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IBM Research AI at KDD 2020

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The Twenty-Sixth ACM SIGKDD Conference on Knowledge Discovery and Data Mining will be held virtually in 2020 due to the COVID-19 pandemic. Join IBM Research AI from August 22nd to August 27th to learn more about our work. We will present several demos, talks, tutorials and papers that explore a wide range of topics ranging from healthcare to forecasting, human-centered explainability, optimization, graph representation and automated machine learning.

At the conference, we will showcase some of the work around healthcare resulting from collaborations with Watson Health and Cornell University. One relevant outcome is a novel system that enables the creation and management of predictive AI models throughout all phases of their life cycle, from data ingestion to model productization and deployment in the wild.

Other works focus on interpretable temporal models of patient records to enable clinicians in understanding the progression of a disease and providing interacting human-in-the-loop explanations. We will also be presenting a series of papers and tutorials on automated machine learning, the importance of data quality for machine learning tasks, as well as on time series forecasting.

IBM Research is a gold sponsor of KDD 2020. We hope you will join us at our  virtual booth to chat with our researchers and recruiters about our latest research, career opportunities, internships including the AI Residency Program.

For a full list of our papers, demos, tutorials and workshops, see below.

Demos

*In addition to our  demos featured at the IBM booth, you can try IBM Research Experiments here.

ExBERT: A Visual Tool to Explore BERT : Learn how to uncover insights into what deep Transformer models understand about human language by interactively exploring their learned attentions and contextual embeddings.

Gamma: short for Go Ahead Ask Me Anything: GAAMA is a (multi-lingual) reading comprehension system for question-answering.

AutoAI for Time Series: This demo shows time series forecasting using AutoAI which automatically selects and optimizes statistics and machine learning pipelines.

 

Explainable Link Prediction: Link Prediction using Graph Neural Networks for Master Data Management

Lale: Type-Driven Auto-ML with Scickit is an open-source library of sklearn-compatible, high-level Python interfaces that simplify and unify automated machine learning in a consistent way.

Command Line AI (CLAI): is an open-source project from IBM Research that brings the latest in AI and ML technologies to the command line as “skills” and seeks to make the command line user’s daily life more efficient and productive. Check out the 2020 NLC2CMD Competition on automated translation of English to the command line.

Accepted papers

Research Track

Combinatorial Black-Box Optimization with Expert Advice

Authors: Hamid Dadkhahi; Karthikeyan Shanmugam; Jesus Rios; Payel Das; Samuel Hoffman; Troy David Loeffler; Subramanian Sankaranarayanan
https://arxiv.org/pdf/2006.03963.pdf

Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Authors: Xiaojie Guo; Liang Zhao; Zhao Qin; Lingfei Wu; Amarda Shehu; Yanfang Ye
https://arxiv.org/abs/2006.05385

Applied Data Science Track

Attention-based multi-modal new product sales time-series forecasting
Authors: Vijay Ekamcbaram; Kushagra Manglik; Sumanta Mukherjee; Surya Shravan Kumar Sajja; Satyam Dwivedi; Vikas Rayka

Map Generation from Large Scale Incomplete and Inaccurate Data Labels
Authors: Rui Zhang; Wei Zhang; Conrad Albrecht; Xiaodong Cui; Ulrich Finkler; David Kung; Siyuan Lu
https://arxiv.org/pdf/2005.10053.pdf

Molecular Inverse-Design Platform for Material Industries
Authors: Seiji Takeda; Toshiyuki Hama; Hsiang-Han Hsu; Victoria Piunova; Dmitry Zubarev; Daniel Sanders; Jed Pitera; Makoto Kogoh; Takumi Hongo; Yenwei Cheng; Wolf Bocanett; Hideaki Nakashika; Akihiro Fujita; Yuta Tsuchiya; Katsuhiko Hino; Kentaro Yano; Shuichi Hirose; Hiroki Toda; Yasumitsu Orii; Daiju Nakano
https://arxiv.org/pdf/2004.11521.pdf

Lecture tutorials

Human-Centered Explainability for Healthcare
Presenters: Prithwish Chakraborty; Bum Chul Kwon; Sanjoy Dey; Amit Dhurandhar; Daniel Gruen; Kenney Ng; Daby Sow; Kush R Varshney

Advanced Deep Graph Learning: Deeper, Faster, Robuster, and Unsupervised
Presenters: Yu Rong; Wenbing Huang; Tingyang Xu; Hong Cheng; Junzhou Huang; Yao Ma; Yiqi Wang; Tyler Derr; Lingfei Wu; Tengfei Ma

Overview and Importance of Data Quality for Machine Learning Tasks
Presenters: Hima Patel; Nitin Gupta; Shazia Afzal; Shashank Mujumdar

