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Found 7 papers, 4 papers with code
Function Aligned Regression: A Method Explicitly Learns Functional Derivatives from Data
Regression is a fundamental task in machine learning that has garnered extensive attention over the past decades.
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization
This paper investigates new families of compositional optimization problems, called $\underline{\bf n}$on-$\underline{\bf s}$mooth $\underline{\bf w}$eakly-$\underline{\bf c}$onvex $\underline{\bf f}$inite-sum $\underline{\bf c}$oupled $\underline{\bf c}$ompositional $\underline{\bf o}$ptimization (NSWC FCCO).
LibAUC: A Deep Learning Library for X-Risk Optimization
This paper introduces the award-winning deep learning (DL) library called LibAUC for implementing state-of-the-art algorithms towards optimizing a family of risk functions named X-risks.
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e. g., multiple 2D slices of a CT scan for a patient).
Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices
(i) We benchmark a variety of loss functions with different algorithmic choices for deep AUROC optimization problem.
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee
In this paper, we propose systematic and efficient gradient-based methods for both one-way and two-way partial AUC (pAUC) maximization that are applicable to deep learning.
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity
We study a family of loss functions named label-distributionally robust (LDR) losses for multi-class classification that are formulated from distributionally robust optimization (DRO) perspective, where the uncertainty in the given label information are modeled and captured by taking the worse case of distributional weights.
