2 citations · 5 across the 23 of their papers we have counts for
8 papers · 1 filter
SPDMark: Selective Parameter Displacement for Robust Video Watermarking
Samar Fares, Nurbek Tastan, Karthik Nandakumar
The advent of high-quality video generation models has amplified the need for robust watermarking schemes that can be used to reliably detect and track the provenance of generated…
RobustA: Robust Anomaly Detection in Multimodal Data
Salem AlMarri, Muhammad Irzam Liaqat, Muhammad Zaigham Zaheer +3
In recent years, multimodal anomaly detection methods have demonstrated remarkable performance improvements over video-only models. However, real-world multimodal data is often cor…
Stochastic Self-Organization in Multi-Agent Systems
Nurbek Tastan, Samuel Horvath, Karthik Nandakumar
Multi-agent systems (MAS) based on Large Language Models (LLMs) have the potential to solve tasks that are beyond the reach of any single LLM. However, this potential can only be r…
MOLM: Mixture of LoRA Markers
Samar Fares, Nurbek Tastan, Noor Hussein +1
Generative models can generate photorealistic images at scale. This raises urgent concerns about the ability to detect synthetically generated images and attribute these images to…
LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning
Nurbek Tastan, Stefanos Laskaridis, Martin Takac +2
Large pre-trained models are commonly adapted to downstream tasks using parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA), which injects small trainable lo…
Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks
Nurbek Tastan, Samuel Horvath, Karthik Nandakumar
Collaborative learning enables multiple participants to learn a single global model by exchanging focused updates instead of sharing data. One of the core challenges in collaborati…
A Framework for Double-Blind Federated Adaptation of Foundation Models
Nurbek Tastan, Karthik Nandakumar
Foundation models (FMs) excel in zero-shot tasks but benefit from task-specific adaptation. However, privacy concerns prevent data sharing among multiple data owners, and proprieta…
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning
Nurbek Tastan, Samuel Horvath, Karthik Nandakumar
Collaborative learning (CL) enables multiple participants to jointly train machine learning (ML) models on decentralized data sources without raw data sharing. While the primary go…