Workshops

Lale: Consistent Automated Machine Learning
Authors: Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar
https://arxiv.org/pdf/2007.01977.pdf

User-driven Analysis of Longitudinal Health Data with Hidden Markov Models for Clinical Insights
Authors: Bum Chul Kwon
https://arxiv.org/pdf/2007.12346.pdf

Explicit-Blurred Memory Network for Analyzing Patient Electronic Health Records
Authors: Prithwish Chakraborty, Fei Wang, Jianying Hu, Daby Sow
https://arxiv.org/pdf/1911.06472.pdf

A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records
Authors: Parthasarathy Suryanarayanan, Bhavani Iyer, Prithwish Chakraborty, Bibo Hao, Italo Buleje, Piyush Madan, James Codella, Antonio Foncubierta, Divya Pathak, Sarah Miller, Amol Rajmane, Shannon Harrer, Gigi Yuan-Ree
https://arxiv.org/pdf/2007.12780.pdf

On Machine Learning-Based Short-Term Adjustment of Epidemiological Projections of COVID-19 in US
Authors: Sarah Kefayati, Hu Huang, Prithwish Chakraborty, Fred Roberts, Vishrawas Gopalakrishnan, Raman Srinivasan, Sayali Pethe, Piyush Madan, Ajay Deshpande, Xuan Liu, Jianying Hu and Gretchen Jackson

Trust and Transparency in Contact Tracing Applications 
Authors: Stacy Hobson, Michael Hind, Aleksandra Mojsilovic and Kush Varshney
https://arxiv.org/pdf/2006.11356.pdf

Is Robust Neurons’ Activation Sufficient to Robustify CNNs against Adversarial Attacks?
Authors: Ingkang Wang, Gaoyuan Zhang, Sijia Liu

Identifying Audio Adversarial Examples via Anomalous Pattern Detection
Authors: Victor Akinwande, Celia Cintas, Skyler Speakman, Srihari Sridharan
https://arxiv.org/pdf/2002.05463.pdf

MALOnt: An Ontology for Malware Threat Intelligence
Authors: Nidhi Rastogi, Sharmishtha Dutta, Mohammed Zaki, Alex Gittens, Charu Aggarwal

Hyper-local sustainable assortment planning
Authors: Nupur Aggarwal, Abhishek Bansal, Kushagra Manglik, Kedar Kulkarni, Vikas Raykar viraykar@in.ibm.com

Cultivating Human Expertise Through AI-Assisted Data Science
Authors: Josh Andres, Christine Wolf, Michael Muller, Justin Weisz, Narendra Nath Joshi, Aabhas Sharma, Krissy Brimijoin, Michael Desmond, Zahra Ashktorab, Qian Pan, Evelyn Duesterwald and Casey Dugan

The Next Decade of Data Science
Authors: Justin Weisz and Michael Muller

Human-in-the-Loop Automated Data Science Outperformed Human Data Scientists in Model Building
Authors: Dakuo Wang, Josh Andres, Justin Weisz, Erick Oduor, Udayan Khurana, Horst Samulowitz, Arunima Chaudhary, Abel Valente, Dustin Torres and Casey Dugan.

Hybrid Edge-Cloud based Ensemble Learning for Forecasting Occupancy of Open-plan Offices
Authors: Fatemeh Jalali, Subhrajit Roy, Ramachandra Rao Kolluri, Maneesha Perera, Mahsa Salehi, John D. Vasquez, Julian de Hoog

Explainable AI based interventions for pre-season decision making in fashion retail
Authors: Surya Shravan Kumar Sajja, Nupur Aggarwal, Sumanta Mukherjee, Kushagra Manglik, Satyam Dwivedi, Vikas Raykar

Hyper-local sustainable assortment planning
Authors: Nupur Aggarwal, Abhishek Bansal, Kushagra Manglik, Kedar Kulkarni, Vikas Raykar
https://arxiv.org/pdf/2007.13414.pdf

WhyFlow: Explaining Errors in Data Flows Interactively
Authors: Maeda Hanafi, Azza Abouzied, Marina Danilevsky, Yunyao Li

Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention
Authors: Nasrullah Sheikh, Xiao Qin, Berthold Reinwald, Christoph Miksovic, Thomas Gschwind and Paolo Scotton

Improving Task-Oriented Dialogue Systems In Production with Conversation Logs
Authors: Alon Jacovi, Ori Bar El, Ofer Lavi, David Boaz, David Amid, Inbal Ronen, Ateret Anaby-Tavor

Predicting Account Receivables with Machine Learning
Authors: Ana Paula Appel, Gabriel Louzada Malfatti, Renato Luiz de Freitas Cunha, Bruno Lima, Rogerio de Paula

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