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Found 6 papers, 0 papers with code
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
We provide a rigorous theoretical guarantee for the confidence interval, demonstrating that the coverage is approximately exact with an explicit convergence rate and allowing for high confidence level inference.
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Stochastic Gradient Descent (SGD) is one of the simplest and most popular algorithms in modern statistical and machine learning due to its computational and memory efficiency.
Time-Varying Multivariate Causal Processes
In this paper, we consider a wide class of time-varying multivariate causal processes which nests many classic and new examples as special cases.
Long-term prediction intervals with many covariates
After validating our approach using simulations we also propose a novel bootstrap based method that can boost the coverage of the theoretical intervals.
Prediction Intervals
Time Series Analysis
Methodology
Econometrics
Statistics Theory
Statistics Theory
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach
The broader ambition of this article is to popularize an approach for the fair distribution of the quantity of a system's output to its subsystems, while allowing for underlying complex subsystem level interactions.
Online Covariance Matrix Estimation in Stochastic Gradient Descent
This approach fits in an online setting and takes full advantage of SGD: efficiency in computation and memory.
