Search Results for author: William Paul

Found 14 papers, 3 papers with code

Achieving Utility, Fairness, and Compactness via Tunable Information Bottleneck Measures

no code implementations20 Jun 2022 Adam Gronowski, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina

Designing machine learning algorithms that are accurate yet fair, not discriminating based on any sensitive attribute, is of paramount importance for society to accept AI for critical applications.

Fairness Image Classification +1

Renyi Fair Information Bottleneck for Image Classification

no code implementations9 Mar 2022 Adam Gronowski, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina

We develop a novel method for ensuring fairness in machine learning which we term as the Renyi Fair Information Bottleneck (RFIB).

Classification Fairness +1

Robustness and Adaptation to Hidden Factors of Variation

no code implementations3 Mar 2022 William Paul, Philippe Burlina

We tackle here a specific, still not widely addressed aspect, of AI robustness, which consists of seeking invariance / insensitivity of model performance to hidden factors of variations in the data.

Data Augmentation

Adaptation and Generalization for Unknown Sensitive Factors of Variations

no code implementations28 Jul 2021 William Paul, Philippe Burlina

We also demonstrate how adaptation to real factors of variations can be performed in the semi-supervised case where some target factor labels are known, via automated intervention selection.

Domain Generalization Fairness

Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods

no code implementations4 Mar 2021 William Paul, Yinzhi Cao, Miaomiao Zhang, Phil Burlina

Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations of regulations governing the use of medical data and threaten to compromise their effective deployment in the clinic.

TARA: Training and Representation Alteration for AI Fairness and Domain Generalization

no code implementations11 Dec 2020 William Paul, Armin Hadzic, Neil Joshi, Fady Alajaji, Phil Burlina

Our experiments also demonstrate the ability of these novel metrics in assessing the Pareto efficiency of the proposed methods.

Domain Generalization Fairness +1

AI Progress in Skin Lesion Analysis

no code implementations28 Sep 2020 Philippe M. Burlina, William Paul, Phil A. Mathew, Neil J. Joshi, Alison W. Rebman, John N. Aucott

We examine progress in the use of AI for detecting skin lesions, with particular emphasis on the erythema migrans rash of acute Lyme disease, and other lesions, such as those from conditions like herpes zoster (shingles), tinea corporis, erythema multiforme, cellulitis, insect bites, or tick bites.

Least $k$th-Order and Rényi Generative Adversarial Networks

no code implementations3 Jun 2020 Himesh Bhatia, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina

Another novel GAN generator loss function is next proposed in terms of R\'{e}nyi cross-entropy functionals with order $\alpha >0$, $\alpha\neq 1$.

Fairness

Addressing Artificial Intelligence Bias in Retinal Disease Diagnostics

no code implementations28 Apr 2020 Philippe Burlina, Neil Joshi, William Paul, Katia D. Pacheco, Neil M. Bressler

Using novel generative methods for addressing missing subpopulation training data (DR-referable darker-skin) achieved instead accuracy, for lighter-skin, of 72. 0% (65. 8%, 78. 2%), and for darker-skin, of 71. 5% (65. 2%, 77. 8%), demonstrating closer parity (delta=0. 5%) in accuracy across subpopulations (Welch t-test t=0. 111, P=. 912).

Domain Generalization

Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection

no code implementations25 Feb 2020 William Paul, I-Jeng Wang, Fady Alajaji, Philippe Burlina

Our work focuses on unsupervised and generative methods that address the following goals: (a) learning unsupervised generative representations that discover latent factors controlling image semantic attributes, (b) studying how this ability to control attributes formally relates to the issue of latent factor disentanglement, clarifying related but dissimilar concepts that had been confounded in the past, and (c) developing anomaly detection methods that leverage representations learned in (a).

Anomaly Detection Disentanglement +2

Ray: A Distributed Framework for Emerging AI Applications

4 code implementations16 Dec 2017 Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael. I. Jordan, Ion Stoica

To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state.

reinforcement-learning

Real-Time Machine Learning: The Missing Pieces

2 code implementations11 Mar 2017 Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael. I. Jordan, Ion Stoica

Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making.

BIG-bench Machine Learning Decision Making

Cannot find the paper you are looking for? You can Submit a new open access paper.