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KDD 2017
Halifax, Nova Scotia - Canada
August 13 - 17, 2017

KDD 2017 is a premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

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Keynote Speakers

Cynthia Dwork

Distinguished Scientist
Microsoft Research / Harvard University

What’s Fair?

Data, algorithms, and systems have biases embedded within them reflecting designers’ explicit and implicit choices, historical biases, and societal priorities. They form, literally and inexorably, a codification of values. “Unfairness” of algorithms – for tasks ranging from advertising to recidivism prediction – has attracted considerable attention in the popular press. The talk will discuss the nascent mathematically rigorous study of fairness in classification and scoring.

Bin Yu

Professor
University of California at Berkeley

Three Principles of Data Science: Predictability, Stability, and Computability

In this talk, I’ll discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions. The ultimate importance of prediction lies in the fact that future holds the unique and possibly the only purpose of all human activities, in business, education, research, and government alike. Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future. Stability (relative to data and model perturbations) is also a minimum requirement for interpretability and reproducibility of data driven results. It is closely related to uncertainty assessment. Obviously, both prediction and stability principles can not be employed without feasible computational algorithms, hence the importance of computability. The three principles will be demonstrated through analytical connections, and in the context of two on-going projects, for which “data wisdom” is also indispensable. Specifically, the first project employs deep learning networks (CNNs) to understand pattern selectivities of neurons in the difficult visual cortex V4; and the second project predicts partisanship and tone of political TV ads by employing and comparing different latent variable models with a Lasso-based model.

Renée J. Miller

Professor
University of Toronto

The Future of Data Integration

The value of data explodes when it is integrated. In this talk, I present some important innovations in data integration over the last two decades. These include data exchange, which provides a foundation for reasoning about the correctness of transformed data, and the use of declarative mappings in integration. I discuss how data mining has been used to facilitate data integration and present some important new data integration challenges that arise in data science.

We are adding new speakers regularly. Check back often.

Highlights from KDD 2016: Audience Appreciation Award Finalists

A Multiple Test Correction for Streams and Cascades of Statistical Hypothesis Tests

Gemello: Creating a Detailed Energy Breakdown from just the Monthly Electricity Bill

EMBERS AutoGSR: Automated Coding of Civil Unrest Events

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Question Independent Grading using Machine Learning: The Case of Computer Program Grading


Photos from KDD 2016 in San Francisco

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View more photos on Flickr