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2020 – today
- 2024
[i13]Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence
:
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data. CoRR abs/2405.03879 (2024)- [i12]Leiv Rønneberg, Vidhi Lalchand, Paul D. W. Kirk:
Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer. CoRR abs/2407.00175 (2024) - 2023
- [i11]Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence:
Dimensionality Reduction as Probabilistic Inference. CoRR abs/2304.07658 (2023) - 2022
- [j2]Ryan-Rhys Griffiths, Alexander A. Aldrick, Miguel Garcia-Ortegon, Vidhi Lalchand, Alpha A. Lee:
Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation. Mach. Learn. Sci. Technol. 3(1): 15004 (2022) - [c6]Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence:
Generalised GPLVM with Stochastic Variational Inference. AISTATS 2022: 7841-7864 - [c5]Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence:
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. MLCB 2022: 46-60 - [c4]Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen:
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS 2022 - [i10]Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence:
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference. CoRR abs/2202.12979 (2022) - [i9]Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, J. Scott Hosking:
Kernel Learning for Explainable Climate Science. CoRR abs/2209.04947 (2022) - [i8]Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence:
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. CoRR abs/2209.06716 (2022) - [i7]Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl E. Rasmussen:
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? CoRR abs/2211.02476 (2022) - 2021
- [c3]Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen:
Kernel Identification Through Transformers. NeurIPS 2021: 10483-10495 - [c2]Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen:
Marginalised Gaussian Processes with Nested Sampling. NeurIPS 2021: 13613-13625 - [i6]Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl E. Rasmussen:
Kernel Identification Through Transformers. CoRR abs/2106.08185 (2021) - 2020
- [i5]Vidhi Lalchand:
Extracting more from boosted decision trees: A high energy physics case study. CoRR abs/2001.06033 (2020) - [i4]Vidhi Lalchand:
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application. CoRR abs/2001.06880 (2020) - [i3]Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen:
Marginalised Gaussian Processes with Nested Sampling. CoRR abs/2010.16344 (2020)
2010 – 2019
- 2019
- [c1]Vidhi Lalchand, Carl Edward Rasmussen:
Approximate Inference for Fully Bayesian Gaussian Process Regression. AABI 2019: 1-12 - [i2]Vidhi Lalchand, Carl Edward Rasmussen:
Approximate Inference for Fully Bayesian Gaussian Process Regression. CoRR abs/1912.13440 (2019) - 2018
- [i1]Vidhi Lalchand, A. C. Faul:
A Greedy approximation scheme for Sparse Gaussian process regression. CoRR abs/1811.07199 (2018) - 2013
- [j1]Philip C. Treleaven, Michal Galas, Vidhi Lalchand:
Algorithmic trading review. Commun. ACM 56(11): 76-85 (2013)
Coauthor Index
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