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Found 12 papers, 1 papers with code
A Generalized Acquisition Function for Preference-based Reward Learning
Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task.
Cognitive Model Priors for Predicting Human Decisions
To solve this problem, what is needed are machine learning models with appropriate inductive biases for capturing human behavior, and larger datasets.
Predicting human decisions with behavioral theories and machine learning
Predicting human decisions under risk and uncertainty remains a fundamental challenge across disciplines.
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making
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.
Meta-Learning MCMC Proposals
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.
Signal-based Bayesian Seismic Monitoring
Detecting weak seismic events from noisy sensors is a difficult perceptual task.
Gaussian Process Random Fields
Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications.
Algorithm selection by rational metareasoning as a model of human strategy selection
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
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.
The Extended Parameter Filter
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
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.
Probabilistic detection of short events, with application to critical care monitoring
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).
