Aidong Zhang

Demystify Hyperparameters for Stochastic Optimization with Transferable Representations

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

This paper studies the convergence and generalization of a large class of Stochastic Gradient Descent (SGD) momentum schemes, in both learning from scratch and transferring representations with fine-tuning. Momentum-based acceleration of SGD is the default …

AdvST: Revisiting Data Augmentations for Single Domain Generalization

2023 · arXiv (Cornell University)

Single domain generalization (SDG) aims to train a robust model against unknown target domain shifts using data from a single source domain. Data augmentation has been proven an effective approach to SDG. However, the utility …

ShortcutProbe: Probing Prediction Shortcuts for Learning Robust Models

2024

Deep learning models often achieve high performance by inadvertently learning spurious correlations between targets and non-essential features. For example, an image classifier may identify an object via its background that spuriously correlates with it. This …

IdeaBench: Benchmarking Large Language Models for Research Idea Generation

2025

Large Language Models (LLMs) have revolutionized interactions between human and artificial intelligence (AI) systems, demonstrating state-of-the-art performance across various domains, including scientific discovery and hypothesis generation. However, the absence of a comprehensive and systematic evaluation …