{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/fairness/papers/49","list_of":"/task/fairness","task":"Fairness","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":49,"pages_in_order":57,"rows_per_page":100,"rows":[4801,4900],"of":5676,"counts":{"archive_papers_tagged":5676,"with_a_code_link":1714,"where_syntology_ran_a_sample":404,"not_listed_spam_title":0,"listed":5676,"listed_where_code_ran":404,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":329,"every_run_a_failure_of_syntologys_instrument":75,"listed_with_a_run_with_no_instrument_failure":329,"listed_every_run_a_failure_of_syntologys_instrument":75,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/fairness","prev":"/task/fairness/papers/48","next":"/task/fairness/papers/50","papers":[{"url":null,"slug":"everything-is-relative-understanding-fairness","title":"Everything is Relative: Understanding Fairness with Optimal Transport","date":"2021-02-20","arxiv_id":"2102.10349","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-sparse-regression-with-clustering-an","title":"Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem","date":"2021-02-19","arxiv_id":"2102.09704","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-subgrouping-in-multicast-massive-mimo","title":"User Subgrouping in Multicast Massive MIMO over Spatially Correlated Rayleigh Fading Channels","date":"2021-02-19","arxiv_id":"2102.09935","repositories_listed":0,"syntology":null},{"url":null,"slug":"strategic-bidding-in-freight-transport-using","title":"Strategic bidding in freight transport using deep reinforcement learning","date":"2021-02-18","arxiv_id":"2102.09253","repositories_listed":0,"syntology":null},{"url":null,"slug":"sustainable-and-resilient-systems-for","title":"Sustainable and Resilient Systems for Intergenerational Justice","date":"2021-02-18","arxiv_id":"2102.09122","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-fairness-of-machine-learning","title":"Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information","date":"2021-02-16","arxiv_id":"2102.08410","repositories_listed":0,"syntology":null},{"url":null,"slug":"lexicographically-fair-learning-algorithms","title":"Lexicographically Fair Learning: Algorithms and Generalization","date":"2021-02-16","arxiv_id":"2102.08454","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-right-kind-of-fairness-in-ai","title":"Towards the Right Kind of Fairness in AI","date":"2021-02-16","arxiv_id":"2102.08453","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-ethics-needs-good-data","title":"AI Ethics Needs Good Data","date":"2021-02-15","arxiv_id":"2102.07333","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-and-optimal-cohort-selection-for-linear","title":"Fair and Optimal Cohort Selection for Linear Utilities","date":"2021-02-15","arxiv_id":"2102.07684","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-impact-of-device-and-behavioral","title":"On the Impact of Device and Behavioral Heterogeneity in Federated Learning","date":"2021-02-15","arxiv_id":"2102.07500","repositories_listed":0,"syntology":null},{"url":null,"slug":"where-to-locate-covid-19-mass-vaccination","title":"Where to locate COVID-19 mass vaccination facilities?","date":"2021-02-15","arxiv_id":"2102.07309","repositories_listed":0,"syntology":null},{"url":null,"slug":"state-visitation-fairness-in-average-reward","title":"Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment","date":"2021-02-14","arxiv_id":"2102.07120","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-stage-decentralized-matching-markets","title":"Learning in Multi-Stage Decentralized Matching Markets","date":"2021-02-13","arxiv_id":"2102.06988","repositories_listed":0,"syntology":null},{"url":null,"slug":"mimic-if-interpretability-and-fairness","title":"MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset","date":"2021-02-12","arxiv_id":"2102.06761","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-challenges-for-training-fair-neural","title":"Technical Challenges for Training Fair Neural Networks","date":"2021-02-12","arxiv_id":"2102.06764","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-representations-from-non-1","title":"Disentangled Representations from Non-Disentangled Models","date":"2021-02-11","arxiv_id":"2102.06204","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-aware-learning-from-corrupted-data","title":"Fairness-Aware PAC Learning from Corrupted Data","date":"2021-02-11","arxiv_id":"2102.06004","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-through-regularization-for-learning","title":"Fairness Through Regularization for Learning to Rank","date":"2021-02-11","arxiv_id":"2102.05996","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-trade-offs-in-utility-fairness","title":"Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks","date":"2021-02-11","arxiv_id":"2102.05975","repositories_listed":0,"syntology":null},{"url":null,"slug":"regret-stability-and-fairness-in-matching","title":"Regret, stability & fairness in matching markets with bandit learners","date":"2021-02-11","arxiv_id":"2102.06246","repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-framework-for-black-box-ai-models","title":"Testing Framework for Black-box AI