Reformulation invariance on inference problems forces minimization of the Kullback-Leibler divergence, narrowing from f-divergences to alpha-divergences to KL.
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Momentum SGD incurs a provable drift-amplification penalty in nonstationary stochastic optimization that makes it worse than vanilla SGD in drift-dominated regimes, confirmed by finite-time upper bounds and minimax lower bounds under gradient-variation constraints.
Conditional normalizing flows approximate intractable likelihoods arising from cell division history to conclude that glc3 is mostly inactive under nutrient stress in yeast, with brief transient expression.
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Reformulation Invariance and the Axiomatic Foundations of Inference
Reformulation invariance on inference problems forces minimization of the Kullback-Leibler divergence, narrowing from f-divergences to alpha-divergences to KL.
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On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
Momentum SGD incurs a provable drift-amplification penalty in nonstationary stochastic optimization that makes it worse than vanilla SGD in drift-dominated regimes, confirmed by finite-time upper bounds and minimax lower bounds under gradient-variation constraints.