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Found 11 papers, 0 papers with code
Experimental Design for Active Transductive Inference in Large Language Models
We design the LLM prompt by adaptively choosing few-shot examples for a given inference query.
Efficient and Interpretable Bandit Algorithms
We propose CODE, a bandit algorithm based on a Constrained Optimal DEsign, that is interpretable and maximally reduces the uncertainty.
SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits
In this paper, we study the problem of optimal data collection for policy evaluation in linear bandits.
Safety Aware Changepoint Detection for Piecewise i.i.d. Bandits
We provide regret bounds for our algorithms and show that the bounds are comparable to their counterparts from the safe bandit and piecewise i. i. d.
ReVar: Strengthening Policy Evaluation via Reduced Variance Sampling
This paper studies the problem of data collection for policy evaluation in Markov decision processes (MDPs).
Nearly Optimal Algorithms for Level Set Estimation
The threshold value $\alpha$ can either be \emph{explicit} and provided a priori, or \emph{implicit} and defined relative to the optimal function value, i. e. $\alpha = (1-\epsilon)f(x_\ast)$ for a given $\epsilon > 0$ where $f(x_\ast)$ is the maximal function value and is unknown.
Chernoff Sampling for Active Testing and Extension to Active Regression
Active learning can reduce the number of samples needed to perform a hypothesis test and to estimate the parameters of a model.
Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits
The second strategy \ImpCPD makes use of the knowledge of $T$ to achieve the order optimal regret bound of $\min\big\lbrace O(\sum\limits_{i=1}^{K} \sum\limits_{g=1}^{G}\frac{\log(T/H_{1, g})}{\Delta^{opt}_{i, g}}), O(\sqrt{GT})\big\rbrace$, (where $H_{1, g}$ is the problem complexity) thereby closing an important gap with respect to the lower bound in a specific challenging setting.
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting
We consider a finite-armed structured bandit problem in which mean rewards of different arms are known functions of a common hidden parameter $\theta^*$.
Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates
We propose a novel variant of the UCB algorithm (referred to as Efficient-UCB-Variance (EUCBV)) for minimizing cumulative regret in the stochastic multi-armed bandit (MAB) setting.
Thresholding Bandits with Augmented UCB
In this paper we propose the Augmented-UCB (AugUCB) algorithm for a fixed-budget version of the thresholding bandit problem (TBP), where the objective is to identify a set of arms whose quality is above a threshold.
