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Found 17 papers, 0 papers with code
Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods.
Efficient Sparse Least Absolute Deviation Regression with Differential Privacy
To fast solve the non-smooth loss under a given privacy budget, we develop a Fast Robust And Privacy-Preserving Estimation (FRAPPE) algorithm for least absolute deviation regression.
Distributed Semi-Supervised Sparse Statistical Inference
The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters.
Transductive Matrix Completion with Calibration for Multi-Task Learning
Moreover, transduction with matrix completion is a useful method in multi-label learning.
Majority Vote for Distributed Differentially Private Sign Selection
Simulation studies are conducted to demonstrate the effectiveness of our proposed method.
Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach
Decentralized sparsity learning has attracted a significant amount of attention recently due to its rapidly growing applications.
Nonparametric Feature Selection by Random Forests and Deep Neural Networks
Random forests are a widely used machine learning algorithm, but their computational efficiency is undermined when applied to large-scale datasets with numerous instances and useless features.
One-Bit Matrix Completion with Differential Privacy
In this paper, we propose a unified framework for ensuring a strong privacy guarantee of one-bit matrix completion with DP.
Applying Differential Privacy to Tensor Completion
Tensor completion aims at filling the missing or unobserved entries based on partially observed tensors.
SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System
In this work, we propose a novel and general self-adaptive module, the Self-adaptive Attention Module (SAM), which adjusts the selection bias by capturing contextual information based on its representation.
Matrix Completion with Model-free Weighting
In particular, the proposed method achieves a stronger guarantee than existing work in terms of the scaling with respect to the observation probabilities, under asymptotically heterogeneous missing settings (where entry-wise observation probabilities can be of different orders).
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Based on the proposed VRMOM estimator, we develop a general distributed inference algorithm that is robust against Byzantine failures.
Median DC for Sign Recovery: Privacy can be Achieved by Deterministic Algorithms
Privacy-preserving data analysis becomes prevailing in recent years.
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy
In this paper, we consider the problem of discrete distribution estimation under local differential privacy constraints.
Median Matrix Completion: from Embarrassment to Optimality
In this paper, we consider matrix completion with absolute deviation loss and obtain an estimator of the median matrix.
Distributed High-dimensional Regression Under a Quantile Loss Function
This paper studies distributed estimation and support recovery for high-dimensional linear regression model with heavy-tailed noise.
Matrix Completion under Low-Rank Missing Mechanism
Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism.
