{"list":"other","total":73,"rows":[{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"id":"1411.1784","title":"Conditional Generative Adversarial Nets","created":"2014-11-06","cited_by_count":2162},{"id":"2002.02126","title":"LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation","created":"2020-02-06","cited_by_count":1139},{"id":"1706.03825","title":"SmoothGrad: removing noise by adding noise","created":"2017-06-12","cited_by_count":1109},{"id":"1711.00399","title":"Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR","created":"2017-11-01","cited_by_count":1089},{"id":"1806.07421","title":"RISE: Randomized Input Sampling for Explanation of Black-box Models","created":"2018-06-19","cited_by_count":838},{"id":"1703.06856","title":"Counterfactual Fairness","created":"2017-03-20","cited_by_count":804},{"id":"1809.06514","title":"Actionable Recourse in Linear Classification","created":"2018-09-18","cited_by_count":328},{"id":"1810.00069","title":"Adversarial Attacks and Defences: A Survey","created":"2018-09-28","cited_by_count":259},{"id":"2002.06278","title":"Algorithmic Recourse: from Counterfactual Explanations to Interventions","created":"2020-02-14","cited_by_count":250},{"id":"1802.07814","title":"Learning to Explain: An Information-Theoretic Perspective on Model Interpretation","created":"2018-02-21","cited_by_count":248},{"id":"1705.07857","title":"Real Time Image Saliency for Black Box Classifiers","created":"2017-05-22","cited_by_count":246},{"id":"1808.00033","title":"Techniques for Interpretable Machine Learning","created":"2018-07-31","cited_by_count":238},{"id":"1907.02584","title":"Interpretable Counterfactual Explanations Guided by Prototypes","created":"2019-07-03","cited_by_count":223},{"id":"1905.11190","title":"Model-Agnostic Counterfactual Explanations for Consequential Decisions","created":"2019-05-27","cited_by_count":204},{"id":"2005.00631","title":"Evaluating and Aggregating Feature-based Model Explanations","created":"2020-05-01","cited_by_count":188},{"id":"1807.05358","title":"Beyond Data and Model Parallelism for Deep Neural Networks","created":"2018-07-14","cited_by_count":179},{"id":"2004.11165","title":"Multi-Objective Counterfactual Explanations","created":"2020-04-23","cited_by_count":157},{"id":"2012.01536","title":"Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression","created":"2020-12-02","cited_by_count":154},{"id":"1910.09398","title":"Learning Model-Agnostic Counterfactual Explanations for Tabular Data","created":"2019-10-21","cited_by_count":146},{"id":"1912.03277","title":"Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers","created":"2019-12-06","cited_by_count":144},{"id":"1902.05622","title":"The Shapley Taylor Interaction Index","created":"2019-02-14","cited_by_count":143},{"id":"2006.06831","title":"Algorithmic recourse under imperfect causal knowledge: a probabilistic approach","created":"2020-06-11","cited_by_count":140},{"id":"1907.09615","title":"Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems","created":"2019-07-22","cited_by_count":131},{"id":"2008.05030","title":"Reliable Post hoc Explanations: Modeling Uncertainty in Explainability","created":"2020-08-11","cited_by_count":125},{"id":"2107.07436","title":"FastSHAP: Real-Time Shapley Value Estimation","created":"2021-07-15","cited_by_count":125},{"id":"1908.08843","title":"Fairness in Deep Learning: A Computational Perspective","created":"2019-08-23","cited_by_count":123},{"id":"1903.10992","title":"Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation","created":"2019-03-26","cited_by_count":115},{"id":"1901.04909","title":"Efficient Search for Diverse Coherent Explanations","created":"2019-01-02","cited_by_count":111},{"id":"2104.12199","title":"Sampling Permutations for Shapley Value Estimation","created":"2021-04-25","cited_by_count":102},{"id":"1808.02610","title":"L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data","created":"2018-08-08","cited_by_count":100},{"id":"1910.12336","title":"CXPlain: Causal Explanations for Model Interpretation under Uncertainty","created":"2019-10-27","cited_by_count":98},{"id":"2006.06848","title":"Getting a CLUE: A Method for Explaining Uncertainty Estimates","created":"2020-06-11","cited_by_count":92},{"id":"1705.04977","title":"Detecting Statistical Interactions from Neural Network Weights","created":"2017-05-14","cited_by_count":80},{"id":"2002.12334","title":"A Distributional Framework for Data Valuation","created":"2020-02-27","cited_by_count":80},{"id":"1911.00483","title":"Explanation by Progressive