Zobrazeno 1 - 10
of 25 907
pro vyhledávání: '"Kleinberg, AS"'
Autor:
Zhang, Wenxin, Balseiro, Santiago R., Kleinberg, Robert, Mirrokni, Vahab, Sivan, Balasubramanian, Wydrowski, Bartek
We study distributed load balancing in bipartite queueing systems. Specifically, a set of frontends route jobs to a set of heterogeneous backends with workload-dependent service rates, with an arbitrary bipartite graph representing the connectivity b
Externí odkaz:
http://arxiv.org/abs/2411.17103
The gold standard in human-AI collaboration is complementarity -- when combined performance exceeds both the human and algorithm alone. We investigate this challenge in binary classification settings where the goal is to maximize 0-1 accuracy. Given
Externí odkaz:
http://arxiv.org/abs/2411.15230
Autor:
Fikioris, Giannis, Kleinberg, Robert, Kolumbus, Yoav, Kumar, Raunak, Mansour, Yishay, Tardos, Éva
In many repeated auction settings, participants care not only about how frequently they win but also how their winnings are distributed over time. This problem arises in various practical domains where avoiding congested demand is crucial, such as on
Externí odkaz:
http://arxiv.org/abs/2411.04843
Autor:
Kleinberg, Bennett, Zegers, Jari, Festor, Jonas, Vida, Stefana, Präsent, Julian, Loconte, Riccardo, Peereboom, Sanne
Differentiating between generated and human-written content is important for navigating the modern world. Large language models (LLMs) are crucial drivers behind the increased quality of computer-generated content. Reportedly, humans find it increasi
Externí odkaz:
http://arxiv.org/abs/2410.01675
Autor:
Tang, Zhenwei, Jiao, Difan, McIlroy-Young, Reid, Kleinberg, Jon, Sen, Siddhartha, Anderson, Ashton
There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains through more
Externí odkaz:
http://arxiv.org/abs/2409.20553
Large language models (LLMs) increasingly reach real-world applications, necessitating a better understanding of their behaviour. Their size and complexity complicate traditional assessment methods, causing the emergence of alternative approaches ins
Externí odkaz:
http://arxiv.org/abs/2409.15324
Broad topics in online platforms represent a type of meso-scale between individual user-defined communities and the whole platform; they typically consist of related communities that address different facets of a shared topic. Users often engage with
Externí odkaz:
http://arxiv.org/abs/2407.11794
We introduce the notion of an online matroid embedding, which is an algorithm for mapping an unknown matroid that is revealed in an online fashion to a larger-but-known matroid. The existence of such embedding enables a reduction from the version of
Externí odkaz:
http://arxiv.org/abs/2407.10316
Autor:
Blumer, Katy, Kleinberg, Jon
An increasing amount of attention has been devoted to the problem of "toxic" or antisocial behavior on social media. In this paper we analyze such behavior at very large scales: we analyze toxicity over a 14-year time span on nearly 500 million comme
Externí odkaz:
http://arxiv.org/abs/2407.09365
Autor:
Dagan, Yuval, Daskalakis, Constantinos, Fishelson, Maxwell, Golowich, Noah, Kleinberg, Robert, Okoroafor, Princewill
A set of probabilistic forecasts is calibrated if each prediction of the forecaster closely approximates the empirical distribution of outcomes on the subset of timesteps where that prediction was made. We study the fundamental problem of online cali
Externí odkaz:
http://arxiv.org/abs/2406.13668