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  - [Large Language Models as Markov Chains](https://huggingface.co/papers/2410.02724): theoretical insights on their generalization and convergence properties.
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  - *(NeurIPS'24)* [MANO: Unsupervised Accuracy Estimation Under Distribution Shifts](https://huggingface.co/papers/2405.18979): when logits are enough to estimate generalization of a pre-trained model.
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- - *(NeurIPS'24, Spotlight)* [Analysing Multi-Task Regression via Random Matrix Theory](https://arxiv.org/pdf/2406.10327): insights on a classical approach and its potentiality for time series forecasting.
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- - *(ICML'24, Oral)* [SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting](https://huggingface.co/papers/2402.10198): sharpness-aware minimization and channel-wise attention is all you need.
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  - *(AISTATS'24)* [Leveraging Ensemble Diversity for Robust Self-Training](https://huggingface.co/papers/2310.14814): confidence estimation method for efficient pseudo-labeling under sample selection bias.
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  - *(JMLR, 2024)* [Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data](https://www.jmlr.org/papers/volume25/23-0121/23-0121.pdf) generalization with unlabeled or pseudo-labeled data.
 
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  - [Large Language Models as Markov Chains](https://huggingface.co/papers/2410.02724): theoretical insights on their generalization and convergence properties.
15
  - *(NeurIPS'24)* [MANO: Unsupervised Accuracy Estimation Under Distribution Shifts](https://huggingface.co/papers/2405.18979): when logits are enough to estimate generalization of a pre-trained model.
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+ - *(NeurIPS'24, **Spotlight**)* [Analysing Multi-Task Regression via Random Matrix Theory](https://arxiv.org/pdf/2406.10327): insights on a classical approach and its potentiality for time series forecasting.
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+ - *(ICML'24, **Oral**)* [SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting](https://huggingface.co/papers/2402.10198): sharpness-aware minimization and channel-wise attention is all you need.
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  - *(AISTATS'24)* [Leveraging Ensemble Diversity for Robust Self-Training](https://huggingface.co/papers/2310.14814): confidence estimation method for efficient pseudo-labeling under sample selection bias.
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  - *(JMLR, 2024)* [Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data](https://www.jmlr.org/papers/volume25/23-0121/23-0121.pdf) generalization with unlabeled or pseudo-labeled data.