Jyotirmoy V. Deshmukh | DeepAI

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∙ 09/17/2023

Data-Driven Reachability Analysis of Stochastic Dynamical Systems with Conformal Inference

We consider data-driven reachability analysis of discrete-time stochasti...

0 Navid Hashemi, et al. ∙

∙ 04/05/2023

Convex Optimization-based Policy Adaptation to Compensate for Distributional Shifts

Many real-world systems often involve physical components or operating e...

0 Navid Hashemi, et al. ∙

∙ 12/05/2022

Multi Agent Path Finding using Evolutionary Game Theory

In this paper, we consider the problem of path finding for a set of homo...

0 Sheryl Paul, et al. ∙

∙ 11/03/2022

Conformal Prediction for STL Runtime Verification

We are interested in predicting failures of cyber-physical systems durin...

0 Lars Lindemann, et al. ∙

∙ 10/14/2022

Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives

In this paper, we consider the problem of synthesizing a controller in t...

0 Navid Hashemi, et al. ∙

∙ 07/01/2022

Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic

Natural language is an intuitive way for humans to communicate tasks to ...

0 Sara Mohammadinejad, et al. ∙

∙ 06/29/2022

Formalizing and Evaluating Requirements of Perception Systems for Automated Vehicles using Spatio-Temporal Perception Logic

Automated vehicles (AV) heavily depend on robust perception systems. Cur...

0 Mohammad Hekmatnejad, et al. ∙

∙ 04/12/2022

Learning Performance Graphs from Demonstrations via Task-Based Evaluations

In the learning from demonstration (LfD) paradigm, understanding and eva...

0 Aniruddh G. Puranic, et al. ∙

∙ 02/04/2022

Model-Free Reinforcement Learning for Symbolic Automata-encoded Objectives

Reinforcement learning (RL) is a popular approach for robotic path plann...

0 Anand Balakrishnan, et al. ∙

∙ 07/29/2021

Non-Markovian Reinforcement Learning using Fractional Dynamics

Reinforcement learning (RL) is a technique to learn the control policy f...

0 Gaurav Gupta, et al. ∙

∙ 02/15/2021

Learning from Demonstrations using Signal Temporal Logic

Learning-from-demonstrations is an emerging paradigm to obtain effective...

0 Aniruddh G. Puranic, et al. ∙

∙ 11/10/2020

Model-based Reinforcement Learning from Signal Temporal Logic Specifications

Techniques based on Reinforcement Learning (RL) are increasingly being u...

0 Parv Kapoor, et al. ∙

∙ 07/20/2020

DiffRNN: Differential Verification of Recurrent Neural Networks

Recurrent neural networks (RNNs) such as Long Short Term Memory (LSTM) n...

0 Sara Mohammadinejad, et al. ∙

∙ 05/18/2020

Mining Environment Assumptions for Cyber-Physical System Models

Many complex cyber-physical systems can be modeled as heterogeneous comp...

0 Sara Mohammadinejad, et al. ∙

∙ 07/24/2019

Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques

Cyber-physical system applications such as autonomous vehicles, wearable...

0 Sara Mohammadinejad, et al. ∙

∙ 11/12/2018

Joint Probability Distribution of Prediction Errors of ARIMA

Producing probabilistic guarantee for several steps of a predicted signa...

0 Xin Qin, et al. ∙

∙ 04/11/2018

Reasoning about Safety of Learning-Enabled Components in Autonomous Cyber-physical Systems

We present a simulation-based approach for generating barrier certificat...

0 Cumhur Erkan Tuncali, et al. ∙

∙ 02/24/2018

Time Series Learning using Monotonic Logical Properties

We propose a new paradigm for time-series learning where users implicitl...

0 Marcell Vazquez-Chanlatte, et al. ∙

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