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Found 6 papers, 1 papers with code
Fair Active Learning in Low-Data Regimes
In this work, we address the challenges of reducing bias and improving accuracy in data-scarce environments, where the cost of collecting labeled data prohibits the use of large, labeled datasets.
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
For robust identification, it is well-known that if arms are chosen randomly and non-adaptively from a G-optimal design over $\mathcal{X}$ at each time then the error probability decreases as $\exp(-T\Delta^2_{(1)}/d)$, where $\Delta_{(1)} = \min_{x \neq x^*} (x^* - x)^\top \frac{1}{T}\sum_{t=1}^T \theta_t$.
Active Learning with Safety Constraints
To our knowledge, our results are the first on best-arm identification in linear bandits with safety constraints.
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.
Selective Sampling for Online Best-arm Identification
The main results of this work precisely characterize this trade-off between labeled samples and stopping time and provide an algorithm that nearly-optimally achieves the minimal label complexity given a desired stopping time.
High-Dimensional Experimental Design and Kernel Bandits
We also leverage our new approach in a new algorithm for kernelized bandits to obtain state of the art results for regret minimization and pure exploration.
