124 citations · 528 across the 41 of their papers we have counts for
42 papers
Joint Causal Structure and Cluster Discovery Using Variational Inference
Avni Rajpal, Anubhav Kumar, Rishabh Karnad +2
Causal discovery aims to understand the relationships between individual random variables. In many applications, such as brain imaging and climate modeling, it is more meaningful t…
A Stein Identity for q-Gaussians with Bounded Support
Sophia Sklaviadis, Thomas Moellenhoff, Andre F. T. Martins +2
Stein's identity is a fundamental tool in machine learning with applications in generative models, stochastic optimization, and other problems involving gradients of expectations u…
Exploiting Inferential Structure in Neural Processes
Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric Nalisnick
Neural Processes (NPs) are appealing due to their ability to perform fast adaptation based on a context set. This set is encoded by a latent variable, which is often assumed to fol…
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 Bayes Made Easy
Mohammad Emtiyaz Khan
Variational Bayes is a popular method for approximate inference but its derivation can be cumbersome. To simplify the process, we give a 3-step recipe to identify the posterior for…
The Lie-Group Bayesian Learning Rule
Eren Mehmet Kıral, Thomas Möllenhoff, Mohammad Emtiyaz Khan
The Bayesian Learning Rule provides a framework for generic algorithm design but can be difficult to use for three reasons. First, it requires a specific parameterization of expone…