October 28–31, 2019

Schedule: Ethics, security, privacy sessions

Techniques and considerations for responsible use of machine intelligence.

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2:30pm3:10pm Wednesday, October 30, 2019
Location: Grand Ballroom E
Krzysztof Ostrowski dives into federated learning (FL)—an approach to machine learning where a shared model is trained across many clients that keep their training data local—and goes hands-on with FL using TensorFlow Federated (TFF). He demonstrates step-by-step how to train your TensorFlow model in a federated environment, implement custom federated computations, and set up large simulations. Read more.
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4:10pm4:50pm Wednesday, October 30, 2019
Location: Grand Ballroom E
Tulsee Doshi (Google), Christina Greer (Google)
Average rating: *****
(5.00, 2 ratings)
ML continues to drive monumental change across products and industries. But as we expand ML to even more sectors and users, it's ever more critical to ensure that these pipelines work well for all users. Tulsee Doshi and Christina Greer announce the launch of Fairness Indicators, built on top of TensorFlow Model Analysis, which allows you to measure and improve algorithmic bias. Read more.
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5:00pm5:40pm Wednesday, October 30, 2019
Location: Grand Ballroom E
Ulfar Erlingsson (Google Brain)
Average rating: *****
(5.00, 1 rating)
When evaluating ML models, it can be difficult to tell the difference between what the models have generalized from the training and what the models have simply memorized. And that difference can be crucial in some ML tasks, such as when ML models are trained using sensitive data. Úlfar Erlingsson explains how to offer strong privacy guarantees for ML training data by using TensorFlow Privacy. Read more.

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