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Found 3 papers, 1 papers with code
Robust Preference Optimization with Provable Noise Tolerance for LLMs
The preference alignment aims to enable large language models (LLMs) to generate responses that conform to human values, which is essential for developing general AI systems.
Provably Convergent Subgraph-wise Sampling for Fast GNN Training
During the message passing (MP) in GNNs, subgraph-wise sampling methods discard messages outside the mini-batches in backward passes to avoid the well-known neighbor explosion problem, i. e., the exponentially increasing dependencies of nodes with the number of MP iterations.
LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence
The key idea of LMC is to retrieve the discarded messages in backward passes based on a message passing formulation of backward passes.
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