2 citations · 3 across the 6 of their papers we have counts for
6 papers
Reflected Schrödinger Bridge Matching
Marcus Häggbom, Viktor Nilsson, Pierre Nyquist +1
Recent advances in generative modeling have enabled the efficient computation of Schrödinger bridges (SB) in high-dimensional settings by leveraging partially simulation-free train…
A weak convergence approach to the large deviations of the dynamic Schrödinger problem
Viktor Nilsson, Pierre Nyquist
In this paper, we consider the large deviations for dynamical Schrödinger problems, using the variational approach developed by Dupuis, Ellis, Budhiraja, and others. Recent results…
Large deviations for scaled families of Schrödinger bridges with reflection
Viktor Nilsson, Pierre Nyquist
In this paper, we show a large deviation principle for certain sequences of static Schrödinger bridges, typically motivated by a scale-parameter decreasing towards zero, extending…
REMEDI: Corrective Transformations for Improved Neural Entropy Estimation
Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy +1
Information theoretic quantities play a central role in machine learning. The recent surge in the complexity of data and models has increased the demand for accurate estimation of…
Large deviations for interacting particle dynamics for finding mixed equilibria in zero-sum games
Viktor Nilsson, Pierre Nyquist
Finding equilibrium points in continuous minmax games has become a key problem within machine learning, in part due to its connection to the training of generative adversarial netw…
Thermodynamic Integration Methods, Infinite Swapping and the Calculation of Generalized Averages
J. D. Doll, P. Dupuis, P. Nyquist
In the present paper we examine the risk-sensitive and sampling issues associated with the problem of calculating generalized averages. By combining thermodynamic integration and S…