2 citations · 2 across the 8 of their papers we have counts for
11 papers
The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators
Alex Iacob, Andrej Jovanović, William F. Shen +10
Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains. However, their search methods generally assume a…
Task-Centric Personalized Federated Fine-Tuning of Language Models
Gabriel U. Talasso, Meghdad Kurmanji, Allan M. de Souza +2
Federated Learning (FL) has emerged as a promising technique for training language models on distributed and private datasets of diverse tasks. However, aggregating models trained…
-FUM: Federated Unlearning via min--max and -divergence
Radmehr Karimian, Amirhossein Bagheri, Meghdad Kurmanji +2
Federated Learning (FL) has emerged as a powerful paradigm for collaborative machine learning across decentralized data sources, preserving privacy by keeping data local. However,…
MT-DAO: Multi-Timescale Distributed Adaptive Optimizers with Local Updates
Alex Iacob, Andrej Jovanovic, Mher Safaryan +6
Training large models with distributed data parallelism (DDP) requires frequent communication of gradients across workers, which can saturate bandwidth. Infrequent communication st…
AbbIE: Autoregressive Block-Based Iterative Encoder for Efficient Sequence Modeling
Preslav Aleksandrov, Meghdad Kurmanji, Fernando Garcia Redondo +7
We introduce the Autoregressive Block-Based Iterative Encoder (AbbIE), a novel recursive generalization of the encoder-only Transformer architecture, which achieves better perplexi…
DES-LOC: Desynced Low Communication Adaptive Optimizers for Training Foundation Models
Alex Iacob, Lorenzo Sani, Mher Safaryan +8
Scaling foundation model training with Distributed Data Parallel (DDP) methods is bandwidth-limited. Existing infrequent communication methods like Local SGD were designed to synch…