Due to requests, we are extending the deadline to Wednesday, October 5th 11:59pm Anywhere On Earth for the INTERPOLATE: first Workshop on Interpolation Regularizers and Beyond @ NeurIPS 2022.
Link: https://sites.google.com/view/interpolation-workshop Objectives Interpolation-based methods are an increasingly popular approach to regularize deep models. For example, the mixup data augmentation method constructs synthetic examples by linearly interpolating random pairs of training data points. Recent work has also explored interpolating in the parameters of different models. During their half-decade lifespan, interpolation regularizers have become ubiquitous and fuel state-of-the-art results in virtually all domains, including computer vision and medical diagnosis. Interpolation regularizers are becoming a standard tool in machine learning, but our understanding of why and how they work is still in its infancy. Even in simpler problem settings such as supervised learning, it remains a puzzling fact that one can build a better image classifier by training only on random combinations of pairs of examples. What aspect of deep neural networks are these regularizers enforcing? What is the theoretical basis of “learning from multiple examples”? How can it guide the development of the next generation of fairer, more robust machines? What are the similarities and differences between methods that interpolate data and the ones that interpolate parameters? This workshop brings together researchers and users of interpolation regularizers to foster research and discussion to advance and understand interpolation regularizers. This inaugural meeting will have no shortage of interactions and energy to achieve these exciting goals. *We are reserving a few complimentary workshop registrations for accepted paper authors who would otherwise have difficulty attending. *Please reach out if this applies to you. Suggested topics include, but are not limited to the intersection between interpolation regularizers and: - Domain generalization - Semi-supervised learning - Privacy-preserving ML - Theory - Robustness - Fairness - Vision - NLP - Medical applications Invited speakers - Chelsea Finn, Stanford: Repurposing Mixup for Robustness and Regression - Sanjeev Arora, Princeton: Interpolation and data-preserving machine learning - Kenji Kawaguchi, National Univ. Singapore: On the developments of the theory of Mixup - Youssef Mroueh, IBM: Interpolation-based regularizers and fairness - Alex Lamb, Microsoft Research: What matters in the world? Exploring algorithms for provably ignoring irrelevant details and distractions Important dates - Paper submission deadline: September 22, 2022 Wednesday, October 5th 11:59pm Anywhere On Earth - Paper acceptance notification: October 14, 2022 Wednesday, October 19th - Workshop: December 2, 2022 Submission Information Authors are invited to submit short papers with up to 4 pages, but unlimited number of pages for references and supplementary materials. The submissions must be anonymized as the reviewing process will be double blind. Please use the NeurIPS template <https://neurips.cc/Conferences/2022/PaperInformation/StyleFiles> for submissions. This is a *non-archival* workshop. To foster discussion as much as possible, *we welcome both new submissions and works that have been already published during the COVID-19 pandemic.* The venue of publication should be clearly indicated during submission for such papers. Submission Link: https://openreview.net/group?id=NeurIPS.cc/2022/Workshop/INTERPOLATE Workshop Chairs - Yann N. Dauphin, Google Research - David Lopez-Paz, Meta AI - Vikas Verma, MILA and Aalto University - Boyi Li, Cornell University Contact us at interpolation.works...@gmail.com Program Committee Interested in co-organizing INTERPOLATE @ NeurIPS 2022? We are hiring a program Committee to help us craft a list of high-quality accepted papers. If you are interested, get in touch with us at: interpolation.works...@gmail.com
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