My issue is lack of transparency. If this interpretation is right, it wouldn't be hard to add a line like: "Whether LLMs plagiarize is an emerging topic of discussion, we deliberated and chose to be conservative, we look forward to how things unfold, etc."
@thegautamkamath
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Do Large Language Models Constitute Plagiarism?
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I think it is super debatable whether large language models (and more generally, powerful ML models) count as plagiarism. This seems like a big question that we will have to grapple with as a community and society.
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Major ML Conferences Need More Transparency in Policy Changes
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I feel like, in the last couple of years, NeurIPS/ICML/ICLR have made a number of changes, often drastic. Change can be good. But I wish these changes were made with a bit more transparency, rationale, and potentially input from the community.
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Submissions vs Papers: Research Collaboration and Student Impact
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To be clear, 14 submissions neq 14 papers, since some are resubmissions. I of course can't know absolutely everything about every paper, e.g., I don't read every line of code. It's hard to say whether great students or collaborators are more important, I'm blessed w both.
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Paper Review Practices at Major AI Conferences
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Yes, that is probably true. I put out my numbers publicly to give at least n = 1 data point. I would be curious to know how many papers are reviewed by those who submit 30+ papers to a single conference. Though maybe their large group would make up any deficit.
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Balancing Peer Review Contributions in Academic Publishing
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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.
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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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