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Stories from my life
This is going to be an unusual AI conference keynote talk. When we talk about why the technological landscape is the way it is, we talk a lot about the macro shifts – the internet, the data, the compute. We don’t talk about the micro threads, the individual human stories as much, even though it is these individual human threads that cumulatively lead to the macro phenomenon. We should talk about these stories more! So that we can learn from each other, inspire each other. So we can be more robust; more effective in our endeavors. By strengthening our individual threads and our connections, we can weave a stronger fabric together. This talk is about some of my stories from my 20-year journey so far – about following up on all threads, about learnt reward functions, about fleeting opportunities, about multidimensional impact landscapes, and about curiosity for new experiences. It might seem narcissistic, but hopefully it will also feel authentic and vulnerable. And hopefully you will get something out of it.
Blog Track Session 5
Tiny Papers Poster Session 5
Rene Vidal is the Herschel Seder Professor of Biomedical Engineering and the Director of the Mathematical Institute for Data Science, the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning, and the NSF TRIPODS Institute on the Foundations of Graph and Deep Learning at The Johns Hopkins University. He has secondary appointments in Applied Mathematics and Statistics, Computer Science, Electrical and Computer Engineering, and Mechanical Engineering. He is also a faculty member in the Center for Imaging Science (CIS), the Institute for Computational Medicine (ICM) and the Laboratory for Computational Sensing and Robotics (LCSR). He is also an Amazon Scholar, Chief Scientist at NORCE, and Associate Editor in Chief of TPAMI. Vidal's research focuses on the development of theory and algorithms for the analysis of complex high-dimensional datasets such as images, videos, time-series and biomedical data. His current major research focus is understanding the mathematical foundations of deep learning and its applications in computer vision and biomedical data science. His lab has pioneered the development of methods for dimensionality reduction and clustering, such as Generalized Principal Component Analysis and Sparse Subspace Clustering, and their applications to face recognition, object recognition, motion segmentation and action recognition. His lab creates new technologies for a variety of biomedical applications, including detection, classification and counting of blood cells in holographic images, classification of embryonic cardio-myocytes in optical images, assessment of surgical skill in kinematic and video data. His lab also develops computer vision technology for pedriatric rehabilitation therapy, autism, and Tourette syndrome.
Your new Scholar profile
Google Scholar is widely used to form opinions about researchers, but it is not a passive measuring tool. Its deliberate decisions on what to show and what to hide have a massive impact on how science is done today: they influence what researchers decide to work on, their methodologies, and career advancements.
We believe Google Scholar profiles are not serving science in the best way. We wish to share our vision of a Better Scholar for the future and gather your observations and feedback.
AI Safety
We aim to foster meaningful discussions among researchers, practitioners, and policymakers dedicated to creating safer AI technologies. Whether you're deeply entrenched in safety research or keen to learn about the intersection of AI and ethics, your insights and experiences are invaluable to us.
Erin Grant is a Senior Research Fellow at the Gatsby Computational Neuroscience Unit and the Sainsbury Wellcome Centre at University College London. Erin studies prior knowledge and learning mechanisms in minds, brains, and machines using a combination of behavioral experiments, computational simulations, and analytical techniques, with the goal of grounding higher-level cognitive phenomena in a neural implementation. Erin earned her Ph.D. from the University of California, Berkeley in 2022 with support from Canada’s Natural Sciences and Engineering Research Council. During her Ph.D., Erin spent time at OpenAI, Google Brain, and DeepMind. Erin currently serves on the Women in Machine Learning Board of Directors.
Andrew Gordon Wilson is an Associate Professor at the Courant Institute of Mathematical Sciences and Center for Data Science at NYU. Prof. Wilson wishes to develop a prescriptive foundation for building intelligent autonomous systems, with work involving Bayesian inference, distribution shifts, scientific discovery, and generalization in deep learning. He has been Workshop Chair, Tutorial Chair, EXPO Chair, and Senior Area Chair for major machine learning conferences, and has received numerous awards, including the NSF CAREER Award, the Amazon Research Award, and best paper, reviewer, area chair, and dissertation awards.
Masashi Sugiyama is a Professor of Computer Science at the University of Tokyo and Director of the RIKEN Center for Advanced Intelligence Project (AIP), Japan. His research interests are in theoretical and algorithmic aspects of machine learning, including weakly supervised learning, transfer learning, density ratio estimation, and noise-robust learning. He has written several books on them. He was a program co-chair for NeurIPS2015, AISTATS2019, and ACML2010/2020---it was not an easy job, but an unforgettably fruitful experience! RIKEN-AIP is a national research project center in Japan, and he established and leads the research groups on generic machine learning technologies, goal-oriented AI technologies for science and society, and social aspects of AI. He is happy to talk about various topics such as research, career, team management, and conference organization.
We discuss how years of research advances now power the private training of Gboard LMs, since the proof-of-concept development of federated learning (FL) in 2017 and formal differential privacy (DP) guarantees in 2022. FL enables mobile phones to collaboratively learn a model while keeping all the training data on device, and DP provides a quantifiable measure of data anonymization. Formally, DP is often characterized by (ε, δ) with smaller values representing stronger guarantees. Machine learning (ML) models are considered to have reasonable DP guarantees for ε=10 and strong DP guarantees for ε=1 when δ is small. As of today, all NWP neural network LMs in Gboard are trained with FL with formal DP guarantees, and all future launches of Gboard LMs trained on user data require DP. These 30+ Gboard on-device LMs are launched in 7+ languages and 15+ countries, and satisfy (ɛ, δ)-DP guarantees of small δ of 10-10 and ɛ between 0.994 and 13.69. To the best of our knowledge, this is the largest known deployment of user-level DP in production at Google or anywhere, and the first time a strong DP guarantee of ɛ < 1 is announced for models trained directly on user data.
