All Research Papers Published In Foundations of Data Science

Gaussian entropic optimal transport: Schrödinger bridges and the Sinkhorn algorithm

Adaptive random Fourier features training stabilized by resampling with applications in image regression

DAWN-FM: Data-aware and noise-informed flow matching for solving inverse problems

Nested smoothing algorithms for inference and tracking of heterogeneous multi-scale state-space systems

Persistent Stanley–Reisner theory

Convergence rates of non-stationary and deep Gaussian process regression

Curse of dimensionality on persistence diagrams

Reduced basis approximations of parameterized dynamical partial differential equations via neural networks

Deep learning with Gaussian continuation

On the choice of the non-trainable internal weights in random feature maps for forecasting chaotic dynamical systems