CS Colloquium Speaker Lydia Liu TODAY at 12:30pm
Speaker: Lydia Liu, Cornell University Date: Wednesday, April 5 Time: 12:30pm EST Location: CS 105 Hosts: Olga Russakovsky and Peter Ramadge Event page: https://www.cs.princeton.edu/events/26353 Title: Towards Responsible Machine Learning in Societal Systems Abstract: Machine learning systems are deployed in consequential domains such as education, employment, and credit, where decisions have profound effects on socioeconomic opportunity and life outcomes. High stakes decision settings present new statistical, algorithmic, and ethical challenges. In this talk, we examine the distributive impact of machine learning algorithms in societal contexts, and investigate the algorithmic and sociotechnical interventions that bring machine learning systems into alignment with societal values---equity and long-term welfare. First, we study the dynamic interactions between machine learning algorithms and populations, for the purpose of mitigating disparate impact in applications such as algorithmic lending and hiring. Next, we consider data-driven decision systems in competitive environments such as markets, and devise learning algorithms to ensure efficiency and allocative fairness. We end by outlining future directions for responsible machine learning in societal systems that bridge the gap between the optimization of predictive models and the evaluation of downstream decisions and impact. Bio: Lydia T. Liu is a postdoctoral researcher in Computer Science at Cornell University, working with Jon Kleinberg, Karen Levy, and Solon Barocas. Her research examines the theoretical foundations of machine learning and algorithmic decision-making, with a focus on societal impact and human welfare. She obtained her PhD in Electrical Engineering and Computer Sciences from UC Berkeley, advised by Moritz Hardt and Michael Jordan, and has received a Microsoft Ada Lovelace Fellowship, an Open Philanthropy AI Fellowship, an NUS Development Grant, and a Best Paper Award at the International Conference on Machine Learning. This talk is co-sponsored with Electrical and Computer Engineering and the Center for Information Technology Policy.
Speaker: Yang Liu, Stanford University Date: Monday, April 10 Time: 12:30pm EST Location: CS 105 Host: Gillat Kol Event page: https://www.cs.princeton.edu/events/26400 Title: Graphs, Optimization, Geometry, and Fast Algorithms Abstract: Discrete combinatorial structures such as graphs and Boolean matrices are prevalent in modern computation. The massive size of modern data motivates the design of efficient algorithms for processing these combinatorial datasets. In this talk, I will describe how to use techniques from continuous optimization and geometry to gain insights into the structure of problems in these combinatorial settings. Using these insights, I will present new efficient algorithms for several fundamental problems at the intersection of combinatorial algorithms, continuous optimization, and high-dimensional geometry, including maximum flows in almost linear time, discrepancy minimization, and linear regression. We conclude by discussing the exciting new lines of research and open problems that these techniques have opened up. Bio: Yang P. Liu is a fifth year PhD student at Stanford advised by Aaron Sidford. He completed his undergraduate studies at MIT in 2018. He has broad interests in computer science, and his research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high-dimensional geometry. His work has been recognized by ITCS and STOC best student paper awards, and a FOCS best paper award.
Speaker: Yao Lu, Microsoft Research Date: Monday, April 17 Time: 12:30pm EST Location: CS 105 Host: Kai Li Event page: https://www.cs.princeton.edu/events/26389 Title: Towards Intelligent Data Systems Abstract: From single-box databases, data systems are evolving into multi-tenant compute and storage platforms that host not only structured data analytics but also AI workloads and AI-enhanced system components. The result of this evolution, which I call an “intelligent” data system, creates new opportunities and challenges for research and production at the intersection of machine learning and systems. Key considerations in these systems include efficiency and cost, ML support and a flexible runtime for heterogeneous jobs. I will describe our work on query optimizers both for AI and aided by AI. For ML inference workloads over unstructured data, our optimizer injects proxy models for queries with complex predicates leading to a many-fold improvement in processing time; for query optimization in classic data analytics, our pre-trained models summarize structured datasets, answer cardinality estimation calls, and avoid the high training cost in recent instance-optimized database components. I will also describe our query processor and optimizer that enable and accelerate ML inference workflows on hybrid/IoT cloud. These efforts, combined with a few missing pieces that I will outline, contribute to better data systems where users can build, deploy, and optimize data analytics and AI applications with ease. Bio: Yao Lu is a researcher at the Data Systems group, Microsoft Research Redmond. He works at the intersection of machine learning and data systems towards improved data and compute platforms for cloud machine learning, as well as using machine learning to improve current data platforms. He received his Ph.D. from the University of Washington in 2018.
