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stat.ML2026
Adversarial Contamination Meets Hard Thresholding: An Iterative Algorithm with Signal Adaptivity and Minimax Optimality
Shixiang Liu, Hanming Yang
Pervasive data contamination -- stemming from measurement errors, outliers, or adversarial corruption -- has motivated the development of robust statistical methods. In this contex…
stat.ML2026
High-dimensional online learning via asynchronous decomposition: Non-divergent results, dynamic regularization, and beyond
Shixiang Liu, Zhifan Li, Hanming Yang +1
Existing high-dimensional online learning methods often face the challenge that their error bounds, or per-batch sample sizes, diverge as the number of data batches increases. To a…
stat.ML2024
Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts
Naimeng Ye, Hongseok Namkoong
Intelligent agents must be able to articulate its own uncertainty. In this work, we show that pre-trained sequence models are naturally capable of probabilistic reasoning over exch…