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Found 5 papers, 1 papers with code
Don't Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget
We study how to best spend a budget of noisy labels to compare the accuracy of two binary classifiers.
Do personality tests generalize to Large Language Models?
With large language models (LLMs) appearing to behave increasingly human-like in text-based interactions, it has become popular to attempt to evaluate various properties of these models using tests originally designed for humans.
Human-Guided Fair Classification for Natural Language Processing
While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully align with human intuition (e. g., in cases of asymmetric counterfactuals).
Algorithmic collusion: A critical review
The prospect of collusive agreements being stabilized via the use of pricing algorithms is widely discussed by antitrust experts and economists.
Measuring Progress in Deep Reinforcement Learning Sample Efficiency
Sampled environment transitions are a critical input to deep reinforcement learning (DRL) algorithms.
