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Creators/Authors contains: "Pareek, Divyansh"

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  1. ; ; (, 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025))
    The success of modern multimodal representation learning relies on internet-scale datasets. Due to the low quality of a large fraction of raw web data, data curation has become a critical step in the training pipeline. Filtering using a trained model (i.e., teacher-based filtering) has emerged as a successful solution, leveraging a pre-trained model to compute quality scores. To explain the empirical success of teacher-based filtering, we characterize the performance of filtered contrastive learning under the standard bimodal data generation model. Denoting as the fraction of data with correctly matched modalities among paired samples, we utilize a linear contrastive learning setup to show a provable benefit of data filtering: the error without filtering is upper and lower bounded by , and the error with teacher-based filtering is upper bounded by in the large regime, and by in the small regime. 
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    Accepted Manuscript Full Text Available
  2. ; ; (, 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024))
    Self-Distillation is a special type of knowledge distillation where the student model has the same architecture as the teacher model. Despite using the same architecture and the same training data, self-distillation has been empirically observed to improve performance, especially when applied repeatedly. For such a process, there is a fundamental question of interest: How much gain is possible by applying multiple steps of self-distillation? To investigate this relative gain, we propose using the simple but canonical task of linear regression. Our analysis shows that the excess risk achieved by multi-step self-distillation can significantly improve upon a single step of self-distillation, reducing the excess risk by a factor of , where is the input dimension. Empirical results on regression tasks from the UCI repository show a reduction in the learnt model's risk (MSE) by up to %. 
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    Accepted Manuscript Full Text Available