no code implementations • 20 Mar 2023 • Binbin Wang, Mingming Li, Zhixiong Zeng, Jingwei Zhuo, Songlin Wang, Sulong Xu, Bo Long, Weipeng Yan
Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) methods are prevailing.
1 code implementation • 12 Aug 2022 • Yiming Qiu, Chenyu Zhao, Han Zhang, Jingwei Zhuo, TianHao Li, Xiaowei Zhang, Songlin Wang, Sulong Xu, Bo Long, Wen-Yun Yang
BERT-style models pre-trained on the general corpus (e. g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: question answering, text classification, sequence labeling and so on.
no code implementations • 28 Feb 2022 • Jingwei Zhuo, Bin Liu, Xiang Li, Han Zhu, Xiaoqiang Zhu
Motivated by the observation that model-free methods like behavioral retargeting (BR) and item-based collaborative filtering (ItemCF) hit different parts of the user-item relevance compared to neural sequential recommendation models, we propose a novel model-agnostic training approach called WSLRec, which adopts a three-stage framework: pre-training, top-$k$ mining, and fine-tuning.
1 code implementation • ICML 2020 • Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, Kun Gai
Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.
1 code implementation • 1 Feb 2019 • Chang Liu, Jingwei Zhuo, Jun Zhu
It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs).
1 code implementation • 4 Jul 2018 • Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu, Lawrence Carin
Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian inference literature, for their capacity to yield flexible and accurate approximations.
no code implementations • 7 Dec 2017 • Jianqiao Wangni, Jingwei Zhuo, Jun Zhu
Since the algorithm consumes a major computation cost in the testing phase, we propose a novel teacher-learner framework of learning computation-efficient kernel embeddings from specific data.
no code implementations • ICML 2018 • Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods.
no code implementations • ICML 2018 • Yichi Zhou, Jun Zhu, Jingwei Zhuo
Thompson sampling has impressive empirical performance for many multi-armed bandit problems.