Zobrazeno 1 - 10
of 357
pro vyhledávání: '"Dickerson, John P."'
Autor:
Sullivan, Ryan, Pégoud, Ryan, Rahmen, Ameen Ur, Yang, Xinchen, Huang, Junyun, Verma, Aayush, Mitra, Nistha, Dickerson, John P.
Curriculum learning has been a quiet yet crucial component of many of the high-profile successes of reinforcement learning. Despite this, none of the major reinforcement learning libraries directly support curriculum learning or include curriculum le
Externí odkaz:
http://arxiv.org/abs/2411.11318
Autor:
Stein, Alex, Sharpe, Samuel, Bergman, Doron, Kumar, Senthil, Bruss, C. Bayan, Dickerson, John, Goldstein, Tom, Goldblum, Micah
Many real-world applications of tabular data involve using historic events to predict properties of new ones, for example whether a credit card transaction is fraudulent or what rating a customer will assign a product on a retail platform. Existing a
Externí odkaz:
http://arxiv.org/abs/2410.10648
Autor:
Feuer, Benjamin, Goldblum, Micah, Datta, Teresa, Nambiar, Sanjana, Besaleli, Raz, Dooley, Samuel, Cembalest, Max, Dickerson, John P.
The release of ChatGPT in November 2022 sparked an explosion of interest in post-training and an avalanche of new preference optimization (PO) methods. These methods claim superior alignment by virtue of better correspondence with human pairwise pref
Externí odkaz:
http://arxiv.org/abs/2409.15268
Clustering is a fundamental problem in machine learning and operations research. Therefore, given the fact that fairness considerations have become of paramount importance in algorithm design, fairness in clustering has received significant attention
Externí odkaz:
http://arxiv.org/abs/2406.15960
In barter exchanges agents enter seeking to swap their items for other items on their wishlist. We consider a centralized barter exchange with a set of agents and items where each item has a positive value. The goal is to compute a (re)allocation of
Externí odkaz:
http://arxiv.org/abs/2406.13983
We study the canonical fair clustering problem where each cluster is constrained to have close to population-level representation of each group. Despite significant attention, the salient issue of having incomplete knowledge about the group membershi
Externí odkaz:
http://arxiv.org/abs/2406.00599
Responsible artificial intelligence (RAI) is increasingly recognized as a critical concern. However, the level of corporate RAI prioritization has not kept pace. In this work, we conduct 16 semi-structured interviews with practitioners to investigate
Externí odkaz:
http://arxiv.org/abs/2405.03855
Large language models (LLMs) are increasing in capability and popularity, propelling their application in new domains -- including as replacements for human participants in computational social science, user testing, annotation tasks, and more. In ma
Externí odkaz:
http://arxiv.org/abs/2402.01908
Autor:
Verma, Sahil, Bhatt, Gantavya, Schwarzschild, Avi, Singhal, Soumye, Das, Arnav Mohanty, Shah, Chirag, Dickerson, John P, Bilmes, Jeff
Despite the advanced capabilities of contemporary machine learning (ML) models, they remain vulnerable to adversarial and backdoor attacks. This vulnerability is particularly concerning in real-world deployments, where compromised models may exhibit
Externí odkaz:
http://arxiv.org/abs/2311.14948
Research in fair machine learning, and particularly clustering, has been crucial in recent years given the many ethical controversies that modern intelligent systems have posed. Ahmadian et al. [2020] established the study of fairness in \textit{hier
Externí odkaz:
http://arxiv.org/abs/2311.12501