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Provably Robust Detection of Out-of-distribution Data (almost) for free
Jasper Snoek · Balaji Lakshminarayanan
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Jasper Snoek (Google Brain)
Balaji Lakshminarayanan (Google Brain)
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2021 : A simple fix to Mahalanobis distance for improving near-OOD detection »
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2021 : Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data »
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2021 : Precise characterization of the prior predictive distribution of deep ReLU networks »
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2021 : Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect »
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2021 : Exploring the Limits of Out-of-Distribution Detection »
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2021 : Repulsive Deep Ensembles are Bayesian »
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2021 : Calibrated Out-of-Distribution Detection with Conformal P-values »
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2021 : Are Bayesian neural networks intrinsically good at out-of-distribution detection? »
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2021 : Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results »
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2021 : Rethinking Assumptions in Deep Anomaly Detection »
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2021 : Multiple Moment Matching Inference: A Flexible Approximate Inference Algorithm »
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2021 : PAC Prediction Sets Under Covariate Shift »
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2021 : Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations »
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2021 : Do We Really Need to Learn Representations from In-domain Data for Outlier Detection? »
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2021 : DATE: Detecting Anomalies in Text via Self-Supervision of Transformers »
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2021 : Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification »
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2021 : Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification »
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2021 : Implicit Ensemble Training for Efficient and Robust Multiagent Reinforcement Learning »
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2021 : Failures of Uncertainty Estimation on Out-Of-Distribution Samples: Experimental Results from Medical Applications Lead to Theoretical Insights »
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2021 : On Out-of-distribution Detection with Energy-Based Models »
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2021 : Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty »
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2021 : Transfer and Marginalize: Explaining Away Label Noise with Privileged Information »
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2021 : Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error »
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2021 : Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes »
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2021 : Towards improving robustness of compressed CNNs »
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2021 : SAND-mask: An Enhanced Gradient Masking Strategy for Invariant Prediction in Domain Generalization »
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2021 : Efficient Gaussian Neural Processes for Regression »
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2021 : Simple, Attack-Agnostic Defense Against Targeted Training Set Attacks Using Cosine Similarity »
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2021 : Safety & Exploration: A Comparative Study of Uses of Uncertainty in Reinforcement Learning »
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2021 : Rethinking Function-Space Variational Inference in Bayesian Neural Networks »
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2021 : Understanding the Under-Coverage Bias in Uncertainty Estimation »
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2021 : BETH Dataset: Real Cybersecurity Data for Anomaly Detection Research »
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2021 : Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations »
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2021 : Deep Ensemble Uncertainty Fails as Network Width Increases: Why, and How to Fix It »
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2021 : Exact and Efficient Adversarial Robustness with Decomposable Neural Networks »
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2021 : Consistency Regularization for Training Confidence-Calibrated Classifiers »
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2021 : Diverse and Amortised Counterfactual Explanations for Uncertainty Estimates »
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2021 : Quantization of Bayesian neural networks and its effect on quality of uncertainty »
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2021 : Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition »
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2021 : Bayesian Neural Networks with Soft Evidence »
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2021 : Anomaly Detection for Event Data with Temporal Point Processes »
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2021 : Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression »
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2021 : An Empirical Study of Invariant Risk Minimization on Deep Models »
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2021 : A Bayesian Approach to Invariant Deep Neural Networks »
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2021 : Practical posterior Laplace approximation with optimization-driven second moment estimation »
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2021 : Variational Generative Flows for Reconstruction Uncertainty Estimation »
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2021 : Improving the Accuracy-Robustness Trade-Off for Dual-Domain Adversarial Training »
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2021 : Consistency Regularization Can Improve Robustness to Label Noise »
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2021 : Neural Variational Gradient Descent »
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2021 : Evaluating the Use of Reconstruction Error for Novelty Localization »
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2021 : Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization »
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2021 : The Hidden Uncertainty in a Neural Network’s Activations »
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2021 : On the Calibration of Deterministic Epistemic Uncertainty »
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2021 : Objective Robustness in Deep Reinforcement Learning »
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2021 : Epistemic Uncertainty in Learning Chaotic Dynamical Systems »
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2021 : Towards Stochastic Neural Networks via Inductive Wasserstein Embeddings »
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2021 : Distribution-free uncertainty quantification for classification under label shift »
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2021 : How does a Neural Network's Architecture Impact its Robustness to Noisy Labels? »
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2021 : Top-label calibration »
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2021 : Learning to Align the Support of Distributions »
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2021 : Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition »
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2021 : Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective »
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2021 : Contrastive Predictive Coding for Anomaly Detection and Segmentation »
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2021 : Multi-headed Neural Ensemble Search »
