activity
20182026
most citedA Comparative Exploration of ML Techniques for Tuning Query Degree of Parallelism

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.DB2026

CACTUSDB: Unlock Co-Optimization Opportunities for SQL and AI/ML Inferences

Lixi Zhou, Kanchan Chowdhury, Lulu Xie +5

There is a growing demand for supporting inference queries that combine Structured Query Language (SQL) and Artificial Intelligence / Machine Learning (AI/ML) model inferences in d…

cs.DB2025

FlowLog: Efficient and Extensible Datalog via Incrementality

Hangdong Zhao, Zhenghong Yu, Srinag Rao +3

Datalog-based languages are regaining popularity as a powerful abstraction for expressing recursive computations in domains such as program analysis and graph processing. However,…

cs.LG2023

Naive Bayes Classifiers over Missing Data: Decision and Poisoning

Song Bian, Xiating Ouyang, Zhiwei Fan +1

We study the certifiable robustness of ML classifiers on dirty datasets that could contain missing values. A test point is certifiably robust for an ML classifier if the classifier…

cs.DB2020★ 1 cited

A Comparative Exploration of ML Techniques for Tuning Query Degree of Parallelism

Zhiwei Fan, Rathijit Sen, Paraschos Koutris +1

There is a large body of recent work applying machine learning (ML) techniques to query optimization and query performance prediction in relational database management systems (RDB…

cs.DB2018

Scaling-Up In-Memory Datalog Processing: Observations and Techniques

Zhiwei Fan, Jianqiao Zhu, Zuyu Zhang +3

Recursive query processing has experienced a recent resurgence, as a result of its use in many modern application domains, including data integration, graph analytics, security, pr…

stat.ML2018

Scalable inference of topic evolution via models for latent geometric structures

Mikhail Yurochkin, Zhiwei Fan, Aritra Guha +2

We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference. Our model is…