arXiv AI
Aug 12
TACTICL: Task-Aware Compression of Tabular ICL Models
arXiv:2608. 10837v1 Announce Type: cross Abstract: The strong performance of foundation models for tabular tasks comes at substantial inference costs.
Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation
arXiv:2607. 04809v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have demonstrated strong empirical performance as black-box inference engines through in-context learning.
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arXiv Machine Learning
Jun 9
In-Context Learning for Latent Space Bayesian Optimization
arXiv:2606. 09664v1 Announce Type: new Abstract: Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects such as molecules and proteins.
arXiv AI
Jun 18
Bounded Context Management for Tabular Foundation Models on Stream Learning
arXiv:2606. 18677v1 Announce Type: cross Abstract: Tabular stream learning requires predictions on sequentially arriving examples under distribution shift.
arXiv Machine Learning
Aug 4
TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction
arXiv:2608. 01400v1 Announce Type: new Abstract: Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity.
arXiv Machine Learning
Jun 9
Hierarchical Projection for Adaptive Knowledge Transfer
arXiv:2606. 08691v1 Announce Type: new Abstract: Modern data-driven applications increasingly involve learning from multiple heterogeneous sources, where a target dataset is limited but related information is available across domains.
arXiv AI
Jul 24
Source-Prior-Driven Selective Adaptation for Efficient Diffusion Model Finetuning
arXiv:2607. 20913v1 Announce Type: new Abstract: Fine-tuning large diffusion models for new domains or styles involves a trade-off: improving target-specific generation often degrades the pretrained model's broad generative capability.
arXiv AI
Jun 24
ARIA: Adaptive Region-Based Importance Allocation for Conditional Diffusion Distillation
arXiv:2606. 23898v1 Announce Type: cross Abstract: Distilling conditional diffusion models aims to transfer the behavior of a large teacher to a smaller student while preserving alignment across conditioning inputs.
arXiv Machine Learning
Jul 23
Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning
arXiv:2602. 00072v2 Announce Type: replace Abstract: The performance of machine learning surrogates is critically dependent on data quality and quantity.
arXiv AI
Jun 10
MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.
arXiv AI
Jun 30
KnowsTFM: Knowledge-Informed Fine-Tuning of Small Tabular Foundation Models
arXiv:2606. 30258v1 Announce Type: cross Abstract: Tabular foundation models have advanced deep learning for tabular data by delivering strong default performance across many small and medium tasks.
arXiv Machine Learning
Jul 1
When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts
arXiv:2507. 14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled target data.
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.