{"list":"other","total":79,"rows":[{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1703.04977","title":"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?","created":"2017-03-15","cited_by_count":2352},{"id":"2004.07780","title":"Shortcut Learning in Deep Neural Networks","created":"2020-04-16","cited_by_count":1969},{"id":"1910.09457","title":"Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods","created":"2019-10-21","cited_by_count":913},{"id":"2002.08347","title":"On Adaptive Attacks to Adversarial Example Defenses","created":"2020-02-19","cited_by_count":555},{"id":"2011.03395","title":"Underspecification Presents Challenges for Credibility in Modern Machine Learning","created":"2020-11-06","cited_by_count":496},{"id":"1902.06705","title":"On Evaluating Adversarial Robustness","created":"2019-02-18","cited_by_count":447},{"id":"1801.01489","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","created":"2018-01-04","cited_by_count":373},{"id":"2103.11251","title":"Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges","created":"2021-03-20","cited_by_count":367},{"id":"1912.05511","title":"Measurement and Fairness","created":"2019-12-11","cited_by_count":308},{"id":"2103.03098","title":"Accounting for Variance in Machine Learning Benchmarks","created":"2021-03-01","cited_by_count":201},{"id":"2111.05328","title":"Data Augmentation Can Improve Robustness","created":"2021-11-09","cited_by_count":194},{"id":"1902.06977","title":"Evaluating model calibration in classification","created":"2019-02-19","cited_by_count":168},{"id":"1909.06677","title":"Predictive Multiplicity in Classification","created":"2019-09-14","cited_by_count":166},{"id":"2109.14545","title":"Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark","created":"2021-09-29","cited_by_count":155},{"id":"2303.00654","title":"How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy","created":"2023-03-01","cited_by_count":153},{"id":"1705.06270","title":"A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications","created":"2017-05-17","cited_by_count":129},{"id":"2207.07068","title":"Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey","created":"2022-07-14","cited_by_count":125},{"id":"2106.16163","title":"The MultiBERTs: BERT Reproductions for Robustness Analysis","created":"2021-06-30","cited_by_count":92},{"id":"2211.13972","title":"Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?","created":"2022-11-25","cited_by_count":91},{"id":"1911.02969","title":"BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance","created":"2019-11-07","cited_by_count":89},{"id":"2109.08203","title":"Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision","created":"2021-09-16","cited_by_count":81},{"id":"2209.08040","title":"Exploring the Whole Rashomon Set of Sparse Decision Trees","created":"2022-09-16","cited_by_count":81},{"id":"2010.01279","title":"Do Wider Neural Networks Really Help Adversarial Robustness?","created":"2020-10-03","cited_by_count":79},{"id":"2007.04131","title":"General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models","created":"2020-07-08","cited_by_count":75},{"id":"2101.00352","title":"Characterizing Fairness Over the Set of Good Models Under Selective Labels","created":"2021-01-02","cited_by_count":65},{"id":"2203.08124","title":"Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective","created":"2022-03-15","cited_by_count":57},{"id":"2407.04846","title":"Amazing Things Come From Having Many Good Models","created":"2024-07-05","cited_by_count":55},{"id":"2102.12594","title":"Directional Bias Amplification","created":"2021-02-24","cited_by_count":54},{"id":"2206.01295","title":"Rashomon Capacity: A Metric for Predictive Multiplicity in Classification","created":"2022-06-02","cited_by_count":47},{"id":"2305.11997","title":"Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees","created":"2023-05-19","cited_by_count":45},{"id":"2012.11788","title":"Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shifts","created":"2020-12-22","cited_by_count":36},{"id":"2310.19726","title":"A Path to Simpler Models Starts With Noise","created":"2023-10-30","cited_by_count":33},{"id":"2301.11562","title":"Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification","created":"2023-01-27","cited_by_count":32},{"id":"2309.13775","title":"The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance","created":"2023-09-24","cited_by_count":29},{"id":"1702.08540","title":"Active Learning Using Uncertainty Information","created":"2017-02-27","cited_by_count":28},{"id":"2307.04138","title":"On The Impact of Machine Learning Randomness on Group Fairness","created":"2023-07-09","cited_by_count":26},{"id":"2306.15786","title":"An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning","created":"2023-06-27","cited_by_count":25},{"id":"2005.00497","title":"The Grammar of Interactive Explanatory Model Analysis","created":"2020-05-01","cited_by_count":24},{"id":"2111.08230","title":"Selective Ensembles for Consistent Predictions","created":"2021-11-16","cited_by_count":23},{"id":"2210.05846","title":"FasterRisk: Fast and Accurate Interpretable