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DTSTART;TZID=America/Los_Angeles:20231108T110000
DTEND;TZID=America/Los_Angeles:20231108T120000
DTSTAMP:20260520T025835
CREATED:20250828T203219Z
LAST-MODIFIED:20260430T152347Z
UID:7324-1699441200-1699444800@tilos.ai
SUMMARY:TILOS-OPTML++ Seminar: Optimization\, Robustness and Privacy in Deep Neural Networks: Insights from the Neural Tangent Kernel
DESCRIPTION:Marco Mondelli\, Institute of Science and Technology Austria \nAbstract: A recent line of work has analyzed the properties of deep over-parameterized neural networks through the lens of the Neural Tangent Kernel (NTK). In this talk\, I will show how concentration bounds on the NTK (and\, specifically\, on its smallest eigenvalue) provide insights on (i) the optimization of the network via gradient descent\, (ii) its adversarial robustness\, and (iii) its privacy guarantees. I will start by proving tight bounds on the smallest eigenvalue of the NTK for deep neural networks with minimum over-parameterization. This implies that the network optimized by gradient descent interpolates the training dataset (i.e.\, reaches 0 training loss)\, as soon as the number of parameters is information-theoretically optimal. Next\, I will focus on two properties of the interpolating solution: robustness and privacy. A thought-provoking paper by Bubeck and Sellke has proposed a “universal law of robustness”: interpolating smoothly the data necessarily requires many more parameters than simple memorization. By providing sharp bounds on random features (RF) and NTK models\, I will show that\, while the RF model is never robust (regardless of the over-parameterization)\, the NTK model saturates the universal law of robustness\, addressing a conjecture by Bubeck\, Li and Nagaraj. Finally\, I will study the safety of RF and NTK models against a family of powerful black-box information retrieval attacks: the proposed analysis shows that safety provably strengthens with an increase in the generalization capability\, unveiling the role of the model and of its activation function. \n\nMarco Mondelli received the B.S. and M.S. degree in Telecommunications Engineering from the University of Pisa\, Italy\, in 2010 and 2012\, respectively. In 2016\, he obtained his Ph.D. degree in Computer and Communication Sciences at the École Polytechnique Fédérale de Lausanne (EPFL)\, Switzerland. He is currently an Assistant Professor at the Institute of Science and Technology Austria (ISTA). Prior to that\, he was a Postdoctoral Scholar in the Department of Electrical Engineering at Stanford University\, USA\, from February 2017 to August 2019. He was also a Research Fellow with the Simons Institute for the Theory of Computing\, UC Berkeley\, USA\, for the program on Foundations of Data Science from August to December 2018. His research interests include data science\, machine learning\, information theory\, and modern coding theory. He was the recipient of a number of fellowships and awards\, including the Jack K. Wolf ISIT Student Paper Award in 2015\, the STOC Best Paper Award in 2016\, the EPFL Doctorate Award in 2018\, the Simons-Berkeley Research Fellowship in 2018\, the Lopez-Loreta Prize in 2019\, and Information Theory Society Best Paper Award in 2021.
URL:https://tilos.ai/event/optimization-robustness-and-privacy-in-deep-neural-networks-insights-from-the-neural-tangent-kernel/
LOCATION:Virtual
CATEGORIES:TILOS - OPTML++ Seminar Series,TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/mondelli-marco-scaled-e1711659727954-z3UC0d.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230918T100000
DTEND;TZID=America/Los_Angeles:20230918T110000
DTSTAMP:20260520T025835
CREATED:20250828T203818Z
LAST-MODIFIED:20260430T154334Z
UID:7327-1695031200-1695034800@tilos.ai
SUMMARY:TILOS Seminar: Machine Learning from Weak\, Noisy\, and Biased Supervision
DESCRIPTION:Masashi Sugiyama\, University of Tokyo and RIKEN \nAbstract: In statistical inference and machine learning\, we face a variety of uncertainties such as training data with insufficient information\, label noise\, and bias. In this talk\, I will give an overview of our research on reliable machine learning\, including weakly supervised classification (positive unlabeled classification\, positive confidence classification\, complementary label classification\, etc.)\, noisy label classification (noise transition estimation\, instance-dependent noise\, clean sample selection\, etc.)\, and transfer learning (joint importance-predictor estimation for covariate shift adaptation\, dynamic importance estimation for full distribution shift\, continuous distribution shift\, etc.).
