Nitesh Trivedi — accepted AI-conference papers

2026

Mining Statistically Likely k-Reachable States in Probabilistic Programs

Arnab Ray, Nitesh Trivedi, Ansuman Banerjee, Sourav Chakraborty, Arijit Ghosh, Subhajit Roy

IJCAI 2026

We propose the notion of statistically likely k-step reachable set in probabilistic programs, a statistically robust notion for high-probability k-step reachable program states. We design an inductive algorithm to capture this set as a symbolic representation in propositional logic for Boolean proba

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2023

Data-Driven Invariant Learning for Probabilistic Programs (Extended Abstract)

Jialu Bao, Nitesh Trivedi, Drashti Pathak, Justin Hsu, Subhajit Roy

IJCAI 2023poster

The weakest pre-expectation framework from Morgan and McIver for deductive verification of probabilistic programs generalizes binary state assertions to real-valued expectations to measure expected values of expressions over probabilistic program variables. While loop-free programs can be analyzed b…

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