GRINCO performs acquisition in the quotient space induced by a transformation group using invariant embeddings or canonical representatives, pairs it with orbit-averaged loss, derives a generalization bound, and reports better orbit coverage and label efficiency than standard coresets on synthetic a
Active learning literature survey
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
The paper formalizes fixed-set worst-case corruption in PBE, implements corruption searches on a string DSL, and shows VPA recovers some margin-1 tasks but fails on public SyGuS where vote margins are near one.
HDRL-MoE is a hierarchical DRL framework with MoE that decouples slow inference decisions from fast UAV trajectory control in a constrained POMDP to maximize inference accuracy under mission constraints.
citing papers explorer
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Group-invariant Coresets for Data-efficient Active Learning
GRINCO performs acquisition in the quotient space induced by a transformation group using invariant embeddings or canonical representatives, pairs it with orbit-averaged loss, derives a generalization bound, and reports better orbit coverage and label efficiency than standard coresets on synthetic a
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Fixed-Set Robustness in Programming by Example: Example Corruption and Semantic Partition Recovery
The paper formalizes fixed-set worst-case corruption in PBE, implements corruption searches on a string DSL, and shows VPA recovers some margin-1 tasks but fails on public SyGuS where vote margins are near one.
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UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning
HDRL-MoE is a hierarchical DRL framework with MoE that decouples slow inference decisions from fast UAV trajectory control in a constrained POMDP to maximize inference accuracy under mission constraints.