4.2k citations · 4.7k across the 41 of their papers we have counts for
5 papers · 1 filter
Learning ODEs via Diffeomorphisms for Fast and Robust Integration
Weiming Zhi, Tin Lai, Lionel Ott +2
Advances in differentiable numerical integrators have enabled the use of gradient descent techniques to learn ordinary differential equations (ODEs). In the context of machine lear…
Bayesian Curiosity for Efficient Exploration in Reinforcement Learning
Tom Blau, Lionel Ott, Fabio Ramos
Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like -greedy. This…
Bayesian optimisation under uncertain inputs
Rafael Oliveira, Lionel Ott, Fabio Ramos
Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however,…
Online Adaptation of Deep Architectures with Reinforcement Learning
Thushan Ganegedara, Lionel Ott, Fabio Ramos
Online learning has become crucial to many problems in machine learning. As more data is collected sequentially, quickly adapting to changes in the data distribution can offer seve…
Integer Programming Relaxations for Integrated Clustering and Outlier Detection
Lionel Ott, Linsey Pang, Fabio Ramos +2
In this paper we present methods for exemplar based clustering with outlier selection based on the facility location formulation. Given a distance function and the number of outlie…