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Langevin Monte Carlo algorithm provably learns depth-two neural nets

By PulseAugur Editorial · [1 sources] ·

A new research paper demonstrates that the Langevin Monte Carlo (LMC) algorithm can effectively train depth-two neural networks. The study proves that LMC can minimize the population risk for these networks, regardless of their size or the training data used. This is achieved by showing that LMC iterates converge to a Gibbs distribution for specific regularized losses, with convergence rates that depend on the Poincare constant of the Gibbs measure. AI

IMPACT Provides theoretical guarantees for training specific neural network architectures, potentially informing future algorithm development.

RANK_REASON Academic paper detailing a new theoretical finding about an ML algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Langevin Monte Carlo algorithm provably learns depth-two neural nets

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Academic paper detailing a new theoretical finding about an ML algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Dibyakanti Kumar, Samyak Jha, Anirbit Mukherjee ·

    Langevin Monte-Carlo Provably Learns Depth Two Neural Nets at Any Size and Data

    arXiv:2503.10428v4 Announce Type: replace Abstract: In this note we prove that the Langevin Monte Carlo (LMC) algorithm can minimize the population risk of appropriately regularized depth-2 nets with no restrictions on the size of the net and for any choice of training data --- a…

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