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Uncertainty quantification is a central challenge in reliable and trustworthy machine learning.
Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing
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Spin glass theory and beyond: An Introduction to the Replica Method and Its Applications
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Neural networks and the bias/variance dilemma
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Bayesian Interpolation
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Hyperparameters evidence and generalisation for an unrealisable rule
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A statistical mechanical analysis of a bayesian inference scheme for an unrealizable rule
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Hyperparameters: Optimize, or Integrate Out?
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Learning curves for gaussian processes
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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Gaussian process regression with mismatched models
Sollich, P. (2001) · 2001
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Margin maximizing loss functions
Rosset, S., Zhu, J., and Hastie, T. (2003) · 2003
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Universality in sherrington-kirkpatrick’s spin glass model
Carmona, P. and Hu, Y. (2004) · 2004
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A simple invariance theorem
Chatterjee, S. (2005) · 2005
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Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R. (2005) · 2005
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Mean-field spin glass models from the cavity–rost perspective
Aizenman, M., Sims, R., and Starr, S. L. (2006) · 2006
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Deterministic equivalents for certain functionals of large random matrices
Hachem, W., Loubaton, P., and Najim, J. (2007) · 2007
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Random features for large-scale kernel machines
Rahimi, A. and Recht, B. (2007) · 2007
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Large sample covariance matrices without independence structures in columns
Bai, Z. and Zhou, W. (2008) · 2008
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Universality of random matrices and local relaxation flow
Erdos, L., Schlein, B., and Yau, H.-T. (2009) · 2009
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Concentration of measure and spectra of random matrices: Applications to correlation matrices, elliptical distributions and beyond
Karoui, N. E. (2009) · 2009
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Information, physics, and computation
Mezard, M. and Montanari, A. (2009) · 2009
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Bulk universality for generalized wigner matrices
Erdos, L., Yau, H.-T., and Yin, J. (2010) · 2010
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The spectrum of kernel random matrices
Karoui, N. E. (2010) · 2010
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Korada, S. B. and Montanari, A. (2010) · 2010
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The dynamics of message passing on dense graphs, with applications to compressed sensing
Bayati, M. and Montanari, A. (2011) · 2011
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Practical variational inference for neural networks
Graves, A. (2011) · 2011
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Generalized approximate message passing for estimation with random linear mixing
Rangan, S. (2011) · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W. (2011) · 2011
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Probabilistic reconstruction in compressed sensing: algorithms, phase diagrams, and threshold achieving matrices
Krzakala, F., Mézard, M., Sausset, F., Sun, Y., and Zdeborová, L. (2012) · 2012
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Random matrices: universal properties of eigenvectors
Tao, T. and Vu, V. (2012) · 2012
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On robust regression with high-dimensional predictors
Karoui, N. E., Bean, D., Bickel, P. J., Lim, C., and Yu, B. (2013) · 2013
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A framework to characterize performance of lasso algorithms
Stojnic, M. (2013) · 2013
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All of Statistics: A Concise Course in Statistical Inference
Wasserman, L. (2013) · 2013
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J. (2015) · 2015
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Regularized linear regression: A precise analysis of the estimation error
Thrampoulidis, C., Oymak, S., and Hassibi, B. (2015) · 2015
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High dimensional robust m-estimation: Asymptotic variance via approximate message passing
Donoho, D. and Montanari, A. (2016) · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
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On last-layer algorithms for classification: Decoupling representation from uncertainty estimation
Brosse, N., Riquelme, C., Martin, A., Gelly, S., and Moulines, E. (2020) · 2020
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The estimation error of general first order methods
Celentano, M., Montanari, A., and Wu, Y. (2020) · 2020
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Double trouble in double descent: Bias and variance(s) in the lazy regime
D’Ascoli, S., Refinetti, M., Biroli, G., and Krzakala, F. (2020) · 2020
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A precise performance analysis of learning with random features
Dhifallah, O. and Lu, Y. M. (2020) · 2020
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Generalisation error in learning with random features and the hidden manifold model
Gerace, F., Loureiro, B., Krzakala, F., Mezard, M., and Zdeborova, L. (2020) · 2020
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Modeling the influence of data structure on learning in neural networks: The hidden manifold model
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Zdeborová, L. and Krzakala, F. (2016) · 2016
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The lasso risk for gaussian matrices
Bayati, M. and Montanari, A. (2012) · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q. (2017) · 2017
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Anisotropic local laws for random matrices
Knowles, A. and Yin, J. (2017) · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2017) · 2017
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A universal analysis of large-scale regularized least squares solutions
Panahi, A. and Hassibi, B. (2017) · 2017
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Nonlinear random matrix theory for deep learning
Pennington, J. and Worah, P. (2017) · 2017
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Goldt, S., Mézard, M., Krzakala, F., and Zdeborová, L. (2020) · 2020
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Universality laws for high-dimensional learning with random features
Hu, H. and Lu, Y. M. (2020) · 2020
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Being bayesian, even just a bit, fixes overconfidence in ReLU networks
Kristiadi, A., Hein, M., and Hennig, P. (2020) · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Liu, J., Lin, Z., Padhy, S., Tran, D., Bedrax Weiss, T., and Lakshminarayanan, B. (2020) · 2020
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Calibrating deep neural networks using focal loss
Mukhoti, J., Kulharia, V., Sanyal, A., Golodetz, S., Torr, P., and Dokania, P. (2020) · 2020
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Deep double descent: Where bigger models and more data hurt
Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., and Sutskever, I. (2020) · 2020
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., Makarenkov, V., and Nahavandi, S. (2021) · 2021
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The spiked matrix model with generative priors
Aubin, B., Loureiro, B., Maillard, A., Krzakala, F., and Zdeborová, L. (2021) · 2021
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Don’t just blame over-parametrization for over-confidence: Theoretical analysis of calibration in binary classification
Bai, Y., Mei, S., Wang, H., and Xiong, C. (2021) · 2021
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Eigenvalue distribution of some nonlinear models of random matrices
Benigni, L. and Péché, S. (2021) · 2021
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Laplace Redux - Effortless Bayesian Deep Learning
Daxberger, E., Kristiadi, A., Immer, A., Eschenhagen, R., Bauer, M., and Hennig, P. (2021) · 2021
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Graph-based approximate message passing iterations
Gerbelot, C. and Berthier, R. (2021) · 2021
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Hessian eigenspectra of more realistic nonlinear models
Liao, Z. and Mahoney, M. W. (2021) · 2021
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Optimal regularization can mitigate double descent
Nakkiran, P., Venkat, P., Kakade, S. M., and Ma, T. (2021) · 2021
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Structured stochastic gradient MCMC
Alexos, A., Boyd, A. J., and Mandt, S. (2022) · 2022
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Theoretical characterization of uncertainty in high-dimensional linear classification
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Learning curves for the multi-class teacher-student perceptron
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A model of double descent for high-dimensional binary linear classification
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A survey of uncertainty in deep neural networks
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Gaussian universality of linear classifiers with random labels in high-dimension
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The gaussian equivalence of generative models for learning with shallow neural networks
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Surprises in high-dimensional ridgeless least squares interpolation
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Hands-on bayesian neural networks—a tutorial for deep learning users
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The generalization error of random features regression: Precise asymptotics and the double descent curve
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Universality of empirical risk minimization
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Universality laws for gaussian mixtures in generalized linear models
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Deterministic equivalent and error universality of deep random features learning
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