Speaker: Ananya Kumar, Stanford University Date: Tuesday, April 18 Time: 12:30pm EST Location: CS 105 Host: Tom Griffiths Event page: https://www.cs.princeton.edu/events/26370 Title: Foundation Models for Robust Machine Learning Abstract: Machine learning systems are not robust—they suffer large drops in accuracy when deployed in different environments from what they were trained on. In this talk, I show that the foundation model paradigm—adapting models that are pretrained on broad unlabeled data—is a principled solution that leads to state-of-the-art robustness. I will focus on the key ingredients: how we should pretrain and adapt models for robustness. (1) First, I show that contrastive pretraining on unlabeled data learns transferable representations that improves accuracy even on domains where we had no labels. We explain why pretraining works in a very different way from some classical intuitions of collapsing representations (domain invariance). Our theory predicts phenomena on real datasets, and leads to improved pretraining methods. (1) Next, I will show that the standard approach of adaptation (updating all the model's parameters) can distort pretrained representations and perform poorly out-of-distribution. Our theoretical analysis leads to better methods for adaptation and state-of-the-art accuracies on ImageNet and in applications such as satellite remote sensing, wildlife conservation, and radiology. Bio: Ananya Kumar is a Ph.D. candidate in the Department of Computer Science at Stanford University, advised by Percy Liang and Tengyu Ma. His work focuses on representation learning, foundation models, and reliable machine learning. His papers have been recognized with several Spotlight and Oral presentations at NeurIPS, ICML, and ICLR, and his research is supported by a Stanford Graduate Fellowship.
Speaker: Saadia Gabriel, University of Washington Date: Thursday, April 20 Time: 12:30pm EST Location: CS 105 Host: Olga Troyanskya Event page: https://www.cs.princeton.edu/events/26380 Title: Socially Responsible and Factual Reasoning for Equitable AI Systems Abstract: Understanding the implications underlying a text is critical to assessing its impact. This requires endowing artificial intelligence (AI) systems with pragmatic reasoning, for example to infer that the statement “Epidemics and cases of disease in the 21st century are “staged”” relates to unfounded conspiracy theories. In this talk, I discuss how shortcomings in the ability of current AI systems to reason about pragmatics leads to inequitable detection of false or harmful language. I demonstrate how these shortcomings can be addressed by imposing human-interpretable structure on deep learning architectures using insights from linguistics. In the first part of the talk, I describe how adversarial text generation algorithms can be used to improve model robustness. I then introduce a pragmatic formalism for reasoning about harmful implications conveyed by social media text. I show how this pragmatic approach can be combined with generative neural language models to uncover implications of news headlines. I also address the bottleneck to progress in text generation posed by gaps in evaluation of factuality. I conclude with an interdisciplinary study showing how content moderation informed by pragmatics can be used to ensure safe interactions with conversational agents, and my future vision for development of context-aware systems. Bio: Saadia Gabriel is a PhD candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Prof. Yejin Choi and Prof. Franziska Roesner. Her research revolves around natural language processing and machine learning, with a particular focus on building systems for understanding how social commonsense manifests in text (i.e. how do people typically behave in social scenarios), as well as mitigating spread of false or harmful text (e.g. Covid-19 misinformation). Her work has been covered by a wide range of media outlets like Forbes and TechCrunch. It has also received a 2019 ACL best short paper nomination, a 2019 IROS RoboCup best paper nomination and won a best paper award at the 2020 WeCNLP summit. Prior to her PhD, Saadia received a BA summa cum laude from Mount Holyoke College in Computer Science and Mathematics.
CS Distinguished Colloquium Speaker Speaker: Corey Sanders '04, Microsoft Date: Monday, April 24 Time: 12:30pm EST Location: CS 105 Host: Adam Finkelstein Event page: https://www.cs.princeton.edu/events/26405 Title: The Future of Cloud Infrastructure for Large AI Models Abstract: Corey Sanders, CVP, Microsoft Cloud for Industry, will join us to share how Microsoft is building the infrastructure requirements for scaling AI and Large Language Model (LLM) services, with a specific focus on Azure, GPU sourcing and the architecture of OpenAI-specific data centers. Corey will highlight the impact of advanced AI models, such as Github CoPilot, including the workings and quality of these tools and models with real-world examples of how advanced AI models are already transforming the software development landscape. Bio: Corey Sanders is the Corporate Vice President for Microsoft Cloud for Industry, an organization dedicated to serving our customers with tailored industry solutions as they transform into successful digital businesses. Prior to this role, Corey led Microsoft Commercial Solution Areas, owning sales strategy and corporate technical sales across Solution Areas and Teams that include Digital Application Innovation, Azure Infrastructure & IoT, Azure Data & AI, Business Applications, Security, and Modern Workplace. His focus also included selling the full value of Microsoft cross-cloud solutions and advancing the technical depth of the Microsoft Solutions team. Earlier, Corey was Head of Product for Azure Compute and the founder of Microsoft Azure’s Infrastructure as a Service (IaaS) business. During that time, he was responsible for products, strategy and technical vision aligned to core Azure compute services. He also led program management for multiple Azure services. Earlier in his career, Corey was a developer in the Windows Serviceability team with ownership across the networking and kernel stack for Windows. In his first role at Microsoft in 2003, Corey served as an intern on the Windows team, after graduating from Princeton University, where he earned his Bachelor S.E. in Computer Science. Today, Corey resides with his family in New Jersey.
