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Predictive Churn with the Set of Good Models

12 February 2024

Papers citing "Predictive Churn with the Set of Good Models"

50 / 57 papers shown

Title
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance J. Donnelly Srikar Katta Cynthia Rudin E. Browne FAtt 66 15 0 24 Sep 2023
On The Impact of Machine Learning Randomness on Group Fairness Prakhar Ganesh Hong Chang Martin Strobel Reza Shokri FaML 58 30 0 09 Jul 2023
Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints J. Watson-Daniels Solon Barocas Jake M. Hofman Alexandra Chouldechova 52 11 0 23 Jun 2023
Arbitrariness Lies Beyond the Fairness-Accuracy Frontier Carol Xuan Long Hsiang Hsu Wael Alghamdi Flavio du Pin Calmon FaML 52 7 0 15 Jun 2023
The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions Anna P. Meyer Aws Albarghouthi Loris Dántoni 67 13 0 20 Apr 2023
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models Chudi Zhong Zhi Chen Jiachang Liu Margo Seltzer Cynthia Rudin 64 11 0 28 Mar 2023
Arbitrary Decisions are a Hidden Cost of Differentially Private Training B. Kulynych Hsiang Hsu Carmela Troncoso Flavio du Pin Calmon 71 18 0 28 Feb 2023
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach Dennis L. Wei Rahul Nair Amit Dhurandhar Kush R. Varshney Elizabeth M. Daly Moninder Singh FAtt 65 9 0 02 Nov 2022
Plex: Towards Reliability using Pretrained Large Model Extensions Dustin Tran J. Liu Michael W. Dusenberry Du Phan Mark Collier ... D. Sculley Y. Gal Zoubin Ghahramani Jasper Snoek Balaji Lakshminarayanan VLM 108 126 0 15 Jul 2022
Rashomon Capacity: A Metric for Predictive Multiplicity in Classification Hsiang Hsu Flavio du Pin Calmon 45 40 0 02 Jun 2022
Predictive Multiplicity in Probabilistic Classification J. Watson-Daniels David C. Parkes Berk Ustun 56 40 0 02 Jun 2022
Measuring and Reducing Model Update Regression in Structured Prediction for NLP Deng Cai Elman Mansimov Yi-An Lai Yixuan Su Lei Shu Yi Zhang KELM 101 9 0 07 Feb 2022
Backward-Compatible Prediction Updates: A Probabilistic Approach Frederik Trauble Julius von Kügelgen Matthäus Kleindessner Francesco Locatello Bernhard Schölkopf Peter V. Gehler 80 16 0 02 Jul 2021
Churn Reduction via Distillation Heinrich Jiang Harikrishna Narasimhan Dara Bahri Andrew Cotter Afshin Rostamizadeh 121 15 0 04 Jun 2021
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests Victor Veitch Alexander DÁmour Steve Yadlowsky Jacob Eisenstein OOD 55 93 0 31 May 2021
Enhanced Isotropy Maximization Loss: Seamless and High-Performance Out-of-Distribution Detection Simply Replacing the SoftMax Loss David Macêdo Teresa B Ludermir OODD 78 13 0 30 May 2021
Accounting for Model Uncertainty in Algorithmic Discrimination Junaid Ali Adish Singla Krishna P. Gummadi FaML 75 21 0 10 May 2021
Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates Yuqing Xie Yi-An Lai Yuanjun Xiong Yi Zhang Stefano Soatto UQCV 40 16 0 07 May 2021
Locally Adaptive Label Smoothing for Predictive Churn Dara Bahri Heinrich Jiang NoLa 57 8 0 09 Feb 2021
Hyperparameter Optimization Is Deceiving Us, and How to Stop It A. Feder Cooper Yucheng Lu Jessica Zosa Forde Christopher De Sa 45 32 0 05 Feb 2021
Characterizing Fairness Over the Set of Good Models Under Selective Labels Amanda Coston Ashesh Rambachan Alexandra Chouldechova FaML 93 85 0 02 Jan 2021
Continual Lifelong Learning in Natural Language Processing: A Survey Magdalena Biesialska Katarzyna Biesialska Marta R. Costa-jussá KELM CLL 86 220 0 17 Dec 2020