Exaggeration","created":"2019-11-01","cited_by_count":76},{"id":"2006.10965","title":"How does this interaction affect me? Interpretable attribution for feature interactions","created":"2020-06-19","cited_by_count":73},{"id":"1509.01644","title":"Reinforcement Learning with Parameterized Actions","created":"2015-09-05","cited_by_count":63},{"id":"2103.01890","title":"Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations","created":"2021-03-02","cited_by_count":59},{"id":"2103.10226","title":"Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations","created":"2021-03-18","cited_by_count":56},{"id":"2203.06877","title":"Rethinking Stability for Attribution-based Explanations","created":"2022-03-14","cited_by_count":52},{"id":"2210.08443","title":"CLEAR: Generative Counterfactual Explanations on Graphs","created":"2022-10-16","cited_by_count":48},{"id":"2206.05282","title":"Learning to Estimate Shapley Values with Vision Transformers","created":"2022-06-10","cited_by_count":45},{"id":"2104.02297","title":"Shapley Explanation Networks","created":"2021-04-06","cited_by_count":41},{"id":"2106.06631","title":"Optimal Counterfactual Explanations in Tree Ensembles","created":"2021-06-11","cited_by_count":36},{"id":"2111.07668","title":"Fast Axiomatic Attribution for Neural Networks","created":"2021-11-15","cited_by_count":35},{"id":"1905.07857","title":"CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models","created":"2019-05-20","cited_by_count":34},{"id":"2203.15064","title":"Cycle-Consistent Counterfactuals by Latent Transformations","created":"2022-03-28","cited_by_count":33},{"id":"2006.10966","title":"Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection","created":"2020-06-19","cited_by_count":32},{"id":"2109.09847","title":"Fast TreeSHAP: Accelerating SHAP Value Computation for Trees","created":"2021-09-20","cited_by_count":29},{"id":"2110.11960","title":"ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models","created":"2021-10-22","cited_by_count":25},{"id":"2106.03962","title":"Amortized Generation of Sequential Algorithmic Recourses for Black-box Models","created":"2021-06-07","cited_by_count":24},{"id":"2112.02797","title":"ML Attack Models: Adversarial Attacks and Data Poisoning Attacks","created":"2021-12-06","cited_by_count":24},{"id":"2010.04965","title":"Scaling Guarantees for Nearest Counterfactual Explanations","created":"2020-10-10","cited_by_count":22},{"id":"2110.14270","title":"Counterfactual Shapley Additive Explanations","created":"2021-10-27","cited_by_count":22},{"id":"1806.08867","title":"xGEMs: Generating Examplars to Explain Black-Box Models","created":"2018-06-22","cited_by_count":21},{"id":"2106.12228","title":"groupShapley: Efficient prediction explanation with Shapley values for feature groups","created":"2021-06-23","cited_by_count":21},{"id":"2212.10847","title":"VCNet: A self-explaining model for realistic counterfactual generation","created":"2022-12-21","cited_by_count":20},{"id":"1810.09656","title":"Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space","created":"2018-10-23","cited_by_count":15},{"id":"1812.01280","title":"Learning to Explain with Complemental Examples","created":"2018-12-04","cited_by_count":15},{"id":"2103.01096","title":"Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms","created":"2021-03-01","cited_by_count":14},{"id":"2106.08971","title":"Model-Based Counterfactual Synthesizer for Interpretation","created":"2021-06-16","cited_by_count":14},{"id":"2206.08529","title":"Accelerating Shapley Explanation via Contributive Cooperator Selection","created":"2022-06-17","cited_by_count":13},{"id":"1807.00400","title":"Antithetic and Monte Carlo kernel estimators for partial rankings","created":"2018-07-01","cited_by_count":11},{"id":"2101.06930","title":"Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation","created":"2021-01-18","cited_by_count":11},{"id":"2103.15429","title":"Efficient Explanations from Empirical Explainers","created":"2021-03-29","cited_by_count":9},{"id":"2012.05773","title":"Influence-Driven Explanations for Bayesian Network Classifiers","created":"2020-12-10","cited_by_count":6},{"id":"2103.00370","title":"Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning","created":"2021-02-28","cited_by_count":6},{"id":"2108.04126","title":"Improved Feature Importance Computations for Tree Models: Shapley vs. Banzhaf","created":"2021-08-09","cited_by_count":4},{"id":"2108.04855","title":"Attention-like feature explanation for tabular data","created":"2021-08-10","cited_by_count":3}],"next_cursor":null}