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 Stuart J Russell

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Average citations per article47.74
Citation Count6,445
Publication count135
Publication years1985-2017
Available for download19
Average downloads per article516.05
Downloads (cumulative)9,805
Downloads (12 Months)1,154
Downloads (6 Weeks)154
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135 results found Export Results: bibtexendnoteacmrefcsv

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1
August 2017 IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

In the context of hierarchical reinforcement learning, the idea of hierarchies of abstract machines (HAMs) is to write a partial policy as a set of hierarchical finite state machines with unspecified choice states, and use reinforcement learning to learn an optimal completion of this partial policy. Given a HAM with ...

2
August 2017 IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 1

Intuitively, obedience - following the order that a human gives - seems like a good property for a robot to have. But, we humans are not perfect and we may give orders that are not best aligned to our preferences. We show that when a human is not perfectly rational ...

3
August 2017 IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

It is clear that one of the primary tools we can use to mitigate the potential risk from a misbehaving AI system is the ability to turn the system off. As the capabilities of AI systems improve, it is important to ensure that such systems do not adopt subgoals that ...

4
December 2016 NIPS'16: Proceedings of the 30th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 2,   Downloads (12 Months): 11,   Downloads (Overall): 11

Full text available: PDFPDF
For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans. We propose a formal definition ...

5
July 2016 IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 1

State abstraction is an important technique for scaling MDP algorithms. As is well known, however, it introduces difficulties due to the non-Markovian nature of state-abstracted models. Whereas prior approaches rely upon ad hoc fixes for this issue, we propose instead to view the state-abstracted model as a POMDP and show ...

6
July 2016 IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines--even the compiled ones--incur significant runtime interpretation overhead, especially for ...

7
February 2016 AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

STRIPS-like languages (SLLs) have fostered immense advances in automated planning. In practice, SLLs are used to express highly abstract versions of real-world planning problems, leading to more concise models and faster solution times. Unfortunately, as we show in the paper, simple ways of abstracting solvable real-world problems may lead to ...

8 published by ACM
January 2016 AI Matters: Volume 2 Issue 2, December 2015
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 3,   Downloads (12 Months): 58,   Downloads (Overall): 363

Full text available: PDFPDF
These are boom times for AI. Articles celebrating the success of AI research appear frequently in the international press. Every day, millions of people routinely use AI-based systems that the founders of the field would hail as miraculous. And there is a palpable sense of excitement about impending applications of ...

9
December 2015 NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2
Publisher: MIT Press
Bibliometrics:
Citation Count: 1

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 Gaussian Process Random Field (GPRF), in which local GPs are coupled via pairwise potentials. The GPRF likelihood is a ...

10
July 2015 UAI'15: Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 0

Split-merge moves are a standard component of MCMC algorithms for tasks such as multi-target tracking and fitting mixture models with unknown numbers of components. Achieving rapid mixing for split-merge MCMC has been notoriously difficult, and state-of-the-art methods do not scale well. We explore the reasons for this and propose a ...

11 published by ACM
June 2015 Communications of the ACM: Volume 58 Issue 7, July 2015
Publisher: ACM
Bibliometrics:
Citation Count: 6
Downloads (6 Weeks): 36,   Downloads (12 Months): 408,   Downloads (Overall): 2,909

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Open-universe probability models show merit in unifying efforts.

12
January 2015 AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 6

We create a unified framework for analyzing and synthesizing plans with loops for solving problems with non-deterministic numeric effects and a limited form of partial observability. Three different action models—with deterministic, qualitative non-deterministic and Boolean non-deterministic semantics—are handled using a single abstract representation. We establish the conditions under which the ...

13
December 2014 NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2
Publisher: MIT Press
Bibliometrics:
Citation Count: 1

Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. ...

14
July 2014 UAI'14: Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 0

Gaussian processes (GP) are a powerful tool for nonparametric regression; unfortunately, calculating the posterior variance in a standard GP model requires time O ( n 2 ) in the size of the training set. Previous work by Shen et al. (2006) used a k -d tree structure to approximate the ...

15
July 2014 UAI'14: Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 1

Open-universe probability models, representable by a variety of probabilistic programming languages (PPLs), handle uncertainty over the existence and identity of objects—forms of uncertainty occurring in many real-world situations. We examine the problem of extending a declarative PPL to define decision problems (specifically, POMDPs) and identify non-trivial representational issues in describing ...

16
December 2013 NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 5

Data in the sciences frequently occur as sequences of multidimensional arrays called tensors. How can hidden, evolving trends in such data be extracted while preserving the tensor structure? The model that is traditionally used is the linear dynamical system (LDS) with Gaussian noise, which treats the latent state and observation ...

17
July 2013 AW'13: Proceedings of the 2013 UAI Conference on Application Workshops: Big Data meet Complex Models and Models for Spatial, Temporal and Network Data - Volume 1024
Publisher: CEUR-WS.org
Bibliometrics:
Citation Count: 0

Gaussian process (GP) regression is a powerful technique for nonparametric regression; unfortunately, calculating the predictive variance in a standard GP model requires time O ( n 2 ) in the size of the training set. This is cost prohibitive when GP likelihood calculations must be done in the inner loop ...

18
June 2013 ICML'13: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28
Publisher: JMLR.org
Bibliometrics:
Citation Count: 0

The parameters of temporal models, such as dynamic Bayesian networks, may be modelled in a Bayesian context as static or atemporal variables that in fluence transition probabilities at every time step. Particle filters fail for models that include such variables, while methods that use Gibbs sampling of parameter variables may ...

19 published by ACM
April 2013 CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems
Publisher: ACM
Bibliometrics:
Citation Count: 10
Downloads (6 Weeks): 1,   Downloads (12 Months): 25,   Downloads (Overall): 394

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Recent technologies in vision sensors are capable of capturing 3D finger positions and movements. We propose a novel way to control and interact with computers by moving fingers in the air. The positions of fingers are precisely captured by a computer vision device. By tracking the moving patterns of fingers, ...
Keywords: dynamic time warping, hand gesture, handwriting recognition, time series

20
September 2012 SUM'12: Proceedings of the 6th international conference on Scalable Uncertainty Management
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 0

Standard temporal models assume that observation times are correct, whereas in many real-world settings (particularly those involving human data entry) noisy time stamps are quite common. Serious problems arise when these time stamps are taken literally. This paper introduces a modeling framework for handling uncertainty in observation times and describes ...



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