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07/15/2023
The Interpolating Information Criterion for Overparameterized Models
The problem of model selection is considered for the setting of interpol...
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07/04/2023
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
Algorithm- and data-dependent generalization bounds are required to expl...
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06/15/2023
A Heavy-Tailed Algebra for Probabilistic Programming
Despite the successes of probabilistic models based on passing noise thr...
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05/21/2023
When are ensembles really effective?
Ensembling has a long history in statistical data analysis, with many im...
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10/14/2022
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
The quality of many modern machine learning models improves as model com...
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05/16/2022
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows
While fat-tailed densities commonly arise as posterior and marginal dist...
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02/06/2022
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data
The search for effective and robust generalization metrics has been the ...
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08/02/2021
Generalization Properties of Stochastic Optimizers via Trajectory Analysis
Despite the ubiquitous use of stochastic optimization algorithms in mach...
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07/23/2021
Taxonomizing local versus global structure in neural network loss landscapes
Viewing neural network models in terms of their loss landscapes has a lo...
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06/11/2020
Multiplicative noise and heavy tails in stochastic optimization
Although stochastic optimization is central to modern machine learning, ...
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02/21/2020
Stochastic Normalizing Flows
We introduce stochastic normalizing flows, an extension of continuous no...
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01/25/2020
The reproducing Stein kernel approach for post-hoc corrected sampling
Stein importance sampling is a widely applicable technique based on kern...
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03/29/2019