13 citations · 16 across the 8 of their papers we have counts for
5 papers · 1 filter
Amplification Effects in Test-Time Reinforcement Learning: Safety and Reasoning Vulnerabilities
Vanshaj Khattar, Md Rafi ur Rashid, Moumita Choudhury +4
Test-time training (TTT) has recently emerged as a promising method to improve the reasoning abilities of large language models (LLMs), in which the model directly learns from test…
Detecting Zero-Day Attacks in Digital Substations via In-Context Learning
Faizan Manzoor, Vanshaj Khattar, Akila Herath +5
The occurrences of cyber attacks on the power grids have been increasing every year, with novel attack techniques emerging every year. In this paper, we address the critical challe…
Optimization Solution Functions as Deterministic Policies for Offline Reinforcement Learning
Vanshaj Khattar, Ming Jin
Offline reinforcement learning (RL) is a promising approach for many control applications but faces challenges such as limited data coverage and value function overestimation. In t…
A CMDP-within-online framework for Meta-Safe Reinforcement Learning
Vanshaj Khattar, Yuhao Ding, Bilgehan Sel +2
Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience. However, the aspect of constraint violations has no…
On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds
Ming Jin, Vanshaj Khattar, Harshal Kaushik +2
We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension. The main results are: \emph{(1)} the class of…