AI for DevSecOps: A Landscape and Future Opportunities

no code implementations • 7 Apr 2024 • Michael Fu, Jirat Pasuksmit, Chakkrit Tantithamthavorn

Drawing insights from our findings, we discussed the state-of-the-art AI-driven security approaches, highlighted challenges in existing research, and proposed avenues for future opportunities.

Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities

1 code implementation • 26 May 2023 • Michael Fu, Trung Le, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Phung

Prior studies found that vulnerabilities across different vulnerable programs may exhibit similar vulnerable scopes, implicitly forming discernible vulnerability patterns that can be learned by DL models through supervised training.

Vulnerability Detection

FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing

3 code implementations • 22 Apr 2019 • Peng Zhang, Fuhao Zou, Zhiwen Wu, Nengli Dai, Skarpness Mark, Michael Fu, Juan Zhao, Kai Li

Face Anti-spoofing gains increased attentions recently in both academic and industrial fields.

Face Anti-Spoofing

Risk-Sensitive Reinforcement Learning via Policy Gradient Search

no code implementations • 22 Oct 2018 • Prashanth L. A., Michael Fu

In this book, we consider risk-sensitive RL in two settings: one where the goal is to find a policy that optimizes the usual expected value objective while ensuring that a risk constraint is satisfied, and the other where the risk measure is the objective.

Policy Gradient Methods reinforcement-learning +1

Random directions stochastic approximation with deterministic perturbations

1 code implementation • 8 Aug 2018 • Prashanth L. A, Shalabh Bhatnagar, Nirav Bhavsar, Michael Fu, Steven I. Marcus

We introduce deterministic perturbation schemes for the recently proposed random directions stochastic approximation (RDSA) [17], and propose new first-order and second-order algorithms.

Bandit algorithms to emulate human decision making using probabilistic distortions

no code implementations • 30 Nov 2016 • Ravi Kumar Kolla, Prashanth L. A., Aditya Gopalan, Krishna Jagannathan, Michael Fu, Steve Marcus

For the $K$-armed bandit setting, we derive an upper bound on the expected regret for our proposed algorithm, and then we prove a matching lower bound to establish the order-optimality of our algorithm.

Decision Making Multi-Armed Bandits

Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control

no code implementations • 8 Jun 2015 • Prashanth L. A., Cheng Jie, Michael Fu, Steve Marcus, Csaba Szepesvári

Cumulative prospect theory (CPT) is known to model human decisions well, with substantial empirical evidence supporting this claim.

reinforcement-learning Reinforcement Learning (RL)

Adaptive system optimization using random directions stochastic approximation

1 code implementation • 19 Feb 2015 • Prashanth L. A., Shalabh Bhatnagar, Michael Fu, Steve Marcus

We prove the unbiasedness of both gradient and Hessian estimates and asymptotic (strong) convergence for both first-order and second-order schemes.