Designing Interpretable Approximations to Deep Reinforcement Learning with Soft Decision Trees. - Nathan Dahlin; Krishna Chaitanya Kalagarla; Nikhil Naik 001; Rahul Jain 002; Pierluigi Nuzzo 002 (CoRR, 2020)

Synthesis of Discounted-Reward Optimal Policies for Markov Decision Processes Under Linear Temporal Logic Specifications. - Krishna Chaitanya Kalagarla; Rahul Jain 002; Pierluigi Nuzzo 002 (CoRR, 2020)

A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints. - Krishna Chaitanya Kalagarla; Rahul Jain 002; Pierluigi Nuzzo 002 (AAAI, 2021)

Optimal Control of Discounted-Reward Markov Decision Processes Under Linear Temporal Logic Specifications. - Krishna Chaitanya Kalagarla; Rahul Jain 002; Pierluigi Nuzzo 002 (ACC, 2021)

Model-Free Reinforcement Learning for Optimal Control of Markov Decision Processes Under Signal Temporal Logic Specifications. - Krishna Chaitanya Kalagarla; Rahul Jain 002; Pierluigi Nuzzo 002 (CDC, 2021)

Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications. - Krishna Chaitanya Kalagarla; Rahul Jain 002; Pierluigi Nuzzo 002 (CoRR, 2021)

Practical Control Design for the Deep Learning Age: Distillation of Deep RL-Based Controllers. - Nathan Dahlin; Kevin Chang 007; Krishna Chaitanya Kalagarla; Rahul Jain 002; Pierluigi Nuzzo 002 (Allerton, 2022)

Optimal control of partially observable Markov decision processes with finite linear temporal logic constraints. - Krishna Chaitanya Kalagarla; Dhruva Kartik; Dongming Shen; Rahul Jain 002; Ashutosh Nayyar; Pierluigi Nuzzo 002 (UAI, 2022)

Safe Posterior Sampling for Constrained MDPs with Bounded Constraint Violation. - Krishna Chaitanya Kalagarla; Rahul Jain 002; Pierluigi Nuzzo 002 (CoRR, 2023)

Optimal Control of Logically Constrained Partially Observable and Multi-Agent Markov Decision Processes. - Krishna Chaitanya Kalagarla; Dhruva Kartik; Dongming Shen; Rahul Jain 002; Ashutosh Nayyar; Pierluigi Nuzzo 002 (CoRR, 2023)

Compositional Planning for Logically Constrained Multi-Agent Markov Decision Processes. - Krishna Chaitanya Kalagarla; Matthew Low; Rahul Jain 002; Ashutosh Nayyar; Pierluigi Nuzzo 002 (CDC, 2024)

A Safe Bayesian Learning Algorithm for Constrained MDPs with Bounded Constraint Violation. - Krishna Chaitanya Kalagarla; Rahul Jain 002; Pierluigi Nuzzo 002 (AISTATS, 2025)

SAVER: A Toolbox for SAmpling-Based, Probabilistic VERification of Neural Networks. - Vignesh Sivaramakrishnan; Krishna Chaitanya Kalagarla; Rosalyn A. Devonport; Joshua Pilipovsky; Panagiotis Tsiotras; Meeko Oishi (HSCC, 2025)

Optimal Control of Logically Constrained Partially Observable and Multiagent Markov Decision Processes. - Krishna Chaitanya Kalagarla; Dhruva Kartik; Dongming Shen; Rahul Jain 002; Ashutosh Nayyar; Pierluigi Nuzzo 002 (IEEE Trans. Autom. Control., 2025)