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Machine Learning

Distributed

Automap: A Platform-Independent Partitioner for Data, Model, and Pipeline Parallelization

Automap: Towards Ergonomic Automated Parallelism for ML Models

We present the prototype of an automated partitioner that enables parallelism that encompasses data parallelism and parameter/activation sharding.

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

Computer Vision

In the Wild: From ML Models to Pragmatic ML Systems

Enabling robust intelligence in the wild entails learning systems that offer uninterrupted inference while affording sustained training, with varying amounts of data & supervision. Such a pragmatic ML

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Machine Learning

Artificial Intelligence

Rethinking and Recomputing the Value of ML Models

In this paper, we argue that the way we have been training and evaluating ML models has largely forgotten the fact that they are applied in an organization or societal context as they provide value to

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Machine Learning

Computers and Society

Artificial Intelligence

Making ML models fairer through explanations: the case of LimeOut

Algorithmic decisions are now being used on a daily basis, and based on Machine Learning (ML) processes that may be complex and biased. This raises several concerns given the critical impact that bias

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Machine Learning

Software Engineering

Feature Interactions on Steroids: On the Composition of ML Models

The lack of specifications is a key difference between traditional software engineering and machine learning. We discuss how it drastically impacts how we think about divide-and-conquer approaches to

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Machine Learning

Computer Vision

Artificial Intelligence

Reconstructing Training Data from Diverse ML Models by Ensemble Inversion

Model Inversion (MI), in which an adversary abuses access to a trained Machine Learning (ML) model attempting to infer sensitive information about its original training data, has attracted increasing

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Chemical Physics

Predicting DFT-quality barrier heights from semiempirical quantum-mechanical calculations using machine learning

Barrier height prediction by machine learning correction of semiempirical calculations

Different machine learning (ml) models are proposed in the present work to predict barrier heights (bhs) from semiempirical quantum-mechanical calculations.

The obtained mean absolute errors (maes) are similar or slightly better than previous models considering the same number of data points.

Machine Learning

Computers and Society

Cryptography and Security

Software Engineering

Advancing the Research and Development of Assured Artificial Intelligence and Machine Learning Capabilities

Artificial intelligence (AI) and machine learning (ML) have become increasingly vital in the development of novel defense and intelligence capabilities across all domains of warfare. An adversarial AI

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Machine Learning

Computers and Society

Computation and Language

Transparency and Accountability in Machine Learning

Accountable and Explainable Methods for Complex Reasoning over Text

This thesis presents my research that expands our collective knowledge in the areas of accountability and transparency of machine learning (ml) models developed for complex reasoning tasks over text.

Accountabilitymethods, such as adversarial attacks and diagnostic datasets, expose vulnerabilities of ml models that could lead to malicious manipulations or systematic faults in their predictions.

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Machine Learning

Quantum Physics

Quark: A Gradient-Free Quantum Learning Framework

Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks

We introduce a gradient-free quantum learning framework that optimizes quantum machine learning (qml) models using quantumoptimization.

The method does not rely on gradient computation and therefore avoids barren plateau (i.e., gradient vanishing) and frequent classical-quantum interactions.

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Machine Learning

Human-Computer Interaction

RadialNet Chart: A Visual Approach to Comparing Machine Learning Models with Dynamic Number of Features

Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation

This paper proposes a novel visualisation approach to compare generated substantial amounts of machine learning (ml) models trained with a different number of features of a given data set while revealing implicit dependent relations such as feature importance for ml explanations.

The dependence of ml models with dynamic number of features is encoded into the structure of visualisation, where the dependence of ml models and their dependent features are directly revealed from related line connections.

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Machine Learning

Computers and Society

Cryptography and Security

Monitoring Machine Learning Performance Under Data Issues

AI Total: Analyzing Security ML Models with Imperfect Data in Production

Development of new machine learning models is typically done on manuallycurated data sets, making them unsuitable for evaluating the models'performance during operations, where the evaluation needs to be performed automatically on incoming streams of new data.

With this in mind, we developed a web-based visualization system that allows the users to quickly gather headline performance numbers while maintaining confidence that the underlying data pipeline is functioning properly.

Machine Learning

Stats Machine Learning

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust. Explainability, or the ability of an ML model to justify its outcomes and assist clin

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Machine Learning

Computer Science and Game Theory

Strategic Repeated Interactions between a Machine Learning Model and an End-User

A Game-theoretic Understanding of Repeated Explanations in ML Models

This paper formally models the strategic repeated interactions between a machine learning (ml) model and associated explanationmethod, and an end-user who is seeking a prediction/label and its explanationfor a query/input, by means of game theory.

In this game, a malicious end-user must strategically decide when to stop querying and attempt to compromise the system, while the system must strategically decide how much information (in the form of noisy explanations) it should share with the end-user and when to stopsharing, all without knowing the type (honest/malicious) of the end-user.

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Artificial Intelligence

Software Engineering

A Taxonomy of Quality Assurance Challenges for Machine Learning Software Applications

Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases

This paper conducts an in-depth literaturereview of a large volume of research papers that focused on the qualityassurance of machine learning (ml) models into software systems.

We developed a taxonomy of quality assurance issues of machine learning software applications (mlsas) by mapping the various ml adoption challenges across different phases of software development life cycles (sdlc).

Machine Learning

Cryptography and Security

Stats Machine Learning

Stealing Machine Learning Models via Prediction APIs

Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being de

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Machine Learning

Computers and Society

Artificial Intelligence

Reducing Unintended Bias of ML Models on Tabular and Textual Data

Unintended biases in machine learning (ML) models are among the major concerns that must be addressed to maintain public trust in ML. In this paper, we address process fairness of ML models that consi

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Materials Science

Disordered Systems and Neural Networks

Predicting the magnetic properties of FeMGs using machine learning

Machine learning prediction of magnetic properties of Fe-based metallic glasses considering local structures

Magnetism prediction is of great significance for metallic glasses, which have shown great commercial value.

In this work, machine learning (ml) models learned from a large amount of experimental data were trained based on extreme gradient boosting (xgboost), artificial neural networks (ann), and random forest to predict the magnetic properties of metallic glasses.

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