[ICML] Int'l Conference on Machine Learning

[ICML] Int'l Conference on Machine Learning

Computers and Electronics Manufacturing

The Int'l Conf on ML is the premier gathering of professionals for the advancement of the branch of AI known as ML.

About us

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

Website
https://icml.cc/
Industry
Computers and Electronics Manufacturing
Company size
2-10 employees
Type
Nonprofit
Founded
1996

Employees at [ICML] Int'l Conference on Machine Learning

Updates

  • We’re thrilled to have Abu Dhabi Investment join ICML in Vienna,Austria!

    We will join the global machine learning community at the [ICML] Int'l Conference on Machine Learning from 21-27 July at the Messe Wien Exhibition Congress Center in Vienna.   ADIA is a sponsor of the event, one of the fastest growing artificial intelligence conferences in the world. Members of ADIA’s Quantitative Research & Development team will discuss the latest machine learning developments with their fellow ICML participants, which include academic and industrial researchers, engineers, graduate students and postdocs.

  • Be sure to pick up your Spotlight sign at the poster pick up desk.

    View profile for Lerrel Pinto, graphic

    Assistant Professor of Computer Science at New York University.

    Happy to see our VQ-BeT paper be selected as a Spotlight paper at [ICML] Int'l Conference on Machine Learning. VQ-BeT takes a fresh look at modeling robot actions and treats them as discrete, vector-quantized (VQ) tokens. These VQ actions combined with our Behavior Transformer (BeT) architecture allows us to predict continuous, multi-modal, and high dim behaviors. This improves performance on a variety of benchmarks including simulated kitchen tasks, self-driving, and real-world manipulation. Code for VQ-BeT is fully open-sourced and has been recently integrated into Hugging Face's LeRobot codebase: https://lnkd.in/e64NF_DK More details on the project are here: https://sjlee.cc/vq-bet/ This work was led primarily by Seungjae Lee and Mahi Shafiullah with wonderful collaborators from NYU and SNU.

  • The Forty-first International Conference on Machine Learning @ Messe Wien Exhibition Congress Center, Vienna, Austria welcomes #LucillaSioli. Ms Lucilla Sioli is the Director of the "EU AI Office" within Directorate-General CONNECT at the European Commission. She is responsible for the coordination of the European AI strategy, including the implementation of the AI Act and international collaboration in trustworthy AI and AI for good. The directorate is also responsible for R&D&I activities in AI and for the implementation of the AI Innovation Package. Lucilla holds a PhD in economics from the University of Southampton (UK) and one from the Catholic University of Milan (Italy) and has been a civil servant with the European Commission since 1997. #ICML #PhD #EUAI

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  • The Forty-first International Conference on Machine Learning @ Messe Wien Exhibition Congress Center, Vienna, Austria welcomes #JavierDuarte. Javier Duarte is an Associate Professor of Physics at UC San Diego and a member of the CMS experiment at the CERN Large Hadron Collider. He leads a research group developing new artificial intelligence (AI) techniques for high-energy particle collisions to better measure the properties and interactions of elementary particles, like the Higgs boson, and search for new physics. Before joining UC San Diego, he was a Lederman postdoctoral fellow at Fermilab and received his Ph.D. in Physics at Caltech and his B.S. in Physics and Mathematics at MIT. Prof. Duarte has received the APS Henry Primakoff Award for Early-Career Particle Physics, Sloan Research Fellowship, RCSA Cottrell Scholar Award, DOE Early Career Award, and is a co-PI of the NSF HDR Institute for Accelerated AI Algorithms for Data-Driven Discovery (A3D3). #ICML #AI #UCSD #CERN

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  • The Forty-first International Conference on Machine Learning @ Messe Wien Exhibition Congress Center, Vienna, Austria welcomes #VukosiMarivate. Prof Vukosi Marivate is an Associate Professor of Computer Science and holds the ABSA UP Chair of Data Science at the University of Pretoria. He specialises in developing Machine Learning (ML) and Artificial Intelligence (AI) methods to extract insights from data, with a particular focus on the intersection of ML/AI and Natural Language Processing (NLP). His research is dedicated to improving the methods, tools and availability of data for local or low-resource languages. As the leader of the Data Science for Social Impact research group in the Computer Science department, Vukosi is interested in using data science to solve social challenges. He has worked on projects related to science, energy, public safety, and utilities, among others. Prof Marivate is a co-founder of Lelapa AI, an African startup focused on AI for Africans by Africans. Vukosi is co-founder and advisor to Masakhane Research Foundation, which aims to develop NLP technologies for African languages. Vukosi is also a co-founder of the Deep Learning Indaba, the leading grassroots Machine Learning and Artificial Intelligence conference on the African continent that aims to empower and support African researchers and practitioners in the field. #ICML #ML #DeepLearning #DEI