URL:https://tilos.ai/event/tilos-seminar-machine-learning-from-weak-noisy-and-biased-supervision/
LOCATION:Virtual
CATEGORIES:TILOS - OPTML++ Seminar Series,TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/Sugiyama-1-e1711659352629-5zhb7G.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230515T143000
DTEND;TZID=America/Los_Angeles:20230515T153000
DTSTAMP:20260520T025835
CREATED:20250828T202516Z
LAST-MODIFIED:20260430T154604Z
UID:7336-1684161000-1684164600@tilos.ai
SUMMARY:TILOS Seminar: The Hidden Convex Optimization Landscape of Deep Neural Networks
DESCRIPTION:Mert Pilanci\, Stanford University \nAbstract: Since deep neural network training problems are inherently non-convex\, their recent dramatic success largely relies on non-convex optimization heuristics and experimental findings. Despite significant advancements\, the non-convex nature of neural network training poses two central challenges: first\, understanding the underlying mechanisms that contribute to model performance\, and second\, achieving efficient training with low computational cost and energy consumption. The performance of non-convex models is notably influenced by the selection of optimization methods and hyperparameters\, including initialization\, mini-batching\, and step sizes. Conversely\, convex optimization problems are characterized by their robustness to these choices\, allowing for the efficient and consistent achievement of globally optimal solutions\, irrespective of optimization parameters. In this talk\, we explore a novel perspective by examining multilayer neural networks equipped with ReLU activation functions through the framework of convex optimization. We introduce exact convex optimization formulations of ReLU network training problems. We show that two-layer ReLU networks can be globally trained via convex programs with the number of variables polynomial in the number of training samples\, feature dimension\, and the number of hidden neurons. We show that our analysis extends to deeper networks and that these convex programs possess an intuitive geometric interpretation. Our results provide an equivalent characterization of neural networks as convex models where a mixture of locally linear models are fitted to the data with sparsity inducing convex regularization. Moreover\, we show that standard convolutional neural networks can be globally optimized in fully polynomial time. We discuss extensions to batch normalization\, generative adversarial networks and transformers. Finally\, we present numerical simulations verifying our claims and illustrating that the proposed convex approach is faster and more reliable than standard local search heuristics such as SGD and variants. \n\nMert Pilanci is an assistant professor of Electrical Engineering at Stanford University. He received his Ph.D. in Electrical Engineering and Computer Science from UC Berkeley in 2016. Prior to joining Stanford\, he was an assistant professor of Electrical Engineering and Computer Science at the University of Michigan. In 2017\, he was a Math+X postdoctoral fellow working with Emmanuel Candès at Stanford University. Mert’s research interests are in neural networks\, machine learning\, optimization\, and signal processing. His group develops theory and algorithms for solving large scale optimization problems in machine learning. His research also seeks to develop safe and interpretable artificial intelligence and information theoretic foundations of distributed computing.
