Vidhi Lalchand
Vidhi Lalchand
University of Cambridge; Broad Institute of MIT & Harvard
Verified email at - Homepage
Cited by
Cited by
Algorithmic trading review
P Treleaven, M Galas, V Lalchand
Communications of the ACM 56 (11), 76-85, 2018
Approximate Inference for Fully Bayesian Gaussian Process Regression
V Lalchand, CE Rasmussen
2nd Symposium on Advances in Approximate Bayesian Inference (AABI 2019), 2019
Physics-informed Gaussian process for online optimization of particle accelerators
A Hanuka, X Huang, J Shtalenkova, D Kennedy, A Edelen, VR Lalchand, ...
Physical Review Accelerators and Beams 24 (7), 2020
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
RR Griffiths, M Garcia-Ortegon, AA Aldrick, V Lalchand, AA Lee
Machine Learning: Science and Technology, 2021, 2019
Generalised GPLVM with Stochastic Variational Inference
V Lalchand, A Ravuri, ND Lawrence
International Conference on Artificial Intelligence and Statistics, 7841-7864, 2022
Marginalised Gaussian Processes with Nested Sampling
F Simpson*, V Lalchand*, CE Rasmussen
NeurIPS 2021, 2021
Kernel Identification Through Transformers
F Simpson, I Davies, V Lalchand, A Vullo, N Durrande, C Rasmussen
NeurIPS 2021, 2021
Extracting more from boosted decision trees: A high energy physics case study
V Lalchand
2nd Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019), 2019
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs
V Lalchand, A Ravuri, E Dann, N Kumasaka, D Sumanaweera, ...
Machine Learning in Computational Biology, 2022, 46-60, 2022
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
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
V Lalchand, WP Bruinsma, DR Burt, CE Rasmussen
NeurIPS 2022, 2022
Kernel Learning for Explainable Climate Science
V Lalchand, K Tazi, TM Cheema, RE Turner, S Hosking
16th Bayesian Modelling Workshop (UAI 2022), 2022
Shared Stochastic Gaussian process Decoders: A Probabilistic Generative model for Quasar Spectra
V Lalchand, AC Eilers
Machine Learning for Astrophysics. Workshop at the Fortieth International …, 2023
Dimensionality Reduction as Probabilistic Inference
A Ravuri, F Vargas, V Lalchand, ND Lawrence
arXiv preprint arXiv:2304.07658, 2023
Gaussian Process Latent Variable Flows for Massively Missing Data
V Lalchand, A Ravuri, ND Lawrence
Third Symposium on Advances in Approximate Bayesian Inference, 2020
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application
V Lalchand
MPhil Thesis (Physics),, 2017
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data
S Zhao, A Ravuri, V Lalchand, ND Lawrence
ICLR 2024 Workshop on Machine Learning for Genomics Explorations, 0
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