| Algorithmic trading review P Treleaven, M Galas, V Lalchand Communications of the ACM 56 (11), 76-85, 2013 | 95 | 2013 |
| Approximate Inference for Fully Bayesian Gaussian Process Regression V Lalchand, CE Rasmussen 2nd Symposium on Advances in Approximate Bayesian Inference (NeurIPS 2019), 2019 | 7 | 2019 |
| 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 | 2 | 2020 |
| 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 | 2 | 2019 |
| 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 | 1 | 2013 |
| 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 | | |
Michal GalasUniversity College LondonVerified email at cs.ucl.ac.uk
