Vidhi Lalchand
Title
Cited by
Cited by
Year
Algorithmic trading review
P Treleaven, M Galas, V Lalchand
Communications of the ACM 56 (11), 76-85, 2013
952013
Approximate Inference for Fully Bayesian Gaussian Process Regression
V Lalchand, CE Rasmussen
2nd Symposium on Advances in Approximate Bayesian Inference (NeurIPS 2019), 2019
72019
Physics-informed Gaussian process for online optimization of particle accelerators
A Hanuka, X Huang, J Shtalenkova, D Kennedy, A Edelen, VR Lalchand, ...
arXiv preprint arXiv:2009.03566, 2020
22020
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
RR Griffiths, M Garcia-Ortegon, AA Aldrick, V Lalchand, AA Lee
arXiv preprint arXiv:1910.07779, 2019
22019
The magazine archive includes every article published in Communications of the ACM for over the past 50 years.
P Treleaven, M Galas, V Lalchand
Communications of the ACM 56 (11), 76-85, 2013
12013
Marginalised Gaussian Processes with Nested Sampling
F Simpson, V Lalchand, CE Rasmussen
arXiv preprint arXiv:2010.16344, 2020
2020
Extracting more from boosted decision trees: A high energy physics case study
V Lalchand
Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019 …, 2019
2019
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes
V Lalchand, AC Faul
38th International Workshop on Bayesian Inference and Maximum Entropy …, 2018
2018
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application
V Lalchand
http://inspirehep.net/record/1776757, 2016
2016
Gaussian Process Latent Variable Flows for Massively Missing Data
V Lalchand, A Ravuri, ND Lawrence
Marginalised Spectral Mixture Kernels with Nested Sampling
F Simpson, V Lalchand, C Rasmussen
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Articles 1–11