no code implementations • 18 Aug 2023 • Siva Athreya, Soumik Pal, Raghav Somani, Raghavendra Tripathi
In both cases we show that, as the size of the graph goes to infinity, the random trajectories of the stochastic processes converge to deterministic curves on the space of measure-valued graphons.
no code implementations • 31 Jul 2023 • Nabarun Deb, Young-Heon Kim, Soumik Pal, Geoffrey Schiebinger
This limit, which we call the Sinkhorn flow, is an example of a Wasserstein mirror gradient flow, a concept we introduce here inspired by the well-known Euclidean mirror gradient flows.
no code implementations • 2 Oct 2022 • Zaid Harchaoui, Sewoong Oh, Soumik Pal, Raghav Somani, Raghavendra Tripathi
The limiting curve of graphons is characterized by a family of stochastic differential equations with reflections and can be thought of as an extension of the classical McKean-Vlasov limit for interacting diffusions.
no code implementations • 31 Dec 2021 • Nicholas J. Irons, Meyer Scetbon, Soumik Pal, Zaid Harchaoui
Triangular flows, also known as Kn\"{o}the-Rosenblatt measure couplings, comprise an important building block of normalizing flow models for generative modeling and density estimation, including popular autoregressive flow models such as real-valued non-volume preserving transformation models (Real NVP).
1 code implementation • 31 Dec 2021 • Lang Liu, Soumik Pal, Zaid Harchaoui
We introduce an independence criterion based on entropy regularized optimal transport.
no code implementations • 18 Nov 2021 • Sewoong Oh, Soumik Pal, Raghav Somani, Raghavendra Tripathi
Wasserstein gradient flows on probability measures have found a host of applications in various optimization problems.
no code implementations • 17 Nov 2020 • Zaid Harchaoui, Lang Liu, Soumik Pal
We consider instead in this paper the problem where each matching is endowed with a Gibbs probability weight proportional to the exponential of the negative total cost of that matching.
no code implementations • 11 Apr 2019 • Soumik Pal, Yizhe Zhu
We consider the community detection problem in sparse random hypergraphs.