Recent & Upcoming Talks

William Gregory: Improving Arctic Sea Ice Predictability with Gaussian Processes

Arctic sea ice is a major component of the Earth’s climate system, as well as an integral platform for travel, subsistence, and habitat. Since the late 1970s, significant advancements have been made in our ability to closely monitor the state of the …

Geoff Pleiss: Understanding Neural Networks through Gaussian Processes, and Vice Versa

Neural networks and Gaussian processes represent different learning paradigms: the former are parametric and rely on ERM-based training, while the latter are non-parametric and employ Bayesian inference. Despite these differences, I will discuss how …

Rachel Prudden: Stochastic Downscaling for Convective Regimes with Gaussian Random Fields

Downscaling aims to link the behaviour of the atmosphere at fine scales to properties measurable at coarser scales, and has the potential to provide high resolution information at a lower computational and storage cost than numerical simulation …

Willie Neiswanger: Going Beyond Global Optima with Bayesian Algorithm Execution

In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function evaluations. One example is budget constrained global optimization of f, for which Bayesian optimization is a popular …

Florin Gogianu and Tudor Berariu: Spectral Normalisation in Deep Reinforcement Learning

We are happy to present this joint work with Mihaela Roșca, Răzvan Pascanu, Lucian Bușunoiu and Claudia Clopath on the effect of spectral normalisation in deep reinforcement learning. Most of the recent deep reinforcement learning advances take an …

Steve James: Learning Portable Hierarchies for Task-Level Planning

In this work, we tackle the problem of learning symbolic representations of low-level and continuous environments. We present a framework for autonomously learning portable hierarchies that are suitable for planning. Such abstractions can be …

Fariba Yousefi: Multi-task Learning for Aggregated Data using Gaussian Processes

Aggregated data is commonplace in areas such as epidemiology and demography. For example, census data for a population is usually given as averages defined over time periods or spatial resolutions (cities, regions or countries). In this paper, we …

Ruha Benjamin: Reimagining the Default Settings of Technology & Society

From everyday apps to complex algorithms, technology has the potential to hide, speed, and deepen discrimination, while appearing neutral and even benevolent when compared to racist practices of a previous era. In this talk, Ruha Benjamin explores a …

César Lincoln C. Mattos: Probabilistic ML-Applications and Modeling Investigations

Probabilistic modeling is a core component of modern machine learning. Nevertheless, at least from our local perspective, educational barriers and practical implementation issues have undermined its applicability in both academy and industry. In …

Jackie Kay: Fairness for Unobserved Characteristics: Insights from Technological Impacts on Que​​​​​​​er Communities

Recent advances in algorithmic fairness have largely omitted sexual orientation and gender identity. We explore the concerns of the queer community in privacy, censorship, language, online safety, health, and employment to study the positive and …