A rough count gave about 14. Each of these papers has at least 3 authors (most have more), and each got ~3 reviews, meaning that to remain review neutral, I'd need to do ~14 reviews.
@thegautamkamath
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2022 Conference Review Activity Across Seven ML Programs
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2022's year in reviews: I did 28 conference reviews, plus 42 meta-reviews, on 7 program committees (COLT, UAI, NeurIPS, SaTML, ICLR, USENIX Security, ALT). I also chaired two #ICML2022 workshops (TPDP and UpML). Numbers are down from last year, so why do I feel more tired…
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Differential Privacy and Secure Aggregation in Federated Learning
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It's a bit complicated. Methods like differential privacy and secure aggregation on top of FL generally help, but there are some caveats. See "Is it possible to Prevent Our Passive and Active Attacks?" of http://
cleverhans.io/2022/04/17/fl-
privacy.html
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Federated Learning: Heterogeneity and Privacy Perspectives
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I like FL as a lens through which to study heterogeneity in clients, which may have different distributions, resources, or capabilities. But not for privacy. Here is another perspective on privacy of FL, which is a bit more conspiratorial than my own https://
x.com/le_science4all
/status/1602432680657928193
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Federated Learning Privacy Misconceptions Debunked
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~30% of true/false voters made the biggest misconception in privacy-preserving ML. Fact: federated learning is not private. @jasondeanlee and @tomgoldsteincs posted some nice papers showing this. Also, here's a blog explainer I like by @fraboeni et al.: http://
cleverhans.io/2022/04/17/fl-
privacy.html
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Vision Transformer Citations: Identifying False Positives in Scholar Data
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I feel like this might be picking up some false positives. Scholar also says that the vision transformer paper (
https://
arxiv.org/abs/2010.11929) only has about 10k citations total. There must be some way to see how many are from 2022… -
NeurIPS 2023 Program Chair Nomination Open Call
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You can nominate yourself as Program Chair for #NeurIPS2023. Who is up for the challenge? https://
x.com/NeurIPSConf/st
/NeurIPSConf/status/1607783374843781121
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Technical Debate on AI Example Among Research Community
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Here is a (very) long conversation involving @ccanonne_ @Aaroth @frankmcsherry and @matloff on this very example (warning that threading gets a bit weird at times), which didn't seem to get resolved in the end https://
mobile.twitter.com/frankmcsherry/
status/1396537641865289728
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Organizations Getting AI and Data Privacy Right
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Right, there are certainly bad actors out there. But nonetheless there remain a few organizations that seem to do it right (some that immediately come to mind include Microsoft, Google, @uscensusbureau
, @TumultLabs, and @opendp_org
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Differential Privacy: Industry Funding for Theoretical AI Research
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Another corollary, besides Jelani's, is that if you want to do interesting theoretical work in industry, differential privacy is a great way to trick a company into paying you to do theory (Observation attributed to an anonymous party)