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Dive into the research topics where Roy Fox is active.

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Featured researches published by Roy Fox.


conference on decision and control | 2016

Minimum-information LQG control Part II: Retentive controllers

Roy Fox; Naftali Tishby

Retentive (memory-utilizing) sensing-acting agents may operate under limitations on the communication between their sensing, memory and acting components, requiring them to trade off the external cost that they incur with the capacity of their communication channels. In this paper we formulate this problem as a sequential rate-distortion problem of minimizing the rate of information required for the controllers operation under a constraint on its external cost. We reduce this bounded retentive control problem to the memoryless one, studied in Part I of this work [1], by viewing the memory reader as one more sensor and the memory writer as one more actuator. We further investigate the structure of the resulting optimal solution and demonstrate its interesting phenomenology.


conference on decision and control | 2016

Minimum-information LQG control part I: Memoryless controllers

Roy Fox; Naftali Tishby

With the increased demand for power efficiency in feedback-control systems, communication is becoming a limiting factor, raising the need to trade off the external cost that they incur with the capacity of the controllers communication channels. With a proper design of the channels, this translates into a sequential rate-distortion problem, where we minimize the rate of information required for the controllers operation under a constraint on its external cost. Memoryless controllers are of particular interest both for the simplicity and frugality of their implementation and as a basis for studying more complex controllers. In this paper we present the optimality principle for memoryless linear controllers that utilize minimal information rates to achieve a guaranteed external-cost level. We also study the interesting and useful phenomenology of the optimal controller, such as the principled reduction of its order.


uncertainty in artificial intelligence | 2016

Taming the noise in reinforcement learning via soft updates

Roy Fox; Ari Pakman; Naftali Tishby


neural information processing systems | 2013

A multi-agent control framework for co-adaptation in brain-computer interfaces

Josh Merel; Roy Fox; Tony Jebara; Liam Paninski


arXiv: Robotics | 2017

DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations

Sanjay Krishnan; Roy Fox; Ion Stoica; Ken Goldberg


arXiv: Learning | 2015

G-Learning: Taming the Noise in Reinforcement Learning via Soft Updates.

Roy Fox; Ari Pakman; Naftali Tishby


conference on automation science and engineering | 2017

Statistical data cleaning for deep learning of automation tasks from demonstrations

Caleb Chuck; Michael Laskey; Sanjay Krishnan; Ruta Joshi; Roy Fox; Ken Goldberg


international conference on machine learning | 2012

Bounded Planning in Passive POMDPs

Roy Fox; Naftali Tishby


arXiv: Learning | 2017

DART: Noise Injection for Robust Imitation Learning.

Michael Laskey; Jonathan Lee; Roy Fox; Anca D. Dragan; Ken Goldberg


arXiv: Learning | 2016

Principled Option Learning in Markov Decision Processes.

Roy Fox; Michal Moshkovitz; Naftali Tishby

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Ken Goldberg

University of California

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Michael Laskey

University of California

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Naftali Tishby

Hebrew University of Jerusalem

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Ion Stoica

University of California

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Jonathan Lee

University of California

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Richard Liaw

University of California

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Eric Liang

University of California

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Philipp Moritz

University of California

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