Positive-Congruent Training: Towards Regression-Free Model Updates Sijie Yan Yuanjun Xiong Kaustav Kundu Shuo Yang Siqi Deng Meng Wang Wei Xia Stefano Soatto BDL 67 53 0 18 Nov 2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander DÁmour Katherine A. Heller D. Moldovan Ben Adlam B. Alipanahi ... Kellie Webster Steve Yadlowsky T. Yun Xiaohua Zhai D. Sculley OffRL 117 687 0 06 Nov 2020
Class-incremental learning: survey and performance evaluation on image classification Marc Masana Xialei Liu Bartlomiej Twardowski Mikel Menta Andrew D. Bagdanov Joost van de Weijer CLL 79 692 0 28 Oct 2020
Characterising Bias in Compressed Models Sara Hooker Nyalleng Moorosi Gregory Clark Samy Bengio Emily L. Denton 67 185 0 06 Oct 2020
On Counterfactual Explanations under Predictive Multiplicity Martin Pawelczyk Klaus Broelemann Gjergji Kasneci 126 86 0 23 Jun 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Zhe Liu Zi Lin Shreyas Padhy Dustin Tran Tania Bedrax-Weiss Balaji Lakshminarayanan UQCV BDL 173 451 0 17 Jun 2020
Towards Backward-Compatible Representation Learning Yantao Shen Yuanjun Xiong Wei Xia Stefano Soatto 61 81 0 26 Mar 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 86 287 0 24 Feb 2020
Deep Double Descent: Where Bigger Models and More Data Hurt Preetum Nakkiran Gal Kaplun Yamini Bansal Tristan Yang Boaz Barak Ilya Sutskever 121 942 0 04 Dec 2019
On Empirical Comparisons of Optimizers for Deep Learning Dami Choi Christopher J. Shallue Zachary Nado Jaehoon Lee Chris J. Maddison George E. Dahl 78 260 0 11 Oct 2019
Predictive Multiplicity in Classification Charles Marx Flavio du Pin Calmon Berk Ustun 123 145 0 14 Sep 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 91 635 0 14 Aug 2019
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning Sebastian Farquhar Michael A. Osborne Y. Gal UQCV BDL 56 57 0 01 Jul 2019
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 170 1,695 0 06 Jun 2019
Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models Jiayun Dong Cynthia Rudin FAtt 45 26 0 10 Jan 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 240 1,650 0 28 Dec 2018
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals Andrew Cotter Heinrich Jiang S. Wang Taman Narayan Maya R. Gupta Seungil You Karthik Sridharan 79 157 0 11 Sep 2018
Knowledge Distillation by On-the-Fly Native Ensemble Xu Lan Xiatian Zhu S. Gong 290 479 0 12 Jun 2018
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy Lance M. Kaplan M. Kandemir OOD UQCV EDL BDL 182 996 0 05 Jun 2018
Collaborative Learning for Deep Neural Networks Guocong Song Wei Chai FedML 43 196 0 30 May 2018
Large scale distributed neural network training through online distillation Rohan Anil Gabriel Pereyra Alexandre Passos Róbert Ormándi George E. Dahl Geoffrey E. Hinton FedML 320 408 0 09 Apr 2018
Predictive Uncertainty Estimation via Prior Networks A. Malinin Mark Gales UD BDL EDL UQCV PER 191 920 0 28 Feb 2018
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling C. Riquelme George Tucker Jasper Snoek BDL 74 366 0 26 Feb 2018
Continual Lifelong Learning with Neural Networks: A Review G. I. Parisi Ronald Kemker Jose L. Part Christopher Kanan S. Wermter KELM CLL 193 2,888 0 21 Feb 2018
DOC: Deep Open Classification of Text Documents Lei Shu Hu Xu Bing-Quan Liu VLM 45 301 0 25 Sep 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 299 5,833 0 14 Jun 2017
Deep Mutual Learning Ying Zhang Tao Xiang Timothy M. Hospedales Huchuan Lu FedML 151 1,653 0 01 Jun 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 840 5,821 0 05 Dec 2016