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Found 6 papers, 2 papers with code
Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting
Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change.
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits
We propose a novel Bayesian-Optimistic Frequentist Upper Confidence Bound (BOF-UCB) algorithm for stochastic contextual linear bandits in non-stationary environments.
PAC-Bayesian Soft Actor-Critic Learning
Actor-critic algorithms address the dual goals of reinforcement learning (RL), policy evaluation and improvement, via two separate function approximators.
How to Combine Variational Bayesian Networks in Federated Learning
Federated Learning enables multiple data centers to train a central model collaboratively without exposing any confidential data.
Continual Learning of Multi-modal Dynamics with External Memory
We study the problem of fitting a model to a dynamical environment when new modes of behavior emerge sequentially.
Evidential Turing Processes
A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e. g.\ class overlap), and iii) accurately identifies queries coming out of the target domain and rejects them.
