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New framework PeTeR enhances probabilistic circuit robustness post-training

By PulseAugur Editorial · [3 sources] ·

Researchers have introduced PeTeR, a new post-training framework designed to enhance the robustness of probabilistic circuits (PCs) against distribution shifts. Unlike existing methods that require training from scratch, PeTeR operates on pre-trained PCs without needing additional data. This approach aims to mitigate issues like overfitting and fragile generalization that arise from noisy data or small sample sizes. Evaluations on density estimation benchmarks show that PeTeR effectively strengthens baseline models against both random and adversarial perturbations, performing comparably to or better than data-dependent robust learning techniques. AI

IMPACT Enhances the reliability of probabilistic models in real-world, noisy conditions.

RANK_REASON The cluster describes a new research paper detailing a novel framework for improving machine learning models.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New framework PeTeR enhances probabilistic circuit robustness post-training

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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Adrian Ciotinga, Yeming Dai, YooJung Choi ·

    PeTeR: Post-Training Robustification of Probabilistic Circuits

    arXiv:2607.07671v1 Announce Type: new Abstract: Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries. However, standard likelihood-based PC learning is vulnerable to overfitting and fragile g…

  2. arXiv cs.LG TIER_1 English(EN) · YooJung Choi ·

    PeTeR: Post-Training Robustification of Probabilistic Circuits

    Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries. However, standard likelihood-based PC learning is vulnerable to overfitting and fragile generalization when confronted with data noise, s…

  3. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    PeTeR hardens probabilistic circuits without retraining New arXiv preprint PeTeR proposes a data-free way to harden pre-trained probabilistic circuits against d

    PeTeR hardens probabilistic circuits without retraining New arXiv preprint PeTeR proposes a data-free way to harden pre-trained probabilistic circuits against distribution shifts without retraining from scratch. https://www. notatechguy.com/peter-hardens- probabilistic-circuits-w…

    LINKS notatechguy.com/peter-hardens-probabilist…

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