[The 60th TrustML Young Scientist Seminar]

Fri, 17 Mar 2023 09:00 - 10:00 JST
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-Passcode: 3pm9LXASXF -Time Zone:JST -The seats are available on a first-come-first-served basis. -When the seats are fully booked, we may stop accepting applications. -Simultaneous interpretation will not be available.


The TrustML Young Scientist Seminars (TrustML YSS) started from January 28, 2022.

The TrustML YSS is a video series that features young scientists giving talks and discoveries in relation with Trustworthy Machine Learning.

Timetable for the TrustML YSS online seminars from March to April. 2023.

For more information please see the following site.

This network is funded by RIKEN-AIP's subsidy and JST, ACT-X Grant Number JPMJAX21AF, Japan.

【The 60th Seminar】

Date and Time: March 17th 9:00 am - 10:00 am(JST)

Speaker: Ludwig Schmidt (U Washington)
Title: A data-centric view on reliable generalization: From ImageNet to LAION-5B

Short Abstract
Researchers have proposed many methods to make neural networks more reliable under distribution shift, yet there is still large room for improvement. Are better training algorithms or training data the more promising way forward? In this talk, we study this question in the context of OpenAI’s CLIP model for learning from image-text data. First, we survey the current robustness landscape based on a large-scale experimental study involving more than 200 different models and test conditions. The CLIP models stand out with unprecedented robustness gains on multiple challenging distribution shifts. To further improve CLIP, we then introduce new methods for reliably fine-tuning models by interpolating the weights of multiple models. Next, we investigate the cause of CLIP’s robustness via controlled experiments to disentangle the influence of language supervision and training distribution. While CLIP leveraged large scale language supervision for the first time, its robustness actually comes from the pre-training dataset. We conclude with a brief overview of ongoing work to improve pre-training datasets: LAION-5B, the largest public image-text dataset, and initial experiments to improve the robustness induced by pre-training data.

Ludwig Schmidt is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Ludwig’s research interests revolve around the empirical foundations of machine learning, often with a focus on datasets, reliable generalization, and large models. Ludwig completed his PhD at MIT under the supervision of Piotr Indyk and was a postdoc at UC Berkeley hosted by Benjamin Recht and Moritz Hardt. Recently, Ludwig's research group contributed to multimodal language & vision models by creating OpenCLIP and the LAION-5B dataset. Ludwig’s research received a new horizons award at EAAMO, best paper awards at ICML & NeurIPS, a best paper finalist at CVPR, and the Sprowls dissertation award from MIT.

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