An Exploration of Multicalibration Uniform Convergence Bounds

Sample Complexity of Uniform Convergence for Multicalibration

There is a growing interest in societal concerns in machine learning systems, especially in fairness. Multicalibration gives a comprehensive methodology to address group fairness. In this work, we address the multicalibration error and decouple it from the prediction error. The importance of decoupling the fairness metric (multicalibration) and the accuracy (prediction error) is due to the inherent trade-off between the two, and the societal decision regarding the "right tradeoff" (as imposed many times by regulators). Our work gives sample complexity bounds for uniform convergence guarantees …

Epsilon-Greedy 探索策略的优化

Optimization of Epsilon-Greedy Exploration

本文提出基于随机梯度下降直接最小化贝叶斯遗憾并结合模型预测控制的框架，用于自动优化推荐系统中 epsilon-greedy 探索速率，实验表明该方法能匹配或超越各设置下的最佳启发式策略。

On the Effectiveness of Mode Exploration in Bayesian Model Averaging for Neural Networks

Multiple techniques for producing calibrated predictive probabilities using deep neural networks in supervised learning settings have emerged that leverage approaches to ensemble diverse solutions discovered during cyclic training or training from multiple random starting points (deep ensembles). However, only a limited amount of work has investigated the utility of exploring the local region around each diverse solution (posterior mode). Using three well-known deep architectures on the CIFAR-10 dataset, we evaluate several simple methods for exploring local regions of the weight space with re…

采用恒定探索的时变高斯过程背包问题的更紧遗憾界

Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration

针对时变高斯过程漂移模型下的贝叶斯优化问题，提出基于每轮局部置信事件的恒定探索GP-UCB方法，推导更紧的时变最大信息增益界与期望、实际遗憾界，仿真验证了探索参数与漂移率倒数的对数依赖关系。

Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm

We design new algorithms for the combinatorial pure exploration problem in the multi-arm bandit framework. In this problem, we are given $K$ distributions and a collection of subsets $\mathcal{V} \subset 2^{[K]}$ of these distributions, and we would like to find the subset $v \in \mathcal{V}$ that has largest mean, while collecting, in a sequential fashion, as few samples from the distributions as possible. In both the fixed budget and fixed confidence settings, our algorithms achieve new sample-complexity bounds that provide polynomial improvements on previous results in some settings. Via an…

Explore no more: Improved high-probability regret bounds for non-stochastic bandits

This work addresses the problem of regret minimization in non-stochastic multi-armed bandit problems, focusing on performance guarantees that hold with high probability. Such results are rather scarce in the literature since proving them requires a large deal of technical effort and significant modifications to the standard, more intuitive algorithms that come only with guarantees that hold on expectation. One of these modifications is forcing the learner to sample arms from the uniform distribution at least $Ω(\sqrt{T})$ times over $T$ rounds, which can adversely affect performance if many of…