This repo contains literature survey and implementation of baselines for predictive uncertainty estimation in deep learning.
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B. Efron and R. Tibshirani. “Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy.” Statistical science, 1986. [Link]
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R. Barber, E. J. Candes, A. Ramdas, and R. J. Tibshirani. “Predictive inference with the jackknife+.” arXiv, 2019. [Link]
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B. Efron. “Jackknife‐after‐bootstrap standard errors and influence functions.” Journal of the Royal Statistical Society: Series B (Methodological), 1992. [Link]
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J. Robins and A. Van Der Vaart. “Adaptive nonparametric confidence sets.” The Annals of Statistics, 2006. [Link]
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V. Vovk, et al., “Cross-conformal predictive distributions.” JMLR, 2018. [Link]
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M. H Quenouille., “Approximate tests of correlation in time-series.” Journal of the Royal Statistical Society, 1949. [Link]
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M. H Quenouille. “Notes on bias in estimation.” Biometrika, 1956. [Link]
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J. Tukey. “Bias and confidence in not quite large samples.” Ann. Math. Statist, 1958.
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R. G. Miller. “The jackknife–a review.” Biometrika, 1974. [Link]
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B. Efron. “Bootstrap methods: Another look at the jackknife.” Ann. Statist., 1979. [Link]
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R. A Stine. “Bootstrap prediction intervals for regression.” Journal of the American Statistical Association, 1985. [Link]
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R. F. Barber, E. J. Candes, A. Ramdas, and R. J. Tibshirani. “Conformal prediction under covariate shift.” arXiv preprint arXiv:1904.06019, 2019. [Link]
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R. F. Barber, E. J. Candes, A. Ramdas, and R. J. Tibshirani. “The limits of distribution-free conditional predictive inference.” arXiv preprint arXiv:1903.04684, 2019b. [Link]
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J. Lei, M. G’Sell, A. Rinaldo, R. J. Tibshirani, and L. Wasserman. “Distribution-free predictive inference for regression.” Journal of the American Statistical Association, 2018. [Link]
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R. Giordano, M. I. Jordan, and T. Broderick. “A Higher-Order Swiss Army Infinitesimal Jackknife.” arXiv, 2019. [Link]
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P. W. Koh, K. Ang, H. H. K. Teo, and P. Liang. “On the Accuracy of Influence Functions for Measuring Group Effects.” arXiv, 2019. [Link]
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D. H. Wolpert. “Stacked generalization.” Neural networks, 1992. [Link]
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R. D. Cook, and S. Weisberg. “Residuals and influence in regression.” New York: Chapman and Hall, 1982. [Link]
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R. Giordano, W. Stephenson, R. Liu, M. I. Jordan, and T. Broderick. “A Swiss Army Infinitesimal Jackknife.” arXiv preprint arXiv:1806.00550, 2018. [Link]
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P. W. Koh, and P. Liang. “Understanding black-box predictions via influence functions.” ICML, 2017. [Link]
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S. Wager and S. Athey. “Estimation and inference of heterogeneous treatment effects using random forests.” Journal of the American Statistical Association, 2018. [Link]
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J. F. Lawless, and M. Fredette. “Frequentist prediction intervals and predictive distributions.” Biometrika, 2005. [Link]
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F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel. “Robust statistics: the approach based on influence functions.” John Wiley and Sons, 2011. [Link]
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P. J. Huber and E. M. Ronchetti. “Robust Statistics.” John Wiley and Sons, 1981.
