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    • Python
      0000Updated Oct 26, 2024Oct 26, 2024
    • Summer 2021 project analyzing bias in algorithms using inductive orientation vectors
      Python
      0000Updated Sep 2, 2024Sep 2, 2024
    • Python
      0000Updated May 18, 2024May 18, 2024
    • Source code for Hom, C., Yik, W., Montañez, G. (Accepted 2023). Finite-Sample Bounds for Two-Distribution Hypothesis Tests. IEEE International Conference on Data Science and Advance Analytics (DSAA).
      Jupyter Notebook
      0000Updated Aug 15, 2023Aug 15, 2023
    • Source code for Yik, W., Serafini, L., Lindsey, T., Montañez, G. (2022). Identifying Bias in Data Using Two-Distribution Hypothesis Tests. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society. https://doi.org/10.1145/3514094.3534169
      Jupyter Notebook
      0000Updated Jan 5, 2023Jan 5, 2023
    • A public repository of the code and data for research shown in "The Gopher Grounds: Testing the Link Between Structure and Function in Simple Machines," supported in part by the National Science Foundation under Grant No. 1950885.
      Python
      3200Updated Jun 11, 2022Jun 11, 2022
    • A public repository of the code and data for research shown in "The Gopher’s Gambit: Survival Advantages of Artifact-Based Intention Perception," supported in part by the National Science Foundation under Grant No. 1950885.
      Python
      1000Updated May 28, 2021May 28, 2021
    • A public repository of the code and data for research shown in "The Predator's Purpose: Survival Advantages of Intentional Perception in Simulated Agents," supported in part by the National Science Foundation under Grant No. 1950885.
      Python
      1000Updated May 28, 2021May 28, 2021
    • A public repository of the code and data for research shown in "The Hero's Dilemma: Survival Advantages of Intentional-Stance Perception in Virtual Agents," supported in part by the National Science Foundation under Grant No. 1950885.
      Python
      1100Updated May 28, 2021May 28, 2021
    • Source code for the paper: Espinosa Dice N, Kaye M, Ahmed H, Montanez G, "A Probabilistic Theory of Abductive Reasoning," ICAART 2021. Work supported in part by the National Science Foundation under Grant No. 1950885.
      Jupyter Notebook
      2000Updated Dec 1, 2020Dec 1, 2020
    • Source code for the paper: Allen J, Lay C, Montañez G, “A Castro Consensus: Understanding the Role of Dependence in Consensus Formation” Conference on Truth and Trust Online (TTO 2020), October 16-17, 2020. This work was supported in part by the National Science Foundation under Grant No. 1950885.
      Python
      1000Updated Nov 30, 2020Nov 30, 2020
    • Functions to investigate learning deterministic finite automata (DFA). Summer 2019 Harvey Mudd College AMISTAD lab.
      Python
      2000Updated Jul 26, 2019Jul 26, 2019