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Found 10 papers, 4 papers with code
2-Rectifications are Enough for Straight Flows: A Theoretical Insight into Wasserstein Convergence
In this paper, we make two key theoretical contributions: 1) we provide the first theoretical analysis of the Wasserstein distance between the sampling distribution of RF and the target distribution.
Feature Selection from Differentially Private Correlations
Additionally, high-dimensional regression can leak information about individual datapoints in a dataset.
FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits
High dimensional sparse linear bandits serve as an efficient model for sequential decision-making problems (e. g. personalized medicine), where high dimensional features (e. g. genomic data) on the users are available, but only a small subset of them are relevant.
On the Computational Complexity of Private High-dimensional Model Selection
We consider the problem of model selection in a high-dimensional sparse linear regression model under privacy constraints.
Understanding Best Subset Selection: A Tale of Two C(omplex)ities
Furthermore, we show that a margin condition depending on similar margin quantity and complexity measures is also necessary for model consistency of BSS.
On Consistency and Asymptotic Normality of Least Absolute Deviation Estimators for 2-dimensional Sinusoidal Model
Estimation of the parameters of a 2-dimensional sinusoidal model is a fundamental problem in digital signal processing and time series analysis.
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits
We consider the stochastic linear contextual bandit problem with high-dimensional features.
How does overparametrization affect performance on minority groups?
The benefits of overparameterization for the overall performance of modern machine learning (ML) models are well known.
Emergence of Monogamy under Static and Dynamic Scenarios
Characterizing multipartite quantum correlations beyond two parties is of utmost importance for building cutting edge quantum technologies, although the comprehensive picture is still missing.
Quantum Physics
Robustness of Higher Dimensional Nonlocality against dual noise and sequential measurements
Specifically, we report that with the increase of dimension, the maximally violating states show a greater enhancement in the area of nonlocal region in comparison to the maximally entangled states and the scaling of the increment, in this case, grows faster than visibility.
Quantum Physics
