Bhaskar Ramasubramanian | DeepAI

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∙ 08/30/2023

MDTD: A Multi Domain Trojan Detector for Deep Neural Networks

Machine learning models that use deep neural networks (DNNs) are vulnera...

0 Arezoo Rajabi, et al. ∙

∙ 04/04/2023

Risk-Aware Distributed Multi-Agent Reinforcement Learning

Autonomous cyber and cyber-physical systems need to perform decision-mak...

0 Abdullah Al Maruf, et al. ∙

∙ 12/03/2022

LDL: A Defense for Label-Based Membership Inference Attacks

The data used to train deep neural network (DNN) models in applications ...

0 Arezoo Rajabi, et al. ∙

∙ 07/13/2022

Game of Trojans: A Submodular Byzantine Approach

Machine learning models in the wild have been shown to be vulnerable to ...

0 Dinuka Sahabandu, et al. ∙

∙ 03/25/2022

Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning

Machine learning (ML) models that use deep neural networks are vulnerabl...

0 Arezoo Rajabi, et al. ∙

∙ 03/18/2022

Privacy-Preserving Reinforcement Learning Beyond Expectation

Cyber and cyber-physical systems equipped with machine learning algorith...

0 Arezoo Rajabi, et al. ∙

∙ 02/19/2022

Shaping Advice in Deep Reinforcement Learning

Reinforcement learning involves agents interacting with an environment t...

0 Baicen Xiao, et al. ∙

∙ 01/12/2022

Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning

This paper considers multi-agent reinforcement learning (MARL) tasks whe...

0 Baicen Xiao, et al. ∙

∙ 09/02/2021

Resilience to Denial-of-Service and Integrity Attacks: A Structured Systems Approach

The resilience of cyberphysical systems to denial-of-service (DoS) and i...

0 Bhaskar Ramasubramanian, et al. ∙

∙ 03/29/2021

Reinforcement Learning Beyond Expectation

The inputs and preferences of human users are important considerations i...

0 Bhaskar Ramasubramanian, et al. ∙

∙ 03/29/2021

Shaping Advice in Deep Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning involves multiple agents interacting ...

0 Baicen Xiao, et al. ∙

∙ 09/20/2020

Safety-Critical Online Control with Adversarial Disturbances

This paper studies the control of safety-critical dynamical systems in t...

0 Bhaskar Ramasubramanian, et al. ∙

∙ 07/27/2020

Privacy-Preserving Resilience of Cyber-Physical Systems to Adversaries

A cyber-physical system (CPS) is expected to be resilient to more than o...

0 Bhaskar Ramasubramanian, et al. ∙

∙ 07/22/2020

Secure Control in Partially Observable Environments to Satisfy LTL Specifications

This paper studies the synthesis of control policies for an agent that h...

0 Bhaskar Ramasubramanian, et al. ∙

∙ 01/19/2020

FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback

Reinforcement learning has been successful in training autonomous agents...

6 Baicen Xiao, et al. ∙

∙ 10/27/2019

Linear Temporal Logic Satisfaction in Adversarial Environments using Secure Control Barrier Certificates

This paper studies the satisfaction of a class of temporal properties fo...

0 Bhaskar Ramasubramanian, et al. ∙

∙ 07/20/2019

Potential-Based Advice for Stochastic Policy Learning

This paper augments the reward received by a reinforcement learning agen...

6 Baicen Xiao, et al. ∙

∙ 03/16/2019

Secure Control under Partial Observability with Temporal Logic Constraints

This paper studies the synthesis of control policies for an agent that h...

0 Bhaskar Ramasubramanian, et al. ∙

∙ 03/16/2019

Notions of Centralized and Decentralized Opacity in Linear Systems

We formulate notions of opacity for cyberphysical systems modeled as dis...

0 Bhaskar Ramasubramanian, et al. ∙

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