CS Distinguished Colloquium Speaker: Kevin Murphy, Google Brain Date: Monday, May 1 Time: 12:30pm EST Location: CS 105 Host: Elad Hazan Event page: [ https://www.cs.princeton.edu/events/26404 | https://www.cs.princeton.edu/events/26404 ] View live-stream online here: [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: The Four Pillars of Machine Learning Abstract: I will present a unified perspective on the field of machine learning, following the structure of my recent book, "Probabilistic Machine Learning: Advanced Topics" which is centered on the "4 pillars of ML": predictions, decisions, discovery and generation. For each of these tasks, I will give a brief summary of some recent methods, including a few of my own contributions. Bio: Kevin was born in Ireland, but grew up in England. He got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He then did a postdoc at MIT, and was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada, from 2004 to 2012. After getting tenure, he went to Google in California on his sabbatical and then ended up staying. He currently runs a team of 6 researchers inside of Google Brain; the team works on generative models, Bayesian inference, and various other topics. Kevin has published over 125 papers in refereed conferences and journals, as well 3 textbooks on machine learning published in 2012, 2022 and 2023 by MIT Press. (The 2012 book was awarded the DeGroot Prize for best book in the field of Statistical Science.) Kevin was also the (co) Editor-in-Chief of JMLR 2014--2017.
CS Distinguished Colloquium Speaker: Kevin Murphy, Google Brain Date: Monday, May 1 Time: 12:30pm EST Location: CS 105 Host: Elad Hazan Event page: [ https://www.cs.princeton.edu/events/26404 | https://www.cs.princeton.edu/events/26404 ] View live-stream online here: [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: The Four Pillars of Machine Learning Abstract: I will present a unified perspective on the field of machine learning, following the structure of my recent book, "Probabilistic Machine Learning: Advanced Topics" which is centered on the "4 pillars of ML": predictions, decisions, discovery and generation. For each of these tasks, I will give a brief summary of some recent methods, including a few of my own contributions. Bio: Kevin was born in Ireland, but grew up in England. He got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He then did a postdoc at MIT, and was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada, from 2004 to 2012. After getting tenure, he went to Google in California on his sabbatical and then ended up staying. He currently runs a team of 6 researchers inside of Google Brain; the team works on generative models, Bayesian inference, and various other topics. Kevin has published over 125 papers in refereed conferences and journals, as well 3 textbooks on machine learning published in 2012, 2022 and 2023 by MIT Press. (The 2012 book was awarded the DeGroot Prize for best book in the field of Statistical Science.) Kevin was also the (co) Editor-in-Chief of JMLR 2014--2017.
CS Colloquium Speaker Speaker: Kaiming He, Facebook AI Research (FAIR) Date: Tuesday, April 25 Time: 12:30pm EST Location: CS 105 Host: Jia Deng / Kai Li Event page: [ https://www.cs.princeton.edu/events/26383 | https://www.cs.princeton.edu/events/26383 ] Title: In Pursuit of Visual Intelligence Abstract: Last decade's deep learning revolution in part began in the area of computer vision. The intrinsic complexity of visual perception problems urged the community to explore effective methods for learning abstractions from data. In this talk, I will review a few major breakthroughs that stemmed from computer vision. I will discuss my work on Deep Residual Networks (ResNets) that enabled deep learning to get way deeper, and its influence on the broader artificial intelligence areas over the years. I will also review my work on enabling deep learning to solve complex object detection and segmentation problems in simple and intuitive ways. On top of this progress, I will introduce recent research on learning from visual observations without human supervision, a topic known as visual self-supervised learning. I will discuss my research that contributed to shaping the two frontier directions on this topic. This research sheds light on future directions. I will discuss the opportunities for self-supervised learning in the visual world. I will also discuss how the research on computer vision may continue influencing broader areas, e.g., by generalizing self-supervised learning to scientific observations from nature. Bio: Kaiming He is a Research Scientist Director at Facebook AI Research (FAIR). Before joining FAIR in 2016, he was with Microsoft Research Asia from 2011 to 2016. He received his PhD degree from the Chinese University of Hong Kong in 2011, and his B.S. degree from Tsinghua University in 2007. His research areas include deep learning and computer vision. He is best-known for his work on Deep Residual Networks (ResNets), which have made significant impact on computer vision and broader artificial intelligence. He received several outstanding paper awards at top-tier conferences, including CVPR, ICCV, and ECCV. He received the PAMI Young Researcher Award in 2018. His publications have over 400,000 citations.
participants (1)
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Emily C. Lawrence