machine learning (ml)

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Machine Learning

Stats Machine Learning

Continual Learning in Practice

This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Le

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Machine Learning

Networking and Internet Architecture

Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON)

Machine learning (ML) is included in Self-organizing Networks (SONs) that are key drivers for enhancing the Operations, Administration, and Maintenance (OAM) activities. It is included in the 5G Stand

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Instrumentation and Methods for Astrophysics

Cosmology and Nongalactic Astrophysics

The Role of Machine Learning in the Next Decade of Cosmology

In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but wi

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Computer Vision

Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that

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Instrumentation and Methods for Astrophysics

Earth and Planetary Astrophysics

Stats Machine Learning

Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of

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Machine Learning

Computers and Society

Human-Computer Interaction

Artificial Intelligence

GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of

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Machine Learning

Distributed

Federated Learning: Opportunities and Challenges

This report discusses the opportunities and challenges in federated learning (fl), in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server.

Given the growing interest in the fl domain, this report discusses the opportunities and challenges in federated learning, in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server.

Machine Learning

Computer Vision

Artificial Intelligence

Geophysics

Machine Learning for the Geosciences: Challenges and Opportunities

Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, machine learning (M

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Econometrics

Stats Machine Learning

Machine Learning Methods Economists Should Know About

We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods and settings between the ML literature and th

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Machine Learning

Artificial Intelligence

Stats Machine Learning

Exploiting Explainable Machine Learning

Proposed Guidelines for the Responsible Use of Explainable Machine Learning

Explainable machine learning (ml) enables human learning from ml, humanappeal of automated model decisions, regulatory compliance, and security audits of machine learning models.

Explainable machine learning has been implemented in numerous open source and commercial packages and explainable ml is also an important, mandatory, or embedded aspect of commercial predictive modeling in industries like financial services.

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Machine Learning

Artificial Intelligence

Cryptography and Security

Differential Privacy in Machine Learning

Differential Privacy: What is all the noise about?

Differential privacy is a formal definition of privacy that provides rigorous guarantees against risks of privacy breaches during data processing.

It makes no assumptions about the knowledge or computational power of adversaries, and provides an interpretable, quantifiable and composable formalism.

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Machine Learning

Artificial Intelligence

Stats Machine Learning

Detecting Unexpected Performance in Streaming Machine Learning Models

Rethinking Streaming Machine Learning Evaluation

We discuss how the nature of streaming machine learning problems introduces new real-world challenges (e.g., delayed arrival of labels) and recommend additional metrics to assess streaming machine learning performance.

Machine Learning

Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy

Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users understand not only w

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Machine Learning

Distributed

Federated Learning on Edge Computing

On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge

Federated learning (fl) was originally developed to improve communication efficiency compared to conventional distributed machine learning (ml).

The original fl assumes a central aggregation server to aggregate locally optimized parameters and might bring reliability and latency issues.

Machine Learning

Databases

Tensor Relational Algebra for Machine Learning System Design

Machine learning (ML) systems have to support various tensor operations. However, such ML systems were largely developed without asking: what are the foundational abstractions necessary for building m

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Machine Learning

Software Engineering

Trustworthy Machine Learning

Empowering the trustworthiness of ML-based critical systems through engineering activities

This paper reviews the entire engineering process of trustworthy machine learning algorithms designed to equip critical systems with advanced analytics and decision functions.

We start from the fundamental principles of machine learning and describe the core elements conditioning its trust, particularly through its design : namely domain specification, data engineering, design of the mlalgorithms, their implementation, evaluation and deployment.

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Machine Learning

Optimization and Control

Artificial Intelligence

Stats Machine Learning

Fairness in Machine Learning

Long Term Fairness for Minority Groups via Performative Distributionally Robust Optimization

We identify four key shortcomings of several formal fairness criteria which provide formal definitions of what it means for an ml model to be fair.

We identify four key shortcomings of these formal fairness criteria, and aim to help to address them by extending performative prediction to include a distributionally robust objective.

Materials Science

Development of exchange-correlation functionals assisted by machine learning

With the recent rapid progress in the machine-learning (ML), there have emerged a new approach using the ML methods to the exchange-correlation functional of density functional theory. In this chapter

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