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Daniel AugustoNew paper accepted! In which circunstances can we use abundant proxy preferences to quickly learn true preferences? I'm glad to announce our paper explores and proposes a model for one of these cases. Check out more on Yuchen's thread in Bluesky <a href="https://bsky.app/profile/zhuyuchen.bsky.social/post/3lo4n2tspys2w" target="_blank" rel="nofollow noopener" translate="no">https://bsky.app/profile/zhuyuchen.bsky.social/post/3lo4n2tspys2w</a> . <a href="https://sigmoid.social/tags/ICML2025" class="mention hashtag" rel="tag">#ICML2025</a> <a href="https://sigmoid.social/tags/ICML" class="mention hashtag" rel="tag">#ICML</a>

AnthonySpeaking of machine learning, I once had a paper rejected from <a href="https://buc.ci?t=icml" class="mention hashtag" rel="nofollow noopener" target="_blank">#ICML</a> (International Conference on Machine Learning) in the early 2000s because it "wasn't about machine learning" (minor paraphrase of comments in 2 of the 3 reviews if I recall correctly). That field was consolidating--in a bad way, in my view--around a very small set of ideas even back then. My co-author and I wrote a rebuttal to the rejection, which we had the opportunity to do, arguing that our work was well within the scope of machine learning as set out by Arthur Samuel's pioneering work in the late 1950s/early 1960s that literally gave the field its name (Samuel 1959, Some studies in machine learning using the game of checkers). Their retort was that machine learning consisted of: learning probability distributions of data (unsupervised learning); learning discriminative or generative probabilistic models from data (supervised learning); or reinforcement learning. Nothing else. OK maybe I'm missing one, but you get the idea. We later expanded this work and landed it as a chapter in a 2008 book Multiobjective Problem Solving from Nature, which is downloadable from <a href="https://link.springer.com/book/10.1007/978-3-540-72964-8" rel="nofollow noopener" target="_blank">https://link.springer.com/book/10.1007/978-3-540-72964-8</a> . You'll see the chapter starting on page 357 of that PDF (p 361 in the PDF's pagination). We applied a technique from the theory of coevolutionary algorithms to examine small instances of the game of Nim, and were able to make several interesting statements about that game. Arthur Samuel's original papers on checkers were about learning by self-play, a particularly simple form of coevolutionary algorithm, as I argue in the introductory chapter of my PhD dissertation. Our technique is applicable to Samuel's work and any other work in that class--in other words, it's squarely "machine learning" in the sense Samuel meant the term. Whatever you may think of this particular work of mine, it's bad news when a field forgets and rejects its own historical origins and throws away the early fruitful lines of work that led to its own birth. <a href="https://buc.ci?t=generativeai" class="mention hashtag" rel="nofollow noopener" target="_blank">#GenerativeAI</a> threatens to have a similar wilting effect on artificial intelligence and possibly on computer science more generally. The marketplace of ideas is monopolizing, the ecosystem of ideas collapsing. Not good. <a href="https://buc.ci?t=machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://buc.ci?t=ml" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> <a href="https://buc.ci?t=ai" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://buc.ci?t=computerscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#ComputerScience</a> <a href="https://buc.ci?t=coevolution" class="mention hashtag" rel="nofollow noopener" target="_blank">#Coevolution</a> <a href="https://buc.ci?t=coevoutionaryalgorithm" class="mention hashtag" rel="nofollow noopener" target="_blank">#CoevoutionaryAlgorithm</a> <a href="https://buc.ci?t=checkers" class="mention hashtag" rel="nofollow noopener" target="_blank">#checkers</a> <a href="https://buc.ci?t=nim" class="mention hashtag" rel="nofollow noopener" target="_blank">#Nim</a> <a href="https://buc.ci?t=boardgames" class="mention hashtag" rel="nofollow noopener" target="_blank">#BoardGames</a>

