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AutonoML: Towards an Integrated Framework for Autonomous Machine Learning

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arXiv 2020年

作者： Kedziora, David Jacob Musial, Katarzyna Gabrys, Bogdan Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney SydneyNSW2007 Australia

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms. Central to this drive is the appeal of engineering a computational system that both discovers and deploys high-performance solutions to arbitrary ML problems with minimal human interaction. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. However, these ambitions are unlikely to be achieved in a robust manner without the broader synthesis of various mechanisms and theoretical frameworks, which, at the present time, remain scattered across numerous research threads. Accordingly, this review seeks to motivate a more expansive perspective on what constitutes an automated/autonomous ML system, alongside consideration of how best to consolidate those elements. In doing so, we survey developments in the following research areas: hyperparameter optimisation, multi-component models, neural architecture search, automated feature engineering, meta-learning, multi-level ensembling, dynamic adaptation, multi-objective evaluation, resource constraints, flexible user involvement, and the principles of generalisation. We also develop a conceptual framework throughout the review, augmented by each topic, to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system. Ultimately, we conclude that the notion of architectural integration deserves more discussion, without which the field of automated ML risks stifling both its technical advantages and general uptake. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

Foundations and modelling of dynamic networks using dynamic graph neural networks: A survey

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arXiv 2020年

作者： Skarding, Joakim Gabrys, Bogdan Musial, Katarzyna Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney SydneyNSW2007 Australia

Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

The Resume Paradox: Greater Language Differences, Smaller Pay Gaps

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arXiv 2023年

作者： Minot, Joshua R. Maier, Marc Demarest, Bradford Cheney, Nicholas Danforth, Christopher M. Dodds, Peter Sheridan Frank, Morgan R. Computational Story Lab The University of Vermont BurlingtonVT05401 United States Vermont Complex Systems Center The University of Vermont BurlingtonVT05401 United States Department of Computer Science The University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States Neurobotics Lab The University of Vermont BurlingtonVT05401 United States Department of Mathematics and Statistics The University of Vermont BurlingtonVT05401 United States Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15260 United States Digital Economy Lab Stanford University StanfordCA94305 United States Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

Over the past decade, the gender pay gap has remained steady with women earning 84 cents for every dollar earned by men on average. Many studies explain this gap through demand-side bias in the labor market represented through employers' job postings. However, few studies analyze potential bias from the worker supply-side. Here, we analyze the language in millions of US workers' resumes to investigate how differences in workers' self-representation by gender compare to differences in earnings. Across US occupations, language differences between male and female resumes correspond to 11% of the variation in gender pay gap. This suggests that females' resumes that are semantically similar to males' resumes may have greater wage parity. However, surprisingly, occupations with greater language differences between male and female resumes have lower gender pay gaps. A doubling of the language difference between female and male resumes results in an annual wage increase of $2,797 for the average female worker. This result holds with controls for gender-biases of resume text and we find that per-word bias poorly describes the variance in wage gap. The results demonstrate that textual data and self-representation are valuable factors for improving worker representations and understanding employment inequities. Copyright © 2023, The Authors. All rights reserved.

关键词： Employment

Hurricanes and hashtags: Characterizing online collective attention for natural disasters

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arXiv 2020年

作者： Arnold, Michael V. Dewhurst, David Rushing Alshaabi, Thayer Minot, Joshua R. Adams, Jane L. Danforth, Christopher M. Dodds, Peter Sheridan MassMutual Center of Excellence in Complex Systems and Data Science Computational Story Lab Vermont Complex Systems Center Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States MassMutual Data Science BostonMA02110 United States

We study collective attention paid towards hurricanes through the lens of n-grams on Twitter, a social media platform with global reach. Using hurricane name mentions as a proxy for awareness, we find that the exogenous temporal dynamics are remarkably similar across storms, but that overall collective attention varies widely even among storms causing comparable deaths and damage. We construct 'hurricane attention maps' and observe that hurricanes causing deaths on (or economic damage to) the continental United States generate substantially more attention in English language tweets than those that do not. We find that a hurricanes Saffir-Simpson wind scale category assignment is strongly associated with the amount of attention it receives. Higher category storms receive higher proportional increases of attention per proportional increases in number of deaths or dollars of damage, than lower category storms. The most damaging and deadly storms of the 2010s, Hurricanes Harvey and Maria, generated the most attention and were remembered the longest, respectively. On average, a category 5 storm receives 4.6 times more attention than a category 1 storm causing the same number of deaths and economic damage. Copyright © 2020, The Authors. All rights reserved.

关键词： Hurricanes

Compressing network populations with modal networks reveals structural diversity

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arXiv 2022年

作者： Kirkley, Alec Rojas, Alexis Rosvall, Martin Young, Jean-Gabriel Institute of Data Science University of Hong Kong Hong Kong Department of Urban Planning and Design University of Hong Kong Hong Kong Urban Systems Institute University of Hong Kong Hong Kong Department of Computer Science University of Helsinki Helsinki00100 Finland Integrated Science Lab Department of Physics Umea University UmeaSE-901 87 Sweden Department of Mathematics and Statistics University of Vermont BurlingtonVT United States Vermont Complex Systems Center University of Vermont BurlingtonVT United States

Analyzing relational data consisting of multiple samples or layers involves critical challenges: How many networks are required to capture the variety of structures in the data? And what are the structures of these representative networks? We describe efficient nonparametric methods derived from the minimum description length principle to construct the network representations automatically. The methods input a population of networks or a multilayer network measured on a fixed set of nodes and output a small set of representative networks together with an assignment of each network sample or layer to one of the representative networks. We identify the representative networks and assign network samples to them with an efficient Monte Carlo scheme that minimizes our description length objective. For temporally ordered networks, we use a polynomial time dynamic programming approach that restricts the clusters of network layers to be temporally contiguous. These methods recover planted heterogeneity in synthetic network populations and identify essential structural heterogeneities in global trade and fossil record networks. Our methods are principled, scalable, parameter-free, and accommodate a wide range of data, providing a unified lens for exploratory analyses and preprocessing large sets of network samples. © 2022, CC BY.

