63 citations · 164 across the 23 of their papers we have counts for
5 papers · 1 filter
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
Xin Qiu, Yulu Gan, Conor F. Hayes +6
Fine-tuning large language models (LLMs) for downstream tasks is an essential stage of modern AI deployment. Reinforcement learning (RL) has emerged as the dominant fine-tuning par…
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
Xin Qiu, Elliot Meyerson, Risto Miikkulainen
Neural Networks (NNs) have been extensively used for a wide spectrum of real-world regression tasks, where the goal is to predict a numerical outcome such as revenue, effectiveness…
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
Elliot Meyerson, Risto Miikkulainen
As deep learning applications continue to become more diverse, an interesting question arises: Can general problem solving arise from jointly learning several such diverse tasks? T…
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back
Elliot Meyerson, Risto Miikkulainen
Deep multitask learning boosts performance by sharing learned structure across related tasks. This paper adapts ideas from deep multitask learning to the setting where only a singl…
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering
Elliot Meyerson, Risto Miikkulainen
Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared struct…