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Found 3 papers, 1 papers with code
ROPO: Robust Preference Optimization for Large Language Models
Preference alignment is pivotal for empowering large language models (LLMs) to generate helpful and harmless responses.
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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