Header logo is


2020


Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures

Marco, A., Rohr, A. V., Baumann, D., Hernández-Lobato, J. M., Trimpe, S.

2020 (proceedings) In revision

Abstract
When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a tolerable failure in exchange for a faster learning process, as it provides rich information about an undesired behavior. In the context of Bayesian optimization under unknown constraints (BOC), typical strategies for safe learning explore conservatively and avoid failures by all means. On the other side of the spectrum, non conservative BOC algorithms that allow failing may fail an unbounded number of times before reaching the optimum. In this work, we propose a novel decision maker grounded in control theory that controls the amount of risk we allow in the search as a function of a given budget of failures. Empirical validation shows that our algorithm uses the failures budget more efficiently in a variety of optimization experiments, and generally achieves lower regret, than state-of-the-art methods. In addition, we propose an original algorithm for unconstrained Bayesian optimization inspired by the notion of excursion sets in stochastic processes, upon which the failures-aware algorithm is built.

ics am

arXiv code (python) PDF [BibTex]

2013


no image
Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24

Deisenroth, M., Szepesvári, C., Peters, J.

pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

ei

Web [BibTex]

2013


Web [BibTex]

2010


no image
JMLR Workshop and Conference Proceedings: Volume 6

Guyon, I., Janzing, D., Schölkopf, B.

pages: 288, MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS Workshop) , February 2010 (proceedings)

ei

Web [BibTex]

2010


Web [BibTex]

2008


no image
CogRob 2008: The 6th International Cognitive Robotics Workshop

Lespérance, Y., Lakemeyer, G., Peters, J., Pirri, F.

Proceedings of the 6th International Cognitive Robotics Workshop (CogRob 2008), pages: 35, Patras University Press, Patras, Greece, 6th International Cognitive Robotics Workshop (CogRob), July 2008 (proceedings)

ei

Web [BibTex]

2008


Web [BibTex]

2007


no image
Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference

Schölkopf, B., Platt, J., Hofmann, T.

Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), pages: 1690, MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (proceedings)

Abstract
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists--interested in theoretical and applied aspects of modeling, simulating, and building neural-like or intelligent systems. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.

ei

Web [BibTex]

2007


Web [BibTex]