| Algorithmic trading review P Treleaven, M Galas, V Lalchand Communications of the ACM 56 (11), 76-85, 2018 | 199* | 2018 |
| Approximate Inference for Fully Bayesian Gaussian Process Regression V Lalchand, CE Rasmussen 2nd Symposium on Advances in Approximate Bayesian Inference (AABI 2019), 2019 | 66 | 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 | 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 | 35 | 2019 |
| 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 | 11 | 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 | 11 | 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 | 9 | 2022 |
| Kernel Learning for Explainable Climate Science V Lalchand, K Tazi, TM Cheema, RE Turner, S Hosking 16th Bayesian Modelling Workshop (UAI 2022), 2022 | 6 | 2022 |
| Sparse Gaussian Process Hyperparameters: Optimize or Integrate? V Lalchand, WP Bruinsma, DR Burt, CE Rasmussen NeurIPS 2022, 2022 | 5 | 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 | 5 | 2018 |
| Dimensionality Reduction as Probabilistic Inference A Ravuri, F Vargas, V Lalchand, ND Lawrence arXiv preprint arXiv:2304.07658, 2023 | 1 | 2023 |
| 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 | | |
Neil D. LawrenceUniversity of Cambridge在 cl.cam.ac.uk 的电子邮件经过验证
