Vidhi Lalchand

23 papers A* 3A 1Journal 17Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative Model for Quasar Spectra. CoRR Vidhi Lalchand, Anna-Christina Eilers
2025	J	`jnl`	Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative model for Quasar spectra. Trans. Mach. Learn. Res. Vidhi Lalchand, Anna-Christina Eilers
2024	J	`jnl`	Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer. CoRR Leiv Rønneberg, Vidhi Lalchand, Paul D. W. Kirk
2024	J	`jnl`	Scalable Amortized GPLVMs for Single Cell Transcriptomics Data. CoRR Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence
2023	J	`jnl`	Dimensionality Reduction as Probabilistic Inference. CoRR Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence
2022	J	`jnl`	Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation. Mach. Learn. Sci. Technol. Ryan-Rhys Griffiths, Alexander A. Aldrick, Miguel Garcia-Ortegon, Vidhi Lalchand, Alpha A. Lee
2022	A	`conf`	Generalised GPLVM with Stochastic Variational Inference. AISTATS Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
2022	J	`jnl`	Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference. CoRR Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
2022	J	`jnl`	Kernel Learning for Explainable Climate Science. CoRR Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, J. Scott Hosking
2022	—	`conf`	Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. MLCB Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence
2022	J	`jnl`	Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. CoRR Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence
2022	A*	`conf`	Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen
2022	J	`jnl`	Sparse Gaussian Process Hyperparameters: Optimize or Integrate? CoRR Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl E. Rasmussen
2021	A*	`conf`	Kernel Identification Through Transformers. NeurIPS Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen
2021	J	`jnl`	Kernel Identification Through Transformers. CoRR Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl E. Rasmussen
2021	A*	`conf`	Marginalised Gaussian Processes with Nested Sampling. NeurIPS Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
2020	J	`jnl`	A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application. CoRR Vidhi Lalchand
2020	J	`jnl`	Extracting more from boosted decision trees: A high energy physics case study. CoRR Vidhi Lalchand
2020	J	`jnl`	Marginalised Gaussian Processes with Nested Sampling. CoRR Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
2019	—	`conf`	Approximate Inference for Fully Bayesian Gaussian Process Regression. AABI Vidhi Lalchand, Carl Edward Rasmussen
2019	J	`jnl`	Approximate Inference for Fully Bayesian Gaussian Process Regression. CoRR Vidhi Lalchand, Carl Edward Rasmussen
2018	J	`jnl`	A Greedy approximation scheme for Sparse Gaussian process regression. CoRR Vidhi Lalchand, A. C. Faul
2013	J	`jnl`	Algorithmic trading review. Commun. ACM Philip C. Treleaven, Michal Galas, Vidhi Lalchand