Matching problems are ubiquitous in data science as they enable the alignment of structured objects and distributions. While existing solvers are often tailored to specific matching formulations, we unify a broad class of such problems within a common mathematical and optimization framework based on duality theory. The...
Guillaume Houry, SU FerdinandGenans, Lpsm et al.· 0 citations
Drifting methods have recently been introduced as a promising new paradigm for training generative models, and have attracted a lot of attention so far. In this work, we focus on how the optimization procedure shapes their convergence properties, shedding light on the underlying machinery that may explain their empiric...
Flows on measure spaces have long been examined in stochastic analysis and have recently attracted significant interest in machine learning, leading to intriguing research questions that often fall outside the scope of existing theory. Normalizing flows, score-based diffusion, and flow matching models are among the mos...
P. Rigollet, Giuseppe Savaré, Gabriele Steidl et al.· Oberwolfach Reports· 0 citations
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