Ying Chen, Aoxi Li, Jihun Kim +1
The massive computational costs of scaling modern deep learning architectures have driven the widespread use of parameter-efficient low-rank structures, such as LoRA and low-rank f…
StyleBench: Evaluating thinking styles in Large Language Models
Junyu Guo, Shangding Gu, Ming Jin +2
Structured reasoning can improve the inference performance of large language models (LLMs), but it also introduces computational cost and control constraints. When additional reaso…
Huber-based Robust System Identification with Near-Optimal Guarantees Across Independent and Adversarial Regimes
Jihun Kim, Javad Lavaei
Dynamical systems can confront one of two extreme types of disturbances: persistent zero-mean independent noise, and sparse nonzero-mean adversarial attacks, depending on the speci…
A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
Yuchen Fang, Jihun Kim, Sen Na +2
In this paper, we propose a trust-region interior-point stochastic sequential quadratic programming (TR-IP-SSQP) method for solving optimization problems with a stochastic objectiv…