2 citations · 5 across the 6 of their papers we have counts for
6 papers
Sequential Causal Discovery with Noisy Language Model Priors
Prakhar Verma, David Arbour, Sunav Choudhary +3
Causal discovery from observational data typically assumes access to complete data and availability of perfect domain experts. In practice, data often arrive in batches, are subjec…
Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation
Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha +3
We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation. While exis…
Memory-Based Dual Gaussian Processes for Sequential Learning
Paul E. Chang, Prakhar Verma, S. T. John +2
Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning. In such cases, errors can accum…
Variational Gaussian Process Diffusion Processes
Prakhar Verma, Vincent Adam, Arno Solin
Diffusion processes are a class of stochastic differential equations (SDEs) providing a rich family of expressive models that arise naturally in dynamic modelling tasks. Probabilis…
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Paul E. Chang, Prakhar Verma, ST John +3
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with dat…
Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation
Arno Solin, Ella Tamir, Prakhar Verma
Simulation-based techniques such as variants of stochastic Runge-Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning. Th…