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 Stuart J Russell

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Average citations per article51.03
Citation Count6,022
Publication count118
Publication years1985-2016
Available for download19
Average downloads per article435.37
Downloads (cumulative)8,272
Downloads (12 Months)1,192
Downloads (6 Weeks)120
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129 results found Export Results: bibtex | endnote | acmref | csv

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1
Markovian state and action abstractions for MDPS via hierarchical MCTS
Aijun Bai, Siddharth Srivastava, Stuart Russell
July 2016 IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

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 ...

2
Swift: compiled inference for probabilistic programming languages
Yi Wu, Lei Li, Stuart Russell, Rastislav Bodik
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 ...

3
Metaphysics of planning domain descriptions
Siddharth Srivastava, Stuart Russell, Alessandro Pinto
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 ...

4 published by ACM
Eric Eaton, Tom Dietterich, Maria Gini, Barbara J. Grosz, Charles L. Isbell, Subbarao Kambhampati, Michael Littman, Francesca Rossi, Stuart Russell, Peter Stone, Toby Walsh, Michael Wooldridge
January 2016 AI Matters: Volume 2 Issue 2, December 2015
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 5,   Downloads (12 Months): 92,   Downloads (Overall): 288

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 ...

5
Gaussian process Random fields
David A. Moore, Stuart J. Russell
December 2015 NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems
Publisher: MIT Press
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0,   Downloads (12 Months): 1,   Downloads (Overall): 1

Full text available: PDFPDF
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 ...

6
A smart-dumb/dumb-smart algorithm for efficient split-merge MCMC
Wei Wang, Stuart Russell
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 ...

7 published by ACM
Unifying logic and probability
Stuart Russell
June 2015 Communications of the ACM: Volume 58 Issue 7, July 2015
Publisher: ACM
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 26,   Downloads (12 Months): 409,   Downloads (Overall): 2,356

Full text available: HtmlHtml  PDFPDF  PDF Chinese translationPDF Chinese translation
Open-universe probability models show merit in unifying efforts.

8
Tractability of planning with loops
Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell
January 2015 AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 4

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 ...

9
Algorithm selection by rational metareasoning as a model of human strategy selection
Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas J. Hay, Thomas L. Griffiths
December 2014 NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems
Publisher: MIT Press
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0,   Downloads (12 Months): 0,   Downloads (Overall): 0

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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. ...

10
First-order open-universe POMDPs
Siddharth Srivastava, Stuart Russell, Paul Ruan, Xiang Cheng
July 2014 UAI'14: Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 0

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 ...

11
Fast Gaussian process posteriors with product trees
David A. Moore, Stuart Russell
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 ...

12
Multilinear dynamical systems for tensor time series
Mark Rogers, Lei Li, Stuart Russell
December 2013 NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 1,   Downloads (12 Months): 2,   Downloads (Overall): 2

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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 ...

13
Product trees for Gaussian process covariance in sublinear time
David A. Moore, Stuart Russell
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
Downloads (6 Weeks): 0,   Downloads (12 Months): 0,   Downloads (Overall): 0

Full text available: PDFPDF
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 ...

14
The extended parameter filter
Yusuf B. Erol, Lei Li, Bharath Ramsundar, Stuart Russell
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 ...

15 published by ACM
Writing and sketching in the air, recognizing and controlling on the fly
Sharad Vikram, Lei Li, Stuart Russell
April 2013 CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems
Publisher: ACM
Bibliometrics:
Citation Count: 6
Downloads (6 Weeks): 5,   Downloads (12 Months): 44,   Downloads (Overall): 355

Full text available: PDFPDF
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: hand gesture, time series, dynamic time warping, handwriting recognition

16
Uncertain observation times
Shaunak Chatterjee, Stuart Russell
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 ...

17
Selecting computations: theory and applications
Nicholas Hay, Stuart Russell, David Tolpin, Solomon Eyal Shimony
August 2012 UAI'12: Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 1

Sequential decision problems are often approximately solvable by simulating possible future action sequences. Metalevel decision procedures have been developed for selecting which action sequences to simulate, based on estimating the expected improvement in decision quality that would result from any particular simulation; an example is the recent work on using ...

18
Global seismic monitoring: a bayesian approach
Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik Sudderth
August 2011 AAAI'11: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

The automated processing of multiple seismic signals to detect and localize seismic events is a central tool in both geophysics and nuclear treaty verification. This paper reports on a project, begun in 2009, to reformulate this problem in a Bayesian framework. A Bayesian seismic monitoring system, NET-VISA, has been built ...

19
Bounded intention planning
Jason Wolfe, Stuart Russell
July 2011 IJCAI'11: Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Publisher: AAAI Press
Bibliometrics:
Citation Count: 7

We propose a novel approach for solving unary SAS+ planning problems. This approach extends an SAS+ instance with new state variables representing intentions about how each original state variable will be used or changed next, and splits the original actions into several stages of intention followed by eventual execution. The ...

20
A temporally abstracted Viterbi algorithm
Shaunak Chatterjee, Stuart Russell
July 2011 UAI'11: Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 0

Hierarchical problem abstraction, when applicable, may offer exponential reductions in computational complexity. Previous work on coarse-to-fine dynamic programming (CFDP) has demonstrated this possibility using state abstraction to speed up the Viterbi algorithm. In this paper, we show how to apply temporal abstraction to the Viterbi problem. Our algorithm uses bounds ...



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