3 papers
cs.LG2026
Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space
Long Minh Bui, Tuan Anh Le Van, Tung Phi Duc +3
Model merging aims to combine existing single-task solutions into a multi-task solution without additional data-driven fine-tuning.~Most existing approaches achieve this using geom…
cs.LG2026
Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks
Azza Fadhel, The Hung Tran, Trong Nghia Hoang +1
We consider the problem of offline black-box optimization, where the goal is to discover optimal designs (e.g., molecules or materials) from past experimental data. A key challenge…
cs.LG2025
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression
Hooman Shahrokhi, Devjeet Raj Roy, Yan Yan +2
We consider the problem of generating valid and small prediction sets by sampling outputs (e.g., software code and natural language text) from a black-box deep generative model for…