2 citations · 4 across the 12 of their papers we have counts for
5 papers · 1 filter
SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion
Zizhao Hu, Ameya Godbole, Johnny Tian-Zheng Wei +3
Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous knowledge, without costly full…
Multi-modal Synthetic Data Training and Model Collapse: Insights from VLMs and Diffusion Models
Zizhao Hu, Mohammad Rostami, Jesse Thomason
Recent research has highlighted the risk of generative model collapse, where performance progressively degrades when continually trained on self-generated data. However, existing e…
Class-Incremental Learning Using Generative Experience Replay Based on Time-aware Regularization
Zizhao Hu, Mohammad Rostami
Learning new tasks accumulatively without forgetting remains a critical challenge in continual learning. Generative experience replay addresses this challenge by synthesizing pseud…
Cognitively Inspired Cross-Modal Data Generation Using Diffusion Models
Zizhao Hu, Mohammad Rostami
Most existing cross-modal generative methods based on diffusion models use guidance to provide control over the latent space to enable conditional generation across different modal…
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL
Sumedh A Sontakke, Stephen Iota, Zizhao Hu +3
Out-of-distribution (OOD) detection is a well-studied topic in supervised learning. Extending the successes in supervised learning methods to the reinforcement learning (RL) settin…