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Defining the technology of today and tomorrow.

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We strive to create an environment conducive to many different types of research across many different time scales and levels of risk.
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Our researchers drive advancements in computer science through both fundamental and applied research.
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Algorithms & Theory

Data Management

Data Mining & Modeling

Information Retrieval & the Web

Machine Intelligence

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Machine Translation

Natural Language Processing

Speech Processing

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Distributed Systems & Parallel Computing

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Mobile Systems

Networking

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Security, Privacy, & Abuse Prevention

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Climate & Sustainability

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Education Innovation

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Human-Computer Interaction and Visualization
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We regularly open-source projects with the broader research community and apply our developments to Google products.
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Publishing our work allows us to share ideas and work collaboratively to advance the field of computer science.
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We make products, tools, and datasets available to everyone with the goal of building a more collaborative ecosystem.
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Supporting the next generation of researchers through a wide range of programming.
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Participating in the academic research community through meaningful engagement with university faculty.
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Blog

Kevin Canini

Kevin Canini

Research Areas

Machine Intelligence

Authored Publications

Google

Diminishing Returns Shape Constraints for Interpretability and Regularization

Maya Gupta

Dara Bahri

Andy Cotter

Kevin Canini

NIPS 2018 (2018)

Deep Lattice Networks and Partial Monotonic Functions

Seungil You

David Ding

Kevin Canini

Jan Pfeifer

Maya Gupta

NIPS (2017)

Fast and Flexible Monotonic Functions with Ensembles of Lattices

Kevin Canini

Andy Cotter

Mahdi Milani Fard

Maya Gupta

Jan Pfeifer

NIPS (2016)

Launch and Iterate: Reducing Prediction Churn

Quentin Cormier

Mahdi Milani Fard

Kevin Canini

Maya Gupta

NIPS (2016)

Monotonic Calibrated Interpolated Look-Up Tables

Maya Gupta

Andrew Cotter

Jan Pfeifer

Konstantin Voevodski

Kevin Canini

Alexander Mangylov

Wojciech Moczydlowski

Alexander van Esbroeck

Journal Machine Learning Research (JMLR) (2016)

Parallel Boosting with Momentum

Indraneel Mukherjee

Kevin Canini

Rafael Frongillo

Yoram Singer

ECML PKDD 2013, Part III, LNAI 8190, Springer, Heidelberg, pp. 17-32 (to appear)

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