Kody J. H. Law

According to our database, Kody J. H. Law authored at least 36 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Fast deep mixtures of Gaussian process experts.

Clement Etienam, Kody J. H. Law, Sara Wade, Vitaly Zankin

Mach. Learn., 2024

SMC Is All You Need: Parallel Strong Scaling.

Xinzhu Liang, Sanjaya Lohani, Joseph M. Lukens, Brian T. Kirby, Thomas A. Searles, Kody J. H. Law

CoRR, 2024

Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need.

Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin Carlberg, Neil Walton, Kody J. H. Law

CoRR, 2024

2023

A randomized multi-index sequential Monte Carlo method.

Xinzhu Liang, Shangda Yang, Simon L. Cotter, Kody J. H. Law

Stat. Comput., October, 2023

On Unbiased Estimation for Discretized Models.

Jeremy Heng, Ajay Jasra, Kody J. H. Law, Alexander Tarakanov

SIAM/ASA J. Uncertain. Quantification, June, 2023

2022

Sparse Online Variational Bayesian Regression.

Kody J. H. Law, Vitaly Zankin

SIAM/ASA J. Uncertain. Quantification, March, 2022

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security.

Joseph M. Lukens, Ali Passian, Srikanth B. Yoginath, Kody J. H. Law, Joel A. Dawson

Sensors, 2022

Certified dimension reduction in nonlinear Bayesian inverse problems.

Olivier Zahm, Tiangang Cui, Kody J. H. Law, Alessio Spantini, Youssef M. Marzouk

Math. Comput., 2022

Mixtures of Gaussian Process Experts with SMC².

Teemu Härkönen, Sara Wade, Kody J. H. Law, Lassi Roininen

CoRR, 2022

Convergence Rates for Stochastic Approximation on a Boundary.

Kody J. H. Law, Neil S. Walton, Shangda Yang

CoRR, 2022

Digital Fingerprinting of Microstructures.

Michael D. White, Alexander Tarakanov, Christopher P. Race, Philip J. Withers, Kody J. H. Law

CoRR, 2022

Multilevel Bayesian Deep Neural Networks.

Neil K. Chada, Ajay Jasra, Kody J. H. Law, Sumeetpal S. Singh

CoRR, 2022

Multi-index Sequential Monte Carlo ratio estimators for Bayesian Inverse problems.

Kody J. H. Law, Neil Walton, Shangda Yang, Ajay Jasra

CoRR, 2022

2021

Unbiased estimation of the gradient of the log-likelihood in inverse problems.

Ajay Jasra, Kody J. H. Law, Deng Lu

Stat. Comput., 2021

Multilevel ensemble Kalman filtering for spatio-temporal processes.

Alexey Chernov, Håkon Hoel, Kody J. H. Law, Fabio Nobile, Raúl Tempone

Numerische Mathematik, 2021

Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions.

Neil K. Chada, Jordan Franks, Ajay Jasra, Kody J. H. Law, Matti Vihola

SIAM/ASA J. Uncertain. Quantification, 2021

Materials Fingerprinting Classification.

Adam Spannaus, Kody J. H. Law, Piotr Luszczek, Farzana Nasrin, Cassie Putman Micucci, Peter K. Liaw, Louis Joseph Santodonato, David J. Keffer, Vasileios Maroulas

Comput. Phys. Commun., 2021

Randomized multilevel Monte Carlo for embarrassingly parallel inference.

Ajay Jasra, Kody J. H. Law, Fangyuan Yu

CoRR, 2021

Randomized Multilevel Monte Carlo for Embarrassingly Parallel Inference.

Ajay Jasra, Kody J. H. Law, Alexander Tarakanov, Fangyuan Yu

Proceedings of the Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, 2021

2020

Ultra-fast Deep Mixtures of Gaussian Process Experts.

Clement Etienam, Kody J. H. Law, Sara Wade

CoRR, 2020

Unbiased Filtering of a Class of Partially Observed Diffusions.

Ajay Jasra, Kody J. H. Law, Fangyuan Yu

CoRR, 2020

2019

Correction to: Multilevel particle filters for Lévy-driven stochastic differential equations.

Ajay Jasra, Kody J. H. Law, Prince Peprah Osei

Stat. Comput., 2019

Multilevel particle filters for Lévy-driven stochastic differential equations.

Ajay Jasra, Kody J. H. Law, Prince Peprah Osei

Stat. Comput., 2019

Cluster, Classify, Regress: A General Method For Learning Discountinous Functions.

David E. Bernholdt, Mark R. Cianciosa, Clement Etienam, David L. Green, Kody J. H. Law, Jin Myung Park

CoRR, 2019

2018

Bayesian Static Parameter Estimation for Partially Observed Diffusions via Multilevel Monte Carlo.

Ajay Jasra, Kengo Kamatani, Kody J. H. Law, Yan Zhou

SIAM J. Sci. Comput., 2018

Accuracy of Some Approximate Gaussian Filters for the Navier-Stokes Equation in the Presence of Model Error.

Michal Branicki, Andrew J. Majda, Kody J. H. Law

Multiscale Model. Simul., 2018

Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals.

Alexandros Beskos, Ajay Jasra, Kody J. H. Law, Youssef M. Marzouk, Yan Zhou

SIAM/ASA J. Uncertain. Quantification, 2018

2017

Multilevel Sequential Monte Carlo Samplers for Normalizing Constants.

Pierre Del Moral, Ajay Jasra, Kody J. H. Law, Yan Zhou

ACM Trans. Model. Comput. Simul., 2017

Multilevel Particle Filters.

Ajay Jasra, Kengo Kamatani, Kody J. H. Law, Yan Zhou

SIAM J. Numer. Anal., 2017

2016

Deterministic Mean-Field Ensemble Kalman Filtering.

Kody J. H. Law, Hamidou Tembine, Raúl Tempone

SIAM J. Sci. Comput., 2016

Accelerated Dimension-Independent Adaptive Metropolis.

Yuxin Chen, David E. Keyes, Kody J. H. Law, Hatem Ltaief

SIAM J. Sci. Comput., 2016

Multilevel ensemble Kalman filtering.

Håkon Hoel, Kody J. H. Law, Raúl Tempone

SIAM J. Numer. Anal., 2016

Dimension-independent likelihood-informed MCMC.

Tiangang Cui, Kody J. H. Law, Youssef M. Marzouk

J. Comput. Phys., 2016

2014

Proposals which speed up function-space MCMC.

Kody J. H. Law

J. Comput. Appl. Math., 2014

2011

Evaluating Data Assimilation Algorithms

Kody J. H. Law, Andrew M. Stuart

CoRR, 2011

2010

Interaction of Excited States in Two-Species Bose-Einstein Condensates: A Case Study.

Todd Kapitula, Kody J. H. Law, Panayotis G. Kevrekidis

SIAM J. Appl. Dyn. Syst., 2010