Zobrazeno 1 - 10
of 11 677
pro vyhledávání: '"Ringel, A."'
The risk-controlling prediction sets (RCPS) framework is a general tool for transforming the output of any machine learning model to design a predictive rule with rigorous error rate control. The key idea behind this framework is to use labeled hold-
Externí odkaz:
http://arxiv.org/abs/2412.11174
Autor:
Ringel, Claus Michael
We deal with the category of finitely generated modules over an artin algebra $A$. Recall that an object in an abelian category is said to be a brick provided its endomorphism ring is a division ring. Simple modules are, of course, bricks, but in cas
Externí odkaz:
http://arxiv.org/abs/2411.18427
Autor:
Park, Joon Sung, Zou, Carolyn Q., Shaw, Aaron, Hill, Benjamin Mako, Cai, Carrie, Morris, Meredith Ringel, Willer, Robb, Liang, Percy, Bernstein, Michael S.
The promise of human behavioral simulation--general-purpose computational agents that replicate human behavior across domains--could enable broad applications in policymaking and social science. We present a novel agent architecture that simulates th
Externí odkaz:
http://arxiv.org/abs/2411.10109
Cetacean Calves keep up with their mothers while rapidly swimming, by a hydrodynamical effect called drafting. This has been observed in the wild and enclosed areas, and has been mathematically analyzed in the past, but no quantitative measures of th
Externí odkaz:
http://arxiv.org/abs/2411.06118
Robot design has traditionally been costly and labor-intensive. Despite advancements in automated processes, it remains challenging to navigate a vast design space while producing physically manufacturable robots. We introduce Text2Robot, a framework
Externí odkaz:
http://arxiv.org/abs/2406.19963
Autor:
Shen, Hua, Knearem, Tiffany, Ghosh, Reshmi, Alkiek, Kenan, Krishna, Kundan, Liu, Yachuan, Ma, Ziqiao, Petridis, Savvas, Peng, Yi-Hao, Qiwei, Li, Rakshit, Sushrita, Si, Chenglei, Xie, Yutong, Bigham, Jeffrey P., Bentley, Frank, Chai, Joyce, Lipton, Zachary, Mei, Qiaozhu, Mihalcea, Rada, Terry, Michael, Yang, Diyi, Morris, Meredith Ringel, Resnick, Paul, Jurgens, David
Recent advancements in general-purpose AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment. However, the lack of clar
Externí odkaz:
http://arxiv.org/abs/2406.09264
Advances in deep learning systems have allowed large models to match or surpass human accuracy on a number of skills such as image classification, basic programming, and standardized test taking. As the performance of the most capable models begin to
Externí odkaz:
http://arxiv.org/abs/2406.04446
Autor:
Lavie, Itay, Ringel, Zohar
Kernel ridge regression (KRR) and Gaussian processes (GPs) are fundamental tools in statistics and machine learning with recent applications to highly over-parameterized deep neural networks. The ability of these tools to learn a target function is d
Externí odkaz:
http://arxiv.org/abs/2406.02663
We consider the category $\mathcal S(n)$ of all pairs $X = (U,V)$, where $V$ is a finite-dimensional vector space with a nilpotent operator $T$ with $T^n = 0$, and $U$ is a subspace of $V$ such that $T(U) \subseteq U$. Our main interest in an object
Externí odkaz:
http://arxiv.org/abs/2405.18592
Autor:
Fischer, Kirsten, Lindner, Javed, Dahmen, David, Ringel, Zohar, Krämer, Michael, Helias, Moritz
A key property of neural networks driving their success is their ability to learn features from data. Understanding feature learning from a theoretical viewpoint is an emerging field with many open questions. In this work we capture finite-width effe
Externí odkaz:
http://arxiv.org/abs/2405.10761