Mansi Rankawat — accepted AI-conference papers

2026

From Lyapunov Analysis to Algorithm Design in two-sided PL Minimax Optimization

Mansi Rankawat, Michael Muehlebach, Simon Lacoste-Julien, Damien Scieur

ICML 2026poster

We derive algorithms for smooth nonconvex nonconcave minimax optimization and establish linear convergence rates for problems that satisfy the two-sided Polyak-Lojasiewicz (PL) inequality. At the core of our approach is the observation that Lyapunov functions can be used not only to certify converge…

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2022

Bayesian structure learning with generative flow networks

Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer +1

UAI 2022poster

In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data. Defining such a distribution is very challenging, due to the combinatorially large sample space, and approximations based on MCMC are often r…

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