Statistical physics of inference: Thresholds and algorithms
Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …
problems: some partial, or noisy, observations are performed over a set of variables and the …
Recent advances in algorithmic high-dimensional robust statistics
I Diakonikolas, DM Kane - ar** field of
statistical learning with sparsity. A sparse statistical model is one having only a small …
statistical learning with sparsity. A sparse statistical model is one having only a small …
Planting undetectable backdoors in machine learning models
Given the computational cost and technical expertise required to train machine learning
models, users may delegate the task of learning to a service provider. Delegation of learning …
models, users may delegate the task of learning to a service provider. Delegation of learning …
Unsupervised alignment of embeddings with wasserstein procrustes
We consider the task of aligning two sets of points in high dimension, which has many
applications in natural language processing and computer vision. As an example, it was …
applications in natural language processing and computer vision. As an example, it was …
Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees
Optimization problems with rank constraints arise in many applications, including matrix
regression, structured PCA, matrix completion and matrix decomposition problems. An …
regression, structured PCA, matrix completion and matrix decomposition problems. An …
Statistical query lower bounds for robust estimation of high-dimensional gaussians and gaussian mixtures
We describe a general technique that yields the first Statistical Query lower bounds for a
range of fundamental high-dimensional learning problems involving Gaussian distributions …
range of fundamental high-dimensional learning problems involving Gaussian distributions …
The computational complexity of the restricted isometry property, the nullspace property, and related concepts in compressed sensing
This paper deals with the computational complexity of conditions which guarantee that the
NP-hard problem of finding the sparsest solution to an underdetermined linear system can …
NP-hard problem of finding the sparsest solution to an underdetermined linear system can …
Tensor SVD: Statistical and computational limits
In this paper, we propose a general framework for tensor singular value decomposition
(tensor singular value decomposition (SVD)), which focuses on the methodology and theory …
(tensor singular value decomposition (SVD)), which focuses on the methodology and theory …
Notes on computational hardness of hypothesis testing: Predictions using the low-degree likelihood ratio
These notes survey and explore an emerging method, which we call the low-degree
method, for understanding statistical-versus-computational tradeoffs in high-dimensional …
method, for understanding statistical-versus-computational tradeoffs in high-dimensional …