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Found 9 papers, 1 papers with code
Auditing and Enforcing Conditional Fairness via Optimal Transport
Many algorithmic fairness techniques exist to target demographic parity, but CDP is much harder to achieve, particularly when the conditioning variable has many levels and/or when the model outputs are continuous.
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
To mitigate this issue, several successful approaches have been proposed, including replacing the empirical distribution in training with: (i) a worst-case distribution within an ambiguity set, leading to a distributionally robust (DR) counterpart of ARO; or (ii) a mixture of the empirical distribution with one derived from an auxiliary dataset (e. g., synthetic, external, or out-of-domain).
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data.
Online Learning for Mixture of Multivariate Hawkes Processes
Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors.
Differentially Private Learning of Hawkes Processes
Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data.
A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression
This paper considers the problem of matrix-variate logistic regression.
Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms
This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning.
STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery
In recent years, a class of dictionaries have been proposed for multidimensional (tensor) data representation that exploit the structure of tensor data by imposing a Kronecker structure on the dictionary underlying the data.