关键词： Network layers

Sentiment and structure in word co-occurrence networks on Twitter

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arXiv 2021年

作者： Fudolig, Mikaela Irene Alshaabi, Thayer Arnold, Michael V. Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT United States Advanced Bioimaging Center UC Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States

We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks, where nodes in the network are the words, and the weight of an edge is the number of tweets in the corpus for which the two connected words co-occur. In particular, we consider tweets with hashtags #imwithher and #crookedhillary, both relating to Hillary Clinton’s presidential bid in 2016. We then analyze the network properties in conjunction with the word scores by comparing with null models to separate the effects of the network structure and the score distribution. Neutral words are found to be dominant and most words, regardless of polarity, tend to co-occur with neutral words. We do not observe any score homophily among positive and negative words. However, when we perform network backboning, community detection results in word groupings with meaningful narratives, and the happiness scores of the words in each group correspond to its respective theme. Thus, although we observe no clear relationship between happiness scores and co-occurrence at the node or edge level, a community-centric approach can isolate themes of competing sentiments in a corpus. © 2021, CC BY.

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AI exposure predicts unemployment risk

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arXiv 2023年

作者： Frank, Morgan R. Ahn, Yong-Yeol Moro, Esteban Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15216 United States Digital Economy Lab Institute for Human-Centered Artificial Intelligence Stanford University StanfordCA94305 United States Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Connection Science Massachusetts Institute of Technology CambridgeMA United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Indiana University Network Science Institute Indiana University BloomingtonIN United States Department of Mathematics & GISC Universidad Carlos III de Madrid Leganes28911 Spain Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States

Is artificial intelligence (AI) disrupting jobs and creating unemployment? Despite many attempts to quantify occupations' exposure to AI, inconsistent validation obfuscates the relative benefits of each approach. A lack of disaggregated labor outcome data, including unemployment data, further exacerbates the issue. Here, we assess which models of AI exposure predict job separations and unemployment risk using new occupation-level unemployment data by occupation from each US state's unemployment insurance office spanning 2010 through 2020. Although these AI exposure scores have been used by governments and industry, we find that individual AI exposure models are not predictive of unemployment rates, unemployment risk, or job separation rates. However, an ensemble of those models exhibits substantial predictive power suggesting that competing models may capture different aspects of AI exposure that collectively account for AI's variable impact across occupations, regions, and time. Our results also call for dynamic, context-aware, and validated methods for assessing AI exposure. Interactive visualizations for this study are available at https://***/~mrfrank/uiRiskDemo/. © 2023, CC BY.

关键词： Visualization

The sleep loss insult of spring daylight savings in the US is absorbed by Twitter users within 48 hours

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arXiv 2020年

作者： Linnell, Kelsey Alshaabi, Thayer McAndrew, Thomas Lim, Jeanie Dodds, Peter Sheridan Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems & Data Science University of Vermont Department of Mathematics & Statistics University of Vermont Department of Biostatistics & Epidemiology School of Public Health & Health Sciences University of Massachusetts at Amherst MassMutual Data Science

Sleep loss has been linked to heart disease, diabetes, cancer, and an increase in accidents, all of which are among the leading causes of death in the United States. Population-scale sleep studies have the potential to advance public health by helping to identify at-risk populations, changes in collective sleep patterns, and to inform policy change. Prior research suggests other kinds of health indicators such as depression and obesity can be estimated using social media activity. However, the inability to effectively measure collective sleep with publicly available data has limited large-scale academic studies. Here, we investigate the passive estimation of sleep loss through a proxy analysis of Twitter activity profiles. We use "Spring Forward" events, which occur at the beginning of Daylight Savings Time in the United States, as a natural experimental condition to estimate spatial differences in sleep loss across the United States. On average, peak Twitter activity occurs roughly 45 minutes later on the Sunday following Spring Forward. By Monday morning however, activity curves are realigned with the week before, suggesting that at least on Twitter, the lost hour of early Sunday morning has been quickly absorbed. Copyright © 2020, The Authors. All rights reserved.

关键词： Sleep research

Computational Paremiology: Charting the temporal, ecological dynamics of proverb use in books, news articles, and tweets

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arXiv 2021年

作者： Davis, Ethan Danforth, Christopher M. Mieder, Wolfgang Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05401 United States Department of German and Russian University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States

Proverbs are an essential component of language and culture, and though much attention has been paid to their history and currency, there has been comparatively little quantitative work on changes in the frequency with which they are used over time. With wider availability of large corpora reflecting many diverse genres of documents, it is now possible to take a broad and dynamic view of the importance of the proverb. Here, we measure temporal changes in the relevance of proverbs within three corpora, differing in kind, scale, and time frame: Millions of books over centuries;hundreds of millions of news articles over twenty years;and billions of tweets over a decade. We find that proverbs present heavy-tailed frequency-of-usage rank distributions in each venue;exhibit trends reflecting the cultural dynamics of the eras covered;and have evolved into contemporary forms on social media. Copyright © 2021, The Authors. All rights reserved.

关键词： History