Tiny Papers Oral Session 3
Danqi Chen is an Assistant Professor of Computer Science at Princeton University and co-leads the Princeton NLP Group. She is also the associate director of Princeton Language and Intelligence, an initiative that seeks to develop fundamental research of large AI models (e.g., LLMs). Her recent research focuses on training, adapting and understanding large language models, and developing scalable and efficient NLP systems. Before joining Princeton, Danqi worked as a visiting scientist at Facebook AI Research. She received her Ph.D. from Stanford University (2018) and B.E. from Tsinghua University (2012), both in Computer Science. Her research was recognized by a Sloan Fellowship, an NSF CAREER award, a Samsung AI Researcher of the Year award, outstanding paper awards from ACL and EMNLP, and multiple industrial faculty awards.
Piotr Koniusz is a principal research scientist in Machine Learning Research Group at Data61❤CSIRO (former NICTA). He is also appointed as an honorary associate professor (level D) at the Australian National University (ANU). Between 2013-2015, he was a post-doctoral researcher in the team LEAR, INRIA, Grenoble. He received my BSc degree in Telecommunications and Software Eng in 2004 from the Warsaw University of Technology, Poland, and my PhD degree in Computer Vision in 2013 at CVSSP, University of Surrey, United Kingdom. His interests include representation learning (e.g., contrastive/self-supervised learning, foundation models, deep neural nets), graph neural networks, image classificaton/action recognition, zero-, one- and few-shot learning, domain adaptation and incremental learning, object segmentation and detection, 3D point clouds, generative networks, adversarial robustness, spectral and tensor learning, RKHS, optimal transportation (OT).
Kyunghyun Cho is a professor of computer science and data science at New York University and a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He is also a CIFAR Fellow of Learning in Machines & Brains and an Associate Member of the National Academy of Engineering of Korea. He served as a (co-)Program Chair of ICLR 2020, NeurIPS 2022 and ICML 2022. He is also a founding co-Editor-in-Chief of the Transactions on Machine Learning Research (TMLR). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving MSc and PhD degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He received the Samsung Ho-Am Prize in Engineering in 2021. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.
Amy X. Zhang is an assistant professor at University of Washington's Allen School of Computer Science and Engineering. Previously, she was a 2019-20 postdoctoral researcher at Stanford University's Computer Science Department after completing her Ph.D. at MIT CSAIL in 2019, where she received the George Sprowls Best Ph.D. Thesis Award at MIT in computer science. During her Ph.D., she was an affiliate and 2018-19 Fellow at the Berkman Klein Center at Harvard University, a Google Ph.D. Fellow, and an NSF Graduate Research Fellow. Her work has received a best paper award at ACM CSCW, a best paper honorable mention award at ACM CHI, and has been profiled on BBC's Click television program, CBC radio, and featured in articles by ABC News, The Verge, New Scientist, and Poynter. She is a founding member of the Credibility Coalition, a group dedicated to research and standards for information credibility online. She has interned at Microsoft Research and Google Research. She received an M.Phil. in Computer Science at the University of Cambridge on a Gates Fellowship and a B.S. in Computer Science at Rutgers University, where she was captain of the Division I Women's tennis team.
The ChatGLM's Road to AGI
Large language models have substantially advanced the state of the art in various AI tasks, such as natural language understanding and text generation, and image processing, multimodal modeling. In this talk, we will first introduce the development of AI in the past decades, in particular from the angle of China. We will also talk about the opportunities, challenges, and risks of AGI in the future. In the second part of the talk, we will use ChatGLM, an alternative but open-sourced model to ChatGPT, as an example to explain our understanding and insights derived during the implementation of the model.
Tiny Papers Poster Session 6
Blog Track Session 6
ML Safety Social
Designing systems to operate safely in real-world settings is a topic of growing interest in machine learning. We want to host a meet-up for researchers who are currently working on or interested in topics relating to AI safety and security, such as adversarial robustness, interpretability, and backdoors, to foster discussion and collaboration. We hosted similar events at NeurIPS and ICML in 2023 which were very well attended (>200 and >150 concurrent attendees, respectively).
GenAI Startups
A premier networking event between AI researchers, tech entrepreneurs, and investors. Focused on leveraging generative AI for startups, it invites participants to explore ideas, investment opportunities, share insights, and discuss the future of AI in entrepreneurship. Perfect for innovators seeking to blend cutting-edge ML with society impacting applications.
Data-Centric Machine Learning Research Meetup
The companion social to the DMLR workshop at ICLR. An opportunity to meet others and talk about the future of data and data-centric AI! This is a semi-structured meet up. We'll have roundtables set up with different themes to foster discussion and help you find your people. Possible topics include: data collection, benchmarking techniques, data cleaning, data governance, active learning, data-centric approaches to AI alignment, etc... If time, we will reconvene at end to summarize discussions.