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Found 6 papers, 1 papers with code
Beyond Lengthscales: No-regret Bayesian Optimisation With Unknown Hyperparameters Of Any Type
Previously proposed algorithms with the no-regret property were only able to handle the special case of unknown lengthscales, reproducing kernel Hilbert space norm and applied only to the frequentist case.
Why Can Large Language Models Generate Correct Chain-of-Thoughts?
This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting.
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
Learning decompositions of expensive-to-evaluate black-box functions promises to scale Bayesian optimisation (BO) to high-dimensional problems.
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction
Centralised training with decentralised execution (CT-DE) serves as the foundation of many leading multi-agent reinforcement learning (MARL) algorithms.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints
In this paper, we introduce a reinforcement learning (RL) framework named \textbf{L}earnable \textbf{I}mpulse \textbf{C}ontrol \textbf{R}einforcement \textbf{A}lgorithm (LICRA), for learning to optimally select both when to act and which actions to take when actions incur costs.
Modelling nonlinear dependencies in the latent space of inverse scattering
The conducted meta-analysis also shows a clear practical advantage of such constructed generative models in terms of the efficiency of their training process compared to existing generative models for images.
