Frontiers in Machine Learning 2020

Session Leads: Alex Shamis, Microsoft and Stavros Volos, Microsoft

Session Abstract: In the recent years, Machine Learning (ML) has facilitated key applications, such as medical imaging, video analytics, and financial forecasting. Understanding the massive computing requirements of ML, cloud providers have been investing in accelerated ML computing and a range of ML services. A key concern in such systems, however, is the privacy of the sensitive data being analyzed and the confidentiality of the trained models. Confidential cloud computing provides a vehicle for privacy-preserving ML, enabling multiple entities to collaborate and train accurate models using sensitive data, and to serve these models with assurance that their data and models remain protected, even from privileged attackers. In this session, our speakers will demonstrate applications and advancements in Confidential ML: (i) how confidential computing hardware can accelerate multi-party and collaborative training, creating an incentive for data sharing; and (ii) how emerging cloud accelerator systems can be re-designed to deliver strong privacy guarantees, overcoming the limited performance of CPU-based confidential computing.

Raluca Ada Popa, University of California, Berkeley
Towards A Secure Collaborative Learning Platform

Emmett Witchel, University of Texas at Austin
Secure Computing with Cloud GPUs

Session Lead: Emre Kiciman, Microsoft

Session Abstract: Machine learning has enabled many advances in processing visual, language, and other digital data signals and, as a result, is quickly becoming integrated in a variety of real-world systems with important societal and business purposes. However, as with any computer technology deployed at scale or in critical domains, ML systems face motivated adversaries who might wish to cause undesired behavior or violate security restrictions. In this session, participants will discuss the security challenges of today’s AI-driven systems and opportunities to mitigate adversarial attacks for more robust systems.

Dawn Song, University of California, Berkeley
AI & Security: Challenges, Lessons & Future Directions

Jerry Li, Microsoft
Algorithmic Aspects of Secure Machine Learning

Q&A panel with all 3 speakers

Moderator: Mary Gray, Microsoft

Session Abstract: At its core, machine learning is the artful science of statistically divining patterns from stores of data—typically, lots of data. Much of these data are drawn from sources as diverse as tweets and Creative Commons images to COVID-19 patient health records. Machine learning uses innovative techniques to draw what it can from the data on hand to push the boundaries of such problems as reliability and robustness in algorithmic modeling; theories and applications of causal inference; development of stable, predictive models from sparse data; uses of interpretable machine learning for course-correcting models that confound reason; and finding new ways to use noisy or sparse annotated training data to drive insights. While societal impact and social equity are relevant to the frontiers above, this panel asks: How might ML take up data and questions across a variety of domains such as education, development, discrimination, housing, health disparities, inequality in labor markets, to advance our understanding of systemic inequities and challenges? These systems, arguably, tacitly shape the data, theory, and methods core to ML. How might centering questions of social equity advance the frontiers of the field?

Irene Lo, Stanford University

Augustin Chaintreau, Columbia University

(9:30 AM – 11:00 AM IST
Wednesday)

Session Lead: Amit Sharma, Microsoft

Session Abstract: Causal relationships are stable across distribution shifts. Models based on causal knowledge have the potential to generalize to unseen domains and offer counterfactual predictions: how do outcomes change if a certain feature is changed in the real world. In recent years, machine learning methods based on causal reasoning have led to advances in out-of-domain generalization, fairness and explanation, and robustness to data selection biases. ¬ In this session, we discuss big ideas at the intersections of causal inference and machine learning towards building stable predictive models and discovering causal insights from data.

Susan Athey, Stanford University
Causal Inference, Consumer Choice, and the Value of Data

Elias Bareinboim, Columbia University
On the Causal Foundations of Artificial Intelligence (Explainability & Decision-Making)

Cheng Zhang, Microsoft
A causal view on Robustness of Neural Networks

Q&A panel with all 3 speakers