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Found 18 papers, 0 papers with code
Permuted and Unlinked Monotone Regression in $\mathbb{R}^d$: an approach based on mixture modeling and optimal transport
We study permutation recovery in the permuted regression setting and develop a computationally efficient and easy-to-use algorithm for denoising based on the Kiefer-Wolfowitz [Ann.
Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group
In the analysis of data sets consisting of (X, Y)-pairs, a tacit assumption is that each pair corresponds to the same observation unit.
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data
In this paper, we present an Asynchronous Online Federated Learning (ASO-Fed) framework, where the edge devices perform online learning with continuous streaming local data and a central server aggregates model parameters from clients.
A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data
In this paper, we present a method to adjust for such mismatches under ``partial shuffling" in which a sufficiently large fraction of (predictors, response)-pairs are observed in their correct correspondence.
The Benefits of Diversity: Permutation Recovery in Unlabeled Sensing from Multiple Measurement Vectors
For the case in which both the signal and permutation are unknown, the problem is reformulated as a bi-convex optimization problem with an auxiliary variable, which can be solved by the Alternating Direction Method of Multipliers (ADMM).
A Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data
A tacit assumption in linear regression is that (response, predictor)-pairs correspond to identical observational units.
A Note on Coding and Standardization of Categorical Variables in (Sparse) Group Lasso Regression
Categorical regressor variables are usually handled by introducing a set of indicator variables, and imposing a linear constraint to ensure identifiability in the presence of an intercept, or equivalently, using one of various coding schemes.
Simple strategies for recovering inner products from coarsely quantized random projections
Random projections have been increasingly adopted for a diverse set of tasks in machine learning involving dimensionality reduction.
Linear Regression with Sparsely Permuted Data
In this paper, we consider the situation of "permuted data" in which this basic correspondence has been lost.
On Principal Components Regression, Random Projections, and Column Subsampling
In this paper, we present an analysis showing that for random projections satisfying a Johnson-Lindenstrauss embedding property, the prediction error in subsequent regression is close to that of PCR, at the expense of requiring a slightly large number of random projections than principal components.
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity
Random projections constitute a simple, yet effective technique for dimensionality reduction with applications in learning and search problems.
Methods for Sparse and Low-Rank Recovery under Simplex Constraints
The de-facto standard approach of promoting sparsity by means of $\ell_1$-regularization becomes ineffective in the presence of simplex constraints, i. e.,~the target is known to have non-negative entries summing up to a given constant.
b-bit Marginal Regression
We consider the problem of sparse signal recovery from $m$ linear measurements quantized to $b$ bits.
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices
Over the past few years, trace regression models have received considerable attention in the context of matrix completion, quantum state tomography, and compressed sensing.
Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fields
Consider a random vector with finite second moments.
Matrix factorization with Binary Components
Motivated by an application in computational biology, we consider low-rank matrix factorization with $\{0, 1\}$-constraints on one of the factors and optionally convex constraints on the second one.
Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization
We show that for these designs, the performance of NNLS with regard to prediction and estimation is comparable to that of the lasso.
Sparse recovery by thresholded non-negative least squares
Non-negative data are commonly encountered in numerous fields, making non-negative least squares regression (NNLS) a frequently used tool.
