arXiv Machine Learning
When Is Coarse Supervision Worth It? Cost-Aware Learning under Unknown Aggregation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jul 1
A Unified and Stable Risk Minimization Framework for Weakly Supervised Learning with Theoretical Guarantees
arXiv:2511. 22823v2 Announce Type: replace-cross Abstract: Weakly supervised learning has emerged as a practical alternative to fully supervised learning when complete and accurate labels are costly or infeasible to acquire.
arXiv Machine Learning
Oct 15
Efficient contextual partial monitoring with general function approximation
arXiv:2610.19804v1 Announce Type: new Abstract: We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round...
Hugging Face Trending Papers
Oct 14
Efficient contextual partial monitoring with general function approximation
We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round $t \in [T]$, both player and environment observe...
arXiv AI
Jul 1
TraCeS: Learning Per-Timestep Constraint-Violation Credit from Sparse Trajectory-Level Labels
arXiv:2504. 12557v3 Announce Type: replace-cross Abstract: Ensuring safe behavior in reinforcement learning (RL) is challenging when safety constraints are implicit and cannot be densely measured.
arXiv Machine Learning
Oct 7
AFA-BANDIT: Provably Near-Optimal Online Multi-Feature Classification Under Budget Constraints
arXiv:2610.07615v1 Announce Type: new Abstract: Active Feature Acquisition (AFA) is a classification problem in which an agent decides which costly features to acquire before predicting each sample's...
arXiv AI
Oct 27
Should Pseudo-Labels Share Mini-Batches with Labeled Examples? Semi-Supervised Continual Active Learning with Limited Training Cost
The paper investigates how pseudo‑labeled examples should be incorporated into mini‑batches during continual active learning (CAL) to balance annotation savings and training cost. It formalizes the problem as two decisions: selecting which unlabeled examples receive pseudo‑labels and determining their share of each mini‑batch. The proposed semi‑supervised continual active learning (SSCAL) method, compatible with any acquisition function, uses distance‑based pseudo‑labeling, caps their batch share, and reweights labeled losses to maintain gradient balance, achieving top performance across multiple audio datasets and settings.