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Vidhi Lalchand
Vidhi Lalchand
University of Cambridge; Broad Institute of MIT & Harvard; MIT
Verified email at broad.mit.edu - Homepage
Title
Cited by
Cited by
Year
Algorithmic trading review
P Treleaven, M Galas, V Lalchand
Communications of the ACM 56 (11), 76-85, 2018
200*2018
Approximate Inference for Fully Bayesian Gaussian Process Regression
V Lalchand, CE Rasmussen
2nd Symposium on Advances in Approximate Bayesian Inference (AABI 2019), 2019
682019
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
50*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
352019
Generalised GPLVM with Stochastic Variational Inference
V Lalchand, A Ravuri, ND Lawrence
International Conference on Artificial Intelligence and Statistics, 7841-7864, 2022
26*2022
Marginalised Gaussian Processes with Nested Sampling
F Simpson*, V Lalchand*, CE Rasmussen
NeurIPS 2021, 2021
18*2021
Kernel Identification Through Transformers
F Simpson, I Davies, V Lalchand, A Vullo, N Durrande, C Rasmussen
NeurIPS 2021, 2021
112021
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
112019
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
92022
Kernel Learning for Explainable Climate Science
V Lalchand, K Tazi, TM Cheema, RE Turner, S Hosking
16th Bayesian Modelling Workshop (UAI 2022), 2022
62022
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
V Lalchand, WP Bruinsma, DR Burt, CE Rasmussen
NeurIPS 2022, 2022
52022
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
52018
Dimensionality Reduction as Probabilistic Inference
A Ravuri, F Vargas, V Lalchand, ND Lawrence
arXiv preprint arXiv:2304.07658, 2023
12023
Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer
L Rønneberg, V Lalchand, PDW Kirk
arXiv preprint arXiv:2407.00175, 2024
2024
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data
S Zhao, A Ravuri, V Lalchand, ND Lawrence
ICLR 2024 MLGenX Workshop, 2024
2024
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
2023
Modelling Technical and Biological Effects in single-cell RNA-seq data with Scalable Gaussian Process Latent Variable Models (GPLVMs)
V Lalchand, A Ravuri, E Dann, N Kumasaka, D Sumanaweera, ...
arXiv preprint arXiv:2209.06716, 2022
2022
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application
V Lalchand
MPhil Thesis (Physics), http://inspirehep.net/record/1776757, 2017
2017
Algorithmic Trading Review The competitive nature of AT, the scarcity of expertise, and the vast profits potential, makes for a secretive community where implementation details …
P Treleaven, M Galas, V Lalchand
Gaussian Process Latent Variable Flows for Massively Missing Data
V Lalchand, A Ravuri, ND Lawrence
Third Symposium on Advances in Approximate Bayesian Inference, 0
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