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Found 6 papers, 3 papers with code
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).
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
In this paper, we consider non-convex multi-block bilevel optimization (MBBO) problems, which involve $m\gg 1$ lower level problems and have important applications in machine learning.
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization
In this paper, we aim to optimize a contrastive loss with individualized temperatures in a principled and systematic manner for self-supervised learning.
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization
To tackle this challenge, we present a single-loop randomized stochastic algorithm, which requires updates for only a constant number of blocks at each iteration.
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence
To the best of our knowledge, this is the first time that stochastic algorithms are proposed to optimize NDCG with a provable convergence guarantee.
Randomized Stochastic Variance-Reduced Methods for Multi-Task Stochastic Bilevel Optimization
Although numerous studies have proposed stochastic algorithms for solving these problems, they are limited in two perspectives: (i) their sample complexities are high, which do not match the state-of-the-art result for non-convex stochastic optimization; (ii) their algorithms are tailored to problems with only one lower-level problem.
