arXiv:2609.39525v1 Announce Type: new
Abstract: Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intracta...
By Hans Olischl\"ager, Svenja Jedhoff, \v{S}imon Kucharsk\'y, Aayush Mishra, Stefan T. Radev, Paul B\"urkner
arXiv:2606. 19587v1 Announce Type: cross Abstract: We propose a scalable method for training prediction (machine learning) models in the predict-then-optimize paradigm, where model outputs serve as coefficients for a subsequent linear optimization task.
By Beichen Wan, Mo Liu
The paper introduces LB-MCTS, a trajectory-structured optimization framework that combines Monte Carlo Tree Search, Bayesian Optimization, and Large Language Models to address the CASH problem in AutoML. It uses a shared tree state for algorithm selection, hyperparameter refinement, and synergy between BO and LLM proposals, with a reliability-aware policy that shifts from LLM-driven to BO-driven suggestions as the surrogate model improves. Experiments on 104 AMLB datasets show that LB-MCTS consistently outperforms existing BO-based, LLM-based, and hybrid baselines.
By Beicheng Xu, Weitong Qian, Lingching Tung, Yupeng Lu, Bin Cui
arXiv:2211. 14411v5 Announce Type: replace-cross Abstract: Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some constraints, such as on memory usage or latency, on top of the performance requirement.
By Shuhei Watanabe, Frank Hutter
arXiv:2503. 07325v2 Announce Type: replace Abstract: Understanding and certifying the behavior of modern deep neural networks remains a fundamental challenge in reliable machine learning.
By Khoat Than, Dat Phan
GAMMA is a post‑training framework that learns module‑wise precision preferences for mixed‑precision quantization of large language models. It optimizes a teacher‑forced hidden‑state reconstruction objective under an augmented Lagrangian constraint and then projects the learned preferences into exact budget‑feasible discrete assignments via integer programming. Because the learned preferences encode a stable sensitivity ranking, a single training run can be reused for any deployment budget, reducing per‑budget adaptation from hours to minutes and outperforming fixed‑precision baselines and search‑based methods on Llama and Qwen models.
By Zhangyang Yao, Haiyan Zhao, Haoyu Wang, Xu Han