activity
20072026
most citedGPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

528 citations · 810 across the 28 of their papers we have counts for

collaborators

32 papers

math.NA2026

Mixed-Precision Computing for Scientific Discovery: Formats, Co-Design, and Responsible Approximation

Emmanuel Agullo, Hartwig Anzt, Daniel Bauer +37

Reduced and mixed precision have moved from a niche optimization to a central design axis in scientific computing and engineering, driven by energy constraints, heterogeneous accel…

eess.SY2026

Minimizing the Expected Cost of Synchronization in Lossless Power Networks

Gerald Ogbonna, David Bindel, Lindsay C. Anderson

The reliable operation of large-scale electric power networks is increasingly challenging, particularly with the integration of stochastic renewable generation. In this work, we ad…

physics.plasm-ph2023

Understanding Trade-offs in Stellarator Design with Multi-objective Optimization

David Bindel, Matt Landreman, Misha Padidar

In designing stellarators, any design decision ultimately comes with a trade-off. Improvements in particle confinement, for instance, may increase the burden on engineers to build…

physics.plasm-ph2023★ 16 cited

Direct Optimization of Fast-Ion Confinement in Stellarators

David Bindel, Matt Landreman, Misha Padidar

Confining energetic ions such as alpha particles is a prime concern in the design of stellarators. However, directly measuring alpha confinement through numerical simulation of gui…

cs.LG2022★ 1 cited

Scalable Bayesian Transformed Gaussian Processes

Xinran Zhu, Leo Huang, Cameron Ibrahim +2

The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem and Oliviera, is a fully Bayesian counterpart to the warped Gaussian process (WGP) and marginalizes out a j…

cs.CL2021

On-the-Fly Rectification for Robust Large-Vocabulary Topic Inference

Moontae Lee, Sungjun Cho, Kun Dong +2

Across many data domains, co-occurrence statistics about the joint appearance of objects are powerfully informative. By transforming unsupervised learning problems into decompositi…