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Found 7 papers, 1 papers with code
Correcting Confounding via Random Selection of Background Variables
We propose a method to distinguish causal influence from hidden confounding in the following scenario: given a target variable Y, potential causal drivers X, and a large number of background features, we propose a novel criterion for identifying causal relationship based on the stability of regression coefficients of X on Y with respect to selecting different background features.
Provably Training Overparameterized Neural Network Classifiers with Non-convex Constraints
Training a classifier under non-convex constraints has gotten increasing attention in the machine learning community thanks to its wide range of applications such as algorithmic fairness and class-imbalanced classification.
Comment on 'Semi-Quantum Private Comparison Based on Bell States'
This study points out a semi-quantum protocol for private comparison using Bell states (SQPC) suffering from the double C-NOT attack and the malicious agent attack.
Quantum Physics
Ratio Trace Formulation of Wasserstein Discriminant Analysis
We reformulate the Wasserstein Discriminant Analysis (WDA) as a ratio trace problem and present an eigensolver-based algorithm to compute the discriminative subspace of WDA.
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach
We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation.
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation.
Tensor Canonical Correlation Analysis with Convergence and Statistical Guarantees
In many applications, such as classification of images or videos, it is of interest to develop a framework for tensor data instead of an ad-hoc way of transforming data to vectors due to the computational and under-sampling issues.
