The 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Singapore 2020

114 Lectures · May 11, 2020

About

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. It provides an international forum for researchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications.

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Keynote Speakers

46:53

Multi-Output Prediction: Theory and Practice

Inderjit S. Dhillon

Oct 14, 2020

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The 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Singapore 2020

About

Related categories

Uploaded videos:

Keynote Speakers

Multi-Output Prediction: Theory and Practice

Inderjit S. Dhillon

Data analysis with humans

Samuel Kaski

Most Influential Paper Award Talk

On Link Privacy in Social Networks

Xintao Wu

Industry

Making AI work in practice: Why Data is so Essential to AI and Towards Data Scie...

Usama Fayyad

AI for Health: How to Make the World a Better Place with Machine Learning

Ankur M. Teredesai

Session #A3 (Representation Learning (2) + Scientic Data + Privacy/Security)

Efficient Database Search via Tensor Distribution Sensitive Bucketing

Mihir Mongia

dK-Microaggregation: Anonymizing Graphs with Differential Privacy Guarantees

Masooma Iftikhar

Data-Free Adversarial Perturbations for Practical Black-Box Attack

Zhaoxin Huan

Attention-based Aggregation Graph Networks for Knowledge Graph Information Trans...

Ming Zhao

Secure and Accurate Two-step Hash Encoding for Privacy-Preserving Record Linkage

Thilina Ranbaduge

Assessing Centrality Without Knowing Connections

Leyla Roohi

Deep Cost-sensitive Kernel Machine for Binary Software Vulnerability Detection

Tuan Nguyen

Session #B3 (Sequential/Dynamic Data + Recommendation System (2) + Novel Algorithm)

Modelling Temporal Dynamics and Repeated Behaviors for Recommendation

Xinyi Zhou

Tree-Miner: Mining sequential patterns from SP-Tree

Redwan Ahmed Rizvee

Off-policy Recommendation System without Exploration

Chengwei Wang

MemMAP: Compact and Generalizable Meta-LSTM Models for Memory Access Prediction

Ajitesh Srivastava

Context-Aware Latent Dirichlet Allocation for Topic Segmentation

Li Wenbo

Balancing Exploration and Exploitation in Self-Imitation Learning

Chun-Yao Kang

Adversarial Autoencoder and Multi-Task Semi-Supervised Learning for Multi-Stage ...

Andre Mendes da Silva

Joint Relational Dependency Learning for Sequential Recommendation

Xiangmeng Wang

PEARL: Probabilistic Exact Adaptive Random Forest with Lossy Counting for Data S...

Ocean Wu

Session #A1 (Classication (1) + Unstructured Data)

Optimized Transformer Models for FAQ Answering

Manish Gupta

Multi-Layer Cross Loss Model for Zero-Shot Human Activity Recognition

Tong Wu

Learning to Select Important Context Words for Event Detection

Thien Huu Nguyen

Inferring Restricted Regular Expressions with Interleaving from Positive and Neg...

Yeting Li

SGCN: A Graph Sparsiﬁer based on Graph Convolutional Networks

Jiayu Li

An Advanced Two-step DNN-Based Framework for Arrhythmia Detection

Jia Rong

Hierarchical Gradient Smoothing for Probability Estimation Trees

He Zhang

HIN: Hierarchical Inference Network for Document-Level Relation Extraction

Hengzhu Tang

A Relation Learning Hierarchical Framework for Multi-label Charge Prediction

Wei Duan

Online Algorithms for Multiclass Classification using Partial Labels

Rajarshi Bhattacharjee

Session #B4 (Anomaly Detection + Feature Extraction)

Anomaly Detection via Neighbourhood Contrast

Bo Chen

LoPAD: A Local Prediction Approach to Anomaly Detection

Sha Lu

Cross-data Automatic Feature Engineering via Meta-learning and Reinforcement Lea...

Jianyu Zhang

Discretization and Feature Selection Based on Bias Corrected Mutual Information ...