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When will a system that has performed well in the past continue to do so in the future? How do we design such systems in the presence of novel and potentially adversarial input distributions? What techniques will let us safely build and deploy autonomous systems on a scale where human monitoring becomes difficult or infeasible? Answering these questions is critical to guaranteeing the safety of emerging high stakes applications of AI, such as self-driving cars and automated surgical assistants. This workshop will bring together researchers in areas such as human-robot interaction, security, causal inference, and multi-agent systems in order to strengthen the field of reliability engineering for machine learning systems. We are interested in approaches that have the potential to provide assurances of reliability, especially as systems scale in autonomy and complexity. We will focus on four aspects — robustness (to adversaries, distributional shift, model mis-specification, corrupted data); awareness (of when a change has occurred, when the model might be mis-calibrated, etc.); adaptation (to new situations or objectives); and monitoring (allowing humans to meaningfully track the state of the system). Together, these will aid us in designing and deploying reliable machine learning systems.
Thu 11:40 p.m. - 12:00 a.m.
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Opening Remarks
(
Talk
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Jacob Steinhardt 🔗 |
Fri 12:00 a.m. - 12:30 a.m.
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Rules for Reliable Machine Learning
(
Invited Talk
)
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Martin A Zinkevich 🔗 |
Fri 12:30 a.m. - 12:45 a.m.
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What's your ML Test Score? A rubric for ML production systems
(
Contributed Talk
)
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D. Sculley 🔗 |
Fri 12:45 a.m. - 1:00 a.m.
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Poster Spotlights I
(
Spotlight
)
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🔗 |
Fri 1:30 a.m. - 2:00 a.m.
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Robust Learning and Inference
(
Invited Talk
)
Robust inference is an extension of probabilistic inference, where some of the observations may be adversarially corrupted. We limit the adversarial corruption to a finite set of modification rules. We model robust inference as a zero-sum game between an adversary, who selects a modification rule, and a predictor, who wants to accurately predict the state of nature. There are two variants of the model, one where the adversary needs to pick the modification rule in advance and one where the adversary can select the modification rule after observing the realized uncorrupted input. For both settings we derive efficient near optimal policy runs in polynomial time. Our efficient algorithms are based on methodologies for developing local computation algorithms. We also consider a learning setting where the predictor receives a set of uncorrupted inputs and their classification. The predictor needs to select a hypothesis, from a known set of hypotheses, and is tested on inputs which the adversary corrupts. We show how to utilize an ERM oracle to derive a near optimal predictor strategy, namely, picking a hypothesis that minimizes the error on the corrupted test inputs. Based on joint works with Uriel Feige, Aviad Rubinstein, Robert Schapira, Moshe Tennenholtz, Shai Vardi. |
Yishay Mansour 🔗 |
Fri 2:00 a.m. - 2:30 a.m.
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Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
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Invited Talk
)
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Jennifer Hill 🔗 |
Fri 2:30 a.m. - 2:45 a.m.
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Robust Covariate Shift Classification Using Multiple Feature Views
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Contributed Talk
)
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Anqi Liu 🔗 |
Fri 2:45 a.m. - 3:00 a.m.
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Poster Spotlights II
(
Spotlight
)
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🔗 |
Fri 4:15 a.m. - 4:45 a.m.
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Doug Tygar
(
Invited Talk
)
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Doug Tygar 🔗 |
Fri 4:45 a.m. - 5:15 a.m.
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Adversarial Examples and Adversarial Training
(
Invited Talk
)
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Ian Goodfellow 🔗 |
Fri 5:15 a.m. - 5:30 a.m.
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Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning
(
Contributed Talk
)
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Octavian Suciu 🔗 |
Fri 5:30 a.m. - 5:45 a.m.
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Poster Spotlights III
(
Spotlight
)
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🔗 |
Fri 5:45 a.m. - 6:30 a.m.
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Poster Session
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🔗 |
Fri 6:30 a.m. - 7:00 a.m.
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Learning Reliable Objectives
(
Invited Talk
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Anca Dragan 🔗 |
Fri 7:00 a.m. - 7:30 a.m.
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Building and Validating the AI behind the Next-Generation Aircraft Collision Avoidance System
(
Invited Talk
)
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Mykel J Kochenderfer 🔗 |
Fri 7:30 a.m. - 7:45 a.m.
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Online Prediction with Selfish Experts
(
Contributed Talk
)
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Okke Schrijvers 🔗 |
Fri 7:45 a.m. - 8:00 a.m.
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TensorFlow Debugger: Debugging Dataflow Graphs for Machine Learning
(
Contributed Talk
)
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D. Sculley 🔗 |
Fri 8:00 a.m. - 8:30 a.m.
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What are the challenges to making machine learning reliable in practice?
(
Panel Discussion
)
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🔗 |
Author Information
Dylan Hadfield-Menell (UC Berkeley)
Adrian Weller (University of Cambridge)
Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, where he is also a Turing Fellow leading work on safe and ethical AI. He is a Principal Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards including the Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.
David Duvenaud (University of Toronto)
Jacob Steinhardt (UC Berkeley)
Percy Liang (Stanford University)

Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning. His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).
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Roy Frostig · Sida Wang · Percy Liang · Christopher D Manning -
2013 Poster: Dropout Training as Adaptive Regularization »
Stefan Wager · Sida Wang · Percy Liang -
2013 Spotlight: Dropout Training as Adaptive Regularization »
Stefan Wager · Sida Wang · Percy Liang -
2012 Poster: Identifiability and Unmixing of Latent Parse Trees »
Percy Liang · Sham M Kakade · Daniel Hsu -
2012 Poster: Active Learning of Model Evidence Using Bayesian Quadrature »
Michael A Osborne · David Duvenaud · Roman Garnett · Carl Edward Rasmussen · Stephen J Roberts · Zoubin Ghahramani -
2011 Poster: Additive Gaussian Processes »
David Duvenaud · Hannes Nickisch · Carl Edward Rasmussen -
2009 Workshop: The Generative and Discriminative Learning Interface »
Simon Lacoste-Julien · Percy Liang · Guillaume Bouchard -
2009 Poster: Asymptotically Optimal Regularization in Smooth Parametric Models »
Percy Liang · Francis Bach · Guillaume Bouchard · Michael Jordan -
2008 Workshop: Speech and Language: Unsupervised Latent-Variable Models »
Slav Petrov · Aria Haghighi · Percy Liang · Dan Klein -
2007 Poster: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Spotlight: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Poster: A Probabilistic Approach to Language Change »
Alexandre Bouchard-Côté · Percy Liang · Tom Griffiths · Dan Klein