Search Results for author:
Found 4 papers, 3 papers with code
Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural Networks
In our framework, the fusion occurs in a layer-wise manner and builds on an interpretation of a node in a network as a function of the layer preceding it.
Learning Invariant Representations using Inverse Contrastive Loss
Learning invariant representations is a critical first step in a number of machine learning tasks.
FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret
We provide a detailed technical analysis and present experiments demonstrating that various fairness measures from the literature can be reliably imposed on a number of training tasks in vision in a manner that is interpretable.
Stochastic Bandits with Delayed Composite Anonymous Feedback
The complexity of this problem stems from the anonymous feedback to the player and the stochastic generation of the reward.
