Vidhi Lalchand

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 20

Top Topics

Gaussian Process

Top Venues

Publications

Leiv Rønneberg, Vidhi Lalchand, Paul D. W. Kirk
Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer. CoRR (2024)
Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data. CoRR (2024)
Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence
Dimensionality Reduction as Probabilistic Inference. CoRR (2023)
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)
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS (2022)
Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, J. Scott Hosking
Kernel Learning for Explainable Climate Science. CoRR (2022)
Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference. CoRR (2022)
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl E. Rasmussen
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? CoRR (2022)
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) (2022)
Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
Generalised GPLVM with Stochastic Variational Inference. AISTATS (2022)
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 (2022)
Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen
Kernel Identification Through Transformers. NeurIPS (2021)
Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl E. Rasmussen
Kernel Identification Through Transformers. CoRR (2021)
Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
Marginalised Gaussian Processes with Nested Sampling. NeurIPS (2021)
Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
Marginalised Gaussian Processes with Nested Sampling. CoRR (2020)
Vidhi Lalchand
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application. CoRR (2020)
Vidhi Lalchand
Extracting more from boosted decision trees: A high energy physics case study. CoRR (2020)
Vidhi Lalchand, Carl Edward Rasmussen
Approximate Inference for Fully Bayesian Gaussian Process Regression. AABI (2019)
Vidhi Lalchand, Carl Edward Rasmussen
Approximate Inference for Fully Bayesian Gaussian Process Regression. CoRR (2019)
Vidhi Lalchand, A. C. Faul
A Greedy approximation scheme for Sparse Gaussian process regression. CoRR (2018)
Philip C. Treleaven, Michal Galas, Vidhi Lalchand
Algorithmic trading review. Commun. ACM 56 (11) (2013)