Search Results for author: Stuart J. Russell

Found 11 papers, 1 papers with code

Cognitive Model Priors for Predicting Human Decisions

no code implementations22 May 2019 David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell

To solve this problem, what is needed are machine learning models with appropriate inductive biases for capturing human behavior, and larger datasets.

Decision Making Decision Making Under Uncertainty +1

Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making

no code implementations NeurIPS 2018 Nishant Desai, Andrew Critch, Stuart J. Russell

To gain insight into the dynamics of this new framework, we implement a simple NRL agent and empirically examine its behavior in a simple environment.

Decision Making

Meta-Learning MCMC Proposals

no code implementations NeurIPS 2018 Tongzhou Wang, Yi Wu, David A. Moore, Stuart J. Russell

The learned neural proposals generalize to occurrences of common structural motifs across different models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required.

Meta-Learning Named Entity Recognition

Gaussian Process Random Fields

1 code implementation NeurIPS 2015 David A. Moore, Stuart J. Russell

Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications.

Gaussian Processes

Algorithm selection by rational metareasoning as a model of human strategy selection

no code implementations NeurIPS 2014 Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Tom Griffiths

Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.

Multilinear Dynamical Systems for Tensor Time Series

no code implementations NeurIPS 2013 Mark Rogers, Lei LI, Stuart J. Russell

The MLDS models each time slice of the tensor time series as the multilinear projection of a corresponding member of a sequence of latent, low-dimensional tensors.

Time Series

The Extended Parameter Filter

no code implementations8 May 2013 Yusuf Erol, Lei LI, Bharath Ramsundar, Stuart J. Russell

Drawing on an analogy to the extended Kalman filter, we develop and analyze, both theoretically and experimentally, a Taylor approximation to the parameter posterior that allows Storvik's method to be applied to a broader class of models.

Global seismic monitoring as probabilistic inference

no code implementations NeurIPS 2010 Nimar Arora, Stuart J. Russell, Paul Kidwell, Erik B. Sudderth

The International Monitoring System (IMS) is a global network of sensors whose purpose is to identify potential violations of the Comprehensive Nuclear-Test-Ban Treaty (CTBT), primarily through detection and localization of seismic events.

Bayesian Inference

Probabilistic detection of short events, with application to critical care monitoring

no code implementations NeurIPS 2008 Norm Aleks, Stuart J. Russell, Michael G. Madden, Diane Morabito, Kristan Staudenmayer, Mitchell Cohen, Geoffrey T. Manley

We describe an application of probabilistic modeling and inference technology to the problem of analyzing sensor data in the setting of an intensive care unit (ICU).

Decision Making

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