Optimizing fairness and utility in healthcare machine learning models

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Laser material processing (LMP) has revolutionized modern manufacturing, emerging as a cornerstone technology valued for its precision, speed, and versatility, especially in high-stakes sectors like aerospace, automotive, and medical device production. Des ...

The unifying theme of this thesis are decision trees. Given a boolean function

f

and an input

x

, a decision tree computes the value

f(x)

by repeatedly querying bits of

x

. Our work is two-fold. On one hand, we investigate direct sums and other relat ...

The sum-of-squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method t ...

Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free metho ...

The real-time optimization scheme “modifier adaptation” (MA) has been developed to enforce steady-state plant optimality in the presence of model uncertainty. The key feature of MA is its ability to locally modify the model by adding bias and gradient corr ...

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

By 'social learning,' in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents' decisions evolve dynamically through interactions with neighbors and the environment. The study of social learni ...

We introduce an algorithm to reconstruct a mesh from discrete samples of a shape's Signed Distance Function (SDF). A simple geometric reinterpretation of the SDF lets us formulate the problem through a point cloud, from which a surface can be extracted wit ...

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these da ...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used i ...

In this article, we study the problem of Byzantine fault-tolerance in a federated optimization setting, where there is a group of agents communicating with a centralized coordinator. We allow up to

f

Byzantine-faulty agents, which may not follow a prescr ...

Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem parameters affec ...

Despite the high number of investments for data-based models in the expansion of Industry 4.0, too little effort has been made to ensure the maintenance of those models. In a data-streaming environment, data-based models are subject to concept drifts. A co ...

In medical image segmentation, supervised deep networks' success comes at the cost of requiring abundant labeled data. While asking domain experts to annotate only one or a few of the cohort's images is feasible, annotating all available images is impracti ...

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed ...

Soft Magnetic Composites (SMCs) demonstrate excellent potentials for electromagnetic applications. The isotropy is one critical feature in designing the composite. This work presents a shape optimization algorithm for SMCs subject to isotropy and volume co ...

The research community of dialog generation has been interested in incorporating emotional information into the design of open-domain dialog systems ever since neural networks (sequence-to-sequence models in particular) were adopted for modeling dialogs. T ...

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

Optimizing fairness and utility in healthcare machine learning models

Chattez avec Graph Search

Toward Autonomous Laser Manufacturing: Solving Key Challenges in Optimization, Monitoring, and Control

The Power of Decision Trees for Composition and Search Problems

Theory and applications of the sum-of-squares technique

Neural Redshift: Random Networks are not Random Functions

On speeding-up modifier-adaptation schemes for real-time optimization

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

Socially Intelligent Networks: A framework for decision making over graphs

Statistical mechanics of deep learning

Reach For the Arcs: Reconstructing Surfaces from SDFs via Tangent Points

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" -Part 2

Validating functional redundancy with mixed generative adversarial networks

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Data-based model maintenance in the era of industry 4.0: A methodology

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models

Level-set-based Shape Optimization on Soft Magnetic Composites with Isotropy Constraint

Towards Empathetic Open-Domain Chatbots

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging