Zobrazeno 1 - 10
of 15 562
pro vyhledávání: '"Karthikeyan, P."'
Our work aims to minimize interaction in secure computation due to the high cost and challenges associated with communication rounds, particularly in scenarios with many clients. In this work, we revisit the problem of secure aggregation in the singl
Externí odkaz:
http://arxiv.org/abs/2410.22303
Autor:
Dasgupta, Arpan, Jain, Gagan, Suggala, Arun, Shanmugam, Karthikeyan, Tambe, Milind, Taneja, Aparna
Mobile health (mHealth) programs face a critical challenge in optimizing the timing of automated health information calls to beneficiaries. This challenge has been formulated as a collaborative multi-armed bandit problem, requiring online learning of
Externí odkaz:
http://arxiv.org/abs/2410.21405
Mechanistic interpretability aims to provide human-understandable insights into the inner workings of neural network models by examining their internals. Existing approaches typically require significant manual effort and prior knowledge, with strate
Externí odkaz:
http://arxiv.org/abs/2410.16484
Autor:
Mukherjee, Arpan, Ubaru, Shashanka, Murugesan, Keerthiram, Shanmugam, Karthikeyan, Tajer, Ali
This paper considers the problem of combinatorial multi-armed bandits with semi-bandit feedback and a cardinality constraint on the super-arm size. Existing algorithms for solving this problem typically involve two key sub-routines: (1) a parameter e
Externí odkaz:
http://arxiv.org/abs/2410.10679
With the boom of machine learning (ML) techniques, software practitioners build ML systems to process the massive volume of streaming data for diverse software engineering tasks such as failure prediction in AIOps. Trained using historical data, such
Externí odkaz:
http://arxiv.org/abs/2410.09190
We propose a novel mechanism, dark matter internal pair production (DIPP), to detect dark matter candidates at beam dump facilities. When energetic dark matter scatters in a material, it can create a lepton-antilepton pair by exchanging a virtual pho
Externí odkaz:
http://arxiv.org/abs/2410.07624
Autor:
Mitchell-White, James, Omdivar, Reza, Urwin, Esmond, Sivakumar, Karthikeyan, Li, Ruizhe, Rae, Andy, Wang, Xiaoyan, Mina, Theresia, Chambers, John, Figueredo, Grazziela, Quinlan, Philip R
This paper introduces Llettuce, an open-source tool designed to address the complexities of converting medical terms into OMOP standard concepts. Unlike existing solutions such as the Athena database search and Usagi, which struggle with semantic nua
Externí odkaz:
http://arxiv.org/abs/2410.09076
Autor:
Lee, Bruce W., Padhi, Inkit, Ramamurthy, Karthikeyan Natesan, Miehling, Erik, Dognin, Pierre, Nagireddy, Manish, Dhurandhar, Amit
LLMs have shown remarkable capabilities, but precisely controlling their response behavior remains challenging. Existing activation steering methods alter LLM behavior indiscriminately, limiting their practical applicability in settings where selecti
Externí odkaz:
http://arxiv.org/abs/2409.05907
Autor:
Jafari, Sajad, Bayani, Atiyeh, Parastesh, Fatemeh, Rajagopal, Karthikeyan, del Genio, Charo I., Minati, Ludovico, Boccaletti, Stefano
The Master Stability Function is a robust and useful tool for determining the conditions of synchronization stability in a network of coupled systems. While a comprehensive classification exists in the case in which the nodes are chaotic dynamical sy
Externí odkaz:
http://arxiv.org/abs/2409.04193
Autor:
Padhi, Inkit, Ramamurthy, Karthikeyan Natesan, Sattigeri, Prasanna, Nagireddy, Manish, Dognin, Pierre, Varshney, Kush R.
Aligning large language models (LLMs) to value systems has emerged as a significant area of research within the fields of AI and NLP. Currently, this alignment process relies on the availability of high-quality supervised and preference data, which c
Externí odkaz:
http://arxiv.org/abs/2408.10392