{"list":"other","total":74,"rows":[{"id":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"2012.07805","title":"Extracting Training Data from Large Language Models","created":"2020-12-14","cited_by_count":2189},{"id":"1610.02413","title":"Equality of Opportunity in Supervised Learning","created":"2016-10-07","cited_by_count":2007},{"id":"1703.04730","title":"Understanding Black-box Predictions via Influence Functions","created":"2017-03-14","cited_by_count":1741},{"id":"1606.06565","title":"Concrete Problems in AI Safety","created":"2016-06-21","cited_by_count":1642},{"id":"1803.09010","title":"Datasheets for Datasets","created":"2018-03-23","cited_by_count":1386},{"id":"2012.07421","title":"WILDS: A Benchmark of in-the-Wild Distribution Shifts","created":"2020-12-14","cited_by_count":1375},{"id":"1810.03993","title":"Model Cards for Model Reporting","created":"2018-10-05","cited_by_count":1080},{"id":"2005.04118","title":"Beyond Accuracy: Behavioral Testing of NLP models with CheckList","created":"2020-05-08","cited_by_count":1006},{"id":"1802.08232","title":"The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks","created":"2018-02-22","cited_by_count":864},{"id":"1703.06856","title":"Counterfactual Fairness","created":"2017-03-20","cited_by_count":804},{"id":"1807.00459","title":"How To Backdoor Federated Learning","created":"2018-07-02","cited_by_count":765},{"id":"1803.01498","title":"Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates","created":"2018-03-05","cited_by_count":711},{"id":"2010.09670","title":"RobustBench: a standardized adversarial robustness benchmark","created":"2020-10-19","cited_by_count":661},{"id":"1904.02868","title":"Data Shapley: Equitable Valuation of Data for Machine Learning","created":"2019-04-05","cited_by_count":629},{"id":"1803.02453","title":"A Reductions Approach to Fair Classification","created":"2018-03-06","cited_by_count":587},{"id":"1805.04049","title":"Exploiting Unintended Feature Leakage in Collaborative Learning","created":"2018-05-10","cited_by_count":520},{"id":"1911.11815","title":"Local Model Poisoning Attacks to Byzantine-Robust Federated Learning","created":"2019-11-26","cited_by_count":493},{"id":"1811.00636","title":"Spectral Signatures in Backdoor Attacks","created":"2018-11-01","cited_by_count":484},{"id":"1712.07557","title":"Differentially Private Federated Learning: A Client Level Perspective","created":"2017-12-20","cited_by_count":459},{"id":"2104.14337","title":"Dynabench: Rethinking Benchmarking in NLP","created":"2021-04-07","cited_by_count":459},{"id":"1902.00146","title":"Agnostic Federated Learning","created":"2019-02-01","cited_by_count":456},{"id":"1905.10497","title":"Fair Resource Allocation in Federated Learning","created":"2019-05-25","cited_by_count":394},{"id":"1711.05144","title":"Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness","created":"2017-11-14","cited_by_count":384},{"id":"1804.08333","title":"Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge","created":"2018-04-23","cited_by_count":352},{"id":"1610.05755","title":"Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data","created":"2016-10-18","cited_by_count":348},{"id":"2012.13995","title":"FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping","created":"2020-12-27","cited_by_count":346},{"id":"1802.07927","title":"The Hidden Vulnerability of Distributed Learning in Byzantium","created":"2018-02-22","cited_by_count":329},{"id":"2002.06673","title":"Performative Prediction","created":"2020-02-16","cited_by_count":327},{"id":"1912.13445","title":"Robust Aggregation for Federated Learning","created":"2019-12-31","cited_by_count":317},{"id":"1905.12101","title":"Differential Privacy Has Disparate Impact on Model Accuracy","created":"2019-05-28","cited_by_count":316},{"id":"2007.05084","title":"Attack of the Tails: Yes, You Really Can Backdoor Federated Learning","created":"2020-07-09","cited_by_count":309},{"id":"1902.06156","title":"A Little Is Enough: Circumventing Defenses For Distributed Learning","created":"2019-02-16","cited_by_count":288},{"id":"1902.10275","title":"Towards Efficient Data Valuation Based on the Shapley Value","created":"2019-02-27","cited_by_count":281},{"id":"1802.08908","title":"Scalable Private Learning with PATE","created":"2018-02-24","cited_by_count":269},{"id":"1506.06980","title":"Strategic Classification","created":"2015-06-23","cited_by_count":257},{"id":"1810.11953","title":"Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift","created":"2018-10-29","cited_by_count":243},{"id":"2011.11660","title":"Differentially Private Learning Needs Better Features (or Much More Data)","created":"2020-11-23","cited_by_count":235},{"id":"1805.12317","title":"Multiaccuracy: Black-Box Post-Processing for Fairness in Classification","created":"2018-05-31","cited_by_count":222},{"id":"1811.12469","title":"Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity","created":"2018-11-29","cited_by_count":164},{"id":"1808.04866","title":"Mitigating Sybils in Federated Learning Poisoning","created":"2018-08-14","cited_by_count":161},{"id":"1803.04585","title":"Categorizing Variants of Goodhart's Law","created":"2018-03-13","cited_by_count":149},{"id":"1909.11671","title":"Data Valuation using Reinforcement Learning","created":"2019-09-25","cited_by_count":116},{"id":"1908.04734","title":"Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective","created":"2019-08-13","cited_by_count":108},{"id":"1411.2664","title":"Preserving Statistical Validity in Adaptive Data Analysis","created":"2014-11-10","cited_by_count":103},{"id":"2006.06887","title":"Stochastic Optimization for Performative Prediction","created":"2020-06-12","cited_by_count":93},{"id":"1910.10362","title":"Strategic Classification is Causal Modeling in Disguise","created":"2019-10-23","cited_by_count":92},{"id":"1511.02513","title":"Algorithmic Stability for Adaptive Data Analysis","created":"2015-11-08","cited_by_count":88},{"id":"2103.05633","title":"Proof-of-Learning: Definitions and Practice","created":"2021-03-09","cited_by_count":84},{"id":"1506.02629","title":"Generalization in Adaptive Data Analysis and Holdout Reuse","created":"2015-06-08","cited_by_count":83},{"id":"1803.08917","title":"Byzantine Stochastic Gradient Descent","created":"2018-03-23","cited_by_count":81},{"id":"1812.02696","title":"Differentially Private Fair Learning","created":"2018-12-06","cited_by_count":76},{"id":"1803.09877","title":"DRACO: Byzantine-resilient Distributed Training via Redundant Gradients","created":"2018-03-27","cited_by_count":75},{"id":"2109.02053","title":"GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning","created":"2021-09-05","cited_by_count":74},{"id":"1502.04585","title":"The Ladder: A Reliable Leaderboard for Machine Learning Competitions","created":"2015-02-16","cited_by_count":69},{"id":"2010.00753","title":"Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation","created":"2020-10-02","cited_by_count":60},{"id":"1903.07020","title":"Zeno++: Robust Fully Asynchronous SGD","created":"2019-03-17","cited_by_count":53},{"id":"1908.03092","title":"Motivating Workers in Federated Learning: A Stackelberg Game Perspective","created":"2019-08-06","cited_by_count":45},{"id":"2106.09580","title":"Optimality and Stability in Federated Learning: A Game-theoretic Approach","created":"2021-06-17","cited_by_count":40},{"id":"2002.11711","title":"FedCoin: A Peer-to-Peer Payment System for Federated Learning","created":"2020-02-26","cited_by_count":39},{"id":"2009.10269","title":"An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach","created":"2020-09-22","cited_by_count":39},{"id":"2110.12257","title":"Game of Gradients: Mitigating Irrelevant Clients in Federated Learning","created":"2021-10-23","cited_by_count":35},{"id":"2009.05604","title":"Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning","created":"2020-09-11","cited_by_count":21},{"id":"2406.00569","title":"Redefining Contributions: Shapley-Driven Federated Learning","created":"2024-06-01","cited_by_count":17},{"id":"2106.11814","title":"Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective","created":"2021-06-22","cited_by_count":14},{"id":"2108.05568","title":"A Contract Theory based Incentive Mechanism for Federated Learning","created":"2021-08-12","cited_by_count":13},{"id":"2202.08310","title":"Towards Verifiable Federated Learning","created":"2022-02-15","cited_by_count":11},{"id":"2101.09673","title":"Incentive Mechanism Design for Federated Learning: Hedonic Game Approach","created":"2021-01-24","cited_by_count":8},{"id":"2304.07981","title":"Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation","created":"2023-04-17","cited_by_count":8},{"id":"2306.05764","title":"Fair yet Asymptotically Equal Collaborative Learning","created":"2023-06-09","cited_by_count":8},{"id":"2212.11194","title":"Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks","created":"2022-12-21","cited_by_count":6},{"id":"2404.13244","title":"Intelligent Agents for Auction-based Federated Learning: A Survey","created":"2024-04-20","cited_by_count":4},{"id":"2202.09897","title":"Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy","created":"2022-02-20","cited_by_count":2}],"next_cursor":null}