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2021 : A variational approximate posterior for the deep Wishart process »
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2021 : What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel »
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2021 : On Stein Variational Neural Network Ensembles »
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2021 : Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings »
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2021 : RouBL: A computationally cheap way to go beyond mean-field variational inference »
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2021 : No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets »
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2021 : Out-of-Distribution Generalization with Deep Equilibrium Models »
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2021 : Mixture Proportion Estimation and PU Learning: A Modern Approach »
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2021 : On The Dark Side Of Calibration For Modern Neural Networks »
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2021 : Domain Adaptation with Factorizable Joint Shift »
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2021 : Scaling Laws for the Out-of-Distribution Generalization of Image Classifiers »
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2021 : Learning Invariant Weights in Neural Networks »
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2021 : Relational Deep Reinforcement Learning and Latent Goals for Following Instructions in Temporal Logic »
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2021 : On the Effectiveness of Mode Exploration in Bayesian Model Averaging for Neural Networks »
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2021 : Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate »
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2021 : Detecting OODs as datapoints with High Uncertainty »
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2021 : Multi-task Transformation Learning for Robust Out-of-Distribution Detection »
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2021 : Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data »
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2021 : Deep Learning with Quantified Uncertainty for Free Electron Laser Scientific Facilities »
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2021 : On the reversed bias-variance tradeoff in deep ensembles »
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2021 : Robust Generalization of Quadratic Neural Networks via Function Identification »
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2021 : Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers »
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2021 : Deep Random Projection Outlyingness for Unsupervised Anomaly Detection »
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2021 : Deep Deterministic Uncertainty for Semantic Segmentation »
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2021 : Identifying Invariant and Sparse Predictors in High-dimensional Data »
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2021 : On Misclassification-Aware Smoothing for Robustness and Uncertainty Calibration »
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2021 : On Pitfalls in OoD Detection: Entropy Considered Harmful »
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2021 : PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation »
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2021 : Augmented Invariant Regularization »
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2021 : Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data »
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2021 : Improved Adversarial Robustness via Uncertainty Targeted Attacks »
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2021 : Notes on the Behavior of MC Dropout »
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2021 : Distribution-free Risk-controlling Prediction Sets »
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2021 : Stochastic Bouncy Particle Sampler for Bayesian Neural Networks »
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2021 : Novelty detection using ensembles with regularized disagreement »
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2021 : A Tale Of Two Long Tails »
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2021 : Defending against Adversarial Patches with Robust Self-Attention »
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2021 : Intrinsic uncertainties and where to find them »
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2021 : Dataset to Dataspace: A Topological-Framework to Improve Analysis of Machine Learning Model Performance »
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2021 : Analyzing And Improving Neural Networks By Generating Semantic Counterexamples Through Differentiable Rendering »
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2021 : Thinkback: Task-Specific Out-of-Distribution Detection »
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2021 : Relating Adversarially Robust Generalization to Flat Minima »
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2021 : Deep Quantile Aggregation »
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2021 : What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel »
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2022 : Plex: Towards Reliability using Pretrained Large Model Extensions »
Dustin Tran · Andreas Kirsch · Balaji Lakshminarayanan · Huiyi Hu · Du Phan · D. Sculley · Jasper Snoek · Jeremiah Liu · Jie Ren · Joost van Amersfoort · Kehang Han · E. Kelly Buchanan · Kevin Murphy · Mark Collier · Mike Dusenberry · Neil Band · Nithum Thain · Rodolphe Jenatton · Tim G. J Rudner · Yarin Gal · Zachary Nado · Zelda Mariet · Zi Wang · Zoubin Ghahramani -
2022 : Plex: Towards Reliability using Pretrained Large Model Extensions »
Dustin Tran · Andreas Kirsch · Balaji Lakshminarayanan · Huiyi Hu · Du Phan · D. Sculley · Jasper Snoek · Jeremiah Liu · JIE REN · Joost van Amersfoort · Kehang Han · Estefany Kelly Buchanan · Kevin Murphy · Mark Collier · Michael Dusenberry · Neil Band · Nithum Thain · Rodolphe Jenatton · Tim G. J Rudner · Yarin Gal · Zachary Nado · Zelda Mariet · Zi Wang · Zoubin Ghahramani -
2022 : Plex: Towards Reliability using Pretrained Large Model Extensions »
Dustin Tran · Andreas Kirsch · Balaji Lakshminarayanan · Huiyi Hu · Du Phan · D. Sculley · Jasper Snoek · Jeremiah Liu · JIE REN · Joost van Amersfoort · Kehang Han · Estefany Kelly Buchanan · Kevin Murphy · Mark Collier · Michael Dusenberry · Neil Band · Nithum Thain · Rodolphe Jenatton · Tim G. J Rudner · Yarin Gal · Zachary Nado · Zelda Mariet · Zi Wang · Zoubin Ghahramani -
2021 Workshop: Uncertainty and Robustness in Deep Learning »
Balaji Lakshminarayanan · Dan Hendrycks · Yixuan Li · Jasper Snoek · Silvia Chiappa · Sebastian Nowozin · Thomas Dietterich -
2021 : Welcome »
Balaji Lakshminarayanan -
2020 Workshop: Uncertainty and Robustness in Deep Learning Workshop (UDL) »
Sharon Yixuan Li · Balaji Lakshminarayanan · Dan Hendrycks · Thomas Dietterich · Jasper Snoek -
2020 Poster: Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors »
Mike Dusenberry · Ghassen Jerfel · Yeming Wen · Yian Ma · Jasper Snoek · Katherine Heller · Balaji Lakshminarayanan · Dustin Tran -
2019 Workshop: Uncertainty and Robustness in Deep Learning »
Sharon Yixuan Li · Dan Hendrycks · Thomas Dietterich · Balaji Lakshminarayanan · Justin Gilmer -
2019 Poster: Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems »
Timothy Mann · Sven Gowal · András György · Huiyi Hu · Ray Jiang · Balaji Lakshminarayanan · Prav Srinivasan -
2019 Oral: Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems »
Timothy Mann · Sven Gowal · András György · Huiyi Hu · Ray Jiang · Balaji Lakshminarayanan · Prav Srinivasan -
2019 Oral: Hybrid Models with Deep and Invertible Features »
Eric Nalisnick · Akihiro Matsukawa · Yee-Whye Teh · Dilan Gorur · Balaji Lakshminarayanan -
2019 Poster: Hybrid Models with Deep and Invertible Features »
Eric Nalisnick · Akihiro Matsukawa · Yee-Whye Teh · Dilan Gorur · Balaji Lakshminarayanan -
2017 Workshop: Implicit Generative Models »
Rajesh Ranganath · Ian Goodfellow · Dustin Tran · David Blei · Balaji Lakshminarayanan · Shakir Mohamed