23 citations · 49 across the 14 of their papers we have counts for
14 papers
Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative Model for Quasar Spectra
Vidhi Lalchand, Anna-Christina Eilers
This work proposes a scalable probabilistic latent variable model based on Gaussian processes (Lawrence, 2004) in the context of multiple observation spaces. We focus on an applica…
Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer
Leiv Rønneberg, Vidhi Lalchand, Paul D. W. Kirk
Dose-response prediction in cancer is an active application field in machine learning. Using large libraries of \textit{in-vitro} drug sensitivity screens, the goal is to develop a…
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data
Sarah Zhao, Aditya Ravuri, Vidhi Lalchand +1
Dimensionality reduction is crucial for analyzing large-scale single-cell RNA-seq data. Gaussian Process Latent Variable Models (GPLVMs) offer an interpretable dimensionality reduc…
Dimensionality Reduction as Probabilistic Inference
Aditya Ravuri, Francisco Vargas, Vidhi Lalchand +1
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data. DR is a critical st…
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt +1
The kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel…
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs
Vidhi Lalchand, Aditya Ravuri, Emma Dann +6
Single-cell RNA-seq datasets are growing in size and complexity, enabling the study of cellular composition changes in various biological/clinical contexts. Scalable dimensionality…
Kernel Learning for Explainable Climate Science
Vidhi Lalchand, Kenza Tazi, Talay M. Cheema +2
The Upper Indus Basin, Himalayas provides water for 270 million people and countless ecosystems. However, precipitation, a key component to hydrological modelling, is poorly unders…
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference
Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
Gaussian process latent variable models (GPLVM) are a flexible and non-linear approach to dimensionality reduction, extending classical Gaussian processes to an unsupervised learni…
Kernel Identification Through Transformers
Fergus Simpson, Ian Davies, Vidhi Lalchand +3
Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models, as the chosen kernel determines both the inductive biases and prior support of…
Marginalised Gaussian Processes with Nested Sampling
Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
Gaussian Process (GPs) models are a rich distribution over functions with inductive biases controlled by a kernel function. Learning occurs through the optimisation of kernel hyper…
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application
Vidhi Lalchand
The aim of this work is to propose a meta-algorithm for automatic classification in the presence of discrete binary classes. Classifier learning in the presence of overlapping clas…
Extracting more from boosted decision trees: A high energy physics case study
Vidhi Lalchand
Particle identification is one of the core tasks in the data analysis pipeline at the Large Hadron Collider (LHC). Statistically, this entails the identification of rare signal eve…
Approximate Inference for Fully Bayesian Gaussian Process Regression
Vidhi Lalchand, Carl Edward Rasmussen
Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal…
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes
Vidhi Lalchand, A. C. Faul
In their standard form Gaussian processes (GPs) provide a powerful non-parametric framework for regression and classificaton tasks. Their one limiting property is their $\mathcal{O…