Please bring an ID or a credit card and your registration receipt QR code to check in. Avoid brining your passport to the convention center.
After decades of steady progress and occasional setbacks, the field of AI now finds itself at an inflection point. AI products have exploded into the mainstream, we've yet to hit the ceiling of scaling dividends, and the community is asking itself what comes next. In this talk, Raia will draw on her 20 years experience as an AI researcher and AI leader to examine how our assumptions about the path to Artificial General Intelligence (AGI) have evolved over time, and to explore the unexpected truths that have emerged along the way. From reinforcement learning to distributed architectures and the potential of neural networks to revolutionize scientific domains, Raia argues that embracing lessons from the past offers valuable insights for AI's future research roadmap.
Tiny Papers Poster Session 3
Blog Track Session 3
WiML@ICLR 2024
Founded in 2006, the Women in Machine Learning (WiML) workshop fosters connections, mentorship, and idea exchange among women in machine learning. The social event at ICLR for women and nonbinary individuals, and mentorship sessions, promoting dialogue and collaboration. WiML aims to increase women's representation in tech and foster a diverse, innovative community in the machine learning landscape. Spots are limited - Registration is Required to attend this luncheon - click on Project Page to Register.
The ML Collective Social on Open Collaboration
ML Collective (https://mlcollective.org) is dedicated to the mission of democratizing AI research from a humanistic perspective: providing more opportunities to people in support of their growth in ML research. We have a tradition of hosting socials at major conferences in the past: https://mlcollective.org/events/#social
AI/ML research is progressing at an unprecedented speed; it is more urgent than ever that we break the institutional walls and collaborate.
Andrea Bajcsy is an Assistant Professor in the Robotics Institute at Carnegie Mellon University. She works at the intersection of robotics, machine learning, and human-AI interaction, with the goal of enabling robots to safely learn from and interact with people. Andrea received her Ph.D. in Electrical Engineering & Computer Science from UC Berkeley in 2022 and her B.S. in Computer Science from University of Maryland, College Park in 2016. She is the recipient of the Google Research Scholar Award, an Honorable Mention for the T-RO Best Paper Award, the NSF Graduate Research Fellowship, and has worked at NVIDIA Research.
Samy Bengio has been a senior director of machine learning research at Apple since 2021. Before that, he was a distinguished scientist at Google Research since 2007 where he was heading the Google Brain team, and at IDIAP in the early 2000s where he co-wrote the well-known open-source Torch machine learning library.
His research interests span many areas of machine learning such as deep architectures, representation learning, sequence processing, speech recognition, and image understanding.
He is action editor of the Journal of Machine Learning Research and on the board of the NeurIPS foundation. He was on the editorial board of the Machine Learning Journal, has been program chair (2017) and general chair (2018) of NeurIPS, program chair of ICLR (2015, 2016), general chair of BayLearn (2012-2015), MLMI (2004-2006), as well as NNSP (2002), and on the program committee of several international conferences such as NeurIPS, ICML, ICLR, ECML and IJCAI.
Andrej Risteski is an Assistant Professor at the Machine Learning Department in Carnegie Mellon University. Prior to that, he was a Norbert Wiener Research Fellow jointly in the Applied Math department and IDSS at MIT. He received my PhD in the Computer Science Department at Princeton University under the advisement of Sanjeev Arora. His research interests lie in the intersection of machine learning, statistics, and theoretical computer science, spanning topics like (probabilistic) generative models, algorithmic tools for learning and inference, representation and self-supervised learning, out-of-distribution generalization and applications of neural approaches to natural language processing and scientific domains. More broadly, the goal of his research is principled and mathematical understanding of statistical and algorithmic problems arising in modern machine learning paradigms.
Yao Qin is an Assistant Professor at the Department of Electrical and Computer Engineering at UC Santa Barbara, affiliated with the Department of Computer Science. Meanwhile, she is also a senior research scientist at Google Research. She obtained my PhD degree at UC San Diego in Computer Science, advised by Prof. Garrison W. Cottrell. During her PhD, she was very fortunate to intern under the supervision of Geoffrey Hinton, Ian Goodfellow and many others. Her research interests primarily focus on robustness in machine learning, such as adversarial robustness, out-of-distribution generalization, and fairness. In addition, she is highly passionate about developing reliable AI-driven models tailored for healthcare, with a particular focus on diabetes management.
