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Found 10 papers, 1 papers with code
Jointly Efficient and Optimal Algorithms for Logistic Bandits
Logistic Bandits have recently undergone careful scrutiny by virtue of their combined theoretical and practical relevance.
Regret Bounds for Generalized Linear Bandits under Parameter Drift
Generalized Linear Bandits (GLBs) are powerful extensions to the Linear Bandit (LB) setting, broadening the benefits of reward parametrization beyond linearity.
Improving Offline Contextual Bandits with Distributional Robustness
Our approach relies on the construction of asymptotic confidence intervals for offline contextual bandits through the DRO framework.
Self-Concordant Analysis of Generalized Linear Bandits with Forgetting
Contextual sequential decision problems with categorical or numerical observations are ubiquitous and Generalized Linear Bandits (GLB) offer a solid theoretical framework to address them.
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits
It was shown by Faury et al. (2020) that the learning-theoretic difficulties of Logistic Bandits can be embodied by a large (sometimes prohibitively) problem-dependent constant $\kappa$, characterizing the magnitude of the reward's non-linearity.
Improved Optimistic Algorithms for Logistic Bandits
For logistic bandits, the frequentist regret guarantees of existing algorithms are $\tilde{\mathcal{O}}(\kappa \sqrt{T})$, where $\kappa$ is a problem-dependent constant.
Distributionally Robust Counterfactual Risk Minimization
This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem.
Improving Evolutionary Strategies with Generative Neural Networks
Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization algorithms which rely on search distributions to efficiently optimize a large variety of objective functions.
Neural Generative Models for Global Optimization with Gradients
The aim of global optimization is to find the global optimum of arbitrary classes of functions, possibly highly multimodal ones.
Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces
Learning to optimize - the idea that we can learn from data algorithms that optimize a numerical criterion - has recently been at the heart of a growing number of research efforts.
