72 citations · 218 across the 27 of their papers we have counts for
14 papers · 1 filter
Deep Reinforcement Learning for Efficient Measurement of Quantum Devices
V. Nguyen, S. B. Orbell, D. T. Lennon +9
Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experie…
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint
Xingyue Pu, Siu Lun Chau, Xiaowen Dong +1
The problem of graph learning concerns the construction of an explicit topological structure revealing the relationship between nodes representing data entities, which plays an inc…
Benign Overfitting and Noisy Features
Zhu Li, Weijie Su, Dino Sejdinovic
Modern machine learning often operates in the regime where the number of parameters is much higher than the number of data points, with zero training loss and yet good generalizati…
A Perspective on Gaussian Processes for Earth Observation
Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge +1
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and…
Variational Inference with Continuously-Indexed Normalizing Flows
Anthony Caterini, Rob Cornish, Dino Sejdinovic +1
Continuously-indexed flows (CIFs) have recently achieved improvements over baseline normalizing flows on a variety of density estimation tasks. CIFs do not possess a closed-form ma…
Meta Learning for Causal Direction
Jean-Francois Ton, Dino Sejdinovic, Kenji Fukumizu
The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus…