1 citations · 1 across the 7 of their papers we have counts for
7 papers
Bounds on Spectral Gaps for Non-Reversible Markov Chains with Applications to Temporal Difference Learning
Andrew Lamperski
This work is motivated by the analysis of temporal difference algorithms, where stability can be guaranteed by bounding the eigenvalues of an associated matrix derived from a, typi…
Non-Asymptotic Error Bounds for Causally Conditioned Directed Information Rates of Gaussian Sequences
Yuping Zheng, Andrew Lamperski
Directed information and its causally conditioned variations are often used to measure causal influences between random processes. In practice, these quantities must be measured fr…
A Neural Network Algorithm for KL Divergence Estimation with Quantitative Error Bounds
Mikil Foss, Andrew Lamperski
Estimating the Kullback-Leibler (KL) divergence between random variables is a fundamental problem in statistical analysis. For continuous random variables, traditional information-…
Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential
Yuping Zheng, Andrew Lamperski
Stochastic gradient descent (SGD) is the main algorithm behind a large body of work in machine learning. In many cases, constraints are enforced via projections, leading to project…
Non-Asymptotic Analysis of Classical Spectrum Estimators for -mixing Time-series Data with Estimated Means
Yuping Zheng, Andrew Lamperski
Spectral estimation is an important tool in time series analysis, with applications including economics, astronomy, and climatology. The asymptotic theory for non-parametric estima…
Function Gradient Approximation with Random Shallow ReLU Networks with Control Applications
Andrew Lamperski, Siddharth Salapaka
Neural networks are widely used to approximate unknown functions in control. A common neural network architecture uses a single hidden layer (i.e. a shallow network), in which the…