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Found 7 papers, 0 papers with code
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
Bandit learning algorithms typically involve the balance of exploration and exploitation.
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
We also discuss stochastic process based forms of J-L, RIP, and sketching, to illustrate the generality of the results.
High-Dimensional Structured Quantile Regression
In this work we consider the problem of linear quantile regression in high dimensions where the number of predictor variables is much higher than the number of samples available for parameter estimation.
Structured Stochastic Linear Bandits
The goal in such a problem is to minimize the (pseudo) regret which is the difference between the total expected loss of the algorithm and the total expected loss of the best fixed vector in hindsight.
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs
In contrast, for the sub-exponential setting, we show that the sample complexity and the estimation error will depend on the exponential width of the corresponding sets, and the analysis holds for any norm.
Estimation with Norm Regularization
Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, needs to consider four aspects: the norm, the loss function, the design matrix, and the noise model.
Multi-task Sparse Structure Learning
We illustrate the effectiveness of the proposed model on a variety of synthetic and benchmark datasets for regression and classification.
