Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Lu, Songtao"'
Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. While the SGD-based FL algorithms have demonstrated considerable success in the past, there is a growing trend towards adopting adaptiv
Externí odkaz:
http://arxiv.org/abs/2407.18365
Autor:
Fang, Minghong, Zhang, Zifan, Hairi, Khanduri, Prashant, Liu, Jia, Lu, Songtao, Liu, Yuchen, Gong, Neil
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system follows the server-assisted architecture (server-assisted FL), where the t
Externí odkaz:
http://arxiv.org/abs/2406.10416
Stochastic bilevel optimization tackles challenges involving nested optimization structures. Its fast-growing scale nowadays necessitates efficient distributed algorithms. In conventional distributed bilevel methods, each worker must transmit full-di
Externí odkaz:
http://arxiv.org/abs/2405.18858
Autor:
Zhang, Shuai, Fernando, Heshan Devaka, Liu, Miao, Murugesan, Keerthiram, Lu, Songtao, Chen, Pin-Yu, Chen, Tianyi, Wang, Meng
This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition dynamics. In this setting, the Q-function of each RL problem (task) can be decompo
Externí odkaz:
http://arxiv.org/abs/2405.15920
Transformer-based large language models have displayed impressive in-context learning capabilities, where a pre-trained model can handle new tasks without fine-tuning by simply augmenting the query with some input-output examples from that task. Desp
Externí odkaz:
http://arxiv.org/abs/2402.15607
Stochastic bilevel optimization (SBO) is becoming increasingly essential in machine learning due to its versatility in handling nested structures. To address large-scale SBO, decentralized approaches have emerged as effective paradigms in which nodes
Externí odkaz:
http://arxiv.org/abs/2402.03167
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization
In this paper, we present a novel bilevel optimization-based training approach to training acoustic models for automatic speech recognition (ASR) tasks that we term {bi-level joint unsupervised and supervised training (BL-JUST)}. {BL-JUST employs a l
Externí odkaz:
http://arxiv.org/abs/2401.06980
Soft random sampling (SRS) is a simple yet effective approach for efficient training of large-scale deep neural networks when dealing with massive data. SRS selects a subset uniformly at random with replacement from the full data set in each epoch. I
Externí odkaz:
http://arxiv.org/abs/2311.12727
Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logic
Externí odkaz:
http://arxiv.org/abs/2310.18378
Autor:
Zhang, Shuai, Li, Hongkang, Wang, Meng, Liu, Miao, Chen, Pin-Yu, Lu, Songtao, Liu, Sijia, Murugesan, Keerthiram, Chaudhury, Subhajit
Publikováno v:
Neurips 2023
This paper provides a theoretical understanding of Deep Q-Network (DQN) with the $\varepsilon$-greedy exploration in deep reinforcement learning. Despite the tremendous empirical achievement of the DQN, its theoretical characterization remains undere
Externí odkaz:
http://arxiv.org/abs/2310.16173