Risk Scores","created":"2022-10-12","cited_by_count":21},{"id":"2102.03034","title":"Hyperparameter Optimization Is Deceiving Us, and How to Stop It","created":"2021-02-05","cited_by_count":20},{"id":"2402.13914","title":"Position: Explain to Question not to Justify","created":"2024-02-21","cited_by_count":20},{"id":"1901.03209","title":"Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models","created":"2019-01-10","cited_by_count":19},{"id":"2302.14517","title":"Arbitrary Decisions are a Hidden Cost of Differentially Private Training","created":"2023-02-28","cited_by_count":19},{"id":"2105.04273","title":"Loss-Aversively Fair Classification","created":"2021-05-10","cited_by_count":18},{"id":"2402.00728","title":"Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation","created":"2024-02-01","cited_by_count":18},{"id":"2304.10655","title":"The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions","created":"2023-04-20","cited_by_count":17},{"id":"2402.07745","title":"Predictive Churn with the Set of Good Models","created":"2024-02-12","cited_by_count":15},{"id":"1407.1097","title":"Robust Optimization using Machine Learning for Uncertainty Sets","created":"2014-07-04","cited_by_count":14},{"id":"2312.01082","title":"A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness","created":"2023-12-02","cited_by_count":14},{"id":"1804.08646","title":"A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results","created":"2018-04-23","cited_by_count":13},{"id":"2103.03462","title":"Forward Stability and Model Path Selection","created":"2021-03-05","cited_by_count":12},{"id":"2110.13369","title":"Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set","created":"2021-10-26","cited_by_count":12},{"id":"2204.11285","title":"Computing the Collection of Good Models for Rule Lists","created":"2022-04-24","cited_by_count":12},{"id":"2308.16681","title":"One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions","created":"2023-08-31","cited_by_count":12},{"id":"2308.11446","title":"Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data","created":"2023-08-22","cited_by_count":11},{"id":"2405.01557","title":"An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification","created":"2024-03-22","cited_by_count":11},{"id":"2302.13356","title":"Performance is not enough: the story told by a Rashomon quartet","created":"2023-02-26","cited_by_count":9},{"id":"2203.07139","title":"Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity","created":"2022-03-14","cited_by_count":8},{"id":"2104.00606","title":"Model Selection's Disparate Impact in Real-World Deep Learning Applications","created":"2021-04-01","cited_by_count":7},{"id":"2206.04179","title":"Automating Ambiguity: Challenges and Pitfalls of Artificial Intelligence","created":"2022-06-08","cited_by_count":7},{"id":"2206.11834","title":"Non-Determinism and the Lawlessness of Machine Learning Code","created":"2022-06-23","cited_by_count":7},{"id":"2311.14859","title":"An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification","created":"2023-11-24","cited_by_count":7},{"id":"2404.08592","title":"Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized","created":"2024-04-12","cited_by_count":7},{"id":"2409.12332","title":"Perceptions of the Fairness Impacts of Multiplicity in Machine Learning","created":"2024-09-18","cited_by_count":7},{"id":"2104.10150","title":"Bayesian subset selection and variable importance for interpretable prediction and classification","created":"2021-04-20","cited_by_count":6},{"id":"2206.08890","title":"Disentangling Model Multiplicity in Deep Learning","created":"2022-06-17","cited_by_count":5},{"id":"2305.10181","title":"Exploring the cloud of feature interaction scores in a Rashomon set","created":"2023-05-17","cited_by_count":5},{"id":"2401.08513","title":"X Hacking: The Threat of Misguided AutoML","created":"2024-01-16","cited_by_count":5},{"id":"2405.19667","title":"Reconciling Model Multiplicity for Downstream Decision Making","created":"2024-05-30","cited_by_count":5},{"id":"2411.10490","title":"AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making","created":"2024-11-14","cited_by_count":4},{"id":"2407.18482","title":"Practical Attribution Guidance for Rashomon Sets","created":"2024-07-26","cited_by_count":3},{"id":"2411.11101","title":"Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML","created":"2024-11-17","cited_by_count":3},{"id":"2303.00984","title":"Encoding of data sets and algorithms","created":"2023-03-02","cited_by_count":2},{"id":"2303.07272","title":"Inventing a coin-flip classifier: Accounting for multiplicity in machine learning benchmark performance","created":"2023-03-10","cited_by_count":2},{"id":"2308.07247","title":"Can we Agree? On the Rash\\=omon Effect and the Reliability of Post-Hoc Explainable AI","created":"2023-08-14","cited_by_count":2},{"id":"2404.17746","title":"On the Rashomon ratio of infinite hypothesis sets","created":"2024-04-27","cited_by_count":2},{"id":"2406.09548","title":"Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale","created":"2024-06-13","cited_by_count":1}],"next_cursor":null}