LipnLab🎉 Two papers from the <a href="https://lipn.info/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> and <a href="https://lipn.info/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> teams <a href="https://lipn.info/@LipnLab" class="u-url mention" rel="nofollow noopener" target="_blank">@LipnLab</a> were accepted to <a href="https://lipn.info/tags/ICML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ICML</a>! ▶️ The paper "Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation" by H. Attali, D. Buscladi, N. Pernelle presents a novel graph rewiring method that incorporates node features with low complexity to alleviate both Over-Squashing and Over-Smoothing issues. 🔗 <a href="https://sites.google.com/view/hugoattali/research?authuser=0" rel="nofollow noopener" translate="no" target="_blank">https://sites.google.com/view/hugoattali/research?authuser=0</a>

aijobs.net => foorilla.comHIRING: Founding AI Engineer, Agents / New York👉 <a href="https://ai-jobs.net/J198951/" rel="nofollow noopener" translate="no" target="_blank">https://ai-jobs.net/J198951/</a><a href="https://mstdn.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://mstdn.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://mstdn.social/tags/DataJobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataJobs</a> <a href="https://mstdn.social/tags/Jobsearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#Jobsearch</a> <a href="https://mstdn.social/tags/MLjobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#MLjobs</a> <a href="https://mstdn.social/tags/bigdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#bigdata</a> <a href="https://mstdn.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataScience</a> <a href="https://mstdn.social/tags/AIjobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIjobs</a> <a href="https://mstdn.social/tags/techjobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#techjobs</a> <a href="https://mstdn.social/tags/hiring" class="mention hashtag" rel="nofollow noopener" target="_blank">#hiring</a> <a href="https://mstdn.social/tags/HiringAlert" class="mention hashtag" rel="nofollow noopener" target="_blank">#HiringAlert</a> <a href="https://mstdn.social/tags/Agents" class="mention hashtag" rel="nofollow noopener" target="_blank">#Agents</a> <a href="https://mstdn.social/tags/EngineerJobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#EngineerJobs</a> <a href="https://mstdn.social/tags/NYjobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#NYjobs</a> <a href="https://mstdn.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLMs</a> <a href="https://mstdn.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#python</a> <a href="https://mstdn.social/tags/ICLR" class="mention hashtag" rel="nofollow noopener" target="_blank">#ICLR</a> <a href="https://mstdn.social/tags/NeurIPS" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeurIPS</a> <a href="https://mstdn.social/tags/ICML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ICML</a> <a href="https://mstdn.social/tags/CCVPR" class="mention hashtag" rel="nofollow noopener" target="_blank">#CCVPR</a>

aijobs.net => foorilla.comHIRING: AI Engineer Intern, Agents / New York (Remote for exceptional candidates)👉 <a href="https://ai-jobs.net/J198461/" rel="nofollow noopener" translate="no" target="_blank">https://ai-jobs.net/J198461/</a><a href="https://mstdn.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://mstdn.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://mstdn.social/tags/DataJobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataJobs</a> <a href="https://mstdn.social/tags/Jobsearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#Jobsearch</a> <a href="https://mstdn.social/tags/MLjobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#MLjobs</a> <a href="https://mstdn.social/tags/bigdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#bigdata</a> <a href="https://mstdn.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataScience</a> <a href="https://mstdn.social/tags/AIjobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIjobs</a> <a href="https://mstdn.social/tags/techjobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#techjobs</a> <a href="https://mstdn.social/tags/hiring" class="mention hashtag" rel="nofollow noopener" target="_blank">#hiring</a> <a href="https://mstdn.social/tags/Agents" class="mention hashtag" rel="nofollow noopener" target="_blank">#Agents</a> <a href="https://mstdn.social/tags/internship" class="mention hashtag" rel="nofollow noopener" target="_blank">#internship</a> <a href="https://mstdn.social/tags/NYjobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#NYjobs</a> <a href="https://mstdn.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLMs</a> <a href="https://mstdn.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#python</a> <a href="https://mstdn.social/tags/ICLR" class="mention hashtag" rel="nofollow noopener" target="_blank">#ICLR</a> <a href="https://mstdn.social/tags/NeurIPS" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeurIPS</a> <a href="https://mstdn.social/tags/ICML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ICML</a> <a href="https://mstdn.social/tags/CCVPR" class="mention hashtag" rel="nofollow noopener" target="_blank">#CCVPR</a>