Mechanistic Interpretability Social
Our event aims to gather researchers and practitioners interested in discussing recent advances and promising directions for deep learning interpretability research, with a specific focus on Transformers-based language models and mechanistic approaches aimed at reverse-engineering their behaviors.
The field of machine learning (ML) is experiencing a paradigm shift. While the focus on innovative algorithms and architecture once dominated and might continue to evolve, the spotlight is now on DATA. Large models are becoming commonplace, and real-world effectiveness is paramount. This necessitates a data-centric approach, encompassing the entire data lifecycle, from collection, cleansing, orchestration to supply, to satisfy the hunger of the humongous and ever growing models.
This panel discussion will delve into the industry challenges associated with data efficiency. We will explore: * The rise of data-centricity: Moving beyond algorithms to prioritize data quality, management, and utilization. * Challenges of large models and real-world application: Ensuring data is sufficient, representative, and addresses real-world complexities. * Data lifecycle considerations: Optimizing data collection, storage, transformation, and integration for robust AI systems.
By fostering open dialogue on these critical challenges and opportunities, this panel discussion aims to propel the field of data-centric AI towards a future of responsible, impactful, and collaborative innovation.
An open discussion led by the organizing committee on topics related to ICLR, such as the review process, policy, venue, and D&I.
Tiny Papers Oral Session 2
Eunsol Choi is an assistant professor in the Computer Science department at the University of Texas at Austin. Prior to UT, she spent a year at Google AI as a visiting researcher. Her research area spans natural language processing and machine learning. She is particularly interested in interpreting and reasoning about text in a dynamic real world context. She is a recipient of a Facebook research fellowship, Google faculty research award, Sony faculty award, and an outstanding paper award at EMNLP. She received a Ph.D. in computer science and engineering from University of Washington and B.A in mathematics and computer science from Cornell University.
Aditi is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. She is also affiliated with the Machine Learning Department.
Aditi works broadly in machine learning and her goal is to make machine learning more reliable and robust. Her work spans both theory and practice, and leverages tools and concepts from statistics, convex optimization, and algorithms to improve the robustness of modern systems based on deep learning.
Aditi’s group research is generously supported by an AI2050 Early Career Fellowship from Schmidt Futures, Apple, Google, and Open Philanthropy.
Until recently, she was a postdoc at Berkeley AI Research. Aditi received her PhD from Stanford University in 2021 where she was fortunate to be advised by Percy Liang. Her thesis won the Arthur Samuel Best Thesis award at Stanford. Previously, she obtained her BTech in Computer Science from IIT Madras in 2016.
Recognized worldwide as one of the leading experts in artificial intelligence, Yoshua Bengio is most known for his pioneering work in deep learning, earning him the 2018 A.M. Turing Award, “the Nobel Prize of Computing,” with Geoffrey Hinton and Yann LeCun.
He is Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Scientific Director of IVADO.
In 2019, he was awarded the prestigious Killam Prize and in 2022, became the most cited computer scientist in the world. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France, Officer of the Order of Canada, Member of the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology since 2023 and a Canada CIFAR AI Chair.
Concerned about the social impact of AI, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence.
Auto-Encoding Variational Bayes
Probabilistic modeling is one of the most fundamental ways in which we reason about the world. This paper spearheaded the integration of deep learning with scalable probabilistic inference (amortized mean-field variational inference via a so-called reparameterization trick), giving rise to the Variational Autoencoder (VAE). The lasting value of this work is rooted in its elegance. The principles used to develop VAEs deepened our understanding of the interplay between deep learning and probabilistic modeling, and sparked the development of many subsequent interesting probabilistic models and encoding approaches.
Intriguing properties of neural networks
With the rising popularity of deep neural networks in real applications, it is important to understand when and how neural networks might behave in undesirable ways. This paper highlighted the issue that neural networks can be vulnerable to small almost imperceptible variations to the input. This idea helped spawn the area of adversarial attacks (trying to fool a neural network) as well as adversarial defense (training a neural network to not be fooled).