Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

While deep learning models have replaced hand-designed features across many domains,
these models are still trained with hand-designed optimizers. In this work, we leverage the …

We study the optimal memorization capacity of modern Hopfield models and Kernelized
Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories …

Learned optimizers---neural networks that are trained to act as optimizers---have the
potential to dramatically accelerate training of machine learning models. However, even …

Robust principal component analysis (RPCA) is a critical tool in modern machine learning,
which detects outliers in the task of low-rank matrix reconstruction. In this paper, we propose …

Stochastic Programming is a powerful modeling framework for decision-making under
uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely …

Learning to optimize (L2O) has gained increasing popularity, which automates the design of
optimizers by data-driven approaches. However, current L2O methods often suffer from poor …

Learning to optimize (L2O) has gained increasing attention since it demonstrates a
promising path to automating and accelerating the optimization of complicated problems …

Most decentralized optimization algorithms are handcrafted. While endowed with strong
theoretical guarantees, these algorithms generally target a broad class of problems, thereby …

In many practical applications, usually, similar optimisation problems or scenarios
repeatedly appear. Learning from previous problem-solving experiences can help adjust …