Zobrazeno 1 - 10
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pro vyhledávání: '"Cyrus, A"'
Fairness research in machine learning often centers on ensuring equitable performance of individual models. However, real-world recommendation systems are built on multiple models and even multiple stages, from candidate retrieval to scoring and serv
Externí odkaz:
http://arxiv.org/abs/2412.04655
Understanding human mobility behavior is crucial for numerous applications, including crowd management, location-based recommendations, and the estimation of pandemic spread. Machine learning models can predict the Points of Interest (POIs) that indi
Externí odkaz:
http://arxiv.org/abs/2411.15285
Autor:
Zeighami, Sepanta, Shahabi, Cyrus
Machine learning models have demonstrated substantial performance enhancements over non-learned alternatives in various fundamental data management operations, including indexing (locating items in an array), cardinality estimation (estimating the nu
Externí odkaz:
http://arxiv.org/abs/2411.06243
Autor:
Zeighami, Sepanta, Shahahbi, Cyrus
Use of machine learning to perform database operations, such as indexing, cardinality estimation, and sorting, is shown to provide substantial performance benefits. However, when datasets change and data distribution shifts, empirical results also sh
Externí odkaz:
http://arxiv.org/abs/2411.06241
In this system, we discuss methods to stylize a scene of 3D primitive objects into a higher fidelity 3D scene using novel 3D representations like NeRFs and 3D Gaussian Splatting. Our approach leverages existing image stylization systems and image-to-
Externí odkaz:
http://arxiv.org/abs/2411.06067
Autor:
Joren, Hailey, Zhang, Jianyi, Ferng, Chun-Sung, Juan, Da-Cheng, Taly, Ankur, Rashtchian, Cyrus
Augmenting LLMs with context leads to improved performance across many applications. Despite much research on Retrieval Augmented Generation (RAG) systems, an open question is whether errors arise because LLMs fail to utilize the context from retriev
Externí odkaz:
http://arxiv.org/abs/2411.06037
Human mobility modeling from GPS-trajectories and synthetic trajectory generation are crucial for various applications, such as urban planning, disaster management and epidemiology. Both of these tasks often require filling gaps in a partially specif
Externí odkaz:
http://arxiv.org/abs/2411.04381
Autor:
Hong, Guan Zhe, Dikkala, Nishanth, Luo, Enming, Rashtchian, Cyrus, Wang, Xin, Panigrahy, Rina
Large language models (LLMs) have shown amazing performance on tasks that require planning and reasoning. Motivated by this, we investigate the internal mechanisms that underpin a network's ability to perform complex logical reasoning. We first const
Externí odkaz:
http://arxiv.org/abs/2411.04105
We study fair allocation of constrained resources, where a market designer optimizes overall welfare while maintaining group fairness. In many large-scale settings, utilities are not known in advance, but are instead observed after realizing the allo
Externí odkaz:
http://arxiv.org/abs/2411.02654
The abundance of vehicle trajectory data offers a new opportunity to compute driving routes between origins and destinations. Current graph-based routing pipelines, while effective, involve substantial costs in constructing, maintaining, and updating
Externí odkaz:
http://arxiv.org/abs/2411.01325