5 citations · 6 across the 4 of their papers we have counts for
9 papers
Permutation Encoding for Text Steganography: A Short Tutorial
George D. Montanez
We explore a method of encoding secret messages using factoradic numbering of permuted lists of text or numeric elements. Encoding and decoding methods are provided, with code, and…
Undecidability of Underfitting in Learning Algorithms
Sonia Sehra, David Flores, George D. Montanez
Using recent machine learning results that present an information-theoretic perspective on underfitting and overfitting, we prove that deciding whether an encodable learning algori…
An Information-Theoretic Perspective on Overfitting and Underfitting
Daniel Bashir, George D. Montanez, Sonia Sehra +2
We present an information-theoretic framework for understanding overfitting and underfitting in machine learning and prove the formal undecidability of determining whether an arbit…
Limits of Transfer Learning
Jake Williams, Abel Tadesse, Tyler Sam +2
Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely used in practice, theory for transfer lea…
The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity
Pedro Sandoval Segura, Julius Lauw, Daniel Bashir +4
Algorithm performance in supervised learning is a combination of memorization, generalization, and luck. By estimating how much information an algorithm can memorize from a dataset…
Decomposable Probability-of-Success Metrics in Algorithmic Search
Tyler Sam, Jake Williams, Abel Tadesse +2
Previous studies have used a specific success metric within an algorithmic search framework to prove machine learning impossibility results. However, this specific success metric p…
The Bias-Expressivity Trade-off
Julius Lauw, Dominique Macias, Akshay Trikha +2
Learning algorithms need bias to generalize and perform better than random guessing. We examine the flexibility (expressivity) of biased algorithms. An expressive algorithm can ada…
The Futility of Bias-Free Learning and Search
George D. Montanez, Jonathan Hayase, Julius Lauw +3
Building on the view of machine learning as search, we demonstrate the necessity of bias in learning, quantifying the role of bias (measured relative to a collection of possible da…
The LICORS Cabinet: Nonparametric Algorithms for Spatio-temporal Prediction
George D. Montanez, Cosma Rohilla Shalizi
Spatio-temporal data is intrinsically high dimensional, so unsupervised modeling is only feasible if we can exploit structure in the process. When the dynamics are local in both sp…