Hugging Face Trending Papers
NPBoost: Neural Processes with Gradient-Boosted Fixed Effects
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arXiv Machine Learning
Sep 24
NPBoost: Neural Processes with Gradient-Boosted Fixed Effects
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...
arXiv Machine Learning
Oct 9
MotherTree: Meta-learning on synthetic data improves decision tree training
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 AI
Aug 11
iLTM: Integrated Large Tabular Model
arXiv:2511. 15941v2 Announce Type: replace-cross Abstract: Tabular data underpins decisions across science, industry, and public services.
arXiv Machine Learning
Aug 26
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions
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 Machine Learning
Sep 10
Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning
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 Machine Learning
Jun 17
Reducing Learner Redundancy in Boosting via Residual Orthogonalization
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.