FODS

An AutoML and interpretability are both fundamental to the successful uptake of machine learning by non-expert end users. The former will lower barriers to entry and unlock potent new capabilities that are out of reach when working with ad-hoc models, ...

In this era of big data, not only the large amount of data keeps motivating distributed computing, but concerns on data privacy also put forward the emphasis on distributed learning. To conduct feature selection and to control the false discovery rate ...

We study the problem of merging decision trees: Given k decision trees $T_1,T_2,T_3...,T_k$, we merge these trees into one super tree T with (often) much smaller size. The resultant super tree T, which is an integration of k decision trees with each ...

Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative approach to ...

Ensemble methods based on trees, such as Random Forests, AdaBoost and gradient boosting, are widely recognized as among the best off-the-shelf classifiers: they typically achieve state-of-the-art accuracy in many problems with little effort in tuning ...

In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments. In such high-stakes tasks, false discoveries may lead investigators astray. These applications would therefore ...

Differentially private data releases are often required to satisfy a set of external constraints that reflect the legal, ethical, and logical mandates to which the data curator is obligated. The enforcement of constraints, when treated as post-...

A central goal of algorithmic fairness is to build systems with fairness properties that compose gracefully. A major effort and step towards this goal in data science has been the development offair representations which guarantee demographic parity ...

Ecological forecasts are used to inform decisions that can havesignificant impacts on the lives of individuals and on the healthof ecosystems. These forecasts, or models, embody the ethics oftheir creators as well as many seemingly arbitrary ...

This talk will describe the dramatic creation of the COVID-19 Open Research Dataset (CORD-19) at the Allen Institute for AI and the broad range of efforts, both inside and outside of the Semantic Scholar project, to garner insights into COVID-19 and its ...

Data sets in the form of binary matrices are ubiquitous across scientific domains, and researchers are often interested in identifying and quantifying noteworthy structure. One approach is to compare the observed data to that which might be obtained ...

We study the detection and the reconstruction of a large very dense subgraph in a social graph with n nodes and m edges given as a stream of edges, when the graph follows a power law degree distribution, in the regime when $m=O(n. łog n)$. A subgraph is ...

In recent years, distributed optimization is proven to be an effective approach to accelerate training of large scale machine learning models such as deep neural networks. With the increasing computation power of GPUs, the bottleneck of training speed ...

Stochastic Lipschitz bandit algorithms balance exploration and exploitation, and have been used for a variety of important task domains. In this paper, we present a framework for Lipschitz bandit methods that adaptively learns partitions of context- and ...

We consider the problem of finding Nash equilibrium for two-player turn-based zero-sum games. Inspired by the AlphaGo Zero (AGZ) algorithm, we develop a Reinforcement Learning based approach. Specifically, we propose Explore-Improve-Supervise (EIS) ...

Probabilistic programming is perfectly suited to reliable and transparent data science, as it allows the user to specify their models in a high-level language without worrying about the complexities of how to fit the models. Static analysis of ...

CAPTCHAs are widely deployed for bot detection. Many CAPTCHAs are based on visual perception tasks such as text and objection classification. However, they are under serious threat from advanced visual perception technologies based on deep convolutional ...

This paper develops an inferential framework for high-dimensional linear mixed effect models. Such models are suitable, e.g., when collecting n repeated measurements for M subjects. We consider a scenario where the number of fixed effects p is large (...

Many real-world applications involve longitudinal data, consisting of observations of several variables, where different subsets of variables are sampled at irregularly spaced time points. We introduce the Longitudinal Gaussian Process Latent Variable ...