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Found 5 papers, 1 papers with code
From Pseudorandomness to Multi-Group Fairness and Back
We identify and explore connections between the recent literature on multi-group fairness for prediction algorithms and the pseudorandomness notions of leakage-resilience and graph regularity.
Privately Estimating a Gaussian: Efficient, Robust and Optimal
We prove a new lower bound on differentially private covariance estimation to show that the dependence on the condition number $\kappa$ in the above sample bound is also tight.
Weighed $\ell_1$ on the simplex: Compressive sensing meets locality
Sparse manifold learning algorithms combine techniques in manifold learning and sparse optimization to learn features that could be utilized for downstream tasks.
On the convergence of group-sparse autoencoders
Recent approaches in the theoretical analysis of model-based deep learning architectures have studied the convergence of gradient descent in shallow ReLU networks that arise from generative models whose hidden layers are sparse.
K-Deep Simplex: Deep Manifold Learning via Local Dictionaries
We theoretically analyze the proposed program by relating the weighted $\ell_1$ penalty in KDS to a weighted $\ell_0$ program.
