Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Dorner, Florian"'
High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem. Scalable evaluation methods that avoid costly annotation have therefore become an important research ambition. Many hope to use strong exist
Externí odkaz:
http://arxiv.org/abs/2410.13341
We study a fundamental problem in the evaluation of large language models that we call training on the test task. Unlike wrongful practices like training on the test data, leakage, or data contamination, training on the test task is not a malpractice
Externí odkaz:
http://arxiv.org/abs/2407.07890
There is a growing body of work on learning from human feedback to align various aspects of machine learning systems with human values and preferences. We consider the setting of fairness in content moderation, in which human feedback is used to dete
Externí odkaz:
http://arxiv.org/abs/2406.05902
Autor:
Dorner, Florian E., Hardt, Moritz
We study how to best spend a budget of noisy labels to compare the accuracy of two binary classifiers. It's common practice to collect and aggregate multiple noisy labels for a given data point into a less noisy label via a majority vote. We prove a
Externí odkaz:
http://arxiv.org/abs/2402.02249
With large language models (LLMs) like GPT-4 appearing to behave increasingly human-like in text-based interactions, it has become popular to attempt to evaluate personality traits of LLMs using questionnaires originally developed for humans. While r
Externí odkaz:
http://arxiv.org/abs/2311.05297
Collaborative learning techniques have the potential to enable training machine learning models that are superior to models trained on a single entity's data. However, in many cases, potential participants in such collaborative schemes are competitor
Externí odkaz:
http://arxiv.org/abs/2305.16272
Autor:
Dorner, Florian E., Peychev, Momchil, Konstantinov, Nikola, Goel, Naman, Ash, Elliott, Vechev, Martin
Text classifiers have promising applications in high-stake tasks such as resume screening and content moderation. These classifiers must be fair and avoid discriminatory decisions by being invariant to perturbations of sensitive attributes such as ge
Externí odkaz:
http://arxiv.org/abs/2212.10154
Autor:
Dorner, Florian E.
The prospect of collusive agreements being stabilized via the use of pricing algorithms is widely discussed by antitrust experts and economists. However, the literature is often lacking the perspective of computer scientists, and seems to regularly o
Externí odkaz:
http://arxiv.org/abs/2110.04740
Autor:
Dorner, Florian E.
Sampled environment transitions are a critical input to deep reinforcement learning (DRL) algorithms. Current DRL benchmarks often allow for the cheap and easy generation of large amounts of samples such that perceived progress in DRL does not necess
Externí odkaz:
http://arxiv.org/abs/2102.04881