Every Query Counts: Analyzing the Privacy Loss of Exploratory Data Analyses

Explaining Aggregates for Exploratory Analytics

Analysts wishing to explore multivariate data spaces, typically pose queries involving selection operators, i.e., range or radius queries, which define data subspaces of possible interest and then use aggregation functions, the results of which determine their exploratory analytics interests. However, such aggregate query (AQ) results are simple scalars and as such, convey limited information about the queried subspaces for exploratory analysis. We address this shortcoming aiding analysts to explore and understand data subspaces by contributing a novel explanation mechanism coined XAXA: eXplai…

可查询自组织映射：用于拓扑驱动数据探索的数据库抽象

Queryable Self-Organizing Maps: A Database Abstraction for Topology-Driven Data Exploration

研究针对现代数据系统中SOM与关系数据脱节问题，引入可查询数据映射概念，通过MapDB原型实现，使SOM工件可查询，经实验验证其在适度规模下训练可行、查询具交互性且为探索性SQL提供有意义目标。

Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning

Saliency maps are frequently used to support explanations of the behavior of deep reinforcement learning (RL) agents. However, a review of how saliency maps are used in practice indicates that the derived explanations are often unfalsifiable and can be highly subjective. We introduce an empirical approach grounded in counterfactual reasoning to test the hypotheses generated from saliency maps and assess the degree to which they correspond to the semantics of RL environments. We use Atari games, a common benchmark for deep RL, to evaluate three types of saliency maps. Our results show the exten…

AI引导的搜索体验加速器

AI Guided Accelerator For Search Experience

本工作提出一个框架，通过挖掘eBay用户查询轨迹建模过渡查询，并利用大语言模型生成意图保持的替代查询，以增强电子商务搜索中的相关搜索和意图聚类轮播，实证显示转化和参与度提升。

Offline Inverse Reinforcement Learning

The objective of offline RL is to learn optimal policies when a fixed exploratory demonstrations data-set is available and sampling additional observations is impossible (typically if this operation is either costly or rises ethical questions). In order to solve this problem, off the shelf approaches require a properly defined cost function (or its evaluation on the provided data-set), which are seldom available in practice. To circumvent this issue, a reasonable alternative is to query an expert for few optimal demonstrations in addition to the exploratory data-set. The objective is then to l…

Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data

As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and attacks. This paper studies adversarial machine learning in the practical case when there are rate limitations on API calls. The adversary launches an exploratory (inference) attack by querying the API of an online machine learning system (in particular, a classifier) with input data samples, collecting returned labels to build up the training data, and training an adversarial classifier that is functionally equival…