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Y. Romano, R. F. Barber, C. Sabatti, E. J. Candès. “With Malice Towards None: Assessing Uncertainty via Equalized Coverage.” arXiv, 2019. [Link]
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H. R. Kunsch. “The Jackknife and the Bootstrap for General Stationary Observations.” The annals of Statistics, 1989. [Link]
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I. Osband, Z. Wen, M. Asghari, M. Ibrahimi, X. Lu, and B. Van Roy “Epistemic Neural Networks.” arXiv, 2021. [Link]
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Abdar, Moloud, et al. “A review of uncertainty quantification in deep learning: Techniques, applications and challenges.” Information Fusion, 2021. [Link]
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Gawlikowski, Jakob, et al. “A Survey of Uncertainty in Deep Neural Networks.” arXiv preprint, 2021. [Link]
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P. Morales-Alvarez, D. Hernández-Lobato, R. Molina, J. M. Hernández-Lobato. “Activation-level uncertainty in deep neural networks.” ICLR, 2021. [Link]
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A. Angelopoulos, S. Bates, J. Malik, and M. I. Jordan. “Uncertainty Sets for Image Classifiers using Conformal Prediction.” ICLR, 2021. [Link]
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K. Patel, W. H. Beluch, B. Yang, M. Pfeiffer, D. Zhang. “Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning.” ICLR 2021. [Link]
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B. Adlam, J. Lee, L. Xiao, J. Pennington, J. Snoek. “Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit.” ICLR 2021. [Link]
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A. Harakeh, S. L. Waslander. “Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors.” ICLR 2021. [Link]
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J. Antoran, U. Bhatt, T. Adel, A. Weller, J. M. Hernández-Lobato. “Getting a CLUE: A Method for Explaining Uncertainty Estimates.” ICLR 2021. [Link]
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A.-K. Kopetzki, B. Charpentier, D. Zügner, S. Giri, S. Günnemann. “Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?” ICML 2021. [Link]
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A. Zhou and S. Levine. “Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation.” ICML, 2021. [Link]
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M. Havasi, R. Jenatton, S. Fort, J. Z. Liu, J. Snoek, B. Lakshminarayanan, A. M. Dai, and D. Tran. “Training independent subnetworks for robust prediction.” ICLR, 2021. [Link]
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B. Adlam, J. Lee, L. Xiao, J. Pennington, J. Snoek. “Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit”. ICLR, 2021. [Link]
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A. N. Angelopoulos, S. Bates, T. Zrnic, M. I. Jordan. “Private Prediction Sets.” arXiv, 2021. [Link]
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B. Charpentier, D. Zügner, S. Günnemann. “Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts.” NeurIPS, 2020. [Link]
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L. Meronen, C. Irwanto, A. Solin. “Stationary Activations for Uncertainty Calibration in Deep Learning.” NeurIPS, 2020. [Link]
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F. Wenzel, J. Snoek, D. Tran, R. Jenatton. “Hyperparameter Ensembles for Robustness and Uncertainty Quantification.” NeurIPS, 2020. [Link]
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J. Liu, Z. Lin, S. Padhy, D. Tran, T. Bedrax Weiss, B. Lakshminarayanan. “Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness.” NeurIPS, 2020. [Link]
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J. Lindinger, D. Reeb, C. Lippert, B. Rakitsch. “Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties.” NeurIPS, 2020. [Link]
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J. Antoran, J. Allingham, J. M. Hernández-Lobato. “Depth Uncertainty in Neural Networks.” NeurIPS, 2020. [Link]
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M. Monteiro, L. Le Folgoc, D. C. de Castro, N. Pawlowski, B. Marques, K. Kamnitsas, M. van der Wilk, B. Glocker. “Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty.” NeurIPS, 2020. [Link]
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W. Shi, X. Zhao, F. Chen, Q. Yu. “Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning.” NeurIPS, 2020. [Link]
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R. Krishnan, O. Tickoo. “Improving model calibration with accuracy versus uncertainty optimization.” NeurIPS, 2020. [Link]
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J. A. Leonard, M. A. Kramer, and L. H. Ungar. “A neural network architecture that computes its own reliability.” Computers & chemical engineering, 1992. [Link]
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C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra. “Weight uncertainty in neural networks.” ICML, 2015. [Link]
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B. Lakshminarayanan, A. Pritzel, and C. Blundell. “Simple and scalable predictive uncertainty estimation using deep ensembles.” NeurIPS, 2017. [Link]
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Y. Gal and Z. Ghahramani. “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.” ICML, 2016. [Link]
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V. Kuleshov, N. Fenner, and S. Ermon. “Accurate Uncertainties for Deep Learning Using Calibrated Regression.” ICML, 2018. [Link]
