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Found 9 papers, 4 papers with code
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems
This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e. g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process.
Probabilistic Meta-Learning for Bayesian Optimization
Transfer and meta-learning algorithms leverage evaluations on related tasks in order to significantly speed up learning or optimization on a new problem.
Bayesian Context Aggregation for Neural Processes
Recently, casting probabilistic regression as a multi-task learning problem in terms of conditional latent variable (CLV) models such as the Neural Process (NP) has shown promising results.
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems
To address this shortcoming, we employ Gaussian ODE filtering (a probabilistic numerical method for ODEs) to construct a local Gaussian approximation to the likelihood.
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
We consider the problem of robust optimization within the well-established Bayesian optimization (BO) framework.
Trajectory-Based Off-Policy Deep Reinforcement Learning
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks.
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization.
Probabilistic Recurrent State-Space Models
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.
Probabilistic Movement Primitives
In order to use such a trajectory distribution for robot movement control, we analytically derive a stochastic feedback controller which reproduces the given trajectory distribution.
