↓Skip to main content

🏢 Virginia Tech

Taming Heavy-Tailed Losses in Adversarial Bandits and the Best-of-Both-Worlds Setting
·418 words·2 mins· loading · loading
Machine Learning Reinforcement Learning 🏢 Virginia Tech
This paper proposes novel algorithms achieving near-optimal regret in adversarial and logarithmic regret in stochastic multi-armed bandit settings with heavy-tailed losses, relaxing strong assumptions…
SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead
·2099 words·10 mins· loading · loading
AI Generated Machine Learning Federated Learning 🏢 Virginia Tech
SpaFL: A communication-efficient federated learning framework that optimizes sparse model structures with low computational overhead by using trainable thresholds to prune model parameters.
Fairness-Aware Meta-Learning via Nash Bargaining
·2445 words·12 mins· loading · loading
Machine Learning Meta Learning 🏢 Virginia Tech
Nash bargaining resolves hypergradient conflicts in fairness-aware meta-learning, boosting model performance and fairness.
DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos
·2153 words·11 mins· loading · loading
Computer Vision 3D Vision 🏢 Virginia Tech
DC-Gaussian: A novel method generates high-fidelity novel views from dashcam videos by addressing common windshield obstructions (reflections, occlusions) using adaptive image decomposition, illumina…
Boosting Alignment for Post-Unlearning Text-to-Image Generative Models
·4270 words·21 mins· loading · loading
AI Generated Multimodal Learning Vision-Language Models 🏢 Virginia Tech
This research introduces a novel framework for post-unlearning in text-to-image generative models, optimizing model updates to ensure both effective forgetting and maintained text-image alignment.