Search Results for author: Daniel Martin

Found 7 papers, 0 papers with code

AI Oversight and Human Mistakes: Evidence from Centre Court

no code implementations30 Jan 2024 David Almog, Romain Gauriot, Lionel Page, Daniel Martin

We structurally estimate the psychological costs of being overruled by AI using a model of rational inattentive umpires, and our results suggest that because of these costs, umpires cared twice as much about Type II errors under AI oversight.

Decision Making

Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals

no code implementations19 Nov 2023 Nir Chemaya, Daniel Martin

The emergent abilities of Large Language Models (LLMs), which power tools like ChatGPT and Bard, have produced both excitement and worry about how AI will impact academic writing.

Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations

no code implementations24 Sep 2022 Letian Chen, Sravan Jayanthi, Rohan Paleja, Daniel Martin, Viacheslav Zakharov, Matthew Gombolay

Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics.

Continuous Control reinforcement-learning +1

Calibrating for Class Weights by Modeling Machine Learning

no code implementations10 May 2022 Andrew Caplin, Daniel Martin, Philip Marx

A much studied issue is the extent to which the confidence scores provided by machine learning algorithms are calibrated to ground truth probabilities.

BIG-bench Machine Learning Pneumonia Detection

ScanGAN360: A Generative Model of Realistic Scanpaths for 360$^{\circ}$ Images

no code implementations25 Mar 2021 Daniel Martin, Ana Serrano, Alexander W. Bergman, Gordon Wetzstein, Belen Masia

Generative adversarial approaches could alleviate this challenge by generating a large number of possible scanpaths for unseen images.

Dynamic Time Warping

If Loud Aliens Explain Human Earliness, Quiet Aliens Are Also Rare

no code implementations1 Feb 2021 Robin Hanson, Daniel Martin, Calvin Mccarter, Jonathan Paulson

We fit this three-parameter model of loud aliens to data: 1) birth power from the number of hard steps seen in Earth history, 2) birth constant by assuming a inform distribution over our rank among loud alien birth dates, and 3) expansion speed from our not seeing alien volumes in our sky.

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