On Data Summarization for Machine Learning in Multi-organization Federations

We consider learning of submodular functions from data. These functions are important in machine learning and have a wide range of applications, e.g. data summarization, feature selection and active learning. Despite their combinatorial nature, submodular functions can be maximized approximately with strong theoretical guarantees in polynomial time. Typically, learning the submodular function and optimization of that function are treated separately, i.e. the function is first learned using a proxy objective and subsequently maximized. In contrast, we show how to perform learning and optimization jointly. By interpreting the output of greedy maximization algorithms as distributions over sequences of items and smoothening these distributions, we obtain a differentiable objective. In this way, we can differentiate through the maximization algorithms and optimize the model to work well with the optimization algorithm. We theoretically characterize the error made by our approach, yielding insights into the tradeoff of smoothness and accuracy. We demonstrate the effectiveness of our approach for jointly learning and optimizing on synthetic maxcut data, and on real world applications such as product recommendation and image collection summarization.

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Scalable machine learning computing a data summarization matrix with a parallel array DBMS

Distributed and Parallel Databases ◽

10.1007/s10619-018-7229-1 ◽

2018 ◽

Vol 37 (3) ◽

pp. 329-350 ◽

Cited By ~ 1

Author(s):

Carlos Ordonez ◽

Yiqun Zhang ◽

S. Lennart Johnsson

Keyword(s):

Machine Learning ◽

Data Summarization ◽

Parallel Array

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Efficient machine learning on data science languages with parallel data summarization

Data & Knowledge Engineering ◽

10.1016/j.datak.2021.101930 ◽

2021 ◽

Vol 136 ◽

pp. 101930

Author(s):

Sikder Tahsin Al-Amin ◽

Carlos Ordonez

Keyword(s):

Machine Learning ◽

Data Science ◽

Data Summarization ◽

Parallel Data ◽

Efficient Machine

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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