arXiv Machine Learning
Accelerating Transfer-Learning-Based Autotuning with Predictive LLVM IR Performance Ranking
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arXiv Machine Learning
Sep 14
SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling
SAGE-Loop is a new closed‑loop, self‑adaptive AutoML framework that uses large language models to generate and validate machine learning pipelines in multiple rounds, allowing trial‑and‑repair and adaptive ensemble selection for both supervised and unsupervised tasks. It addresses the lack of instant feedback and correction in existing AutoML by enabling process‑level recovery from failures and dynamic use of model diversity. Experiments on 20 public datasets show consistent improvements in performance and stability across classification, regression, and clustering, and demonstrate the system’s ability to recover from execution failures.
arXiv Machine Learning
Jun 16
GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning
arXiv:2606. 14900v1 Announce Type: new Abstract: Multi-source transfer learning faces a fundamental scalability bottleneck: existing approaches require either loading all K source models into memory simultaneously during parameter fusion, requiring O(K) memory, or deploying all models at inference time, making production deployment infeasible.
arXiv AI
2d ago
Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)
The paper introduces TESS, a scalable data‑selection framework that replaces per‑sample weights with a selection network to improve transferability across datasets and model sizes. It identifies instability in existing meta‑learning for training‑data selection (MTS) due to weight suppression and overreliance on easy features, and proposes a Pointwise Value Matching objective to address these issues. Experiments on large language model safety and instruction tuning show strong transfer from subsets to full corpora and from smaller to larger models.
arXiv AI
Aug 20
AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems
AutoOR is a scalable synthetic data generation and reinforcement learning pipeline that trains large language models to autoformalize operations research problems expressed in natural language across linear, mixed‑integer, and non‑linear categories. By generating verified training data from standard optimization forms and using solver execution feedback as a reward signal, AutoOR enables post‑training of an 8B model to achieve state‑of‑the‑art or competitive results on six established OR benchmarks, matching significantly larger frontier models. For non‑linear problems involving physical dynamics, a curriculum RL strategy bootstraps from limited initial data, making this class tractable for post‑training.
arXiv Machine Learning
Jul 31
AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization
arXiv:2607. 27953v1 Announce Type: new Abstract: Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain.
arXiv AI
Aug 24
AutoOR: Scalably Post-training LLMs to Autoformulate Operations Research Problems
arXiv:2604.16804v4 Announce Type: replace-cross Abstract: Optimization problems are central to decision-making in manufacturing, logistics, scheduling, and other industrial settings. Translating comp...