Models","date":"2021-02-11","arxiv_id":"2102.06166","repositories_listed":0,"syntology":null},{"url":null,"slug":"output-perturbation-for-differentially","title":"Output Perturbation for Differentially Private Convex Optimization: Faster and More General","date":"2021-02-09","arxiv_id":"2102.04704","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularization-strategies-for-quantile","title":"Regularization Strategies for Quantile Regression","date":"2021-02-09","arxiv_id":"2102.05135","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-ranking-approach-to-fair-classification","title":"A Ranking Approach to Fair Classification","date":"2021-02-08","arxiv_id":"2102.04565","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-generate-fair-clusters-from","title":"Learning to Generate Fair Clusters from Demonstrations","date":"2021-02-08","arxiv_id":"2102.03977","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-near-neighbor-search-via","title":"Privacy-Preserving Near Neighbor Search via Sparse Coding with Ambiguation","date":"2021-02-08","arxiv_id":"2102.04274","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-fairness-in-classification-parity","title":"Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare","date":"2021-02-07","arxiv_id":"2102.03717","repositories_listed":0,"syntology":null},{"url":null,"slug":"befair-addressing-fairness-in-the-banking","title":"BeFair: Addressing Fairness in the Banking Sector","date":"2021-02-03","arxiv_id":"2102.02137","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-for-unobserved-characteristics","title":"Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities","date":"2021-02-03","arxiv_id":"2102.04257","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-data-processing-on-fairness-in","title":"Impact of Data Processing on Fairness in Supervised Learning","date":"2021-02-03","arxiv_id":"2102.01867","repositories_listed":0,"syntology":null},{"url":null,"slug":"key-technology-considerations-in-developing","title":"Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice","date":"2021-02-03","arxiv_id":"2102.01979","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-label-one-billion-faces-usage-and","title":"One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision","date":"2021-02-03","arxiv_id":"2102.02320","repositories_listed":0,"syntology":null},{"url":null,"slug":"agent-incentives-a-causal-perspective","title":"Agent Incentives: A Causal Perspective","date":"2021-02-02","arxiv_id":"2102.01685","repositories_listed":0,"syntology":null},{"url":null,"slug":"emergent-unfairness-in-algorithmic-fairness","title":"Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research","date":"2021-02-01","arxiv_id":"2102.01203","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-fair-machine-learning","title":"Quantum Fair Machine Learning","date":"2021-02-01","arxiv_id":"2102.00753","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-allocation-for-mixed-numerology-noma","title":"Resource Allocation for Mixed Numerology NOMA","date":"2021-02-01","arxiv_id":"2102.01005","repositories_listed":0,"syntology":null},{"url":null,"slug":"priority-based-post-processing-bias","title":"Priority-based Post-Processing Bias Mitigation for Individual and Group Fairness","date":"2021-01-31","arxiv_id":"2102.00417","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-through-optimization","title":"Fairness through Social Welfare Optimization","date":"2021-01-30","arxiv_id":"2102.00311","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-the-umpire-is-also-a-player-bias-in","title":"When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces","date":"2021-01-30","arxiv_id":"2102.00141","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-traditional-assumptions-in-fair","title":"Beyond traditional assumptions in fair machine learning","date":"2021-01-29","arxiv_id":"2101.12476","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-resource-allocation-for-demands-with","title":"Fair Resource Allocation for Demands with Sharp Lower Tail Inequalities","date":"2021-01-29","arxiv_id":"2101.12403","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-for-whom-understanding-the-reader-s","title":"Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarization","date":"2021-01-29","arxiv_id":"2101.12406","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-balance-for-fairness-fair-distribution","title":"A Balance for Fairness: Fair Distribution Utilising Physics in Games of Characteristic Function Form","date":"2021-01-27","arxiv_id":"2101.11496","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-2-stage-neural-optimization-for","title":"A Hybrid 2-stage Neural Optimization for Pareto Front Extraction","date":"2021-01-27","arxiv_id":"2101.11684","repositories_listed":0,"syntology":null},{"url":null,"slug":"black-feminist-musings-on-algorithmic","title":"Black Feminist Musings on Algorithmic Oppression","date":"2021-01-25","arxiv_id":"2101.09869","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-of-cell-free-mmwave-massive-mimo","title":"Performance of Cell-Free MmWave Massive MIMO Systems with Fronthaul Compression and DAC