971 citations · 3k across the 74 of their papers we have counts for
7 papers · 2 filters
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…
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…
Learning Inconsistent Preferences with Gaussian Processes
Siu Lun Chau, Javier González, Dino Sejdinovic
We revisit widely used preferential Gaussian processes by Chu et al.(2005) and challenge their modelling assumption that imposes rankability of data items via latent utility functi…
Spectral Ranking with Covariates
Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic
We consider spectral approaches to the problem of ranking n players given their incomplete and noisy pairwise comparisons, but revisit this classical problem in light of player cov…
Large Scale Tensor Regression using Kernels and Variational Inference
Robert Hu, Geoff K. Nicholls, Dino Sejdinovic
We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakn…