A super cool and timely workshop to be co-hosted at #ICLR2023! When can statistical and computational limitations arise in the context of trustworth ML? Deadline is February 8, check out their unique two track system. https://
sites.google.com/view/trustml-u
nlimited/call-for-papers
… Look forward to being there!
@thegautamkamath
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Trustworthy ML Workshop at ICLR 2023: Statistical Computational Limitations
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French Machine Learning Researchers: Numbers and Implications
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It's at odds with the number of French machine learning researchers…
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Major AI Conference Submission Deadlines January 2026
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Some venues that come to mind include COLT, ICML, FAccT, FORC, and EC. These are mostly late January deadlines though.
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Public Domain Data Availability for AI Model Training
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Sure. But is there a surplus of such data in the public domain? Not totally clear to me. Either way, it feels qualitatively different from ImageNet style settings.
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Public and Private AI Training: Different Accuracy Standards
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Also, my personal opinion is that both public pretraining (with certain constraints/limitations) as well as private training from scratch both have roles to play in the space. They of course can't be held to the same accuracy standard though.
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Public vs Proprietary Datasets in AI Model Training
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Thanks Alex. I like the papers that do this, but I also have some concern when this is done on a dataset that is proprietary and only Google has access (JFT). I would like to see a version that is pretrained on LAION. This still has privacy issues, but is at least all public.
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Privacy in AI: Beyond Training and Model Usage
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And finally, privacy is… hard! While a lot of work focuses on training and using models privately, this is a narrow view of privacy, which encapsulates much more. 14/n
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Private ML Benchmarks and Privacy-Focused Evaluation Standards
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The second is to re-focus towards benchmarks that are more appropriate for private ML. We now understand that public data can help for private CIFAR-10 and ImageNet classification, which is great. But maybe we should move towards settings where privacy is more important. 13/n
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Privacy-Respecting Public Pre-Training Datasets for AI Models
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So where do we go from here? We conclude with a number of suggestions for the field. The first ones focuses on making sure we have public pre-training sets which are truly privacy-respecting. Can we make such a dataset/model with comparable utility to what people use now? 12/n
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FHE Limitations for Large Model Inference Privacy
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But FHE is not so practical for these models. And it may be infeasible to use some of these large models on a user's device. So even with private fine-tuning, then privacy at inference time still remains. 11/n