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Found 10 papers, 2 papers with code
Neural-BO: A Black-box Optimization Algorithm using Deep Neural Networks
Bayesian Optimization (BO) is an effective approach for global optimization of black-box functions when function evaluations are expensive.
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data
Modern deep neural networks have achieved impressive performance on tasks from image classification to natural language processing.
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
In particular, whether in the noisy setting, the EI strategy with a standard incumbent converges is still an open question of the Gaussian process bandit optimization problem.
Expected Improvement-based Contextual Bandits
With a linear reward function, we demonstrate that our algorithm achieves a near-optimal regret.
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support
We propose a novel approach that uses a hybrid of offline learning with online exploration.
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions.
Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks
To the best of our knowledge, this is the first theoretical characterization of the sample complexity of offline RL with deep neural network function approximation under the general Besov regularity condition that goes beyond {the linearity regime} in the traditional Reproducing Hilbert kernel spaces and Neural Tangent Kernels.
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
To this end, we propose a novel BO algorithm which expands (and shifts) the search space over iterations based on controlling the expansion rate thought a hyperharmonic series.
Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
Optimising acquisition function in low dimensional subspaces allows our method to obtain accurate solutions within limited computational budget.
Bayesian Optimization with Unknown Search Space
Applying Bayesian optimization in problems wherein the search space is unknown is challenging.
