Robust Bayes-Assisted Conformal Prediction
Ashouritaklimi, K
Cortinovis, S
Caron, F
Louis Thomas Hardin, better known as Moondog, was a blind avant-garde American musician who worked in a variety of genres including jazz and classical, invented a series of his own instruments, influenced a bunch of other composers including the American minimalists, made a lot of his living by begging and briefly appeared in a cloak and horned helmet (he loved Norse mythology) on Sixth Avenue in New York.
But never mind that.
Climate-driven host ecology as a framework for zoonotic disease risk: Understanding transferability across arenavirus systems
Milne, G
Attfield, L
Mariën, J
Kirkpatrick, L
Leirs, H
Jones, K
Donnelly, C
Redding, D
Proceedings of the National Academy of Sciences of the United States of America
volume 123
issue 39
e2625913123
(29 Sep 2026)
doi:10.1073/pnas.2625913123
Initial observations in X-point target divertor discharges on MAST-U
Lonigro, N
Verhaegh, K
Harrison, J
Lipschultz, B
Bowman, C
Federici, F
Flanagan, J
Greenhouse, D
Moulton, D
Ryan, P
Scannell, R
Silburn, S
Wijkamp, T
Brida, D
Theiler, C
Team, T
Nuclear Fusion
volume 66
issue 10
106007
(01 Oct 2026)
doi:10.1088/1741-4326/ae948e
Mon, 09 Nov 2026
15:30 -
16:30
L3
One-Step Generative Modeling via Wasserstein Gradient Flows
RenYuan XU
(Stanford University)
Abstract
Diffusion models and flow-based methods have achieved strong results in image generation, but often rely on costly iterative sampling. We introduce W-Flow, a framework that compresses a Wasserstein gradient flow into a one-step neural generator. By minimizing the Sinkhorn divergence, W-Flow transports a reference distribution toward the data distribution through efficient optimal-transport updates that capture global distributional discrepancies. We prove that, under suitable assumptions, the finite-sample training dynamics converge to the continuous-time distributional dynamics. Empirically, W-Flow sets a new state of the art in one-step ImageNet generation, with improved mode coverage and domain transfer. We will also discuss extensions using alternative energy functionals and applications to post-training.
Mon, 09 Nov 2026
16:30 -
17:30
L4