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  • The Forty-first International Conference on Machine Learning @ Messe Wien Exhibition Congress Center, Vienna, Austria welcomes #SoumithChintala. Soumith is a Scientist-Engineer focused on AI and Robotics, leading influential AI work such as PyTorch, DCGAN and Torch-7; work which is used by several top institutions including NASA, Meta, Google, Tesla, Microsoft, Disney, Genentech, and numerous other Fortune-500 companies and in the curriculum of top-ranked universities such as Stanford, Harvard, Oxford and MIT. He currently leads PyTorch and other AI projects at Meta, is a Visiting Professor at New York University, and maintains advisory roles at various institutions. #ICML #AI #Robotics #Meta

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  • The Forty-first International Conference on Machine Learning @ Messe Wien Exhibition Congress Center, Vienna, Austria welcomes #DrLucíaMagisWeinberg, Assistant Professor in Psychology at the University of Washington. She leads the interACTlab (International Adolescent Connection and Technology Laboratory), focusing on studying how social media impacts adolescent development and mental health. Her lab also designs and evaluates school interventions to promote healthy digital habits. Her work has mostly focused on underserved adolescents in global settings, particularly in Latin America, and is currently funded by the National Institute of Mental Health. Dr. Magis-Weinberg received her MD from the National Autonomous University of México, her MSc in Cognitive Neuroscience and PhD in Experimental Psychology (Developmental) from University College London. Before joining the University of Washington, she conducted research at the Institute of Human Development at University of California, Berkeley, as a postdoctoral scholar. As a longstanding member of the Society for Research in Adolescence’s International Committee, Dr. Magis-Weinberg advocates for inclusive, global research on adolescent development. Committed to science communication, outreach and impact, she is Executive Editor of Neuromexico.org a leading science communication platform for Latin America. Dr. Magis-Weinberg is a member of the American Psychological Association Expert Advisory Panel on Social Media Use in Adolescence. #DEI #MentalHealth #AI #LatinX #ICML

    Bienvenidos

    Bienvenidos

    http://neuromexico.org

  • Fabulous work and contributions to our conference #ICML and community! Congrats to WASP PhD students.

    📢 17 papers by WASP PhD students, postdocs, supervisors and recruited researchers have been accepted at ICML, one of the premier conferences in machine learning. The 41st International Conference on Machine Learning (ICML) will be held in Vienna, Austria, on July 21–27, 2024. Accepted papers: Anahita Baninajjar, Ahmed Rezine, and Amir Aminifar, "VNNs: Verification-Friendly Neural Networks with Hard Robustness Guarantees" Daniel Gedon, Antonio H. Ribeiro, and Thomas B. Schön, "No double descent in principal component regression: A high-dimensional analysis" Jan Gerken and Pan Kessel, "Emergent Equivariance in Deep Ensembles" Alexandra Hotti, Oskar Kviman, Ricky Molén, Víctor Elvira, and Jens Lagergren, "Efficient Mixture Learning in Black-Box Variational Inference" Carl Hvarfner, Erik Orm Hellsten, and Luigi Nardi, "Vanilla Bayesian Optimization Performs Great in High Dimensions" Yassir Jedra, William Reveillard, Stefan Stojanovic, and Alexandre Proutiere, "Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery" Arvi Jonnarth, Jie Zhao, and Michael Felsberg, "Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning" Aleksandr Karakulev, Dave Zachariah, and Prashant Singh, "Adaptive Robust Learning using Latent Bernoulli Variables" Rasmus Kjær Høier and Christopher Zach, "Two Tales of Single-Phase Contrastive Hebbian Learning" Jaron Maene, Vincent Derkinderen, and Luc De Raedt, "On the Hardness of Probabilistic Neurosymbolic Learning" Amir Mohammad Karimi Mamaghan, Panagiotis Tigas, Karl Johansson, Yarin Gal, Yashas Annadani, and Stefan Bauer, "Challenges and Considerations in the Evaluation of Bayesian Causal Discovery" Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck, Andreas Robinson, and Cuong Le, "O$n$ Learning Deep O($n$)-Equivariant Hyperspheres" Alfred Nilsson, Klas Wijk, Sai bharath chandra Gutha, Erik Englesson, Alexandra Hotti, Carlo Saccardi, Oskar Kviman, Jens Lagergren, Ricardo Vinuesa, and Hossein Azizpour, "Indirectly Parameterized Concrete Autoencoders" Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy, and Pierre Nyquist, "REMEDI: Corrective Transformations for Improved Neural Entropy Estimation" Po-An Wang, Kaito Ariu, and Alexandre Proutiere, "On Universally Optimal Algorithms for A/B Testing" Theodor Westny, Arman Mohammadi, Daniel Jung, and Erik Frisk, "Stability-Informed Initialization of Neural Ordinary Differential Equations" Frederic Zheng and Alexandre Proutiere, "Conformal Prediction under Markovian Data" See the ICML 2024 website for all accepted papers: https://lnkd.in/dQb57w4k #WASP #machinelearning #ICML [ICML] Int'l Conference on Machine Learning

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