Quantization","date":"2021-01-25","arxiv_id":"2101.10157","repositories_listed":0,"syntology":null},{"url":null,"slug":"re-imagining-algorithmic-fairness-in-india","title":"Re-imagining Algorithmic Fairness in India and Beyond","date":"2021-01-25","arxiv_id":"2101.09995","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-pre-processing-to-achieve-fairness","title":"Optimal Pre-Processing to Achieve Fairness and Its Relationship with Total Variation Barycenter","date":"2021-01-18","arxiv_id":"2101.06811","repositories_listed":0,"syntology":null},{"url":null,"slug":"responsible-ai-challenges-in-end-to-end","title":"Responsible AI Challenges in End-to-end Machine Learning","date":"2021-01-15","arxiv_id":"2101.05967","repositories_listed":0,"syntology":null},{"url":null,"slug":"reviving-purpose-limitation-and-data","title":"Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems","date":"2021-01-15","arxiv_id":"2101.06203","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-transfer-learning-at-scale-for","title":"Supervised Transfer Learning at Scale for Medical Imaging","date":"2021-01-14","arxiv_id":"2101.05913","repositories_listed":0,"syntology":null},{"url":null,"slug":"bandwidth-allocation-for-multiple-federated","title":"Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks","date":"2021-01-10","arxiv_id":"2101.03627","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tale-of-fairness-revisited-beyond","title":"A Tale of Fairness Revisited: Beyond Adversarial Learning for Deep Neural Network Fairness","date":"2021-01-08","arxiv_id":"2101.02831","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-learning-to-relearning-a-framework-for","title":"From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation","date":"2021-01-07","arxiv_id":"2101.02647","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-with-continuous-optimal-transport","title":"Fairness with Continuous Optimal Transport","date":"2021-01-06","arxiv_id":"2101.02084","repositories_listed":0,"syntology":null},{"url":null,"slug":"characterizing-intersectional-group-fairness","title":"Characterizing Intersectional Group Fairness with Worst-Case Comparisons","date":"2021-01-05","arxiv_id":"2101.01673","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-training-of-decision-tree-classifiers","title":"Fair Training of Decision Tree Classifiers","date":"2021-01-04","arxiv_id":"2101.00909","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-technical-and-normative-investigation-of","title":"A Technical and Normative Investigation of Social Bias Amplification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-birkhoff-von-neumann","title":"Approximate Birkhoff-von-Neumann decomposition: a differentiable approach","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attainability-and-optimality-the-equalized","title":"Attainability and Optimality: The Equalized-Odds Fairness Revisited","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"blind-pareto-fairness-and-subgroup-robustness","title":"Blind Pareto Fairness and Subgroup Robustness","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"counterfactual-fairness-through-data","title":"Counterfactual Fairness through Data Preprocessing","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-differential-privacy-can-mitigate-the","title":"Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-empirical-risk-minimization-via","title":"Fair Empirical Risk Minimization via Exponential Rényi Mutual Information","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-guarantee-in-analysis-of-incomplete","title":"Fairness guarantee in analysis of incomplete data","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-mnas-uncertainty-aware-neural","title":"Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-auto-encoder-non-adversarial","title":"Generative Auto-Encoder: Non-adversarial Controllable Synthesis with Disentangled Exploration","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-fairness-teaching","title":"Generative Fairness Teaching","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"individually-fair-rankings","title":"Individually Fair Rankings","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-and-bias-in-multilingual-nlp","title":"Representation and Bias in Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"socially-responsible-ai-algorithms-issues","title":"Socially Responsible AI Algorithms: Issues, Purposes, and Challenges","date":"2021-01-01","arxiv_id":"2101.02032","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-fairness-with-invisible","title":"Zero-shot Fairness with Invisible Demographics","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-in-machine-learning","title":"Fairness in Machine Learning","date":"2020-12-31","arxiv_id":"2012.15816","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-maximal-correlation-approach-to-imposing","title":"A Maximal Correlation Approach to Imposing Fairness in Machine Learning","date":"2020-12-30","arxiv_id":"2012.15259","repositories_listed":0,"syntology":null},{"url":null,"slug":"provably-training-neural-network-classifiers","title":"Provably Training Overparameterized Neural Network Classifiers with Non-convex