TILOS Seminar: The Dissimilarity Dimension: Sharper Bounds for Optimistic Algorithms
2024-01-18 10:00 - 11:00 (America/Los_Angeles) | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Aldo Pacchiano, Boston University Center for Computing and Data Sciences
Abstract: The principle of Optimism in the Face of Uncertainty (OFU) is one of the foundational algorithmic design choices in Reinforcement Learning and Bandits. Optimistic algorithms balance exploration and exploitation by deploying data collection strategies that maximize expected rewards in plausible models. This is the basis of celebrated algorithms like the Upper Confidence Bound (UCB) for multi-armed bandits. For nearly a decade, the analysis of optimistic algorithms, including Optimistic Least Squares, in the context of rich reward function classes has relied on the concept of eluder dimension, introduced by Russo and Van Roy in 2013. In this talk we shed light on the limitations of the eluder dimension in capturing the true behavior of optimistic strategies in the realm of function approximation. We remediate these by introducing a novel statistical measure, the “dissimilarity dimension”. We show it can be used to provide sharper sample analysis of algorithms like Optimistic Least Squares by establishing a link between regret and the dissimilarity dimension. To illustrate this, we will show that some function classes have arbitrarily large eluder dimension but constant dissimilarity. Our regret analysis draws inspiration from graph theory and may be of interest to the mathematically minded beyond the field of statistical learning theory. This talk sheds new light on the fundamental principle of optimism and its algorithms in the function approximation regime, advancing our understanding of these concepts.

Aldo Pacchiano is an Assistant Professor at the Boston University Center for Computing and Data Sciences and a Fellow at the Eric and Wendy Schmidt Center of the Broad Institute of MIT and Harvard. He obtained his PhD under the supervision of Profs. Michael Jordan and Peter Bartlett at UC Berkeley and was a Postdoctoral Researcher at Microsoft Research, NYC. His research lies in the areas of Reinforcement Learning, Online Learning, Bandits and Algorithmic Fairness. He is particularly interested in furthering our statistical understanding of learning phenomena in adaptive environments and use these theoretical insights and techniques to design efficient and safe algorithms for scientific, engineering, and large-scale societal applications.

TILOS-HDSI Distinguished Colloquium: The Synergy between Machine Learning and the Natural Sciences
2024-02-21 14:00 - 15:30 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Max Welling, University of Amsterdam
Abstract: Traditionally machine learning has been heavily influenced by neuroscience (hence the name artificial neural networks) and physics (e.g. MCMC, Belief Propagation, and Diffusion based Generative AI). We have recently witnessed that the flow of information has also reversed, with new tools developed in the ML community impacting physics, chemistry and biology. Examples include faster DFT, Force-Field accelerated MD simulations, PDE Neural Surrogate models, generating druglike molecules, and many more. In this talk I will review the exciting opportunities for further cross fertilization between these fields, ranging from faster (classical) DFT calculations and enhanced transition path sampling to traveling waves in artificial neural networks.

Prof. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate Prof. Gerard ‘t Hooft.

AI Ethics in Research Webinar
2024-03-08 12:00 - 13:00 | Virtual
Please join Dr. Nisheeth Vishnoi from Yale and Dr. David Danks from UC San Diego who will discuss their Research in AI Ethics. Professor Danks develops practical frameworks and methods to incorporate ethical and policy considerations throughout the AI lifecycle, including different ways to include them in optimization steps. Bias and fairness have been a particular focus given the multiple ways in which they can be measured, represented, and used. Professor Vishnoi uses optimization as a lens to study how subjective human and societal biases emerge in the objective world of artificial algorithms, as well as how to design strategies to mitigate these biases.
This event is a great opportunity to learn about the constantly evolving issues of AI Ethics in research and the societal impact of AI. It will also provide a platform for students to gain insights and valuable advice that can help them in their future career pursuits.

Nisheeth Vishnoi is the A. Bartlett Giamatti Professor of Computer Science and a co-founder of the Computation and Society Initiative at Yale University. He studies the foundations of computation, and his research spans several areas of theoretical computer science, optimization, and machine learning. He is also interested in understanding nature and society from a computational viewpoint. Here, his current focus includes understanding the emergence of intelligence and developing methods to address ethical issues at the interface of artificial intelligence and humanity.

David Danks is Professor of Data Science and Philosophy and affiliate faculty in Computer Science and Engineering at University of California, San Diego. His research interests range widely across philosophy, cognitive science, and machine learning, including their intersection. Danks has examined the ethical, psychological, and policy issues around AI and robotics across multiple sectors, including transportation, healthcare, privacy, and security. He has also done significant research in computational cognitive science and developed multiple novel causal discovery algorithms for complex types of observational and experimental data. Danks is the recipient of a James S. McDonnell Foundation Scholar Award, as well as an Andrew Carnegie Fellowship. He currently serves on multiple advisory boards, including the National AI Advisory Committee.

TILOS Seminar: How Large Models of Language and Vision Help Agents to Learn to Behave
2024-03-20 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Roy Fox, UC Irvine
Abstract: If learning from data is valuable, can learning from big data be very valuable? So far, it has been so in vision and language, for which foundation models can be trained on web-scale data to support a plethora of downstream tasks; not so much in control, for which scalable learning remains elusive. Can information encoded in vision and language models guide reinforcement learning of control policies? In this talk, I will discuss several ways for foundation models to help agents to learn to behave. Language models can provide better context for decision-making: we will see how they can succinctly describe the world state to focus the agent on relevant features; and how they can form generalizable skills that identify key subgoals. Vision and vision–language models can help the agent to model the world: we will see how they can block visual distractions to keep state representations task-relevant; and how they can hypothesize about abstract world models that guide exploration and planning.

Roy Fox is an Assistant Professor of Computer Science at the University of California, Irvine. His research interests include theory and applications of control learning: reinforcement learning (RL), control theory, information theory, and robotics. His current research focuses on structured and model-based RL, language for RL and RL for language, and optimization in deep control learning of virtual and physical agents.

