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Found 6 papers, 2 papers with code
Multi-Objective Optimization Using the R2 Utility
In order to solve this vector-valued optimization problem, practitioners often appeal to the use of scalarization functions in order to transform the multi-objective problem into a collection of single-objective problems.
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds
The privacy preserving properties of Langevin dynamics with additive isotropic noise have been extensively studied.
Joint Entropy Search for Multi-objective Bayesian Optimization
Many real-world problems can be phrased as a multi-objective optimization problem, where the goal is to identify the best set of compromises between the competing objectives.
On stochastic mirror descent with interacting particles: convergence properties and variance reduction
A second alternative is to use a fixed step-size and run independent replicas of the algorithm and average these.
The sharp, the flat and the shallow: Can weakly interacting agents learn to escape bad minima?
As a first step towards understanding this question we formalize it as an optimization problem with weakly interacting agents.
On Adaptive Estimation for Dynamic Bernoulli Bandits
In this paper, we overcome the shortcoming of slow response to change by deploying adaptive estimation in the standard methods and propose a new family of algorithms, which are adaptive versions of $\epsilon$-Greedy, UCB, and Thompson sampling.
