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Found 6 papers, 1 papers with code
Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm
The first algorithm uses a gradient estimator based on randomization over the $\ell_2$ sphere due to Bach and Perchet (2016).
Estimating the minimizer and the minimum value of a regression function under passive design
We propose a new method for estimating the minimizer $\boldsymbol{x}^*$ and the minimum value $f^*$ of a smooth and strongly convex regression function $f$ from the observations contaminated by random noise.
Group Meritocratic Fairness in Linear Contextual Bandits
This is a very strong notion of fairness, since the relative rank is not directly observed by the agent and depends on the underlying reward model and on the distribution of rewards.
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback
We present a novel gradient estimator based on two function evaluations and randomization on the $\ell_1$-sphere.
Distributed Zero-Order Optimization under Adversarial Noise
We study the problem of distributed zero-order optimization for a class of strongly convex functions.
Optimization and Control Statistics Theory Statistics Theory
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits
The gradient is estimated by a randomized procedure involving two function evaluations and a smoothing kernel.
