{"list":"other","total":55,"rows":[{"id":"1603.02754","title":"XGBoost: A Scalable Tree Boosting System","created":"2016-03-09","cited_by_count":5166},{"id":"1706.04599","title":"On Calibration of Modern Neural Networks","created":"2017-06-14","cited_by_count":4960},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1306.0895","title":"Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances","created":"2013-06-04","cited_by_count":2316},{"id":"1908.09635","title":"A Survey on Bias and Fairness in Machine Learning","created":"2019-08-23","cited_by_count":1917},{"id":"1912.02762","title":"Normalizing Flows for Probabilistic Modeling and Inference","created":"2019-12-05","cited_by_count":1323},{"id":"1906.02530","title":"Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift","created":"2019-06-06","cited_by_count":1287},{"id":"1803.00567","title":"Computational Optimal Transport","created":"2018-03-01","cited_by_count":780},{"id":"2110.14795","title":"MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification","created":"2021-10-27","cited_by_count":690},{"id":"2004.05785","title":"Learning under Concept Drift: A Review","created":"2020-04-13","cited_by_count":477},{"id":"1806.10758","title":"A Benchmark for Interpretability Methods in Deep Neural Networks","created":"2018-06-28","cited_by_count":476},{"id":"1710.10547","title":"Interpretation of Neural Networks is Fragile","created":"2017-10-29","cited_by_count":444},{"id":"1911.02508","title":"Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods","created":"2019-11-06","cited_by_count":443},{"id":"1802.03916","title":"Detecting and Correcting for Label Shift with Black Box Predictors","created":"2018-02-12","cited_by_count":366},{"id":"1908.08474","title":"The many Shapley values for model explanation","created":"2019-08-22","cited_by_count":344},{"id":"1901.09392","title":"On the (In)fidelity and Sensitivity for Explanations","created":"2019-01-27","cited_by_count":310},{"id":"1705.08848","title":"Joint Distribution Optimal Transportation for Domain Adaptation","created":"2017-05-24","cited_by_count":282},{"id":"1810.11953","title":"Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift","created":"2018-10-29","cited_by_count":243},{"id":"2011.14878","title":"Explaining by Removing: A Unified Framework for Model Explanation","created":"2020-11-21","cited_by_count":218},{"id":"1803.10081","title":"DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation","created":"2018-03-27","cited_by_count":214},{"id":"1909.06342","title":"Explainable Machine Learning in Deployment","created":"2019-09-13","cited_by_count":211},{"id":"1605.08527","title":"Stochastic Optimization for Large-scale Optimal Transport","created":"2016-05-27","cited_by_count":189},{"id":"2005.00631","title":"Evaluating and Aggregating Feature-based Model Explanations","created":"2020-05-01","cited_by_count":188},{"id":"2202.06861","title":"Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond","created":"2022-02-14","cited_by_count":171},{"id":"1901.05335","title":"A review of domain adaptation without target labels","created":"2019-01-16","cited_by_count":156},{"id":"2011.09926","title":"Challenges in Deploying Machine Learning: a Survey of Case Studies","created":"2020-11-18","cited_by_count":152},{"id":"2201.04234","title":"Leveraging Unlabeled Data to Predict Out-of-Distribution Performance","created":"2022-01-11","cited_by_count":145},{"id":"1906.02337","title":"MNIST-C: A Robustness Benchmark for Computer Vision","created":"2019-06-05","cited_by_count":138},{"id":"1803.05573","title":"Improving GANs Using Optimal Transport","created":"2018-03-15","cited_by_count":123},{"id":"2003.07554","title":"A Unified View of Label Shift Estimation","created":"2020-03-17","cited_by_count":119},{"id":"2106.13799","title":"Assessing Generalization of SGD via Disagreement","created":"2021-06-25","cited_by_count":117},{"id":"2101.08629","title":"Image-to-Image Translation: Methods and Applications","created":"2021-01-21","cited_by_count":116},{"id":"2107.03315","title":"Predicting with Confidence on Unseen Distributions","created":"2021-07-07","cited_by_count":116},{"id":"2207.07605","title":"Algorithms to estimate Shapley value feature attributions","created":"2022-07-15","cited_by_count":115},{"id":"2202.05594","title":"The Shapley Value in Machine Learning","created":"2022-02-11","cited_by_count":98},{"id":"1901.08949","title":"Subspace Robust Wasserstein Distances","created":"2019-01-25","cited_by_count":90},{"id":"2010.05862","title":"Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation","created":"2020-10-12","cited_by_count":87},{"id":"2110.02999","title":"Generative Modeling with Optimal Transport Maps","created":"2021-10-06","cited_by_count":82},{"id":"1807.03418","title":"AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark","created":"2018-07-09","cited_by_count":72},{"id":"2203.07835","title":"Better Uncertainty Calibration via Proper Scores for Classification and Beyond","created":"2022-03-15","cited_by_count":65},{"id":"2012.10988","title":"Post-hoc Uncertainty Calibration for Domain Drift Scenarios","created":"2020-12-20","cited_by_count":61},{"id":"2107.00643","title":"Mandoline: Model Evaluation under Distribution Shift","created":"2021-07-01","cited_by_count":55},{"id":"1906.10827","title":"Hierarchical Optimal Transport for Document Representation","created":"2019-06-26","cited_by_count":53},{"id":"2202.07477","title":"Understanding DDPM Latent Codes Through Optimal Transport","created":"2022-02-14","cited_by_count":45},{"id":"2003.05155","title":"Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology","created":"2020-03-11","cited_by_count":41},{"id":"2102.13384","title":"Why did the distribution change?","created":"2021-02-26","cited_by_count":37},{"id":"2012.10923","title":"Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration","created":"2020-12-20","cited_by_count":34},{"id":"1806.09277","title":"Towards Optimal Transport with Global Invariances","created":"2018-06-25","cited_by_count":33},{"id":"2103.16886","title":"Neural Response Interpretation through the Lens of Critical Pathways","created":"2021-03-31","cited_by_count":32},{"id":"2006.08161","title":"Optimal Transport for Conditional Domain Matching and Label Shift","created":"2020-06-15","cited_by_count":20},{"id":"1910.03860","title":"Spatio-Temporal Alignments: Optimal transport through space and time","created":"2019-10-09","cited_by_count":17},{"id":"2111.13657","title":"Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models","created":"2021-11-26","cited_by_count":16},{"id":"2302.04065","title":"Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps","created":"2023-02-08","cited_by_count":16},{"id":"2206.02868","title":"A Human-Centric Take on Model Monitoring","created":"2022-06-06","cited_by_count":9},{"id":"2210.12369","title":"Explanation Shift: Detecting distribution shifts on tabular data via the explanation space","created":"2022-10-22","cited_by_count":7}],"next_cursor":null}