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Workshop
Bayesian Treatment of the Spectrum of the Empirical Kernel in (Sub)Linear-Width Neural Networks
Ouns El Harzli · Bernardo Grau
Workshop
Improved Depth Estimation of Bayesian Neural Networks
Bart van Erp · Bert de Vries
Workshop
Hi-fi functional priors by learning activations
Marcin Sendera · Amin Sorkhei · Tomasz Kuśmierczyk
Workshop
Convergence Rates of Bayesian Network Policy Gradient for Cooperative Multi-Agent Reinforcement Learning
Dingyang Chen · Zhenyu Zhang · Xiaolong Kuang · Xinyang Shen · Ozalp Ozer · Qi Zhang
Workshop
Variational Last Layers for Bayesian Optimization
Paul Brunzema · Mikkel Jordahn · John Willes · Sebastian Trimpe · Jasper Snoek · James Harrison
Workshop
Learning from Less: Bayesian Neural Networks for Optimization Proxy using Limited Labeled Data
Parikshit Pareek · Kaarthik Sundar · Deep Deka · Sidhant Misra
Workshop
Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
Richard Bergna · Sergio Calvo Ordoñez · Felix Opolka · Pietro Lió · José Miguel Hernández-Lobato
Workshop
Dimension Deficit: Is 3D a Step Too Far for Optimizing Molecules?
Andres Guzman-Cordero · Luca Thiede · Gary Tom · Alan Aspuru-Guzik · Felix Strieth-Kalthoff · Agustinus Kristiadi
Poster
Thu 11:00 TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
Benjamin Feuer · Robin Schirrmeister · Valeriia Cherepanova · Chinmay Hegde · Frank Hutter · Micah Goldblum · Niv Cohen · Colin White
Workshop
Gradient-free variational learning with conditional mixture networks
Conor Heins · Hao Wu · Dimitrije Markovic · Alexander Tschantz · Jeff Beck · Christopher L Buckley
Workshop
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
David Schnurr · Kai Helli · Noah Hollmann · Samuel Müller · Frank Hutter