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Found 7 papers, 0 papers with code
Conditional Stochastic Interpolation for Generative Learning
We propose a conditional stochastic interpolation (CSI) approach to learning conditional distributions.
Wasserstein Generative Regression
Our approach simultaneously estimates a regression function and a conditional generator using a generative learning framework, where a conditional generator is a function that can generate samples from a conditional distribution.
Complexity of Deep Neural Networks from the Perspective of Functional Equivalence
In this paper, we investigate the complexity of feed-forward neural networks by examining the concept of functional equivalence, which suggests that different network parameterizations can lead to the same function.
Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression
We establish error bounds for simultaneously approximating $C^s$ smooth functions and their derivatives using RePU-activated deep neural networks.
Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction
We also derive non-asymptotic upper bounds for the difference of the lengths between the proposed non-crossing conformal prediction interval and the theoretically oracle prediction interval.
Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks
We propose a penalized nonparametric approach to estimating the quantile regression process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep neural networks and introduce a novel penalty function to enforce non-crossing of quantile regression curves.
Non-asymptotic Excess Risk Bounds for Classification with Deep Convolutional Neural Networks
To establish these results, we derive an upper bound for the covering number for the class of general convolutional neural networks with a bias term in each convolutional layer, and derive new results on the approximation power of CNNs for any uniformly-continuous target functions.
