Bayesian Learning
Posterior Concentration for Sparse Deep Learning
Veronika Rockova, nicholas polson
Neural Information Processing Systems http://nips.cc/
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
Meelis Kull, Miquel Perello Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, Peter Flach
Neural Information Processing Systems http://nips.cc/
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks
Neural Information Processing Systems http://nips.cc/
Posterior Collapse of a Linear Latent Variable Model
This work identifies the existence and cause of a type of posterior collapse that frequently occurs in the Bayesian deep learning practice. For a general linear latent variable model that includes linear variational autoencoders as a special case, we precisely identify the nature of posterior collapse to be the competition between the likelihood and the regularization of the mean due to the prior. Our result suggests that posterior collapse may be related to neural collapse and dimensional collapse and could be a subclass of a general problem of learning for deeper architectures.
Learning Hawkes Processes from a handful of events
Farnood Salehi, William Trouleau, Matthias Grossglauser, Patrick Thiran
Neural Information Processing Systems http://nips.cc/
Demystifying the Optimal Performance of Multi-Class Classification
Inspired by the work by Ishida et al. (2023), we propose an estimator
From Stochastic Planning to Marginal MAP
Hao(Jackson) Cui, Radu Marinescu, Roni Khardon
Neural Information Processing Systems http://nips.cc/
Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra
John T. Halloran, David M. Rocke
The most widely used technology to identify the proteins present in a complex biological sample istandem mass spectrometry,which quickly produces alarge collection of spectra representative of thepeptides (i.e., protein subsequences) present in the original sample.