1 citations · 3 across the 8 of their papers we have counts for
8 papers
Bi-Level Optimization for Single Domain Generalization
Marzi Heidari, Hanping Zhang, Hao Yan +1
Generalizing from a single labeled source domain to unseen target domains, without access to any target data during training, remains a fundamental challenge in robust machine lear…
Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation
Hao Yan, Heyan Zhang, Yongyi Guo
The rise of large-scale pretrained models has made it feasible to generate predictive or synthetic features at low cost, raising the question of how to incorporate such surrogate p…
Context-Aware Self-Adaptation for Domain Generalization
Hao Yan, Yuhong Guo
Domain generalization aims at developing suitable learning algorithms in source training domains such that the model learned can generalize well on a different unseen testing domai…
Single Domain Generalization with Adversarial Memory
Hao Yan, Marzi Heidari, Yuhong Guo
Domain Generalization (DG) aims to train models that can generalize to unseen testing domains by leveraging data from multiple training domains. However, traditional DG methods rel…
A Unified Framework for Heterogeneous Semi-supervised Learning
Marzi Heidari, Abdullah Alchihabi, Hao Yan +1
In this work, we introduce a novel problem setup termed as Heterogeneous Semi-Supervised Learning (HSSL), which presents unique challenges by bridging the semi-supervised learning…
Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations
Abdullah Alchihabi, Hao Yan, Yuhong Guo
Class imbalance is pervasive in real-world graph datasets, where the majority of annotated nodes belong to a small set of classes (majority classes), leaving many other classes (mi…
On the KL-Divergence-based Robust Satisficing Model
Haojie Yan, Minglong Zhou, Jiayi Guo
Empirical risk minimization, a cornerstone in machine learning, is often hindered by the Optimizer's Curse stemming from discrepancies between the empirical and true data-generatin…
Lightweight Unsupervised Federated Learning with Pretrained Vision Language Model
Hao Yan, Yuhong Guo
Federated learning aims to tackle the ``isolated data island" problem, where it trains a collective model from physically isolated clients while safeguarding the privacy of users'…