Zobrazeno 1 - 10
of 254
pro vyhledávání: '"Ioannidis, Stratis"'
Autor:
Chee, Jerry, Kalyanaraman, Shankar, Ernala, Sindhu Kiranmai, Weinsberg, Udi, Dean, Sarah, Ioannidis, Stratis
We consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content. We model the impact of recommendations on user behavior, particularly the tendency to consume harmfu
Externí odkaz:
http://arxiv.org/abs/2406.09882
Autor:
Siew, Marie, Zhang, Haoran, Park, Jong-Ik, Liu, Yuezhou, Ruan, Yichen, Su, Lili, Ioannidis, Stratis, Yeh, Edmund, Joe-Wong, Carlee
Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL applications may increasingly require multiple FL tasks to be trained s
Externí odkaz:
http://arxiv.org/abs/2404.13841
Federated learning is a popular distributed learning approach for training a machine learning model without disclosing raw data. It consists of a parameter server and a possibly large collection of clients (e.g., in cross-device federated learning) t
Externí odkaz:
http://arxiv.org/abs/2404.10091
As edge computing capabilities increase, model learning deployments in diverse edge environments have emerged. In experimental design networks, introduced recently, network routing and rate allocation are designed to aid the transfer of data from sen
Externí odkaz:
http://arxiv.org/abs/2401.04996
Autor:
Belgiovine, Mauro, Groen, Joshua, Sirera, Miquel, Tassie, Chinenye, Yıldız, Ayberk Yarkın, Trudeau, Sage, Ioannidis, Stratis, Chowdhury, Kaushik
Spectrum sharing allows different protocols of the same standard (e.g., 802.11 family) or different standards (e.g., LTE and DVB) to coexist in overlapping frequency bands. As this paradigm continues to spread, wireless systems must also evolve to id
Externí odkaz:
http://arxiv.org/abs/2401.04837
Several recent methods for interpretability model feature interactions by looking at the Hessian of a neural network. This poses a challenge for ReLU networks, which are piecewise-linear and thus have a zero Hessian almost everywhere. We propose Smoo
Externí odkaz:
http://arxiv.org/abs/2311.00858
We study monotone submodular maximization under general matroid constraints in the online setting. We prove that online optimization of a large class of submodular functions, namely, weighted threshold potential functions, reduces to online convex op
Externí odkaz:
http://arxiv.org/abs/2309.04339
Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively train a model via minimizing a global objective. Communication bandwidth is a scarce resource; in each round, th
Externí odkaz:
http://arxiv.org/abs/2306.00280
Autor:
Salehi, Batool, Demir, Utku, Roy, Debashri, Pradhan, Suyash, Dy, Jennifer, Ioannidis, Stratis, Chowdhury, Kaushik
Creating a digital world that closely mimics the real world with its many complex interactions and outcomes is possible today through advanced emulation software and ubiquitous computing power. Such a software-based emulation of an entity that exists
Externí odkaz:
http://arxiv.org/abs/2305.10350
Autor:
Wang, Zifeng, Zhan, Zheng, Gong, Yifan, Shao, Yucai, Ioannidis, Stratis, Wang, Yanzhi, Dy, Jennifer
Rehearsal-based approaches are a mainstay of continual learning (CL). They mitigate the catastrophic forgetting problem by maintaining a small fixed-size buffer with a subset of data from past tasks. While most rehearsal-based approaches study how to
Externí odkaz:
http://arxiv.org/abs/2305.00380