Model Monitoring and Robustness of In-Use Machine Learning Models:
Quantifying Data Distribution Shifts Using Population Stability Index

Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index

1 February 2023

Devesh Upadhyay

Papers citing "Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index"

19 / 19 papers shown

Title
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets Nikolaj Thams Michael Oberst David Sontag OOD 63 12 0 31 May 2022
Anomaly Detection in Autonomous Driving: A Survey Daniel Bogdoll Maximilian Nitsche J. Marius Zöllner 55 121 0 17 Apr 2022
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance Saurabh Garg Sivaraman Balakrishnan Zachary Chase Lipton Behnam Neyshabur Hanie Sedghi OODD OOD 62 128 0 11 Jan 2022
Robust fine-tuning of zero-shot models Mitchell Wortsman Gabriel Ilharco Jong Wook Kim Mike Li Simon Kornblith ... Raphael Gontijo-Lopes Hannaneh Hajishirzi Ali Farhadi Hongseok Namkoong Ludwig Schmidt VLM 119 724 0 04 Sep 2021
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization John Miller Rohan Taori Aditi Raghunathan Shiori Sagawa Pang Wei Koh Vaishaal Shankar Percy Liang Y. Carmon Ludwig Schmidt OODD OOD 67 276 0 09 Jul 2021
Predicting with Confidence on Unseen Distributions Devin Guillory Vaishaal Shankar Sayna Ebrahimi Trevor Darrell Ludwig Schmidt UQCV OOD 48 121 0 07 Jul 2021
Partial success in closing the gap between human and machine vision Robert Geirhos Kantharaju Narayanappa Benjamin Mitzkus Tizian Thieringer Matthias Bethge Felix Wichmann Wieland Brendel VLM AAML 69 229 0 14 Jun 2021
A Universal Law of Robustness via Isoperimetry Sébastien Bubeck Mark Sellke 38 218 0 26 May 2021
Unadversarial Examples: Designing Objects for Robust Vision Hadi Salman Andrew Ilyas Logan Engstrom Sai H. Vemprala Aleksander Madry Ashish Kapoor WIGM 100 59 0 22 Dec 2020
WILDS: A Benchmark of in-the-Wild Distribution Shifts Pang Wei Koh Shiori Sagawa Henrik Marklund Sang Michael Xie Marvin Zhang ... A. Kundaje Emma Pierson Sergey Levine Chelsea Finn Percy Liang OOD 172 1,428 0 14 Dec 2020
Learning from Noisy Labels with Deep Neural Networks: A Survey Hwanjun Song Minseok Kim Dongmin Park Yooju Shin Jae-Gil Lee NoLa 99 985 0 16 Jul 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori Achal Dave Vaishaal Shankar Nicholas Carlini Benjamin Recht Ludwig Schmidt OOD 108 546 0 01 Jul 2020
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? Angelos Filos P. Tigas R. McAllister Nicholas Rhinehart Sergey Levine Y. Gal 46 187 0 26 Jun 2020
Adversarial Machine Learning -- Industry Perspectives Ramnath Kumar Magnus Nyström J. Lambert Andrew Marshall Mario Goertzel Andi Comissoneru Matt Swann Sharon Xia AAML SILM 87 235 0 04 Feb 2020
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Yaniv Ovadia Emily Fertig Jie Jessie Ren Zachary Nado D. Sculley Sebastian Nowozin Joshua V. Dillon Balaji Lakshminarayanan Jasper Snoek UQCV 159 1,691 0 06 Jun 2019
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects Michael A. Alcorn Melvin Johnson Zhitao Gong Chengfei Wang Long Mai Naveen Ari Stella Laurenzo 82 298 0 28 Nov 2018
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift Stephan Rabanser Stephan Günnemann Zachary Chase Lipton 54 367 0 29 Oct 2018
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 452 43,277 0 11 Feb 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 269 19,045 0 20 Dec 2014