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Found 10 papers, 3 papers with code
Coverage-Validity-Aware Algorithmic Recourse
Algorithmic recourse emerges as a prominent technique to promote the explainability, transparency and hence ethics of machine learning models.
Approximate Secular Equations for the Cubic Regularization Subproblem
In this paper, we propose and analyze a novel CRS solver based on an approximate secular equation, which requires only some of the Hessian eigenvalues and is therefore much more efficient.
Robust Bayesian Recourse
Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision.
Distributionally Robust Fair Principal Components via Geodesic Descents
Principal component analysis is a simple yet useful dimensionality reduction technique in modern machine learning pipelines.
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Given available data, we investigate novel strategies to synthesize a family of least squares estimator experts that are robust with regard to moment conditions.
Small errors in random zeroth-order optimization are imaginary
Most zeroth-order optimization algorithms mimic a first-order algorithm but replace the gradient of the objective function with some gradient estimator that can be computed from a small number of function evaluations.
Optimization and Control 65D25, 65G50, 65K05, 65Y04, 65Y20, 90C56
A Unified Approach to Synchronization Problems over Subgroups of the Orthogonal Group
In this paper, we consider the class of synchronization problems in which the group is a closed subgroup of the orthogonal group.
On Linear Optimization over Wasserstein Balls
In this technical note we prove that the Wasserstein ball is weakly compact under mild conditions, and we offer necessary and sufficient conditions for the existence of optimal solutions.
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
The likelihood function is a fundamental component in Bayesian statistics.
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions.
