Search Results for author: Wenzhuo Yang

Found 9 papers, 6 papers with code

PyRCA: A Library for Metric-based Root Cause Analysis

1 code implementation20 Jun 2023 Chenghao Liu, Wenzhuo Yang, Himanshu Mittal, Manpreet Singh, Doyen Sahoo, Steven C. H. Hoi

We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps).

Causal Discovery graph construction

AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

no code implementations10 Apr 2023 Qian Cheng, Doyen Sahoo, Amrita Saha, Wenzhuo Yang, Chenghao Liu, Gerald Woo, Manpreet Singh, Silvio Saverese, Steven C. H. Hoi

There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency.

LogAI: A Library for Log Analytics and Intelligence

1 code implementation31 Jan 2023 Qian Cheng, Amrita Saha, Wenzhuo Yang, Chenghao Liu, Doyen Sahoo, Steven Hoi

In order to enable users to perform multiple types of AI-based log analysis tasks in a uniform manner, we introduce LogAI (https://github. com/salesforce/logai), a one-stop open source library for log analytics and intelligence.

Anomaly Detection Log Parsing +2

A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes

no code implementations30 Jun 2022 Wenzhuo Yang, Kun Zhang, Steven C. H. Hoi

In light of the modularity property of causal systems (the causal processes to generate different variables are irrelevant modules), the original problem is divided into a series of separate, simpler, and low-dimensional anomaly detection problems so that where an anomaly happens (root causes) can be directly identified.

Anomaly Detection Time Series +1

OmniXAI: A Library for Explainable AI

2 code implementations1 Jun 2022 Wenzhuo Yang, Hung Le, Tanmay Laud, Silvio Savarese, Steven C. H. Hoi

We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python library of eXplainable AI (XAI), which offers omni-way explainable AI capabilities and various interpretable machine learning techniques to address the pain points of understanding and interpreting the decisions made by machine learning (ML) in practice.

Counterfactual Explanation Decision Making +4

Online Collaborative Learning for Open-Vocabulary Visual Classifiers

no code implementations CVPR 2016 Hanwang Zhang, Xindi Shang, Wenzhuo Yang, Huan Xu, Huanbo Luan, Tat-Seng Chua

Leveraging on the structure of the proposed collaborative learning formulation, we develop an efficient online algorithm that can jointly learn the label embeddings and visual classifiers.

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