12 citations · 15 across the 7 of their papers we have counts for
7 papers
Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs
Kevin Course, Prasanth B. Nair
We consider the problem of inferring latent stochastic differential equations (SDEs) with a time and memory cost that scales independently with the amount of data, the total length…
Sparse random Fourier features based interatomic potentials for high entropy alloys
Gurjot Dhaliwal, Abu Anand, Prasanth B. Nair +1
Computational modeling of high entropy alloys (HEA) is challenging given the scalability issues of Density functional theory (DFT) and the non-availability of Interatomic potential…
Weak Form Generalized Hamiltonian Learning
Kevin L. Course, Trefor W. Evans, Prasanth B. Nair
We present a method for learning generalized Hamiltonian decompositions of ordinary differential equations given a set of noisy time series measurements. Our method simultaneously…
Quadruply Stochastic Gaussian Processes
Trefor W. Evans, Prasanth B. Nair
We introduce a stochastic variational inference procedure for training scalable Gaussian process (GP) models whose per-iteration complexity is independent of both the number of tra…
Discretely Relaxing Continuous Variables for tractable Variational Inference
Trefor W. Evans, Prasanth B. Nair
We explore a new research direction in Bayesian variational inference with discrete latent variable priors where we exploit Kronecker matrix algebra for efficient and exact computa…
Exploiting Structure for Fast Kernel Learning
Trefor W. Evans, Prasanth B. Nair
We propose two methods for exact Gaussian process (GP) inference and learning on massive image, video, spatial-temporal, or multi-output datasets with missing values (or "gaps") in…
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
Trefor W. Evans, Prasanth B. Nair
We introduce a kernel approximation strategy that enables computation of the Gaussian process log marginal likelihood and all hyperparameter derivatives in time. O…