Search Results for author:
Found 14 papers, 3 papers with code
Counterfactually Fair Representation
It was shown in \cite{kusner2017counterfactual} that a sufficient condition for satisfying CF is to \textbf{not} use features that are descendants of sensitive attributes in the causal graph.
Loss Balancing for Fair Supervised Learning
Imposing EL on the learning process leads to a non-convex optimization problem even if the loss function is convex, and the existing fair learning algorithms cannot properly be adopted to find the fair predictor under the EL constraint.
Federated Learning with Reduced Information Leakage and Computation
Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data.
Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions
This composition can be represented in the form of a tree.
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution.
Fair Sequential Selection Using Supervised Learning Models
This observation implies that the fairness notions used in classification problems are not suitable for a selection problem where the applicants compete for a limited number of positions.
Interpreting Black-boxes Using Primitive Parameterized Functions
One approach for interpreting black-box machine learning models is to find a global approximation of the model using simple interpretable functions, which is called a metamodel (a model of the model).
Non-convex Optimization for Learning a Fair Predictor under Equalized Loss Fairness Constraint
In general, finding a fair predictor leads to a constrained optimization problem, and depending on the fairness notion, it may be non-convex.
RewardRating: A Mechanism Design Approach to Improve Rating Systems
Our proposed mechanism \textit{RewardRating} is inspired by the stock market model in which users can invest in their ratings for services and receive a reward based on future ratings.
Computer Science and Game Theory
Improving Fairness and Privacy in Selection Problems
In this work, we study the possibility of using a differentially private exponential mechanism as a post-processing step to improve both fairness and privacy of supervised learning models.
Recycled ADMM: Improving the Privacy and Accuracy of Distributed Algorithms
It can be shown that the privacy-accuracy tradeoff can be improved significantly compared with conventional ADMM.
Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness
Machine Learning (ML) models trained on data from multiple demographic groups can inherit representation disparity (Hashimoto et al., 2018) that may exist in the data: the model may be less favorable to groups contributing less to the training process; this in turn can degrade population retention in these groups over time, and exacerbate representation disparity in the long run.
Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithms
Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems.
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Alternating direction method of multiplier (ADMM) is a popular method used to design distributed versions of a machine learning algorithm, whereby local computations are performed on local data with the output exchanged among neighbors in an iterative fashion.
