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Found 8 papers, 3 papers with code
Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective
Recent results in coupled or temporal graphical models offer schemes for estimating the relationship structure between features when the data come from related (but distinct) longitudinal sources.
Treatment Effects Estimation by Uniform Transformer
In this paper, we investigate weighting methods from a functional estimation perspective and argue that the weights needed for covariate balancing could differ from those needed for treatment effects estimation under low regularity conditions.
Methodology Statistics Theory Statistics Theory
Robust Differential Abundance Test in Compositional Data
However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis in compositional data remains a complicated and unsolved statistical problem.
Methodology Statistics Theory Quantitative Methods Applications Statistics Theory
Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective
To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of multi-view data.
Augmentation Invariant Manifold Learning
Our theoretical investigation characterizes the role of data augmentation in the proposed method and reveals why and how the data representation learned from augmented data can improve the $k$-nearest neighbor classifier in the downstream analysis, showing that a more complex data augmentation leads to more improvement in downstream analysis.
On Linear Separation Capacity of Self-Supervised Representation Learning
Recent advances in self-supervised learning have highlighted the efficacy of data augmentation in learning data representation from unlabeled data.
Language-Codec: Reducing the Gaps Between Discrete Codec Representation and Speech Language Models
Furthermore, we also validate the efficiency of the Language-Codec on downstream speech language models.
Unlocking the Potential of Multimodal Unified Discrete Representation through Training-Free Codebook Optimization and Hierarchical Alignment
The Dual Cross-modal Information Disentanglement (DCID) model, utilizing a unified codebook, shows promising results in achieving fine-grained representation and cross-modal generalization.
