📐 Learning Theory

🤖 AAAI2026 · 3 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (293) · 🧪 ICML2026 (45) · 🧠 NeurIPS2025 (25) · 🧪 ICML2025 (16)

A Switching Framework for Online Interval Scheduling with Predictions

For the irrevocable online interval scheduling problem, this paper proposes the SemiTrust-and-Switch framework and the SmoothMerge randomized algorithm. By switching between or blending a prediction-trusting strategy and a classical greedy algorithm, the approach achieves near-optimal performance when predictions are accurate (consistency) and degrades gracefully when predictions are erroneous (robustness and smoothness). Tightness of the framework on specific instances is also established.

Generalizing Analogical Inference from Boolean to Continuous Domains

This paper revisits the theoretical foundations of analogical inference: it first constructs a counterexample demonstrating the failure of classical generalization bounds in the Boolean domain, then proposes a unified analogical inference framework based on parameterized generalized means, extending discrete classification to continuous regression domains.

Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version

This paper proposes SkyGP (Streaming Kernel-induced Progressively Generated Expert GP), which handles streaming data via kernel-distance-driven progressive expert generation and time-aware configurable aggregation, inheriting the learning guarantees of exact GP while maintaining bounded computational complexity. SkyGP comprehensively outperforms state-of-the-art methods on both benchmark regression tasks and real-time control experiments.