TILOS Seminar: Transformers learn in-context by (functional) gradient descent
2024-04-17 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Xiang Cheng, TILOS Postdoctoral Scholar, MIT
Abstract: Motivated by the in-context learning phenomenon, we investigate how the Transformer neural network can implement learning algorithms in its forward pass. We show that a linear Transformer naturally learns to implement gradient descent, which enables it to learn linear functions in-context. More generally, we show that a non-linear Transformer can implement functional gradient descent with respect to some RKHS metric, which allows it to learn a broad class of functions in-context. Additionally, we show that the RKHS metric is determined by the choice of attention activation, and that the optimal choice of attention activation depends in a natural way on the class of functions that need to be learned. I will end by discussing some implications of our results for the choice and design of Transformer architectures.

TILOS Seminar: Large Datasets and Models for Robots in the Real World
2024-05-22 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Nicklas Hansen, UC San Diego
Abstract: Recent progress in AI can be attributed to the emergence of large models trained on large datasets. However, teaching AI agents to reliably interact with our physical world has proven challenging, which is in part due to a lack of large and sufficiently diverse robot datasets. In this talk, I will cover ongoing efforts of the Open X-Embodiment project–a collaboration between 279 researchers across 20+ institutions–to build a large, open dataset for real-world robotics, and discuss how this new paradigm is rapidly changing the field. Concretely, I will discuss why we need large datasets in robotics, what such datasets may look like, and how large models can be trained and evaluated effectively in a cross-embodiment cross-environment setting. Finally, I will conclude the talk by sharing my perspective on the limitations of current embodied AI agents, as well as how to move forward as a community.

Nicklas Hansen is a Ph.D. student at University of California San Diego advised by Prof. Xiaolong Wang and Prof. Hao Su. His research focuses on developing generalist AI agents that learn from interaction with the physical and digital world. He has spent time at Meta AI (FAIR) and University of California Berkeley (BAIR), and received his B.S. and M.S. degrees from Technical University of Denmark. He is a recipient of the 2024 NVIDIA Graduate Fellowship, and his work has been featured at top venues in machine learning and robotics.

TILOS Seminar: What Kinds of Functions do Neural Networks Learn? Theory and Practical Applications
2024-07-24 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Robert Nowak, University of Wisconsin
Abstract: This talk presents a theory characterizing the types of functions neural networks learn from data. Specifically, the function space generated by deep ReLU networks consists of compositions of functions from the Banach space of second-order bounded variation in the Radon transform domain. This Banach space includes functions with smooth projections in most directions. A representer theorem associated with this space demonstrates that finite-width neural networks suffice for fitting finite datasets. The theory has several practical applications. First, it provides a simple and theoretically grounded method for network compression. Second, it shows that multi-task training can yield significantly different solutions compared to single-task training, and that multi-task solutions can be related to kernel ridge regressions. Third, the theory has implications for improving implicit neural representations, where multi-layer neural networks are used to represent continuous signals, images, or 3D scenes. This exploration bridges theoretical insights with practical advancements, offering a new perspective on neural network capabilities and future research directions.

Robert Nowak is the Grace Wahba Professor of Data Science and Keith and Jane Nosbusch Professor in Electrical and Computer Engineering at the University of Wisconsin-Madison. His research focuses on machine learning, optimization, and signal processing. He serves on the editorial boards of the SIAM Journal on the Mathematics of Data Science and the IEEE Journal on Selected Areas in Information Theory.

TILOS-SDSU Seminar: AI/ML & NLP for UAS/Air Traffic Management
2024-10-02 11:00 - 12:00 | San Diego State University, 5500 Campanile Dr, San Diego, 92182, United States
Krishna Kalyanam, NASA Ames Research Center
Abstract: We introduce several Air Traffic Management (ATM) initiatives envisioned by NASA and FAA for a future airspace that combines conventional traffic and new entrants (e.g., drones) without sacrificing safety. In this framework, we demonstrate the use of state-of-the-art AI/ML modeling and prediction tools that will enable efficient and safe traffic flow in the U.S. National Airspace System (NAS). For example, Natural Language Processing (NLP) tools can help extract data (e.g., airspace constraints) that are currently contained in legacy text and audio format and convert them into digital information. The digitized information can be ingested by route planning, arrival scheduling and other decision support tools both on the ground and in the flight deck. We show how historical data (track, weather & events) can be preprocessed and utilized to create accurate models to predict flight trajectories and events of interest (e.g., Traffic Management Initiatives). We show several application areas within ATM that benefit from AI/ML including trajectory prediction, airport runway configuration management and automatic speech to text. The overarching goal of the work is to accelerate the integration of package delivery drones, air taxis and autonomous cargo aircraft into the NAS without impacting the safety and efficacy of current manned operations. As an example, we also show a strategic deconfliction scenario and demonstrate scalable algorithms that provide conflict free schedules for package delivery drones in an urban setting.

Dr. Krishna Kalyanam is the Autonomy & AI/ML tech lead with the NASA Aeronautics Research Institute (NARI). In his current role, he is focused on delivering state of the art AI/ML algorithms to enable scalable and efficient manned/unmanned operations in a mixed-use National airspace. Prior to joining NASA, Dr. Kalyanam was with AFRL’s Autonomous Controls branch, where he co-designed several multi-UAV cooperative control algorithms that were flight tested as part of the Intelligent Control & Evaluation of Teams (ICE-T) program. Dr. Kalyanam has published 100+ papers on stochastic control, human machine teaming and multi-agent scheduling in IEEE, ASME and AIAA venues. Dr. Kalyanam is a senior member of IEEE and an associate fellow of the AIAA. He is a recipient of the prestigious Research associateship award sponsored by the National Academies. He was also part of the UAV Autonomy team that won the AFRL “Star Team” award for performing the most innovative in-house basic research in 2018.

TILOS Seminar: Data Models for Deep Learning: Beyond i.i.d. Assumptions
2024-11-07 16:00 - 17:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Elchanan Mossel, MIT
Abstract: Classical Machine Learning theory is largely built upon the assumption that data samples are independent and identically distributed (i.i.d.) from general distribution families. In this talk, I will present novel insights that emerge when we move beyond these traditional assumptions, exploring both dependent sampling scenarios and structured generative distributions. These perspectives offer fresh theoretical frameworks and practical implications for modern machine learning approaches.

Elchanan Mossel is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), specializing in probability theory, combinatorics, and theoretical computer science. His research explores a range of complex, interdisciplinary problems, including social choice theory, inference in networks, and the analysis of algorithms, with applications across economics, political science, and genetics. Mossel completed his Ph.D. at the Hebrew University of Jerusalem and held postdoctoral positions at Microsoft Research and UC Berkeley before joining MIT. Recognized for his innovative work, Mossel has received a Sloan fellowship, NSF CAREER award, and COLT best paper award, and is a Fellow of the American Mathematical Society.

TILOS Seminar: Off-the-shelf Algorithmic Stability
2024-11-13 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Rebecca Willett, University of Chicago
Abstract: Algorithmic stability holds when our conclusions, estimates, fitted models, predictions, or decisions are insensitive to small changes to the training data. Stability has emerged as a core principle for reliable data science, providing insights into generalization, cross-validation, uncertainty quantification, and more. Whereas prior literature has developed mathematical tools for analyzing the stability of specific machine learning (ML) algorithms, we study methods that can be applied to arbitrary learning algorithms to satisfy a desired level of stability. First, I will discuss how bagging is guaranteed to stabilize any prediction model, regardless of the input data. Thus, if we remove or replace a small fraction of the training data at random, the resulting prediction will typically change very little. Our analysis provides insight into how the size of the bags (bootstrap datasets) influences stability, giving practitioners a new tool for guaranteeing a desired level of stability. Second, I will describe how to extend these stability guarantees beyond prediction modeling to more general statistical estimation problems where bagging is not as well known but equally useful for stability. Specifically, I will describe a new framework for stable classification and model selection by combining bagging on class or model weights with a stable, “soft” version of the argmax operator. This is joint work with Jake Soloff and Rina Barber.

Rebecca Willett is a Professor of Statistics and Computer Science and the Director of AI in the Data Science Institute at the University of Chicago, and she holds a courtesy appointment at the Toyota Technological Institute at Chicago. Her research is focused on machine learning foundations, scientific machine learning, and signal processing. Willett received the inaugural Data Science Career Prize from the Society of Industrial and Applied Mathematics in 2024, was named a Fellow of the Society of Industrial and Applied Mathematics in 2021, and was named a Fellow of the IEEE in 2022. She is the Deputy Director for Research at the NSF-Simons Foundation National Institute for Theory and Mathematics in Biology, Deputy Director for Research at the NSF-Simons Institute for AI in the Sky (SkAI), and a member of the NSF Institute for the Foundations of Data Science Executive Committee. She is the Faculty Director of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship. She helps direct the Air Force Research Lab University Center of Excellence on Machine Learning. She received the National Science Foundation CAREER Award in 2007, was a DARPA Computer Science Study Group member, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005. She was an Assistant and then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018.

TILOS Seminar: How Transformers Learn Causal Structure with Gradient Descent
2024-11-20 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Jason Lee, Princeton University
Abstract: The incredible success of transformers on sequence modeling tasks can be largely attributed to the self-attention mechanism, which allows information to be transferred between different parts of a sequence. Self-attention allows transformers to encode causal structure which makes them particularly suitable for sequence modeling. However, the process by which transformers learn such causal structure via gradient-based training algorithms remains poorly understood. To better understand this process, we introduce an in-context learning task that requires learning latent causal structure. We prove that gradient descent on a simplified two-layer transformer learns to solve this task by encoding the latent causal graph in the first attention layer. The key insight of our proof is that the gradient of the attention matrix encodes the mutual information between tokens. As a consequence of the data processing inequality, the largest entries of this gradient correspond to edges in the latent causal graph. As a special case, when the sequences are generated from in-context Markov chains, we prove that transformers learn an induction head (Olsson et al., 2022). We confirm our theoretical findings by showing that transformers trained on our in-context learning task are able to recover a wide variety of causal structures.

Jason Lee is an associate professor in Electrical Engineering and Computer Science (secondary) at Princeton University. Prior to that, he was in the Data Science and Operations department at the University of Southern California and a postdoctoral researcher at UC Berkeley working with Michael I. Jordan. Jason received his PhD at Stanford University advised by Trevor Hastie and Jonathan Taylor. His research interests are in the theory of machine learning, optimization, and statistics. Lately, he has worked on the foundations of deep learning, representation learning, and reinforcement learning. He has received the Samsung AI Researcher of the Year Award, NSF Career Award, ONR Young Investigator Award in Mathematical Data Science, Sloan Research Fellowship, NeurIPS Best Student Paper Award and Finalist for the Best Paper Prize for Young Researchers in Continuous Optimization, and Princeton Commendation for Outstanding Teaching.

TILOS Seminar: Unlearnable Facts Cause Hallucinations in Pretrained Language Models
2025-01-29 11:00 - 12:30 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Adam Tauman Kalai, OpenAI
Abstract: Pretrained language models (LMs) tend to preserve many qualities present in their training data, such as grammaticality, formatting, and politeness. However, for specific types of factuality, even LMs pretrained on factually correct statements tend to produce falsehoods at high rates. We explain these “hallucinations” by drawing a connection to binary classification, enabling us to leverage insights from supervised learning. We prove that pretrained LMs (which are “calibrated”) fail to mimic criteria that cannot be learned. Our analysis explains why pretrained LMs hallucinate on facts such as people’s birthdays but not on systematic facts such as even vs. odd numbers. Of course, LM pretraining is only one stage in the development of a chatbot, and thus hallucinations are *not* inevitable in chatbots. This is joint work with Santosh Vempala.

Adam Tauman Kalai is a Research Scientist at OpenAI working on AI Safety and Ethics. He has worked in Algorithms, Fairness, Machine Learning Theory, Game Theory, and Crowdsourcing. He received his PhD from Carnegie Mellon University. He has served as an Assistant Professor at Georgia Tech and TTIC, and is on the science team of the whale-translation Project CETI. He has co-chaired AI and crowdsourcing conferences and has numerous honors, most notably the Majulook prize.

