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Found 7 papers, 1 papers with code
Evaluating Predictive Distributions: Does Bayesian Deep Learning Work?
This paper introduces \textit{The Neural Testbed}, which provides tools for the systematic evaluation of agents that generate such predictions.
Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal Predictions
A fundamental challenge for any intelligent system is prediction: given some inputs $X_1,.., X_\tau$ can you predict outcomes $Y_1,.., Y_\tau$.
Reinforcement Learning, Bit by Bit
Reinforcement learning agents have demonstrated remarkable achievements in simulated environments.
Event-based Motion Segmentation with Spatio-Temporal Graph Cuts
We have developed a method to identify independently moving objects acquired with an event-based camera, i. e., to solve the event-based motion segmentation problem.
Hypermodels for Exploration
This generalizes and extends the use of ensembles to approximate Thompson sampling.
Information-Theoretic Confidence Bounds for Reinforcement Learning
We integrate information-theoretic concepts into the design and analysis of optimistic algorithms and Thompson sampling.
Ensemble Sampling
Thompson sampling has emerged as an effective heuristic for a broad range of online decision problems.