Tiago F. R. RibeiroTimesFM: A decoder-only foundation model for time-series forecasting«Este modelo é baseado em modelos descodificadores pré-treinados num grande “corpus” de séries temporais composto por conjuntos de dados do mundo reais e sintéticos. Os resultados experimentais sugerem que o modelo pode produzir previsões precisas em diferentes domínios, horizontes de previsão e granularidades temporais»:clippy: <a href="https://arxiv.org/html/2310.10688v2" rel="nofollow noopener" translate="no" target="_blank">https://arxiv.org/html/2310.10688v2</a><a href="https://mastodon.social/tags/TimeSeries" class="mention hashtag" rel="nofollow noopener" target="_blank">#TimeSeries</a> <a href="https://mastodon.social/tags/Forecasting" class="mention hashtag" rel="nofollow noopener" target="_blank">#Forecasting</a> <a href="https://mastodon.social/tags/ICML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ICML</a>

Mark ReidA friend attending <a href="https://mastodon.social/tags/ICML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ICML</a> just sent me a photo of this front runner for best poster award.

Harald SackSlides for the <a href="https://sigmoid.social/tags/icml2023" class="mention hashtag" rel="tag">#icml2023</a> tutorial on "Trustworthy Generative AI" by Nazneen Rajani, Hima Lakkaraju, and Krishnaram Kenthapadi Tutorial website: <a href="https://sites.google.com/view/responsible-gen-ai-tutorial" target="_blank" rel="nofollow noopener" translate="no">https://sites.google.com/view/responsible-gen-ai-tutorial</a> Slides: <a href="https://t.co/hNQMkXOqgZ" target="_blank" rel="nofollow noopener" translate="no">https://t.co/hNQMkXOqgZ</a> <a href="https://mastodon.social/@nazneenrajani" class="u-url mention">@nazneenrajani</a> <a href="https://mastodon.social/@ICMLConf" class="u-url mention">@ICMLConf</a> <a href="https://sigmoid.social/tags/trust" class="mention hashtag" rel="tag">#trust</a> <a href="https://sigmoid.social/tags/trustworthyAI" class="mention hashtag" rel="tag">#trustworthyAI</a> <a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="tag">#AI</a> <a href="https://sigmoid.social/tags/icml" class="mention hashtag" rel="tag">#icml</a> <a href="https://sigmoid.social/tags/icml2023" class="mention hashtag" rel="tag">#icml2023</a> <a href="https://sigmoid.social/tags/icml23" class="mention hashtag" rel="tag">#icml23</a> <a href="https://sigmoid.social/tags/generativeAI" class="mention hashtag" rel="tag">#generativeAI</a>

UKRI AI for Healthcare CentresCome and find our Exhibit stand at <a href="https://sigmoid.social/tags/ICML" class="mention hashtag" rel="tag">#ICML</a> 2023 and chat to our brilliant <a href="https://sigmoid.social/tags/AI4Health" class="mention hashtag" rel="tag">#AI4Health</a> researchers about their prelims and ideas @PFestor @alj_jenkins @joshsouthern EdisonLiu GiannisAfentakis <a href="https://sigmoid.social/@analogaldo" class="u-url mention">@analogaldo</a>

