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DTSTART;TZID=America/Los_Angeles:20220511T143000
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CREATED:20250904T173748Z
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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
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DTSTART;TZID=America/Los_Angeles:20220518T100000
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CREATED:20250904T173915Z
LAST-MODIFIED:20250904T173915Z
UID:7344-1652868000-1652871600@tilos.ai
SUMMARY:TILOS Seminar: Deep Generative Models and Inverse Problems
DESCRIPTION:Alexandros G. Dimakis\, Professor\, The University of Texas at Austin \nAbstract: Sparsity has given us MP3\, JPEG\, MPEG\, Faster MRI and many fun mathematical problems. Deep generative models like GANs\, VAEs\, invertible flows and Score-based models are modern data-driven generalizations of sparse structure. We will start by presenting the CSGM framework by Bora et al. to solve inverse problems like denoising\, filling missing data\, and recovery from linear projections using an unsupervised method that relies on a pre-trained generator. We generalize compressed sensing theory beyond sparsity\, extending Restricted Isometries to sets created by deep generative models. Our recent results include establishing theoretical results for Langevin sampling from full-dimensional generative models\, generative models for MRI reconstruction and fairness guarantees for inverse problems.
URL:https://tilos.ai/event/tilos-seminar-deep-generative-models-and-inverse-problems/
LOCATION:Virtual
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/dimakis-alexandros-e1711660493749-oAsHBv.jpg
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