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stat.ML2024
Deep Kernel Posterior Learning under Infinite Variance Prior Weights
Jorge Loría, Anindya Bhadra
Neal (1996) proved that infinitely wide shallow Bayesian neural networks (BNN) converge to Gaussian processes (GP), when the network weights have bounded prior variance. Cho & Saul…
stat.ML2023
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance
Jorge Loría, Anindya Bhadra
From the classical and influential works of Neal (1996), it is known that the infinite width scaling limit of a Bayesian neural network with one hidden layer is a Gaussian process,…
stat.ML2022★ 1 cited
Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization
Carlos Fernández-Loría, Jorge Loría
Who should we prioritize for treatment when causal effects cannot be estimated? In practice, organizations often rely on predictive proxies: ads are targeted using purchase probabi…