TILOS-SDSU Seminar: Challenging Estimation Problems in Vehicle Autonomy
2025-02-12 11:00 - 12:00 | San Diego State University, 5500 Campanile Dr, San Diego, 92182, United States
Rajesh Rajamani, University of Minnesota
Abstract: This talk presents some interesting problems in estimation related to vehicle autonomy. First, a teleoperation application in which a remote operator can intervene to control an autonomous vehicle is considered. Fundamental challenges here include the need to design an effective teleoperation station, bandwidth and time-criticality constraints in wireless communication, and the need for a control system that can handle delays. A predictive display system that uses generative AI to estimate the current video display for the teleoperator from fusion of delayed camera and Lidar images is developed. By estimating trajectories of the ego vehicle and of other nearby vehicles on the road, realistic intermediate updates of the remote vehicle environment are used to compensate for delayed camera data. A different estimation application involving the driving of a vehicle with automated steering control on snow-covered and rural roads is considered next. Since camera-based feedback of lane markers cannot be used, sensor fusion algorithms and RTK-corrected GPS are utilized for lateral position estimation. Finally, the modification of target vehicle tracking methods utilized on autonomous vehicles for use on other low-cost platforms is considered. Applications involving protection of vulnerable road users such as e-scooter riders, bicyclists and construction zone workers is demonstrated. The fundamental theme underlying the different estimation problems in this seminar is the effective use of nonlinear vehicle dynamic models and novel nonlinear observer design algorithms.

Rajesh Rajamani obtained his M.S. and Ph.D. degrees from the University of California at Berkeley and his B.Tech degree from the Indian Institute of Technology at Madras. He joined the faculty in Mechanical Engineering at the University of Minnesota in 1998 where he is currently the Benjamin Y.H. Liu-TSI Endowed Chair Professor and Associate Director (Research) of the Minnesota Robotics Institute. His active research interests include estimation, sensing and control for smart and autonomous systems. Dr. Rajamani has co-authored over 190 journal papers and is a co-inventor on 20+ patents/patent applications. He is a Fellow of IEEE and ASME and has been a recipient of the CAREER award from the National Science Foundation, the O. Hugo Schuck Award from the American Automatic Control Council, the Ralph Teetor Award from SAE, the Charles Stark Draper award from ASME, and a number of best paper awards from journals and conferences. Several inventions from his laboratory have been commercialized through start-up ventures co-founded by industry executives. One of these companies, Innotronics, was recently recognized among the 35 Best University Start-Ups of 2016 by the US National Council of Entrepreneurial Tech Transfer.

TILOS Seminar: Synthetic Tasks as Testbeds for Attributing Model Behavior
2025-03-12 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Surbhi Goel, University of Pennsylvania
Abstract: Understanding how different components of the machine learning pipeline—spanning data composition, architectural choices, and optimization dynamics—shape model behavior remains a fundamental challenge. In this talk, I will argue that synthetic tasks, which enable precise control over data distribution and task complexity, serve as powerful testbeds for analyzing and attributing behaviors in deep learning. Focusing on the sparse parity learning problem, a canonical task in learning theory, I will present insights into: (1) the phenomenon of “hidden progress” in gradient-based optimization, where models exhibit consistent advancement despite stagnating loss curves; (2) nuanced trade-offs between data, compute, model width, and initialization that govern learning success; and (3) the role of progressive distillation in implicitly structuring curricula to accelerate feature learning. These findings highlight the utility of synthetic tasks in uncovering empirical insights into the mechanisms driving deep learning, without the cost of training expensive models. This talk is based on joint work with a lot of amazing collaborators: Boaz Barak, Ben Edelman, Sham Kakade, Bingbin Liu, Eran Malach, Sadhika Malladi, Abhishek Panigrahi, Andrej Risteski, and Cyril Zhang.

Surbhi Goel is the Magerman Term Assistant Professor of Computer and Information Science at the University of Pennsylvania. She is associated with the theory group, the ASSET Center on safe, explainable, and trustworthy AI systems, and the Warren Center for Network and Data Sciences. Surbhi’s research focuses on theoretical foundations of modern machine learning paradigms, particularly deep learning, and is supported by Microsoft Research and OpenAI. Previously, she was a postdoctoral researcher at Microsoft Research NYC and completed her Ph.D. at the University of Texas at Austin under Adam Klivans, receiving the UTCS Bert Kay Dissertation Award. She has also been a visiting researcher at IAS, Princeton, and the Simons Institute at UC Berkeley. Surbhi co-founded the Learning Theory Alliance (LeT‐All) and holds several leadership roles, including Office Hours co-chair for ICLR 2024 and co-treasurer for the Association for Computational Learning Theory.

TILOS Seminar: Single location regression and attention-based models
2025-03-27 14:00 - 15:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Claire Boyer, Université Paris-Saclay
Abstract: Attention-based models, such as Transformer, excel across various tasks but lack a comprehensive theoretical understanding, especially regarding token-wise sparsity and internal linear representations. To address this gap, we introduce the single-location regression task, where only one token in a sequence determines the output, and its position is a latent random variable, retrievable via a linear projection of the input. To solve this task, we propose a dedicated predictor, which turns out to be a simplified version of a non-linear self-attention layer. We study its theoretical properties, by showing its asymptotic Bayes optimality and analyzing its training dynamics. In particular, despite the non-convex nature of the problem, the predictor effectively learns the underlying structure. This work highlights the capacity of attention mechanisms to handle sparse token information and internal linear structures.

TILOS Seminar: Foundational Methods for Foundation Models for Scientific Machine Learning
2025-04-02 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Michael W. Mahoney, ICSI, LBNL, and Department of Statistics, UC Berkeley
Abstract: The remarkable successes of ChatGPT in natural language processing (NLP) and related developments in computer vision (CV) motivate the question of what foundation models would look like and what new advances they would enable, when built on the rich, diverse, multimodal data that are available from large-scale experimental and simulational data in scientific computing (SC), broadly defined. Such models could provide a robust and principled foundation for scientific machine learning (SciML), going well beyond simply using ML tools developed for internet and social media applications to help solve future scientific problems. I will describe recent work demonstrating the potential of the “pre-train and fine-tune” paradigm, widely-used in CV and NLP, for SciML problems, demonstrating a clear path towards building SciML foundation models; as well as recent work highlighting multiple “failure modes” that arise when trying to interface data-driven ML methodologies with domain-driven SC methodologies, demonstrating clear obstacles to traversing that path successfully. I will also describe initial work on developing novel methods to address several of these challenges, as well as their implementations at scale, a general solution to which will be needed to build robust and reliable SciML models consisting of millions or billions or trillions of parameters.

Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He is also an Amazon Scholar as well as head of the Machine Learning and Analytics Group at the Lawrence Berkeley National Laboratory. He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, scientific machine learning, scalable stochastic optimization, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, computational methods for neural network analysis, physics informed machine learning, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. He received his PhD from Yale University with a dissertation in computational statistical mechanics, and he has worked and taught at Yale University in the mathematics department, at Yahoo Research, and at Stanford University in the mathematics department. Among other things, he was on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council’s Committee on the Analysis of Massive Data, he co-organized the Simons Institute’s fall 2013 and 2018 programs on the foundations of data science, he ran the Park City Mathematics Institute’s 2016 PCMI Summer Session on The Mathematics of Data, he ran the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets, and he was the Director of the NSF/TRIPODS-funded FODA (Foundations of Data Analysis) Institute at UC Berkeley. More information is available at https://www.stat.berkeley.edu/~mmahoney/.

TILOS Seminar: Amplifying human performance in combinatorial competitive programming
2025-04-16 11:00 - 12:00 | Virtual
Petar Veličković, Google DeepMind
Abstract: Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady progress has been made, the highest percentiles still remain out of reach for these methods on standard competition platforms such as Codeforces. In this talk, I will describe and dive into our recent work, where we focussed on combinatorial competitive programming. In combinatorial challenges, the target is to find as-good-as-possible solutions to otherwise computationally intractable problems, over specific given inputs. We hypothesise that this scenario offers a unique testbed for human-AI synergy, as human programmers can write a backbone of a heuristic solution, after which AI can be used to optimise the scoring function used by the heuristic. We deploy our approach on previous iterations of Hash Code, a global team programming competition inspired by NP-hard software engineering problems at Google, and we leverage FunSearch to evolve our scoring functions. Our evolved solutions significantly improve the attained scores from their baseline, successfully breaking into the top percentile on all previous Hash Code online qualification rounds, and outperforming the top human teams on several. To the best of our knowledge, this is the first known AI-assisted top-tier result in competitive programming.

TILOS Seminar: Optimal Quantization for LLMs and Matrix Multiplication
2025-05-23 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Yury Polyanskiy, MIT
Abstract: The main building block of large language models is matrix multiplication, which is often bottlenecked by the speed of loading these matrices from memory. A number of recent quantization algorithms (SmoothQuant, GPTQ, QuIP, SpinQuant etc) address this issue by storing matrices in lower precision. We derive optimal asymptotic information-theoretic tradeoff between accuracy of the matrix product and compression rate (number of bits per matrix entry). We also show that a non-asymptotic version of our construction (based on nested Gosset lattices and Conway-Sloan decoding), which we call NestQuant, reduces perplexity deterioration almost three-fold compared to the state-of-the-art algorithms (as measured on LLama-2, Llama-3 with 8B to 70B parameters). Based on a joint work with Or Ordentlich (HUJI), Eitan Porat and Semyon Savkin (MIT EECS).

Yury Polyanskiy is a Cutten Professor of Electrical Engineering and Computer Science, a member of IDSS and LIDS at MIT, and an IEEE Fellow (2024). Yury received M.S. degree in applied mathematics and physics from the Moscow Institute of Physics and Technology in 2005 and Ph.D. degree in electrical engineering from Princeton University in 2010. His research interests span information theory, machine learning and statistics. Dr. Polyanskiy won the 2020 IEEE Information Theory Society James Massey Award, 2013 NSF CAREER award and 2011 IEEE Information Theory Society Paper Award.

TILOS-HDSI Seminar: A New Paradigm for Learning with Distribution Shift
2025-10-01 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Adam Klivans, The University of Texas at Austin
Abstract: We revisit the fundamental problem of learning with distribution shift, where a learner is given labeled samples from training distribution D, unlabeled samples from test distribution D′ and is asked to output a classifier with low test error. The standard approach in this setting is to prove a generalization bound in terms of some notion of distance between D and D′. These distances, however, are difficult to compute, and this has been the main stumbling block for efficient algorithm design over the last two decades.
We sidestep this issue and define a new model called TDS learning, where a learner runs a test on the training set and is allowed to reject if this test detects distribution shift relative to a fixed output classifier. This approach leads to the first set of efficient algorithms for learning with distribution shift that do not take any assumptions on the test distribution. Finally, we discuss how our techniques have recently been used to solve longstanding problems in supervised learning with contamination.

Adam Klivans is a Professor of Computer Science at the University of Texas at Austin and Director of the NSF AI Institute for Foundations of Machine Learning (IFML). His research interests lie in machine learning and theoretical computer science, in particular, Learning Theory, Computational Complexity, Pseudorandomness, Limit Theorems, and Gaussian Space. Dr. Klivans is a recipient of the NSF CAREER Award and serves on the editorial board for the Theory of Computing and Machine Learning Journal.

Optimization for ML and AI Seminar: High-dimensional Optimization with Applications to Compute-Optimal Neural Scaling Laws
2025-10-24 11:00 - 12:00 | CSE 1242 and Virtual, 3235 Voigt Dr, La Jolla, CA, 92093, United States
Courtney Paquette, McGill University
Abstract: Given the massive scale of modern ML models, we now only get a single shot to train them effectively. This restricts our ability to test multiple architectures and hyper-parameter configurations. Instead, we need to understand how these models scale, allowing us to experiment with smaller problems and then apply those insights to larger-scale models. In this talk, I will present a framework for analyzing scaling laws in stochastic learning algorithms using a power-law random features model (PLRF), leveraging high-dimensional probability and random matrix theory. I will then use this scaling law to address the compute-optimal question: How should we choose model size and hyper-parameters to achieve the best possible performance in the most compute-efficient manner? Then using this PLRF model, I will devise a new momentum-based algorithm that (provably) improves the scaling law exponent. Finally, I will present some numerical experiments on LSTMs that show how this new stochastic algorithm can be applied to real data to improve the compute-optimal exponent.

Courtney Paquette is an assistant professor at McGill University in the Mathematics and Statistics department, a CIFAR AI Chair (MILA), and an active member of the Montreal Machine Learning Optimization Group (MTL MLOpt) at MILA. Her research broadly focuses on designing and analyzing algorithms for large-scale optimization problems, motivated by applications in data science, and using techniques that draw from a variety of fields, including probability, complexity theory, and convex and nonsmooth analysis. Dr. Paquette is a lead organizer of the OPT-ML Workshop at NeurIPS since 2020, and a lead organizer (and original creator) of the High-dimensional Learning Dynamics (HiLD) Workshop at ICML.