URL:https://tilos.ai/event/tilos-seminar-the-hidden-convex-optimization-landscape-of-deep-neural-networks/
LOCATION:Virtual
CATEGORIES:TILOS - OPTML++ Seminar Series,TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2023/10/pilanci-mert-e1756408324872.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230426T090000
DTEND;TZID=America/Los_Angeles:20230426T100000
DTSTAMP:20260520T025835
CREATED:20250828T204254Z
LAST-MODIFIED:20260430T154628Z
UID:7361-1682499600-1682503200@tilos.ai
SUMMARY:TILOS-OPTML++ Seminar: Sums of Squares: from Algebra to Analysis
DESCRIPTION:Francis Bach\, NRIA\, ENS\, and PSL Paris \nAbstract: The representation of non-negative functions as sums of squares has become an important tool in many modeling and optimization tasks. Traditionally applied to polynomial functions\, it requires rich tools from algebraic geometry that led to many developments in the last twenty years. In this talk\, I will look at this problem from a functional analysis point of view\, leading to new applications and new results on the performance of sum-of-squares optimization. \n\nFrancis Bach is a researcher at Inria\, leading since 2011 the machine learning team which is part of the Computer Science department at Ecole Normale Supérieure. He graduated from Ecole Polytechnique in 1997 and completed his Ph.D. in Computer Science at U.C. Berkeley in 2005\, working with Professor Michael Jordan. He spent two years in the Mathematical Morphology group at Ecole des Mines de Paris\, then he joined the computer vision project-team at Inria/Ecole Normale Supérieure from 2007 to 2010. Francis Bach is primarily interested in machine learning\, and especially in sparse methods\, kernel-based learning\, large-scale optimization\, computer vision and signal processing. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council\, and received the Inria young researcher prize in 2012\, the ICML test-of-time award in 2014 and 2019\, as well as the Lagrange prize in continuous optimization in 2018\, and the Jean-Jacques Moreau prize in 2019. He was elected in 2020 at the French Academy of Sciences. In 2015\, he was program co-chair of the International Conference in Machine learning (ICML)\, and general chair in 2018; he is now co-editor-in-chief of the Journal of Machine Learning Research.
URL:https://tilos.ai/event/tilos-optml-seminar-sums-of-squares-from-algebra-to-analysis/
LOCATION:Virtual
CATEGORIES:TILOS - OPTML++ Seminar Series,TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/francis_bach_septembre_2016_small-e1711659265321-yFIGFR.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220511T143000
DTEND;TZID=America/Los_Angeles:20220511T153000
DTSTAMP:20260520T025835
CREATED:20250904T173748Z
LAST-MODIFIED:20260430T155843Z
UID:7345-1652279400-1652283000@tilos.ai
SUMMARY:TILOS-OPTML++ Seminar: Constant Regret in Online Decision-Making
DESCRIPTION:Siddhartha Banerjee\, Cornell University \nAbstract: I will present a class of finite-horizon control problems\, where we see a random stream of arrivals\, need to select actions in each step\, and where the final objective depends only on the aggregate type-action counts; this includes many widely-studied control problems including online resource-allocation\, dynamic pricing\, generalized assignment\, online bin packing\, and bandits with knapsacks. For such settings\, I will introduce a unified algorithmic paradigm\, and provide a simple yet general condition under which these algorithms achieve constant regret\, i.e.\, additive loss compared to the hindsight optimal solution which is independent of the horizon and state-space. These results stem from an elementary coupling argument\, which may prove useful for many other questions in online decision-making. Time permitting\, I will illustrate this by showing how we can use this technique to incorporate side information and historical data in these settings\, and achieve constant regret with as little as a single data trace.
URL:https://tilos.ai/event/tilos-optml-seminar-constant-regret-in-online-decision-making/
LOCATION:Virtual
CATEGORIES:TILOS - OPTML++ Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2023/09/banerjee-siddhartha-e1757007458539.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220427T143000
DTEND;TZID=America/Los_Angeles:20220427T153000
DTSTAMP:20260520T025835
CREATED:20250904T173650Z
LAST-MODIFIED:20260430T155915Z
UID:7347-1651069800-1651073400@tilos.ai
SUMMARY:TILOS-OPTML++ Seminar: Equilibrium Computation\, Deep Multi-Agent Learning\, and Multi-Agent Reinforcement Learning
DESCRIPTION:Constantinos Daskalakis\, MIT
URL:https://tilos.ai/event/tilos-optml-seminar-equilibrium-computation-deep-multi-agent-learning-and-multi-agent-reinforcement-learning/
LOCATION:Virtual
CATEGORIES:TILOS - OPTML++ Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2023/10/daskalakis-constantinos.jpg
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