4.2k citations · 4.4k across the 40 of their papers we have counts for
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BORE: Bayesian Optimization by Density-Ratio Estimation
Louis C. Tiao, Aaron Klein, Matthias Seeger +3
Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion enco…
A User's Guide to Calibrating Robotics Simulators
Bhairav Mehta, Ankur Handa, Dieter Fox +1
Simulators are a critical component of modern robotics research. Strategies for both perception and decision making can be studied in simulation first before deployed to real world…
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
Anthony Tompkins, Rafael Oliveira, Fabio Ramos
We establish a general form of explicit, input-dependent, measure-valued warpings for learning nonstationary kernels. While stationary kernels are ubiquitous and simple to use, the…
Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
Rel Guzman, Rafael Oliveira, Fabio Ramos
Model predictive control (MPC) has been successful in applications involving the control of complex physical systems. This class of controllers leverages the information provided b…
Intrinsic Exploration as Multi-Objective RL
Philippe Morere, Fabio Ramos
Intrinsic motivation enables reinforcement learning (RL) agents to explore when rewards are very sparse, where traditional exploration heuristics such as Boltzmann or e-greedy woul…
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,…
Learning Non-Stationary Space-Time Models for Environmental Monitoring
Sahil Garg, Amarjeet Singh, Fabio Ramos
One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena. Many of these phenomena exhibit variations in both sp…
Adaptive Sensing for Learning Nonstationary Environment Models
Sahil Garg, Amarjeet Singh, Fabio Ramos
Most environmental phenomena, such as wind profiles, ozone concentration and sunlight distribution under a forest canopy, exhibit nonstationary dynamics i.e. phenomenon variation c…
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…
Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection
Markus Schneider, Wolfgang Ertel, Fabio Ramos
We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (EXPoSE), is kernel-based and able to e…
Transductive Learning for Multi-Task Copula Processes
Markus Schneider, Fabio Ramos
We tackle the problem of multi-task learning with copula process. Multivariable prediction in spatial and spatial-temporal processes such as natural resource estimation and polluti…
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…