3 papers
cs.LG2026
TabQL: In-Context Q-Learning with Tabular Foundation Models
Qisai Liu, Zhanhong Jiang, Timilehin Ayanlade +4
We propose Tabular Q-Learning (TabQL), a reinforcement learning framework that replaces the conventional parametric Q-network in Deep Q-Learning (DQN) with a tabular foundation mod…
cs.CV2023
Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing
A K Nirala, A Joshi, C Hegde +1
A key benefit of deep vision-language models such as CLIP is that they enable zero-shot open vocabulary classification; the user has the ability to define novel class labels via na…
cs.LG2023
Vulnerability-Aware Instance Reweighting For Adversarial Training
Olukorede Fakorede, Ashutosh Kumar Nirala, Modeste Atsague +1
Adversarial Training (AT) has been found to substantially improve the robustness of deep learning classifiers against adversarial attacks. AT involves obtaining robustness by inclu…