2 citations · 4 across the 16 of their papers we have counts for
10 papers · 1 filter
The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators
Alex Iacob, Andrej Jovanović, William F. Shen +12
Self-improving agents are state-of-the-art on agentic coding benchmarks, yet their search methods assume a stationary evaluation criterion. This ignores a central feature of evolut…
Class-wise Contribution Estimation via Logit Maximization for Federated Learning
Asim Ukaye, Nurbek Tastan, Mubarak Abdu-Aguye +1
Federated learning (FL) enables collaborative learning of computer vision models, where privacy and regulatory constraints prevent centralizing data across devices or organizations…
Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy
Asim Ukaye, Mubarak Abdu-Aguye, Nurbek Tastan +1
Client contribution estimation in Federated Learning is necessary for identifying clients' importance and for providing fair rewards. Current methods often rely on server-side vali…
MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models
Nurbek Tastan, Stefanos Laskaridis, Karthik Nandakumar +1
Mixture-of-Experts (MoE) models scale large language models efficiently by sparsely activating experts, but once an expert is selected, it is executed fully. Hence, the trade-off b…
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…
FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning
Nurbek Tastan, Samuel Horvath, Martin Takac +1
Statistical data heterogeneity is a significant barrier to convergence in federated learning (FL). While prior work has advanced heterogeneous FL through better optimization object…
Redefining Contributions: Shapley-Driven Federated Learning
Nurbek Tastan, Samar Fares, Toluwani Aremu +2
Federated learning (FL) has emerged as a pivotal approach in machine learning, enabling multiple participants to collaboratively train a global model without sharing raw data. Whil…