marco<a href="https://sigmoid.social/tags/CFP" class="mention hashtag" rel="tag">#CFP</a> for `Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators`, a workshop at <a href="https://sigmoid.social/tags/ICML" class="mention hashtag" rel="tag">#ICML</a> <a href="https://differentiable.xyz/" target="_blank" rel="nofollow noopener" translate="no">https://differentiable.xyz/</a><a href="https://twitter.com/FHKPetersen/status/1657283011459821569" target="_blank" rel="nofollow noopener" translate="no">https://twitter.com/FHKPetersen/status/1657283011459821569</a>Differentiable programming is a powerful tool, so I am quite interested in this workshop (especially as a <a href="https://sigmoid.social/tags/JuliaLang" class="mention hashtag" rel="tag">#JuliaLang</a> user, which has fantastic <a href="https://sigmoid.social/tags/AD" class="mention hashtag" rel="tag">#AD</a> support).<a href="https://sigmoid.social/tags/AutomaticDifferentiation" class="mention hashtag" rel="tag">#AutomaticDifferentiation</a> <a href="https://sigmoid.social/tags/ML" class="mention hashtag" rel="tag">#ML</a>

Mark Riedl<a href="https://sigmoid.social/tags/ICML" class="mention hashtag" rel="tag">#ICML</a> has clarified their position on AI-generated text. <a href="https://icml.cc/Conferences/2023/llm-policy" target="_blank" rel="nofollow noopener" translate="no">https://icml.cc/Conferences/2023/llm-policy</a>

Marco 🌳 Zocca<a href="https://mastodon.social/@matrig" class="u-url mention">@matrig</a> am I the only one finding this tight deadline a bit ridiculous? Every year <a href="https://sigmoid.social/tags/icml" class="mention hashtag" rel="tag">#icml</a> is due end of January but somehow a major conference can only finalize the CFP with 3 weeks of notice?

Mark Riedl<a href="https://sigmoid.social/tags/ICML" class="mention hashtag" rel="tag">#ICML</a> has prohibited papers that include text generated by large language models (unless that text is part of shown output or experimental analysis)<a href="https://icml.cc/Conferences/2023/CallForPapers" target="_blank" rel="nofollow noopener" translate="no">https://icml.cc/Conferences/2023/CallForPapers</a>

Liz WoodLooking to follow (and be followed!) <a href="https://sigmoid.social/tags/mlbio" class="mention hashtag" rel="tag">#mlbio</a> <a href="https://sigmoid.social/tags/bioml" class="mention hashtag" rel="tag">#bioml</a> <a href="https://sigmoid.social/tags/biotech" class="mention hashtag" rel="tag">#biotech</a> <a href="https://sigmoid.social/tags/bayes" class="mention hashtag" rel="tag">#bayes</a> <a href="https://sigmoid.social/tags/bayesian" class="mention hashtag" rel="tag">#bayesian</a> <a href="https://sigmoid.social/tags/generativemodels" class="mention hashtag" rel="tag">#generativemodels</a> <a href="https://sigmoid.social/tags/startup" class="mention hashtag" rel="tag">#startup</a> <a href="https://sigmoid.social/tags/startups" class="mention hashtag" rel="tag">#startups</a> <a href="https://sigmoid.social/tags/medicine" class="mention hashtag" rel="tag">#medicine</a> <a href="https://sigmoid.social/tags/biology" class="mention hashtag" rel="tag">#biology</a> <a href="https://sigmoid.social/tags/representationlearning" class="mention hashtag" rel="tag">#representationlearning</a> <a href="https://sigmoid.social/tags/techbio" class="mention hashtag" rel="tag">#techbio</a> <a href="https://sigmoid.social/tags/immunology" class="mention hashtag" rel="tag">#immunology</a> <a href="https://sigmoid.social/tags/neurips" class="mention hashtag" rel="tag">#neurips</a> <a href="https://sigmoid.social/tags/icml" class="mention hashtag" rel="tag">#icml</a> <a href="https://sigmoid.social/tags/iclr" class="mention hashtag" rel="tag">#iclr</a> <a href="https://sigmoid.social/tags/cambridge" class="mention hashtag" rel="tag">#cambridge</a> <a href="https://sigmoid.social/tags/nyc" class="mention hashtag" rel="tag">#nyc</a>

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