Constraints","date":"2020-12-30","arxiv_id":"2012.15274","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-user-scheduling-for-6g-a-fairness","title":"Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning","date":"2020-12-30","arxiv_id":"2012.15081","repositories_listed":0,"syntology":null},{"url":null,"slug":"prosocial-norm-emergence-in-multiagent","title":"Prosocial Norm Emergence in Multiagent Systems","date":"2020-12-29","arxiv_id":"2012.14581","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fair-deep-anomaly-detection","title":"Towards Fair Deep Anomaly Detection","date":"2020-12-29","arxiv_id":"2012.14961","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-regret-bound-for-non-stationary-multi-armed","title":"A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints","date":"2020-12-24","arxiv_id":"2012.13380","repositories_listed":0,"syntology":null},{"url":null,"slug":"confronting-abusive-language-online-a-survey","title":"Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective","date":"2020-12-22","arxiv_id":"2012.12305","repositories_listed":0,"syntology":null},{"url":null,"slug":"unbiased-subdata-selection-for-fair","title":"Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithms","date":"2020-12-22","arxiv_id":"2012.12356","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-welfare-and-equity-in-personalized","title":"Fairness, Welfare, and Equity in Personalized Pricing","date":"2020-12-21","arxiv_id":"2012.11066","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-importance-of-modeling-data-missingness","title":"The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective","date":"2020-12-21","arxiv_id":"2012.11448","repositories_listed":0,"syntology":null},{"url":null,"slug":"biased-models-have-biased-explanations","title":"Biased Models Have Biased Explanations","date":"2020-12-20","arxiv_id":"2012.10986","repositories_listed":0,"syntology":null},{"url":null,"slug":"fundamental-limits-and-tradeoffs-in-invariant-1","title":"Fundamental Limits and Tradeoffs in Invariant Representation Learning","date":"2020-12-19","arxiv_id":"2012.10713","repositories_listed":0,"syntology":null},{"url":null,"slug":"affirmative-algorithms-the-legal-grounds-for","title":"Affirmative Algorithms: The Legal Grounds for Fairness as Awareness","date":"2020-12-18","arxiv_id":"2012.14285","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-for-all-best-effort-fairness-guarantees","title":"Fair for All: Best-effort Fairness Guarantees for Classification","date":"2020-12-18","arxiv_id":"2012.10216","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-and-accuracy-in-federated-learning","title":"Fairness and Accuracy in Federated Learning","date":"2020-12-18","arxiv_id":"2012.10069","repositories_listed":0,"syntology":null},{"url":null,"slug":"hiring-from-a-pool-of-workers","title":"Hiring from a pool of workers","date":"2020-12-17","arxiv_id":"2012.09541","repositories_listed":0,"syntology":null},{"url":null,"slug":"i3dol-incremental-3d-object-learning-without","title":"I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting","date":"2020-12-16","arxiv_id":"2012.09014","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-cf-a-simple-baseline-for-reverse","title":"Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations","date":"2020-12-16","arxiv_id":"2012.09301","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-learning-demands-in-max-min-fairness","title":"Online Learning Demands in Max-min Fairness","date":"2020-12-15","arxiv_id":"2012.08648","repositories_listed":0,"syntology":null},{"url":null,"slug":"bandit-based-communication-efficient-client","title":"Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning","date":"2020-12-14","arxiv_id":"2012.08009","repositories_listed":0,"syntology":null},{"url":null,"slug":"noisy-linear-convergence-of-stochastic","title":"Noisy Linear Convergence of Stochastic Gradient Descent for CV@R Statistical Learning under Polyak-Łojasiewicz Conditions","date":"2020-12-14","arxiv_id":"2012.07785","repositories_listed":0,"syntology":null},{"url":null,"slug":"demysifying-deep-neural-networks-through","title":"Demystifying Deep Neural Networks Through Interpretation: A Survey","date":"2020-12-13","arxiv_id":"2012.07119","repositories_listed":0,"syntology":null},{"url":null,"slug":"trading-the-system-efficiency-for-the-income","title":"Trading the System Efficiency for the Income Equality of Drivers in Rideshare","date":"2020-12-12","arxiv_id":"2012.06850","repositories_listed":0,"syntology":null},{"url":null,"slug":"conceptualization-and-framework-of-hybrid","title":"Conceptualization and Framework of Hybrid Intelligence Systems","date":"2020-12-11","arxiv_id":"2012.06161","repositories_listed":0,"syntology":null},{"url":null,"slug":"renata-representation-and-training-alteration","title":"TARA: Training and Representation Alteration for AI Fairness and Domain Generalization","date":"2020-12-11","arxiv_id":"2012.06387","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-bias-in-image-classification","title":"Investigating Bias in Image Classification using Model Explanations","date":"2020-12-10","arxiv_id":"2012.05463","repositories_listed":0,"syntology":null}],"record_sha256":"16946d8b490eb74e0f804ff0a75ac2e9bf90a5895582d9b307237b5d3b0cfa87","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}