Author name cluster

Zhi Wei

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5 papers

1 author row

IJCAI Conference 2025 Conference Paper

Two-stage Risk Control with Application to Ranked Retrieval

Yunpeng Xu
Mufang Ying
Wenge Guo
Zhi Wei

Practical machine learning systems often operate in multiple sequential stages, as seen in ranking and recommendation systems, which typically include a retrieval phase followed by a ranking phase. Effectively assessing prediction uncertainty and ensuring effective risk control in such systems pose significant challenges due to their inherent complexity. To address these challenges, we developed two-stage risk control methods based on the recently proposed learn-then-test (LTT) and conformal risk control (CRC) frameworks. Unlike the methods in prior work that address multiple risks, our approach leverages the sequential nature of the problem, resulting in reduced computational burden. We provide theoretical guarantees for our proposed methods and design novel loss functions tailored for ranked retrieval tasks. The effectiveness of our approach is validated through experiments on two large-scale, widely-used datasets: MSLR- Web and Yahoo LTRC.

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AAAI Conference 2020 Conference Paper

AirNet: A Calibration Model for Low-Cost Air Monitoring Sensors Using Dual Sequence Encoder Networks

Haomin Yu
Qingyong Li
Yangli-ao Geng
Yingjun Zhang
Zhi Wei

Air pollution monitoring has attracted much attention in recent years. However, accurate and high-resolution monitoring of atmospheric pollution remains challenging. There are two types of devices for air pollution monitoring, i. e. , static stations and mobile stations. Static stations can provide accurate pollution measurements but their spatial distribution is sparse because of their high expense. In contrast, mobile stations offer an effective solution for dense placement by utilizing lowcost air monitoring sensors, whereas their measurements are less accurate. In this work, we propose a data-driven model based on deep neural networks, referred to as AirNet, for calibrating low-cost air monitoring sensors. Unlike traditional methods, which treat the calibration task as a point-to-point regression problem, we model it as a sequence-to-point mapping problem by introducing historical data sequences from both a mobile station (to be calibrated) and the referred static station. Speciﬁcally, AirNet ﬁrst extracts an observation trend feature of the mobile station and a reference trend feature of the static station via dual encoder neural networks. Then, a social-based guidance mechanism is designed to select periodic and adjacent features. Finally, the features are fused and fed into a decoder to obtain a calibrated measurement. We evaluate the proposed method on two real-world datasets and compare it with six baselines. The experimental results demonstrate that our method yields the best performance.

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AAAI Conference 2020 Conference Paper

DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

Chaoran Cheng
Fei Tan
Zhi Wei

We consider the problem of Named Entity Recognition (NER) on biomedical scientiﬁc literature, and more speciﬁcally the genomic variants recognition in this work. Signiﬁcant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domainspeciﬁc areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the ﬁrst successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any handcrafted features or post-processing rules. Our extensive experiments and results may shed light on other similar lowresource NER applications.

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IJCAI Conference 2019 Conference Paper

Success Prediction on Crowdfunding with Multimodal Deep Learning

Chaoran Cheng
Fei Tan
Xiurui Hou
Zhi Wei

We consider the problem of project success prediction on crowdfunding platforms. Despite the information in a project profile can be of different modalities such as text, images, and metadata, most existing prediction approaches leverage only the text dominated modality. Nowadays rich visual images have been utilized in more and more project profiles for attracting backers, little work has been conducted to evaluate their effects towards success prediction. Moreover, meta information has been exploited in many existing approaches for improving prediction accuracy. However, such meta information is usually limited to the dynamics after projects are posted, e. g. , funding dynamics such as comments and updates. Such a requirement of using after-posting information makes both project creators and platforms not able to predict the outcome in a timely manner. In this work, we designed and evaluated advanced neural network schemes that combine information from different modalities to study the influence of sophisticated interactions among textual, visual, and metadata on project success prediction. To make pre-posting prediction possible, our approach requires only information collected from the pre-posting profile. Our extensive experimental results show that the image features could improve success prediction performance significantly, particularly for project profiles with little text information. Furthermore, we identified contributing elements.

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NeurIPS Conference 2008 Conference Paper

A spatially varying two-sample recombinant coalescent, with applications to HIV escape response

Alexander Braunstein
Zhi Wei
Shane Jensen
Jon McAuliffe

Statistical evolutionary models provide an important mechanism for describing and understanding the escape response of a viral population under a particular therapy. We present a new hierarchical model that incorporates spatially varying mutation and recombination rates at the nucleotide level. It also maintains sep- arate parameters for treatment and control groups, which allows us to estimate treatment effects explicitly. We use the model to investigate the sequence evolu- tion of HIV populations exposed to a recently developed antisense gene therapy, as well as a more conventional drug therapy. The detection of biologically rele- vant and plausible signals in both therapy studies demonstrates the effectiveness of the method.

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