Learning With Multi-Group Guarantees For Clusterable Subpopulations
Fuente:
arXiv
Saved in:
| Main Authors: | Dai, Jessica, Haghtalab, Nika, Zhao, Eric |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On-Demand Sampling: Learning Optimally from Multiple Distributions
by: Haghtalab, Nika, et al.
Published: (2022)
by: Haghtalab, Nika, et al.
Published: (2022)
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
by: Dai, Jessica, et al.
Published: (2024)
by: Dai, Jessica, et al.
Published: (2024)
From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
by: Zhao, Eric, et al.
Published: (2025)
by: Zhao, Eric, et al.
Published: (2025)
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
by: Jagadeesan, Meena, et al.
Published: (2023)
by: Jagadeesan, Meena, et al.
Published: (2023)
On Demographic Group Fairness Guarantees in Deep Learning
by: Luo, Yan, et al.
Published: (2024)
by: Luo, Yan, et al.
Published: (2024)
Diffusion Language Models are Provably Optimal Parallel Samplers
by: Jiang, Haozhe, et al.
Published: (2025)
by: Jiang, Haozhe, et al.
Published: (2025)
Evaluating Model Performance Under Worst-case Subpopulations
by: Li, Mike, et al.
Published: (2024)
by: Li, Mike, et al.
Published: (2024)
On Surjectivity of Neural Networks: Can you elicit any behavior from your model?
by: Jiang, Haozhe, et al.
Published: (2025)
by: Jiang, Haozhe, et al.
Published: (2025)
Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?
by: Gölz, Paul, et al.
Published: (2025)
by: Gölz, Paul, et al.
Published: (2025)
Algorithmic Content Selection and the Impact of User Disengagement
by: Calvano, Emilio, et al.
Published: (2024)
by: Calvano, Emilio, et al.
Published: (2024)
Assessing Generalization for Subpopulation Representative Modeling via In-Context Learning
by: Simmons, Gabriel, et al.
Published: (2024)
by: Simmons, Gabriel, et al.
Published: (2024)
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
by: Ananthakrishnan, Nivasini, et al.
Published: (2024)
by: Ananthakrishnan, Nivasini, et al.
Published: (2024)
Truthfulness of Calibration Measures
by: Haghtalab, Nika, et al.
Published: (2024)
by: Haghtalab, Nika, et al.
Published: (2024)
Learning in Stackelberg Games with Non-myopic Agents
by: Haghtalab, Nika, et al.
Published: (2022)
by: Haghtalab, Nika, et al.
Published: (2022)
Panprediction: Optimal Predictions for Any Downstream Task and Loss
by: Balakrishnan, Sivaraman, et al.
Published: (2025)
by: Balakrishnan, Sivaraman, et al.
Published: (2025)
Achievable Fairness on Your Data With Utility Guarantees
by: Taufiq, Muhammad Faaiz, et al.
Published: (2024)
by: Taufiq, Muhammad Faaiz, et al.
Published: (2024)
Smooth Nash Equilibria: Algorithms and Complexity
by: Daskalakis, Constantinos, et al.
Published: (2023)
by: Daskalakis, Constantinos, et al.
Published: (2023)
Sample-Adaptivity Tradeoff in On-Demand Sampling
by: Haghtalab, Nika, et al.
Published: (2025)
by: Haghtalab, Nika, et al.
Published: (2025)
Delegating Data Collection in Decentralized Machine Learning
by: Ananthakrishnan, Nivasini, et al.
Published: (2023)
by: Ananthakrishnan, Nivasini, et al.
Published: (2023)
Fairness-aware Federated Minimax Optimization with Convergence Guarantee
by: Dunda, Gerry Windiarto Mohamad, et al.
Published: (2023)
by: Dunda, Gerry Windiarto Mohamad, et al.
Published: (2023)
A Unified Post-Processing Framework for Group Fairness in Classification
by: Xian, Ruicheng, et al.
Published: (2024)
by: Xian, Ruicheng, et al.
Published: (2024)
From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms
by: Dai, Jessica, et al.
Published: (2025)
by: Dai, Jessica, et al.
Published: (2025)
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
by: Halawi, Danny, et al.
Published: (2024)
by: Halawi, Danny, et al.
Published: (2024)
Statistical Guarantees in the Search for Less Discriminatory Algorithms
by: Hays, Chris, et al.
Published: (2025)
by: Hays, Chris, et al.
Published: (2025)
Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
by: Somerstep, Seamus, et al.
Published: (2024)
by: Somerstep, Seamus, et al.
Published: (2024)
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering
by: Yang, Yifan, et al.
Published: (2024)
by: Yang, Yifan, et al.
Published: (2024)
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
by: Duan, Yuying, et al.
Published: (2024)
by: Duan, Yuying, et al.
Published: (2024)
Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Review
by: Benítez-Peña, Sandra, et al.
Published: (2026)
by: Benítez-Peña, Sandra, et al.
Published: (2026)
Novel Topological Machine Learning Methodology for Stream-of-Quality Modeling in Smart Manufacturing
by: Lee, Jay, et al.
Published: (2024)
by: Lee, Jay, et al.
Published: (2024)
The Fairness-Quality Trade-off in Clustering
by: Hakim, Rashida, et al.
Published: (2024)
by: Hakim, Rashida, et al.
Published: (2024)
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds
by: Chaudhuri, Kamalika, et al.
Published: (2024)
by: Chaudhuri, Kamalika, et al.
Published: (2024)
Migrate Demographic Group For Fair GNNs
by: Hu, YanMing, et al.
Published: (2023)
by: Hu, YanMing, et al.
Published: (2023)
Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing
by: Xian, Ruicheng, et al.
Published: (2025)
by: Xian, Ruicheng, et al.
Published: (2025)
The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics
by: Wang, Yuhao, et al.
Published: (2025)
by: Wang, Yuhao, et al.
Published: (2025)
Fairness Risks for Group-conditionally Missing Demographics
by: Jiang, Kaiqi, et al.
Published: (2024)
by: Jiang, Kaiqi, et al.
Published: (2024)
Bias Amplification Enhances Minority Group Performance
by: Li, Gaotang, et al.
Published: (2023)
by: Li, Gaotang, et al.
Published: (2023)
Fairness in Multi-Task Learning via Wasserstein Barycenters
by: Hu, François, et al.
Published: (2023)
by: Hu, François, et al.
Published: (2023)
From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning
by: Qiu, Eric S., et al.
Published: (2026)
by: Qiu, Eric S., et al.
Published: (2026)
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing
by: Paes, Lucas Monteiro, et al.
Published: (2023)
by: Paes, Lucas Monteiro, et al.
Published: (2023)
What is Fair? Defining Fairness in Machine Learning for Health
by: Gao, Jianhui, et al.
Published: (2024)
by: Gao, Jianhui, et al.
Published: (2024)
Similar Items
-
On-Demand Sampling: Learning Optimally from Multiple Distributions
by: Haghtalab, Nika, et al.
Published: (2022) -
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
by: Dai, Jessica, et al.
Published: (2024) -
From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
by: Zhao, Eric, et al.
Published: (2025) -
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
by: Jagadeesan, Meena, et al.
Published: (2023) -
On Demographic Group Fairness Guarantees in Deep Learning
by: Luo, Yan, et al.
Published: (2024)