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Found 11 papers, 1 papers with code
Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness
Agents can share information through a central server, with the objective of minimizing regret that is accumulating over time $T$ and aggregating over agents.
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization.
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret
We develop a distributed online learning algorithm that achieves order-optimal cumulative regret with low communication cost measured in the total number of bits transmitted over the entire learning horizon.
Distributed Linear Bandits under Communication Constraints
We consider distributed linear bandits where $M$ agents learn collaboratively to minimize the overall cumulative regret incurred by all agents.
Collaborative Learning in Kernel-based Bandits for Distributed Users
We study collaborative learning among distributed clients facilitated by a central server.
Provably and Practically Efficient Neural Contextual Bandits
The non-asymptotic error bounds may be of broader interest as a tool to establish the relation between the smoothness of the activation functions in neural contextual bandits and the smoothness of the kernels in kernel bandits.
As Easy as ABC: Adaptive Binning Coincidence Test for Uniformity Testing
The alternative hypothesis is a composite set of Lipschitz continuous distributions that are at least $\varepsilon$ away in $\ell_1$ distance from the uniform distribution.
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance
We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space.
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
A framework based on iterative coordinate minimization (CM) is developed for stochastic convex optimization.
Disagreement-based Active Learning in Online Settings
We show that the proposed algorithm has a label complexity of $O(dT^{\frac{2-2\alpha}{2-\alpha}}\log^2 T)$ under a constraint of bounded regret in terms of classification errors, where $d$ is the VC dimension of the hypothesis space and $\alpha$ is the Tsybakov noise parameter.
Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization
Online minimization of an unknown convex function over the interval $[0, 1]$ is considered under first-order stochastic bandit feedback, which returns a random realization of the gradient of the function at each query point.
