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Found 8 papers, 2 papers with code
Sparse Linear Regression and Lattice Problems
Furthermore, for well-conditioned (essentially) isotropic Gaussian design matrices, where Lasso is known to behave well in the identifiable regime, we show hardness of outputting any good solution in the unidentifiable regime where there are many solutions, assuming the worst-case hardness of standard and well-studied lattice problems.
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice.
OpenFHE: Open-Source Fully Homomorphic Encryption Library
Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables performing computations over encrypted data without having access to the secret key.
Planting Undetectable Backdoors in Machine Learning Models
Second, we demonstrate how to insert undetectable backdoors in models trained using the Random Fourier Features (RFF) learning paradigm or in Random ReLU networks.
Continuous LWE is as Hard as LWE & Applications to Learning Gaussian Mixtures
Under the (conservative) polynomial hardness of LWE, we show hardness of density estimation for $n^{\epsilon}$ Gaussians for any constant $\epsilon > 0$, which improves on Bruna, Regev, Song and Tang (STOC 2021), who show hardness for at least $\sqrt{n}$ Gaussians under polynomial (quantum) hardness assumptions.
The Fine-Grained Hardness of Sparse Linear Regression
Sparse linear regression is the well-studied inference problem where one is given a design matrix $\mathbf{A} \in \mathbb{R}^{M\times N}$ and a response vector $\mathbf{b} \in \mathbb{R}^M$, and the goal is to find a solution $\mathbf{x} \in \mathbb{R}^{N}$ which is $k$-sparse (that is, it has at most $k$ non-zero coordinates) and minimizes the prediction error $\|\mathbf{A} \mathbf{x} - \mathbf{b}\|_2$.
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
We propose to approximate this family of encoding functions through random deep neural networks.
Computational Limitations in Robust Classification and Win-Win Results
We continue the study of statistical/computational tradeoffs in learning robust classifiers, following the recent work of Bubeck, Lee, Price and Razenshteyn who showed examples of classification tasks where (a) an efficient robust classifier exists, in the small-perturbation regime; (b) a non-robust classifier can be learned efficiently; but (c) it is computationally hard to learn a robust classifier, assuming the hardness of factoring large numbers.
