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robustness

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  1. 1

    PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimization

    PLC‑DPO extends Direct Preference Optimization by using the policy‑reference margin to route each training pair into clean, flipped, or tie categories, actively correcting noisy or ambiguous labels. Across extensive benchmarks it improves mean win‑rate from 55.5 % to 60.5 % and stays stable under injected noise and tie stress tests.

    Hugging Face Daily Papersarxiv.org1 minpaper
  2. 2

    Training-Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Representation

    This paper introduces a training-adaptive Convolutional Sparse Coding (CSC) framework where the sparsity coefficient is learned end-to-end via FISTA unfolding. It uses an information bottleneck perspective to balance representation compression and content preservation, showing improved robustness to input perturbations on CIFAR and ImageNet.

    Hugging Face Daily Papersarxiv.org1 minpaper