arXiv:2609.28122v1 Announce Type: cross Abstract: Neural Processes (NPs) are model-based meta-learners that implicitly learn a stochastic process and adapt to a new task from a small context set. Mos...
MotherTree is a tabular transformer that meta‑learns to produce hard, axis‑aligned decision trees in a single forward pass. It is pre‑trained on a synthetic prior distribution using stochastic gradient descent, without needing reference trees for supervision. On controlled benchmarks, MotherTree matches or surpasses size‑matched trees from traditional algorithms and serves as a strong initializer for task‑specific fine‑tuning.
arXiv:2511. 15941v2 Announce Type: replace-cross Abstract: Tabular data underpins decisions across science, industry, and public services.
arXiv:2501.14271v4 Announce Type: replace Abstract: Meta-learning enables models to rapidly adapt to new tasks by leveraging prior experience, but its adaptation mechanisms remain opaque, especially...
arXiv:2603.02935v2 Announce Type: replace Abstract: Offline meta-reinforcement learning seeks to learn a policy that generalizes to new related tasks online. Context-based methods infer a task repres...
arXiv:2606. 17567v1 Announce Type: new Abstract: While sequential residual fitting is the bedrock of standard boosting frameworks, it inherently breeds learner redundancy by repeatedly revisiting correlated error components.
arXiv:2609.37959v1 Announce Type: new Abstract: Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We p...
The paper documents the migration of a live conversational recommendation system from a gradient‑boosted multiclass model to a pairwise‑binary deep recommender. It explains how reformulating the task, using negative sampling, noise injection, and attention pooling over transcript chunks enabled the new model to handle dynamic, multimodal data and long conversation context. The authors compare several architectures and loss functions, showing that the deep recommender matches or surpasses the CatBoost baseline, especially in later conversational stages.
arXiv:2602. 00329v4 Announce Type: replace-cross Abstract: Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley value serving as the theoretical gold standard.
OrionMoE-v1.0 is a compact tabular foundation model that uses a dual‑axis Transformer and a sparse mixture‑of‑experts architecture to perform in‑context learning on labeled support tables. It selectively activates experts along the feature axis, guided by both individual feature representations and overall dataset context, enabling adaptive computation across diverse tabular problems. Pretrained on synthetic classification tasks, it achieves competitive performance on multiple benchmarks, especially on small‑context, high‑dimensional, and class‑imbalanced datasets.
TabMF is a framework that repurposes pretrained tabular foundation models (TFMs) as encoders for supervised datasets, adding a dataset-level readout and fine‑tuning via contrastive post‑training on synthetic views. The authors instantiate TabMF on TabICLv2 and TabPFN‑2.5 and evaluate the resulting representations on six tasks involving unseen real datasets. The approach achieves the best aggregate performance among both learned and classical dataset representations, excelling in dataset retrieval, compact benchmark construction, and distribution‑shift severity assessment.
arXiv:2509.18349v4 Announce Type: replace Abstract: Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of l...