Zobrazeno 1 - 10
of 35 913
pro vyhledávání: '"Shanmugam, A."'
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
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
Source enumeration, the task of estimating the number of sources from the signal received by the array of antennas, is a critical problem in array signal processing. Numerous methods have been proposed to estimate the number of sources under white or
Externí odkaz:
http://arxiv.org/abs/2409.06563
Language-agnostic many-to-one end-to-end speech translation models can convert audio signals from different source languages into text in a target language. These models do not need source language identification, which improves user experience. In s
Externí odkaz:
http://arxiv.org/abs/2406.10276
Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions. This assumption is not valid in a wide range of applications, and generally
Externí odkaz:
http://arxiv.org/abs/2406.05937
We introduce the Glauber Generative Model (GGM), a new class of discrete diffusion models, to obtain new samples from a distribution given samples from a discrete space. GGM deploys a discrete Markov chain called the heat bath dynamics (or the Glaube
Externí odkaz:
http://arxiv.org/abs/2405.17035
Autor:
Artman, Conor M., Mate, Aditya, Nwankwo, Ezinne, Heching, Aliza, Idé, Tsuyoshi, Navrátil, Jiří, Shanmugam, Karthikeyan, Sun, Wei, Varshney, Kush R., Goldkind, Lauri, Kroch, Gidi, Sawyer, Jaclyn, Watson, Ian
We developed a common algorithmic solution addressing the problem of resource-constrained outreach encountered by social change organizations with different missions and operations: Breaking Ground -- an organization that helps individuals experienci
Externí odkaz:
http://arxiv.org/abs/2403.10638
This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent variables to the observed variables. Linear and general transformations
Externí odkaz:
http://arxiv.org/abs/2402.00849
Autor:
Pierson, Emma, Shanmugam, Divya, Movva, Rajiv, Kleinberg, Jon, Agrawal, Monica, Dredze, Mark, Ferryman, Kadija, Gichoya, Judy Wawira, Jurafsky, Dan, Koh, Pang Wei, Levy, Karen, Mullainathan, Sendhil, Obermeyer, Ziad, Suresh, Harini, Vafa, Keyon
Advances in large language models (LLMs) have driven an explosion of interest about their societal impacts. Much of the discourse around how they will impact social equity has been cautionary or negative, focusing on questions like "how might LLMs be
Externí odkaz:
http://arxiv.org/abs/2312.14804
Autor:
Hegselmann, Stefan, Parziale, Antonio, Shanmugam, Divya, Tang, Shengpu, Asiedu, Mercy Nyamewaa, Chang, Serina, Hartvigsen, Thomas, Singh, Harvineet
A collection of the accepted Findings papers that were presented at the 3rd Machine Learning for Health symposium (ML4H 2023), which was held on December 10, 2023, in New Orleans, Louisiana, USA. ML4H 2023 invited high-quality submissions on relevant
Externí odkaz:
http://arxiv.org/abs/2312.00655