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Found 8 papers, 4 papers with code
Fixing confirmation bias in feature attribution methods via semantic match
Feature attribution methods have become a staple method to disentangle the complex behavior of black box models.
Differentially Private Distributed Bayesian Linear Regression with MCMC
We develop a novel generative statistical model for privately shared statistics, which exploits a useful distributional relation between the summary statistics of linear regression.
Semantic match: Debugging feature attribution methods in XAI for healthcare
Despite valid concerns, we argue that existing criticism on the viability of post-hoc local explainability methods throws away the baby with the bathwater by generalizing a problem that is specific to image data.
Explainable Artificial Intelligence (XAI)
Feature Importance
+1
Machine Learning for K-adaptability in Two-stage Robust Optimization
One of the solution approaches to this class of problems is K-adaptability.
Rule Generation for Classification: Scalability, Interpretability, and Fairness
The method returns a set of rules along with their optimal weights indicating the importance of each rule for learning.
Differentially Private Accelerated Optimization Algorithms
The first algorithm is inspired by Polyak's heavy ball method and employs a smoothing approach to decrease the accumulated noise on the gradient steps required for differential privacy.
HAMSI: A Parallel Incremental Optimization Algorithm Using Quadratic Approximations for Solving Partially Separable Problems
We propose HAMSI (Hessian Approximated Multiple Subsets Iteration), which is a provably convergent, second order incremental algorithm for solving large-scale partially separable optimization problems.
Parallel Stochastic Gradient Markov Chain Monte Carlo for Matrix Factorisation Models
For large matrix factorisation problems, we develop a distributed Markov Chain Monte Carlo (MCMC) method based on stochastic gradient Langevin dynamics (SGLD) that we call Parallel SGLD (PSGLD).
