Search Results for author:
Found 6 papers, 6 papers with code
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO
In this paper, we present a principled framework for learning a small yet generalizable temperature prediction network (TempNet) to improve LFMs.
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.
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.
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance
In this paper, we study contrastive learning from an optimization perspective, aiming to analyze and address a fundamental issue of existing contrastive learning methods that either rely on a large batch size or a large dictionary of feature vectors.
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.
