CleanML
It is widely recognized that the data quality affects machine learning (ML) model performances, and data scientists spend considerable amount of time on data cleaning before model training. However, to date, there does not exist a rigorous study on how exactly does cleaning affect ML — ML community usually focuses on the effects of specific types of noises of certain distributions (e.g., mislabels) on certain ML models, while database (DB) community has been mostly studying the problem of data cleaning alone without considering how data is consumed by downstream analytics. We propose the CleanML benchmark that systematically investigates the impact of data cleaning on downstream ML models. The CleanML benchmark currently includes 13 real-world datasets with real errors, five common error types, and seven different ML models. To ensure that our findings are statistically significant, CleanML carefully controls the randomness in ML experiments using statistical hypothesis testing, and also uses the Benjamini-Yekutieli (BY) procedure to control potential false discoveries due to many hypotheses in the benchmark. We obtain many interesting and non-trivial insights, and identify multiple open research directions. We also release the benchmark and hope to invite future studies on the important problems of joint data cleaning and ML. …

Galam Model
Consider a community where initially, each individual is positive or negative regarding a reform proposal. In each round, individuals gather randomly in fixed rooms of different sizes, and all individuals in a room agree on the majority opinion in the room (with ties broken in favor of the negative opinion). The Galam model—introduced in statistical physics, specifically sociophysics—approximates this basic random process.
Phase Transition in Democratic Opinion Dynamics …

Target-Based Temporal Difference Learning
The use of target networks has been a popular and key component of recent deep Q-learning algorithms for reinforcement learning, yet little is known from the theory side. In this work, we introduce a new family of target-based temporal difference (TD) learning algorithms and provide theoretical analysis on their convergences. In contrast to the standard TD-learning, target-based TD algorithms maintain two separate learning parameters-the target variable and online variable. Particularly, we introduce three members in the family, called the averaging TD, double TD, and periodic TD, where the target variable is updated through an averaging, symmetric, or periodic fashion, mirroring those techniques used in deep Q-learning practice. We establish asymptotic convergence analyses for both averaging TD and double TD and a finite sample analysis for periodic TD. In addition, we also provide some simulation results showing potentially superior convergence of these target-based TD algorithms compared to the standard TD-learning. While this work focuses on linear function approximation and policy evaluation setting, we consider this as a meaningful step towards the theoretical understanding of deep Q-learning variants with target networks. …

Concept Learning
Concept learning, also known as category learning, concept attainment, and concept formation, is defined by Bruner, Goodnow, & Austin (1967) as ‘the search for and listing of attributes that can be used to distinguish exemplars from non exemplars of various categories’. More simply put, concepts are the mental categories that help us classify objects, events, or ideas, building on the understanding that each object, event, or idea has a set of common relevant features. Thus, concept learning is a strategy which requires a learner to compare and contrast groups or categories that contain concept-relevant features with groups or categories that do not contain concept-relevant features. Concept learning also refers to a learning task in which a human or machine learner is trained to classify objects by being shown a set of example objects along with their class labels. The learner simplifies what has been observed by condensing it in the form of an example. This simplified version of what has been learned is then applied to future examples. Concept learning may be simple or complex because learning takes place over many areas. When a concept is difficult, it is less likely that the learner will be able to simplify, and therefore will be less likely to learn. Colloquially, the task is known as learning from examples. Most theories of concept learning are based on the storage of exemplars and avoid summarization or overt abstraction of any kind. …