The recent advancements in deep learning have revolutionized the field of machine learning, enabling unparalleled performance and many new real-world applications. Yet, the developments that led to this success have often been driven by empirical studies, and little is known about the theory behind some of the most successful approaches. While theoretically well-founded deep learning architectures had been proposed in the past, they came at a price of increased complexity and reduced tractability. Recently, we have witnessed considerable interest in principled deep learning. This led to a better theoretical understanding of existing architectures as well as development of more mature deep models with solid theoretical foundations. In this workshop, we intend to review the state of those developments and provide a platform for the exchange of ideas between the theoreticians and the practitioners of the growing deep learning community. Through a series of invited talks by the experts in the field, contributed presentations, and an interactive panel discussion, the workshop will cover recent theoretical developments, provide an overview of promising and mature architectures, highlight their challenges and unique benefits, and present the most exciting recent results.

Topics of interest include, but are not limited to:

  • Deep architectures with solid theoretical foundations
  • Theoretical understanding of deep networks
  • Theoretical approaches to representation learning
  • Algorithmic and optimization challenges, alternatives to backpropagation
  • Probabilistic, generative deep models
  • Symmetry, transformations, and equivariance
  • Practical implementations of principled deep learning approaches
  • Domain-specific challenges of principled deep learning approaches
  • Applications to real-world problems


Invited Speakers

Important Dates

Paper submissionJune 28, 2017 11:59pm PT
Acceptance notificationJuly 28, 2017
Final versionAugust 1, 2017
WorkshopAugust 10, 2017 (Sydney, Australia)


The workshop combines invited talks, presentations of contributed papers and an interactive panel discussion. The panel discussion will be interactive, involving the invited speakers, moderated by the organizers based on questions submitted and voted on by the audience using an online service. Both the invited talks and the panel discussion will be recorded, with answers to audience questions posted to the online service. The workshop will feature presentations of contributed papers during poster sessions, with a few selected submissions allotted a 15 minute time slot for an oral presentation (including 3 minutes for questions).

Tentative program of the workshop

Time Event
08:30-08:45Welcome and opening remarks by the organizers
08:45-10:002 invited talks + 1 contributed presentation
10:00-10:30Coffee break & poster session
10:30-12:002 invited talks + 2 contributed presentations
13:30-15:002 invited talks + 2 contributed presentations
15:00-15:30Coffee break & poster session
15:30-16:50Panel discussion
16.50-17:00Closing remarks


We invite submissions of full papers (max 8 pages excluding references) as well as work-in-progress, position, and challenging problems papers (max 4 pages excluding references). Papers must be formatted using the ICML style and submitted online. Reviewing will be double-blind, therefore no author information should be included in the papers; self-reference that identifies the authors should be avoided or anonymized. Accepted papers will be selected for an oral or a poster presentation. While original contributions are preferred, we also invite submissions of high-quality work that has recently been published in other venues.

Best submissions will be awarded the Google Best Paper Award for the best paper and the Google Best Student Paper Award for the best student paper. Both awards come with a prize of 600$ sponsored by Google.

Program Committee


Andrzej Pronobis is a Research Associate in the Department of Computer Science and Engineering at the University of Washington in Seattle, as well as a Senior Researcher at KTH Royal Institute of Technology in Stockholm, Sweden. His research is at the intersection of robotics, deep learning and computer vision, with focus on perception and spatial understanding mechanisms for mobile robots and their role in the interaction between robots and human environments. His recent interests include application of tractable probabilistic deep models to planning and learning semantic spatial representations. He is a recipient of a prestigious Swedish Research Council Grant for Junior Researchers and a finalist for the Georges Giralt Ph.D. award for the best European Ph.D. thesis in robotics.

Robert Gens is a Research Scientist at Google Seattle. His research interests are in machine learning, deep learning, and computer vision. He received a PhD in Computer Science and Engineering from the University of Washington. He completed an SB in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology in 2009, received an Outstanding Student Paper Award at NIPS 2012, and was supported by the 2014 Google PhD Fellowship in Deep Learning.
Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in both the Computer Science & Engineering and Statistics departments at the University of Washington. Before joining the University of Washington, Dr. Kakade was a principal research scientist at Microsoft Research, New England. Prior to this, Dr. Kakade was an associate professor at the Department of Statistics, Wharton, University of Pennsylvania and an assistant professor at the Toyota Technological Institute at Chicago. He works on both theoretical and applied questions in machine learning and artificial intelligence, focusing on designing both statistically and computationally efficient algorithms for machine learning, statistics, and artificial intelligence. More broadly, Sham has made various contributions in various areas including statistics, optimization, probability theory, machine learning, algorithmic game theory and economics, and computational neuroscience. He is a recipient of numerous awards and served as a chair for many conferences.
Pedro Domingos is a professor of computer science at the University of Washington and the author of "The Master Algorithm". He is a winner of the SIGKDD Innovation Award, the highest honor in data science. He is a Fellow of the Association for the Advancement of Artificial Intelligence, and has received a Fulbright Scholarship, a Sloan Fellowship, the National Science Foundation’s CAREER Award, and numerous best paper awards. His research spans a wide variety of topics in machine learning, artificial intelligence, and data science, including scaling learning algorithms to big data, maximizing word of mouth in social networks, unifying logic and probability, and deep learning.