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J. Hernández-Lobato and R. Adams. “Probabilistic backpropagation for scalable learning of bayesian neural networks.” ICML, 2015. [Link]
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S. Liang, Y. Li, and R. Srikant. “Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.” ICLR, 2018. [Link]
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K. Lee, H. Lee, K. Lee, and J. Shin. “Training Confidence-calibrated classifiers for detecting out-of-distribution samples.” ICLR, 2018. [Link]
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P. Schulam and S. Saria “Can You Trust This Prediction? Auditing Pointwise Reliability After Learning.” AISTATS, 2019. [Link]
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A. Malinin and M. Gales. “Predictive uncertainty estimation via prior networks.” NeurIPS, 2018. [Link]
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D. Hendrycks, M. Mazeika, and T. G. Dietterich. “Deep anomaly detection with outlier exposure.” arXiv preprint arXiv:1812.04606, 2018. [Link]
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A-A. Papadopoulos, M. R. Rajati, N. Shaikh, and J. Wang. “Outlier exposure with confidence control for out-of-distribution detection.” arXiv preprint arXiv:1906.03509, 2019. [Link]
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D. Madras, J. Atwood, A. D’Amour, “Detecting Extrapolation with Influence Functions.” ICML Workshop on Uncertainty and Robustness in Deep Learning, 2019. [Link]
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M. Sensoy, L. Kaplan, and M. Kandemir. “Evidential deep learning to quantify classification uncertainty.” NeurIPS, 2018. [Link]
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W. Maddox, T. Garipov, P. Izmailov, D. Vetrov, and A. G. Wilson. “A simple baseline for bayesian uncertainty in deep learning.” arXiv preprint arXiv:1902.02476, 2019. [Link]
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Y. Ovadia, et al. “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift.” arXiv preprint arXiv:1906.02530, 2019. [Link]
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D. Hendrycks, et al. “Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty.” arXiv preprint arXiv:1906.12340, 2019. [Link]
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A. Kumar, P. Liang, T. Ma. “Verified Uncertainty Calibration.” arXiv preprint, 2019. [Link]
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I. Osband, C. Blundell, A. Pritzel, and B. Van Roy. “Deep Exploration via Bootstrapped DQN.” NeurIPS, 2016. [Link]
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I. Osband. “Risk versus Uncertainty in Deep Learning: Bayes, Bootstrap and the Dangers of Dropout.” NeurIPS Workshop, 2016. [Link]
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J. Postels et al. “Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation.” ICCV, 2019. [Link]
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A. Kendall and Y. Gal. “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” NeurIPS, 2017. [Link]
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N. Tagasovska and D. Lopez-Paz. “Single-Model Uncertainties for Deep Learning.” NeurIPS, 2019. [Link]
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A. Der Kiureghian and O. Ditlevsen. “Aleatory or Epistemic? Does it Matter?.” Structural Safety, 2009. [Link]
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D. Hafner, D. Tran, A. Irpan, T. Lillicrap, and J. Davidson. “Reliable uncertainty estimates in deep neural networks using noise contrastive priors.” arXiv, 2018. [Link]
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S. Depeweg, J. M. Hernández-Lobato, F. Doshi-Velez, and S. Udluft. “Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning.” ICML, 2018. [Link]
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L. Smith and Y. Gal, “Understanding Measures of Uncertainty for Adversarial Example Detection.” UAI, 2018. [Link]
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L. Zhu and N. Laptev. “Deep and Confident Prediction for Time series at Uber.” IEEE International Conference on Data Mining Workshops, 2017. [Link]
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M. W. Dusenberry, G. Jerfel, Y. Wen, Yi-an Ma, J. Snoek, K. Heller, B. Lakshminarayanan, D. Tran. “Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors.” arXiv, 2020. [Link]
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J. van Amersfoort, L. Smith, Y. W. Teh, and Y. Gal. “Uncertainty Estimation Using a Single Deep Deterministic Neural Network.” ICML, 2020. [Link]
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E. Begoli, T. Bhattacharya and D. Kusnezov. “The need for uncertainty quantification in machine-assisted medical decision making.” Nature Machine Intelligence, 2019. [Link]
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T. S. Salem, H. Langseth, and H. Ramampiaro. “Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles.” UAI, 2020. [Link]
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K. Posch, and J. Pilz, “Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks.” IEEE Transactions on Neural Networks and Learning Systems, 2020. [Link]
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B. Kompa, J. Snoek, and A. Beam. “Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures.” arXiv, 2020. [Link]
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