Assessing reliability of protein-protein interactions by gene ontology integration. - George D. Montañez; Young-Rae Cho (CIBCB, 2012)

Information transmission through genetic algorithm fitness maps. - George D. Montañez (IEEE Congress on Evolutionary Computation, 2013)

Bounding the number of favorable functions in stochastic search. - George D. Montañez (IEEE Congress on Evolutionary Computation, 2013)

Cross-Device Search. - George D. Montañez; Ryen W. White; Xiao Huang (CIKM, 2014)

Inertial Hidden Markov Models: Modeling Change in Multivariate Time Series. - George D. Montañez; Saeed Amizadeh; Nikolay Laptev (AAAI, 2015)

The LICORS Cabinet: Nonparametric Algorithms for Spatio-temporal Prediction. - George D. Montañez; Cosma Rohilla Shalizi (CoRR, 2015)

Detecting Intelligence - The Turing Test and Other Design Detection Methodologies. - George D. Montañez (ICAART (2), 2016)

Why Machine Learning Works. - George D. Montañez (2016)

The LICORS cabinet: Nonparametric light cone methods for spatio-temporal modeling. - George D. Montañez; Cosma Rohilla Shalizi (IJCNN, 2017)

Estimating the Prevalence of Religious Content in Intelligent Design Social Media. - George D. Montañez (IRI, 2017)

The famine of forte: Few search problems greatly favor your algorithm. - George D. Montañez (SMC, 2017)

The Futility of Bias-Free Learning and Search. - George D. Montañez; Jonathan Hayase; Julius Lauw; Dominique Macias; Akshay Trikha; Julia Vendemiatti (Australasian Conference on Artificial Intelligence, 2019)

An Information-Theoretic Perspective on Overfitting and Underfitting. - Daniel Bashir; George D. Montañez; Sonia Sehra; Pedro Sandoval Segura; Julius Lauw (Australasian Conference on Artificial Intelligence, 2020)

Minimal Complexity Requirements for Proteins and Other Combinatorial Recognition Systems. - George D. Montañez; Laina Sanders; Howard Deshong (BIOINFORMATICS, 2020)

The Bias-Expressivity Trade-off. - Julius Lauw; Dominique Macias; Akshay Trikha; Julia Vendemiatti; George D. Montañez (ICAART (2), 2020)

Trading Bias for Expressivity in Artificial Learning. - George D. Montañez; Daniel Bashir; Julius Lauw (ICAART (Revised Selected Papers), 2020)

Decomposable Probability-of-Success Metrics in Algorithmic Search. - Tyler Sam; Jake Williams; Abel Tadesse; Huey Sun; George D. Montañez (ICAART (2), 2020)

The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity. - Pedro Sandoval Segura; Julius Lauw; Daniel Bashir; Kinjal Shah; Sonia Sehra; Dominique Macias; George D. Montañez (ICAART (2), 2020)

Limits of Transfer Learning. - Jake Williams; Abel Tadesse; Tyler Sam; Huey Sun; George D. Montañez (LOD (2), 2020)

A Castro Consensus: Understanding the Role of Dependence in Consensus Formation. - Jarred Allen; Cindy Lay; George D. Montañez (TTO, 2020)

The Predator's Purpose: Intention Perception in Simulated Agent Environments. - Amani R. Maina-Kilaas; Cynthia Hom; Kevin Ginta; George D. Montañez (CEC, 2021)

The Hero's Dilemma: Survival Advantages of Intention Perception in Virtual Agent Games. - Amani R. Maina-Kilaas; George D. Montañez; Cynthia Hom; Kevin Ginta; Cindy Lay (CoG, 2021)

A Probabilistic Theory of Abductive Reasoning. - Nicolas A. Espinosa Dice; Megan L. Kaye; Hana Ahmed; George D. Montañez (ICAART (2), 2021)

The Gopher's Gambit: Survival Advantages of Artifact-based Intention Perception. - Cynthia Hom; Amani R. Maina-Kilaas; Kevin Ginta; Cindy Lay; George D. Montañez (ICAART (1), 2021)

The Label Recorder Method - Testing the Memorization Capacity of Machine Learning Models. - Kyle Rong; Aditya Khant; David Flores; George D. Montañez (LOD, 2021)

Hyperparameter Choice as Search Bias in AlphaZero. - Eric M. Weiner; George D. Montañez; Aaron Trujillo; Abtin Molavi (SMC, 2021)

Undecidability of Underfitting in Learning Algorithms. - Sonia Sehra; David Flores; George D. Montañez (CoRR, 2021)

Permutation Encoding for Text Steganography: A Short Tutorial. - George D. Montañez (CoRR, 2021)

Identifying Bias in Data Using Two-Distribution Hypothesis Tests. - William Yik; Limnanthes Serafini; Timothy Lindsey; George D. Montañez (AIES, 2022)

Vectorization of Bias in Machine Learning Algorithms. - Sophie Bekerman; Eric Chen; Lily Lin; George D. Montañez (ICAART (2), 2022)

The Gopher Grounds: Testing the Link between Structure and Function in Simple Machines. - Anshul Kamath; Jiayi Zhao; Nick Grisanti; George D. Montañez (ICAART (2), 2022)

Generating the Gopher's Grounds: Form, Function, Order, and Alignment. - Jiayi Zhao; Anshul Kamath; Nick Grisanti; George D. Montañez (ICAART (Revised Selected Paper, 2022)

Bounding Generalization Error Through Bias and Capacity. - Ramya Ramalingam; Nicolas A. Espinosa Dice; Megan L. Kaye; George D. Montañez (IJCNN, 2022)

Finite-Sample Bounds for Two-Distribution Hypothesis Tests. - Cynthia Hom; William Yik; George D. Montañez (DSAA, 2023)

From Targets to Rewards: Continuous Target Sets in the Algorithmic Search Framework. - Milo Knell; Sahil Rane; Forrest Bicker; Tiger Che; Alan Wu; George D. Montañez (ICAART (3), 2024)

Model Characterization with Inductive Orientation Vectors. - Kerria Pang-Naylor; Eric Chen; George D. Montañez (ICAART (2), 2025)