Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Russell Stuart J"'
Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task. Previous works have shown that actively synthesizing preference queries to maximize information gain abo
Externí odkaz:
http://arxiv.org/abs/2403.06003
Autor:
Russell Stuart J, Tan Christine, O'Keefe Peter, Ashraf Saeed, Zaidi Afzal, Fraser Alan G, Yousef Zaheer R
Publikováno v:
Trials, Vol 13, Iss 1, p 20 (2012)
Abstract Background Heart failure patients with stable angina, acute coronary syndromes and valvular heart disease may benefit from revascularisation and/or valve surgery. However, the mortality rate is increased- 5-30%. Biventricular pacing using te
Externí odkaz:
https://doaj.org/article/8c6bdf004ab448b89436310a33f1c1af
Autor:
Bourgin, David D., Peterson, Joshua C., Reichman, Daniel, Griffiths, Thomas L., Russell, Stuart J.
Publikováno v:
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5133-5141, 2019
Human decision-making underlies all economic behavior. For the past four decades, human decision-making under uncertainty has continued to be explained by theoretical models based on prospect theory, a framework that was awarded the Nobel Prize in Ec
Externí odkaz:
http://arxiv.org/abs/1905.09397
Autor:
Plonsky, Ori, Apel, Reut, Ert, Eyal, Tennenholtz, Moshe, Bourgin, David, Peterson, Joshua C., Reichman, Daniel, Griffiths, Thomas L., Russell, Stuart J., Carter, Evan C., Cavanagh, James F., Erev, Ido
Predicting human decision-making under risk and uncertainty represents a quintessential challenge that spans economics, psychology, and related disciplines. Despite decades of research effort, no model can be said to accurately describe and predict h
Externí odkaz:
http://arxiv.org/abs/1904.06866
Effective implementations of sampling-based probabilistic inference often require manually constructed, model-specific proposals. Inspired by recent progresses in meta-learning for training learning agents that can generalize to unseen environments,
Externí odkaz:
http://arxiv.org/abs/1708.06040
Autor:
Moore, David A., Russell, Stuart J.
Detecting weak seismic events from noisy sensors is a difficult perceptual task. We formulate this task as Bayesian inference and propose a generative model of seismic events and signals across a network of spatially distributed stations. Our system,
Externí odkaz:
http://arxiv.org/abs/1703.00561
Autor:
Moore, David A., Russell, Stuart J.
Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Ga
Externí odkaz:
http://arxiv.org/abs/1511.00054