Outlier detection is the identification of points in a dataset that do not
conform to the norm. Outlier detection is highly sensitive to the choice of the
detection algorithm and the feature subspace used by the algorithm. Extracting
domain-relevant insights from outliers needs systematic exploration of these
choices since diverse outlier sets could lead to complementary insights. This
challenge is especially acute in an interactive setting, where the choices must
be explored in a time-constrained manner. In this work, we present REMIX, the
first system to address the problem of outlier detection in an interactive
setting. REMIX uses a novel mixed integer programming (MIP) formulation for
automatically selecting and executing a diverse set of outlier detectors within
a time limit. This formulation incorporates multiple aspects such as (i) an
upper limit on the total execution time of detectors (ii) diversity in the
space of algorithms and features, and (iii) meta-learning for evaluating the
cost and utility of detectors. REMIX provides two distinct ways for the analyst
to consume its results: (i) a partitioning of the detectors explored by REMIX
into perspectives through low-rank non-negative matrix factorization; each
perspective can be easily visualized as an intuitive heatmap of experiments
versus outliers, and (ii) an ensembled set of outliers which combines outlier
scores from all detectors. We demonstrate the benefits of REMIX through
extensive empirical validation on real-world data.

REMIX 是第一个解决交互式环境下异常检测问题的系统，它使用新颖的混合整数规划 (MIP) 公式，在规定时间内自动选择和执行多样化的异常检测器，包括数据处理算法和特征选择，为分析人员提供两种不同的结果消费方式，即透视图和集成异常检测器。

REMIX: 交互式异常值检测的自动化探索

REMIX: Automated Exploration for Interactive Outlier Detection

This paper studies systematic exploration for reinforcement learning with
rich observations and function approximation. We introduce a new model called
contextual decision processes, that unifies and generalizes most prior
settings. Our first contribution is a complexity measure, the Bellman rank,
that we show enables tractable learning of near-optimal behavior in these
processes and is naturally small for many well-studied reinforcement learning
settings. Our second contribution is a new reinforcement learning algorithm
that engages in systematic exploration to learn contextual decision processes
with low Bellman rank. Our algorithm provably learns near-optimal behavior with
a number of samples that is polynomial in all relevant parameters but
independent of the number of unique observations. The approach uses Bellman
error minimization with optimistic exploration and provides new insights into
efficient exploration for reinforcement learning with function approximation.

本文探讨了如何使用富观测和函数逼近进行强化学习的系统性探索，并介绍了一种新的模型，即上下文决策过程，它统一和概括了大多数之前的设置，并提出了一种新的强化学习算法，我们的算法用 Bellman rank 衡量复杂度，使用乐观探索最小化 Bellman 误差，保证在所有相关参数的时间多项式的情况下学会近似最优行为，为强化学习提供了新的见解。