Search Results for author:
Found 24 papers, 10 papers with code
Open science in machine learning
We present OpenML and mldata, open science platforms that provides easy access to machine learning data, software and results to encourage further study and application.
Multivariate Spearman's rho for aggregating ranks using copulas
Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's \rho, which measures correlation between a set of ranked lists.
A scaled Bregman theorem with applications
Experiments on each of these domains validate the analyses and suggest that the scaled Bregman theorem might be a worthy addition to the popular handful of Bregman divergence properties that have been pervasive in machine learning.
Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches
Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion).
Learning Points and Routes to Recommend Trajectories
Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours.
Hawkes Processes with Stochastic Excitations
We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation.
A Modular Theory of Feature Learning
We propose the idea of a risk gap induced by representation learning for a given prediction context, which measures the difference in the risk of some learner using the learned features as compared to the original inputs.
Revisiting revisits in trajectory recommendation
Trajectory recommendation is the problem of recommending a sequence of places in a city for a tourist to visit.
Provably Fair Representations
These provable properties can be used in a governance model involving a data producer, a data user and a data regulator, where there is a separation of concerns between fairness and target task utility to ensure transparency and prevent perverse incentives.
Monge blunts Bayes: Hardness Results for Adversarial Training
A key feature of our result is that it holds for all proper losses, and for a popular subset of these, the optimisation of this central measure appears to be independent of the loss.
Representation Learning of Compositional Data
Our approach combines the benefits of the log-ratio transformation from compositional data analysis and exponential family PCA.
Cold-start Playlist Recommendation with Multitask Learning
Playlist recommendation involves producing a set of songs that a user might enjoy.
New Tricks for Estimating Gradients of Expectations
We introduce a family of pairwise stochastic gradient estimators for gradients of expectations, which are related to the log-derivative trick, but involve pairwise interactions between samples.
Disentangled behavioural representations
Individual characteristics in human decision-making are often quantified by fitting a parametric cognitive model to subjects' behavior and then studying differences between them in the associated parameter space.
Quantile Bandits for Best Arms Identification
We consider a variant of the best arm identification task in stochastic multi-armed bandits.
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series
We introduce Radflow, a novel model that embodies three key ideas: a recurrent neural network to obtain node embeddings that depend on time, the aggregation of the flow of influence from neighboring nodes with multi-head attention, and the multi-layer decomposition of time series.
Declarative nets that are equilibrium models
We show that using a kernelised generalised linear model (kGLM) as an inner problem in a DDN yields a large class of commonly used DEQ architectures with a closed-form expression for the hidden layer parameters in terms of the kernel.
Factorized Fourier Neural Operators
We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based approach for simulating partial differential equations (PDEs).
Gaussian Process Bandits with Aggregated Feedback
We consider the continuum-armed bandits problem, under a novel setting of recommending the best arms within a fixed budget under aggregated feedback.
Two-Stage Neural Contextual Bandits for Personalised News Recommendation
We consider the problem of personalised news recommendation where each user consumes news in a sequential fashion.
Deep equilibrium models as estimators for continuous latent variables
We consider a generalised setting where the canonical parameters of the exponential family are a nonlinear transformation of the latents.
Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction
In contrast to baseline approaches for prediction interval estimation, we show that the CQR method provides prediction intervals that adjust to the black hole mass and its related properties.
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families
Maximum likelihood and maximum a posteriori estimates in a reparameterisation of the final layer of the intensity function can be obtained by solving a (strongly) convex optimisation problem using projected gradient descent.
