Search Results for author:
Found 15 papers, 0 papers with code
On The Fairness Impacts of Hardware Selection in Machine Learning
In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data.
On the Fairness Impacts of Private Ensembles Models
The Private Aggregation of Teacher Ensembles (PATE) is a machine learning framework that enables the creation of private models through the combination of multiple "teacher" models and a "student" model.
Personalized Privacy Auditing and Optimization at Test Time
A number of learning models used in consequential domains, such as to assist in legal, banking, hiring, and healthcare decisions, make use of potentially sensitive users' information to carry out inference.
Fairness Increases Adversarial Vulnerability
The remarkable performance of deep learning models and their applications in consequential domains (e. g., facial recognition) introduces important challenges at the intersection of equity and security.
Pruning has a disparate impact on model accuracy
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy.
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles
A key characteristic of the proposed model is to enable the adoption of off-the-selves and non-private fair models to create a privacy-preserving and fair model.
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
This paper surveys recent work in the intersection of differential privacy (DP) and fairness.
Differentially Private Empirical Risk Minimization under the Fairness Lens
However, it was recently observed that DP learning systems may exacerbate bias and unfairness for different groups of individuals.
A Fairness Analysis on Private Aggregation of Teacher Ensembles
The Private Aggregation of Teacher Ensembles (PATE) is an important private machine learning framework.
Differentially Empirical Risk Minimization under the Fairness Lens
However, it was recently observed that DP learning systems may exacerbate bias and unfairness for different groups of individuals.
A Privacy-Preserving and Trustable Multi-agent Learning Framework
Distributed multi-agent learning enables agents to cooperatively train a model without requiring to share their datasets.
Decision Making with Differential Privacy under a Fairness Lens
Agencies, such as the U. S. Census Bureau, release data sets and statistics about groups of individuals that are used as input to a number of critical decision processes.
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
A critical concern in data-driven decision making is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age.
Lagrangian Duality for Constrained Deep Learning
In energy domains, the combination of Lagrangian duality and deep learning can be used to obtain state-of-the-art results to predict optimal power flows, in energy systems, and optimal compressor settings, in gas networks.
Gaussian Process for Noisy Inputs with Ordering Constraints
We study the Gaussian Process regression model in the context of training data with noise in both input and output.
