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Found 12 papers, 3 papers with code
Learning to Collaborate for User-Controlled Privacy
As such, users and utility providers should collaborate in data privacy, a paradigm that has not yet been developed in the privacy research community.
Non-contact photoplethysmogram and instantaneous heart rate estimation from infrared face video
One way to avoid these constraints is using infrared cameras, allowing the monitoring of iHR under low light conditions.
Learning data-derived privacy preserving representations from information metrics
We study space-preserving transformations where the utility provider can use the same algorithm on original and sanitized data, a critical and novel attribute to help service providers accommodate varying privacy requirements with a single set of utility algorithms.
Pareto Optimality in No-Harm Fairness
Common fairness definitions in machine learning focus on balancing various notions of disparity and utility.
Fairness With Minimal Harm: A Pareto-Optimal Approach For Healthcare
Common fairness definitions in machine learning focus on balancing notions of disparity and utility.
Instance based Generalization in Reinforcement Learning
Agents trained via deep reinforcement learning (RL) routinely fail to generalize to unseen environments, even when these share the same underlying dynamics as the training levels.
Minimax Pareto Fairness: A Multi Objective Perspective
In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective.
Instance-based Generalization in Reinforcement Learning
Agents trained via deep reinforcement learning (RL) routinely fail to generalize to unseen environments, even when these share the same underlying dynamics as the training levels.
Blind Pareto Fairness and Subgroup Robustness
With the wide adoption of machine learning algorithms across various application domains, there is a growing interest in the fairness properties of such algorithms.
Federating for Learning Group Fair Models
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models.
Minimax Demographic Group Fairness in Federated Learning
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models.
Federated Fairness without Access to Sensitive Groups
Current approaches to group fairness in federated learning assume the existence of predefined and labeled sensitive groups during training.
