The paper derives bounds for GFlowNet convergence, sample complexity, implicit regularization, and robustness, but the proofs do not support the stated rates.
On a modification of chebyshev’s inequ ality and of the error formula of laplace
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Secrets of GFlowNets' Learning Behavior: A Theoretical Study
The paper derives bounds for GFlowNet convergence, sample complexity, implicit regularization, and robustness, but the proofs do not support the stated rates.