TILOS-HDSI Seminar: AI safety theory: the missing middle ground
2025-11-12 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Adam Oberman, McGill University
Abstract: Over the past few years, the capabilities of generative artificial intelligence (AI) systems have advanced rapidly. Along with the benefits of AI, there is also a risk of harm. In order to benefit from AI while mitigating the risks, we need a grounded theoretical framework.
The current AI safety theory, which predates generative AI, is insufficient. Most theoretical AI safety results tend to reason absolutely: a system is a system is “aligned” or “mis-aligned”, “honest” or “dishonest”. But in practice safety is probabilistic, not absolute. The missing middle ground is a quantitative or relative theory of safety — a way to reason formally about degrees of safety. Such a theory is required for defining safety and harms, and is essential for technical solutions as well as for making good policy decisions.
In this talk I will:

Review current AI risks (from misuse, from lack of reliability, and systemic risks to the economy) as well as important future risks (lack of control).
Review theoretical predictions of bad AI behavior and discuss experiments which demonstrate that they can occur in current LLMs.
Explain why technical and theoretical safety solutions are valuable, even by contributors outside of the major labs.
Discuss some gaps in the theory and present some open problems which could address the gaps.

Adam Oberman is a Full Professor of Mathematics and Statistics at McGill University, a Canada CIFAR AI Chair, and an Associate Member of Mila. He is a research collaborator at LawZero, Yoshua Bengio’s AI Safety Institute. He has been researching AI safety since 2024. His research spans generative models, reinforcement learning, optimization, calibration, and robustness. Earlier in his career, he made significant contributions to optimal transport and nonlinear partial differential equations. He earned degrees from the University of Toronto and the University of Chicago, and previously held faculty and postdoctoral positions at Simon Fraser University and the University of Texas at Austin.

TILOS-SDSU Seminar: Certifiably Correct Machine Perception
2025-11-19 11:00 - 12:00 | Lamden Hall 341 (SDSU) and Virtual, San Diego, CA, 92182, United States
David Rosen, Northeastern University
Abstract: Many fundamental machine perception and state estimation tasks require the solution of a high-dimensional nonconvex estimation problem; this class includes (for example) the fundamental problems of simultaneous localization and mapping (in robotics), 3D reconstruction (in computer vision), and sensor network localization (in distributed sensing). Such problems are known to be computationally hard in general, with many local minima that can entrap the smooth local optimization methods commonly applied to solve them. The result is that standard machine perception algorithms (based upon local optimization) can be surprisingly brittle, often returning egregiously wrong answers even when the problem to which they are applied is well-posed.
In this talk, we present a novel class of certifiably correct estimation algorithms that are capable of efficiently recovering provably good (often globally optimal) solutions of generally-intractable machine perception problems in many practical settings. Our approach directly tackles the problem of nonconvexity by employing convex relaxations whose minimizers provide provably good approximate solutions to the original estimation problem under moderate measurement noise. We illustrate the design of this class of methods using the fundamental problem of pose-graph optimization (a mathematical abstraction of robotic mapping) as a running example. We conclude with a brief discussion of open questions and future research directions.

David M. Rosen is an Assistant Professor in the Departments of Electrical & Computer Engineering and Mathematics and the Khoury College of Computer Sciences (by courtesy) at Northeastern University, where he leads the Robust Autonomy Laboratory (NEURAL). Prior to joining Northeastern, he was a Research Scientist at Oculus Research (now Meta Reality Labs) from 2016 to 2018, and a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) from 2018 to 2021. He holds the degrees of B.S. in Mathematics from the California Institute of Technology (2008), M.A. in Mathematics from the University of Texas at Austin (2010), and ScD in Computer Science from the Massachusetts Institute of Technology (2016).

He is broadly interested in the mathematical and algorithmic foundations of trustworthy machine perception, learning, and control. His work has been recognized with the IEEE Transactions on Robotics Best Paper Award (2024), an Honorable Mention for the IEEE Transactions on Robotics Best Paper Award (2021), a Best Student Paper Award at Robotics: Science and Systems (2020), a Best Paper Award at the International Workshop on the Algorithmic Foundations of Robotics (2016), and selection as an RSS Pioneer (2019).

TILOS-SDSU Seminar: 95 Percent: Bridging the Gap Between Prototype and Product
2025-12-03 11:00 - 12:00 | Lamden Hall 341 (SDSU) and Virtual, San Diego, CA, 92182, United States
Jeremy Schwartz, Zoox
Abstract: When transitioning from the academic world to the professional world of engineering, one of the most common pitfalls is failing to understand the difference between a compelling prototype and a successful product. This talk will focus on that distinction. We will discuss the differences between them, and the work required to evolve a good prototype into a real product. We will also discuss some common pitfalls encountered in product development, and some of the practical software design considerations to keep in mind for development of robust, mature code. The talk will include examples from my background developing robotic systems for air, space, and ground.

Jeremy Schwartz is a robotics engineer at Zoox with expertise in a wide variety of areas of mechanical and electrical engineering and computer science. His primary professional expertise is in autonomy and behavioral algorithms, and he has worked in the aerospace industry as well as ground robotics, specializing in autonomous systems of all kinds.

Optimization for AI and ML Seminar: Training Neural Networks at Any Scale
2025-12-03 13:00 - 14:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Volkan Cevher, École Polytechnique Fédérale de Lausanne
Abstract: At the heart of deep learning’s transformative impact lies the concept of scale–encompassing both data and computational resources, as well as their interaction with neural network architectures. Scale, however, presents critical challenges, such as increased instability during training and prohibitively expensive model-specific tuning. Given the substantial resources required to train such models, formulating high-confidence scaling hypotheses backed by rigorous theoretical research has become paramount.
To bridge theory and practice, the talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning, spanning domains from vision to language models. We unify these algorithms under a common master template, making their foundational principles transparent. In doing so, we reveal the interplay between adaptation to smoothness structures via online learning and the exploitation of optimization geometry through non-Euclidean norms. Our exposition moves beyond simply building larger models–it emphasizes strategic scaling, offering insights that promise to advance the field while economizing on resources.

Volkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta, GA in 2005. He was a Research Scientist with the University of Maryland, College Park from 2006-2007 and also with Rice University in Houston, TX, from 2008-2009. Currently, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include machine learning, signal processing theory, optimization theory and methods, and information theory. Dr. Cevher is an ELLIS fellow and was the recipient of the Google Faculty Research award in 2018, the IEEE Signal Processing Society Best Paper Award in 2016, a Best Paper Award at CAMSAP in 2015, a Best Paper Award at SPARS in 2009, and an ERC CG in 2016 as well as an ERC StG in 2011.

Optimization for ML and AI Seminar: Stochastic-Gradient and Diagonal-Scaling Algorithms for Constrained Optimization and Learning
2025-12-05 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Frank E. Curtis, Lehigh University
Abstract: I will motivate and provide an overview of recent efforts in my research group on the design and analysis of stochastic-gradient-based algorithms for solving constrained optimization problems. I will focus in particular on our motivation for informed supervised learning, where constraints in the training problem can be used to impose prior knowledge on the properties that should be possessed by a trained prediction model. In addition, I will provide a detailed look at our newest extensions of heavy-ball and Adam schemes from the unconstrained to the equality-constrained setting, for which we have shown state-of-the-art convergence guarantees. I will demonstrate the impressive practical performance of our methods using a few informed supervised learning problems.

Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he has been employed since 2009. He received a bachelor’s degree from the College of William and Mary in 2003 with a double major in Computer Science and Mathematics, received a master’s degree in 2004 and Ph.D. degree in 2007 from the Department of Industrial Engineering and Management Science at Northwestern University, and spent two years as a Postdoctoral Researcher in the Courant Institute of Mathematical Sciences at New York University from 2007 until 2009. His research focuses on the design, analysis, and implementation of numerical methods for solving large-scale nonlinear optimization problems. He received an Early Career Award from the Advanced Scientific Computing Research (ASCR) program of the U.S. Department of Energy (DoE), and has received funding from various programs of the U.S. National Science Foundation (NSF), including through a TRIPODS Phase I grant awarded to him and his collaborators at Lehigh, Northwestern, and Boston University. He has also received funding from the U.S. Office of Naval Research (ONR) and DoE’s Advanced Research Projects Agency-Energy (ARPA-E). He received, along with Leon Bottou (Meta AI) and Jorge Nocedal (Northwestern), the 2021 SIAM/MOS Lagrange Prize in Continuous Optimization. He was awarded, with James V. Burke (U. of Washington), Adrian Lewis (Cornell), and Michael Overton (NYU), the 2018 INFORMS Computing Society Prize. He and team members Daniel Molzahn (Georgia Tech), Andreas Waechter (Northwestern), Ermin Wei (Northwestern), and Elizabeth Wong (UC San Diego) were awarded second place in the ARPA-E Grid Optimization Competition in 2020. He currently serves as Area Editor for Continuous Optimization for Mathematics of Operations Research and serves as an Associate Editor for Mathematical Programming, SIAM Journal on Optimization, Operations Research, IMA Journal of Numerical Analysis, and Mathematical Programming Computation. He previously served as the Vice Chair for Nonlinear Programming for the INFORMS Optimization Society, and is currently very active in professional societies and groups related to mathematical optimization, including INFORMS, the Mathematics Optimization Society, and the SIAM Activity Group on Optimization.

TILOS-HDSI Seminar: Incentivizing Emergent Behaviors for LLMs via Reinforcement Learning
2025-12-08 10:00 - 11:00 | Qualcomm Conference Center Room B (Jacobs Hall first floor) and Virtual, 9736 Engineers Ln, La Jolla, CA, 92093, United States
Yi Wu, Tsinghua University
Abstract: Reinforcement Learning (RL) has become a powerful post-training method for eliciting advanced behaviors in large language models (LLMs). This talk presents recent results showing how RL can incentivize the emergence of LLM capabilities across three domains: (1) multi-player deduction game, Werewolf, where RL-trained LLM agents develop strategic behaviors and outperform strong human players; (2) agentic search, where large-scale RL enables a 32B model to run multi-step search to answer non-trivial questions beyond commercial baselines; and (3) efficient reasoning, where RL mitigates over-thinking and improves both reliability and compute efficiency.
The papers can be found at

Werewolf: https://arxiv.org/abs/2310.18940 (ICML24), https://arxiv.org/abs/2502.04686 (ICML25)
ASearcher: https://arxiv.org/abs/2508.07976
Thinking Efficiency: https://www.arxiv.org/abs/2506.07104 (NeurIPS25)

All the projects are trained using our large-scale agentic RL system, AReaL, which is open-source at https://github.com/inclusionAI/AReaL with its paper at https://arxiv.org/abs/2505.24298 (NeurIPS25).

Yi Wu is an assistant professor at the Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University. He obtained his Ph.D. from UC Berkeley and was a researcher at OpenAI from 2019 to 2020. His research focuses on reinforcement learning, multi-agent learning, and LLM agents. His representative works include the value iteration network, the MADDPG/MAPPO algorithm, OpenAI’s hide-and-seek project, and the AReaL project. He received the best paper award at NIPS 2016, the best demo award finalist at ICRA 2024, and MIT TR35 Asia Pacific 2025 award.

Optimization for ML and AI Seminar: Randomized linear algebra with subspace injections
2026-01-09 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Joel Tropp, Caltech
Abstract: To achieve the greatest possible speed, practitioners regularly implement randomized algorithms for low-rank approximation and least-squares regression with structured dimension reduction maps. This talk outlines a new perspective on structured dimension reduction, based on the injectivity properties of the dimension reduction map. This approach provides sharper bounds for sparse dimension reduction maps, and it leads to exponential improvements for tensor-product dimension reduction. Empirical evidence confirms that these types of structured random matrices offer exemplary performance for a range of synthetic problems and contemporary scientific applications.
Joint work with Chris Camaño, Ethan Epperly, and Raphael Meyer; available at arXiv:2508.21189.

