Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Li, Shuai"'
Autor:
Kong, Fang, Li, Shuai
The problem of matching markets has been studied for a long time in the literature due to its wide range of applications. Finding a stable matching is a common equilibrium objective in this problem. Since market participants are usually uncertain of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c77e86e78985c7821f345a1e96e6dc7c
http://arxiv.org/abs/2307.10890
http://arxiv.org/abs/2307.10890
Autor:
Wu, Junda, Yu, Tong, Wang, Rui, Song, Zhao, Zhang, Ruiyi, Zhao, Handong, Lu, Chaochao, Li, Shuai, Henao, Ricardo
Soft prompt tuning achieves superior performances across a wide range of few-shot tasks. However, the performances of prompt tuning can be highly sensitive to the initialization of the prompts. We also empirically observe that conventional prompt tun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b5d7d0cf30366e2f099b2503ff5faec
http://arxiv.org/abs/2306.04933
http://arxiv.org/abs/2306.04933
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:6202-6210
Online learning to rank (OLTR) interactively learns to choose lists of items from a large collection based on certain click models that describe users' click behaviors. Most recent works for this problem focus on the stochastic environment where the
Recent research in offline reinforcement learning (RL) has demonstrated that return-conditioned supervised learning is a powerful paradigm for decision-making problems. While promising, return conditioning is limited to training data labeled with rew
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d4d8a1b2f90b867ecab75366137e9ac
http://arxiv.org/abs/2305.16683
http://arxiv.org/abs/2305.16683
Online learning to rank (OLTR) is a sequential decision-making problem where a learning agent selects an ordered list of items and receives feedback through user clicks. Although potential attacks against OLTR algorithms may cause serious losses in r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4dec3fc0e9a9f912f31eb3b0b48fd571
http://arxiv.org/abs/2305.17071
http://arxiv.org/abs/2305.17071
Autor:
Li, Shuai, Keipour, Azarakhsh, Jamieson, Kevin, Hudson, Nicolas, Swan, Charles, Bekris, Kostas
Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to workforce fluctuations. The past few years have seen increas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99a82471e1c3f2549fceedb127ffeb24
http://arxiv.org/abs/2305.10272
http://arxiv.org/abs/2305.10272
Large language models (LLMs) are known for their exceptional performance in natural language processing, making them highly effective in many human life-related or even job-related tasks. The attention mechanism in the Transformer architecture is a c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b93e9172d99ec0b633f124bcd883938
http://arxiv.org/abs/2304.13276
http://arxiv.org/abs/2304.13276
Conversational contextual bandits elicit user preferences by occasionally querying for explicit feedback on key-terms to accelerate learning. However, there are aspects of existing approaches which limit their performance. First, information gained f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4207c3f59f38bc14212b330094d86428
http://arxiv.org/abs/2303.00315
http://arxiv.org/abs/2303.00315
Learning Markov decision processes (MDP) in an adversarial environment has been a challenging problem. The problem becomes even more challenging with function approximation, since the underlying structure of the loss function and transition kernel ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25f03d45d8efb5319f2f24791167769d
http://arxiv.org/abs/2302.06834
http://arxiv.org/abs/2302.06834
We study online influence maximization (OIM) under a new model of decreasing cascade (DC). This model is a generalization of the independent cascade (IC) model by considering the common phenomenon of market saturation. In DC, the chance of an influen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4863f649ed8ee2e68c113f87ccd25b18