Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Yuan, Yancheng"'
In this paper, we aim to accelerate a preconditioned alternating direction method of multipliers (pADMM), whose proximal terms are convex quadratic functions, for solving linearly constrained convex optimization problems. To achieve this, we first re
Externí odkaz:
http://arxiv.org/abs/2403.18618
We investigate certified robustness for GNNs under graph injection attacks. Existing research only provides sample-wise certificates by verifying each node independently, leading to very limited certifying performance. In this paper, we present the f
Externí odkaz:
http://arxiv.org/abs/2403.01423
Autor:
Peng, Keqin, Ding, Liang, Yuan, Yancheng, Liu, Xuebo, Zhang, Min, Ouyang, Yuanxin, Tao, Dacheng
Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL), where a few examples are used to describe a task to the model. However, the performance of ICL varies significantly with t
Externí odkaz:
http://arxiv.org/abs/2401.12087
Predicting in-hospital mortality for intensive care unit (ICU) patients is key to final clinical outcomes. AI has shown advantaged accuracy but suffers from the lack of explainability. To address this issue, this paper proposes an eXplainable Multimo
Externí odkaz:
http://arxiv.org/abs/2312.17624
Autor:
Liao, Jiayi, Li, Sihang, Yang, Zhengyi, Wu, Jiancan, Yuan, Yancheng, Wang, Xiang, He, Xiangnan
Sequential recommendation aims to predict users' next interaction with items based on their past engagement sequence. Recently, the advent of Large Language Models (LLMs) has sparked interest in leveraging them for sequential recommendation, viewing
Externí odkaz:
http://arxiv.org/abs/2312.02445
Autor:
Yang, Zhengyi, Wu, Jiancan, Luo, Yanchen, Zhang, Jizhi, Yuan, Yancheng, Zhang, An, Wang, Xiang, He, Xiangnan
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a common approach is to model item sequences using discrete IDs, learning
Externí odkaz:
http://arxiv.org/abs/2310.20487
Publikováno v:
NeurIPS 2023
Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a
Externí odkaz:
http://arxiv.org/abs/2310.20453
In this paper, we propose an efficient sieving based secant method to address the computational challenges of solving sparse optimization problems with least-squares constraints. A level-set method has been introduced in [X. Li, D.F. Sun, and K.-C. T
Externí odkaz:
http://arxiv.org/abs/2308.07812
In this paper, we propose an adaptive sieving (AS) strategy for solving general sparse machine learning models by effectively exploring the intrinsic sparsity of the solutions, wherein only a sequence of reduced problems with much smaller sizes need
Externí odkaz:
http://arxiv.org/abs/2306.17369
The exclusive lasso (also known as elitist lasso) regularizer has become popular recently due to its superior performance on intra-group feature selection. Its complex nature poses difficulties for the computation of high-dimensional machine learning
Externí odkaz:
http://arxiv.org/abs/2306.14196