Joel A. Tropp is Steele Family Professor of Applied & Computational Mathematics at the California Institute of Technology. His research centers on applied mathematics, machine learning, data science, numerical algorithms, and random matrix theory. Some of his best-known contributions include matching pursuit algorithms, randomized SVD algorithms, matrix concentration inequalities, and statistical phase transitions. Prof. Tropp attained the Ph.D. degree in Computational Applied Mathematics at the University of Texas at Austin in 2004, and he joined Caltech in 2007. He won the PECASE in 2008, and he was recognized as a Highly Cited Researcher in Computer Science each year from 2014–2018. He is co-founder of the SIAM Journal on Mathematics of Data Science (SIMODS), and he was co-chair of the inaugural 2020 SIAM Conference on the Mathematics of Data Science. Prof. Tropp was elected SIAM Fellow in 2019, IEEE Fellow in 2020, and IMS Fellow in 2024. He received the 2025 Richard P. Feynman Prize for Excellence in Teaching at Caltech. He is an invited speaker at the 2026 International Congress of Mathematicians (ICM).

TILOS-HDSI Seminar: Safety, Representations, and Generative Learning in Dynamical Systems
2026-01-28 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Koushil Sreenath, UC Berkeley
Abstract: This talk explores the interplay between model-based guarantees and learning-based flexibility in the control of dynamical systems. I begin with safety-critical control using control barrier functions (CBFs), highlighting that while CBFs enforce state constraints, they may induce unstable internal dynamics. I introduce conditions under which CBF-based safety filters ensure boundedness of the full system state. I then transition to learning representations of hybrid dynamical systems. I present a framework that learns continuous neural representations by exploiting the geometric structure induced by guards and resets, enabling accurate flow prediction without explicit mode switching. Finally, I discuss generative learning approaches for control, emphasizing guided diffusion models that jointly represent states and actions. Through applications to agile humanoid locomotion, motion synthesis, and dynamic manipulation, I demonstrate how generative models can produce versatile, long-horizon behaviors while respecting physical constraints. Together, these results highlight how structure, geometry, and learning can bridge safety guarantees and expressive control in complex dynamical systems.

Koushil Sreenath is an Associate Professor of Mechanical Engineering, at UC Berkeley. He received a Ph.D. degree in Electrical Engineering and Computer Science and a M.S. degree in Applied Mathematics from the University of Michigan at Ann Arbor, MI, in 2011. He was a Postdoctoral Scholar at the GRASP Lab at University of Pennsylvania from 2011 to 2013 and an Assistant Professor at Carnegie Mellon University from 2013 to 2017. His research interest lies at the intersection of highly dynamic robotics and applied nonlinear control. His work on dynamic legged locomotion was featured on The Discovery Channel, CNN, ESPN, FOX, and CBS. His work on dynamic aerial manipulation was featured on the IEEE Spectrum, New Scientist, and Huffington Post. His work on adaptive sampling with mobile sensor networks was published as a book. He received the NSF CAREER, Hellman Fellow, Google Faculty Research Award in Robotics, and Best Paper Awards at Learning for Dynamics and Control (L4DC) and Robotics: Science and Systems (RSS).

[CANCELED] Optimization for ML and AI Seminar: Fantastic Pretraining Optimizers and Where to Find Them
2026-01-30 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Tengyu Ma, Stanford
Abstract: AdamW has long been the dominant optimizer in language model pretraining, despite numerous claims that alternative optimizers offer 1.4 to 2x speedup. We posit that two methodological shortcomings have obscured fair comparisons and hindered practical adoption: (i) unequal hyperparameter tuning and (ii) limited or misleading evaluation setups. To address these two issues, we conduct a systematic study of ten deep learning optimizers across four model scales (0.1B-1.2B parameters) and data-to-model ratios (1-8x the Chinchilla optimum). We find that fair and informative comparisons require rigorous hyperparameter tuning and evaluations across a range of model scales and data-to-model ratios, performed at the end of training. First, optimal hyperparameters for one optimizer may be suboptimal for another, making blind hyperparameter transfer unfair. Second, the actual speedup of many proposed optimizers over well-tuned baselines is lower than claimed and decreases with model size to only 1.1x for 1.2B parameter models. Thirdly, comparing intermediate checkpoints before reaching the target training budgets can be misleading, as rankings between two optimizers can flip during training due to learning rate decay. Through our thorough investigation, we find that all the fastest optimizers such as Muon and Soap, use matrices as preconditioners—multiplying gradients with matrices rather than entry-wise scalars. However, the speedup of matrix-based optimizers is inversely proportional to model scale, decreasing from 1.4x over AdamW for 0.1B parameter models to merely 1.1x for 1.2B parameter models.

Tengyu Ma is an assistant professor of computer science at Stanford University. His research interests broadly include topics in machine learning, algorithms and their theory, such as deep learning, (deep) reinforcement learning, pre-training / foundation models, robustness, non-convex optimization, distributed optimization, and high-dimensional statistics.
Zoom: https://bit.ly/Opt-AI-ML

Optimization for ML and AI Seminar: Extended Convex Lifting for Policy Optimization in Control
2026-02-06 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Yang Zheng, UC San Diego
Abstract: Direct policy search has achieved great empirical success in reinforcement learning. Many recent studies have revisited its theoretical foundation for continuous control, which reveals elegant nonconvex geometry in various benchmark problems. In this talk, we introduce an Extended Convex Lifting (ECL) framework, which reveals hidden convexity in classical optimal and robust control problems from a modern optimization perspective. Our ECL offers a bridge between nonconvex policy optimization and convex reformulations. Despite non-convexity and non-smoothness, the existence of an ECL not only reveals that minimizing the original function is equivalent to a convex problem, but also certifies a class of first-order non-degenerate stationary points to be globally optimal. This ECL framework encompasses many benchmark control problems, including LQR, LQG, state-feedback, and output-feedback H-infinity robust control. We believe that the ECL framework may be of independent interest for analyzing nonconvex problems beyond control.

Yang Zheng is an Assistant Professor in the ECE Department at UC San Diego. His research focuses on control theory, convex and nonconvex optimization, and their applications to autonomous vehicles and traffic systems. He received his DPhil (Ph.D.) in Engineering Science from the University of Oxford in 2019, and his B.E. and M.S. degrees from Tsinghua University in 2013 and 2015, respectively. His work has been recognized with several awards, including the 2019 European Ph.D. Award on Control for Complex and Heterogeneous Systems, the 2022 Best Paper Award from IEEE Transactions on Control of Network Systems, the 2023 Best Graduate Teacher Award from UC San Diego’s ECE Department, the 2024 NSF CAREER Award, and the 2025 Donald P. Eckman Award from the American Automatic Control Council.
