Zobrazeno 71 - 80
of 101 261
pro vyhledávání: '"A, Gaurav"'
We study a variant of causal contextual bandits where the context is chosen based on an initial intervention chosen by the learner. At the beginning of each round, the learner selects an initial action, depending on which a stochastic context is reve
Externí odkaz:
http://arxiv.org/abs/2405.18626
In this paper, we introduce a data augmentation approach specifically tailored to enhance intersectional fairness in classification tasks. Our method capitalizes on the hierarchical structure inherent to intersectionality, by viewing groups as inters
Externí odkaz:
http://arxiv.org/abs/2405.14521
Autor:
Yao, Lingyun, Trapp, Martin, Leslin, Jelin, Singh, Gaurav, Zhang, Peng, Periasamy, Karthekeyan, Andraud, Martin
Probabilistic circuits (PCs) offer a promising avenue to perform embedded reasoning under uncertainty. They support efficient and exact computation of various probabilistic inference tasks by design. Hence, hardware-efficient computation of PCs is hi
Externí odkaz:
http://arxiv.org/abs/2405.13639
Autor:
Ahumada, Tomás, Anand, Shreya, Coughlin, Michael W., Gupta, Vaidehi, Kasliwal, Mansi M., Karambelkar, Viraj R., Stein, Robert D., Waratkar, Gaurav, Swain, Vishwajeet, Laz, Theophile Jegou du, Anumarlapudi, Akash, Andreoni, Igor, Bulla, Mattia, Srinivasaragavan, Gokul P., Toivonen, Andrew, Wold, Avery, Bellm, Eric C., Cenko, S. Bradley, Kaplan, David L., Sollerman, Jesper, Bhalerao, Varun, Perley, Daniel, Salgundi, Anirudh, Suresh, Aswin, Hinds, K-Ryan, Reusch, Simeon, Necker, Jannis, Cook, David O., Pletskova, Natalya, Singer, Leo P., Banerjee, Smaranika, Barna, Tyler, Copperwheat, Christopher M., Healy, Brian, Kiendrebeogo, R. Weizmann, Kumar, Harsh, Kumar, Ravi, Pezzella, Marianna, Sagues-Carracedo, Ana, Sravan, Niharika, Bloom, Joshua S., Chen, Tracy X., Graham, Matthew, Helou, George, Laher, Russ R., Mahabal, Ashish A., Purdum, Josiah, Anupama, G. C., Barway, Sudhanshu, Basu, Judhajeet, Raman, Dhananjay, Roychowdhury, Tamojeet
During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network (IGWN), the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (BNS) and neutron
Externí odkaz:
http://arxiv.org/abs/2405.12403
We study the phenomenon of quantum synchronization from the viewpoint of quantum metrology. By interpreting quantum self-sustained oscillators as dissipative quantum sensors, we develop a framework to characterize several aspects of quantum synchroni
Externí odkaz:
http://arxiv.org/abs/2405.10643
We study the sample complexity of the classical shadows task: what is the fewest number of copies of an unknown state you need to measure to predict expected values with respect to some class of observables? Large joint measurements are likely requir
Externí odkaz:
http://arxiv.org/abs/2405.09525
Autor:
Reeves, Cian C., Harsha, Gaurav, Shee, Avijit, Zhu, Yuanran, Yang, Chao, Whaley, K Birgitta, Zgid, Dominika, Vlcek, Vojtech
Theoretical descriptions of non equilibrium dynamics of quantum many-body systems essentially employ either (i) explicit treatments, relying on truncation of the expansion of the many-body wave function, (ii) compressed representations of the many-bo
Externí odkaz:
http://arxiv.org/abs/2405.08814
In this paper, we invoke a generalized uncertainty principle (GUP) in the symmetry-reduced cosmological Hamiltonian for a universe driven by a quintessence scalar field with potential. Our study focuses on semi-classical regime. In particular, we der
Externí odkaz:
http://arxiv.org/abs/2405.08680
Autor:
Chauhan, Vinod Kumar, Clifton, Lei, Salaün, Achille, Lu, Huiqi Yvonne, Branson, Kim, Schwab, Patrick, Nigam, Gaurav, Clifton, David A.
While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited. One critical factor contributing to this restraint is sample selection bias (SSB) which refers to the study population being less repre
Externí odkaz:
http://arxiv.org/abs/2405.07841
Autor:
Singh, Gaurav, Bali, Kavitesh Kumar
This paper explores the seamless integration of Generative AI (GenAI) and Evolutionary Algorithms (EAs) within the domain of large-scale multi-objective optimization. Focusing on the transformative role of Large Language Models (LLMs), our study inve
Externí odkaz:
http://arxiv.org/abs/2405.07212