Uncertainty
Should We Learn Most Likely Functions or Parameters? Shikai Qiu Tim G. J. Rudner Sanyam Kapoor Andrew Gordon Wilson New York University
Machine learning has matured to the point where we often take key design decisions for granted.
Bayesian Nonparametric Spectral Estimation
Neural Information Processing Systems http://nips.cc/
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi
Such learning problems are formulated as latent or generative model learning assuming that observations were emerged from the low-dimensional latent states, which includes an intractable posterior inference of latent states for given input data.
Evidential Deep Learning to Quantify Classification Uncertainty
Murat Sensoy, Lance Kaplan, Melih Kandemir
While one side of the coin is a boost of interest and investment on deep learning research, theother isanemergent need foritsrobustness, sample efficiency,security,and interpretability.
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $\beta$-Divergences
Jeremias Knoblauch, Jack E. Jewson, Theodoros Damoulas
Neural Information Processing Systems http://nips.cc/
Learning to Shape In-distribution Feature Space for Out-of-distribution Detection
We prove that the ELBO is bounded, and the EM algorithm contributes to the convergence of ELBO.
Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
Neural Information Processing Systems http://nips.cc/
Computationally and statistically efficient learning of causal Bayes nets using path queries
Neural Information Processing Systems http://nips.cc/
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
Shahana Ibrahim, Xiao Fu, Nikolaos Kargas, Kejun Huang
Neural Information Processing Systems http://nips.cc/