21 citations · 31 across the 6 of their papers we have counts for
9 papers
Ranker-agnostic Contextual Position Bias Estimation
Oriol Barbany Mayor, Vito Bellini, Alexander Buchholz +4
Learning-to-rank (LTR) algorithms are ubiquitous and necessary to explore the extensive catalogs of media providers. To avoid the user examining all the results, its preferences ar…
Flatness is a False Friend
Diego Granziol
Hessian based measures of flatness, such as the trace, Frobenius and spectral norms, have been argued, used and shown to relate to generalisation. In this paper we demonstrate that…
A Maximum Entropy approach to Massive Graph Spectra
Diego Granziol, Robin Ru, Stefan Zohren +3
Graph spectral techniques for measuring graph similarity, or for learning the cluster number, require kernel smoothing. The choice of kernel function and bandwidth are typically ch…
Deep Curvature Suite
Diego Granziol, Xingchen Wan, Timur Garipov
We present MLRG Deep Curvature suite, a PyTorch-based, open-source package for analysis and visualisation of neural network curvature and loss landscape. Despite of providing rich…
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
Diego Granziol, Binxin Ru, Stefan Zohren +3
Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing…
Entropic Spectral Learning for Large-Scale Graphs
Diego Granziol, Binxin Ru, Stefan Zohren +3
Graph spectra have been successfully used to classify network types, compute the similarity between graphs, and determine the number of communities in a network. For large graphs,…