Cover Image

On-Demand Sampling: Learning Optimally from Multiple Distributions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Haghtalab, Nika, Jordan, Michael I., Zhao, Eric
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Computers and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning With Multi-Group Guarantees For Clusterable Subpopulations
by: Dai, Jessica, et al.
Published: (2024)

Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
by: Jagadeesan, Meena, et al.
Published: (2023)

Sample-Adaptivity Tradeoff in On-Demand Sampling
by: Haghtalab, Nika, et al.
Published: (2025)

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)

Diffusion Language Models are Provably Optimal Parallel Samplers
by: Jiang, Haozhe, et al.
Published: (2025)

Delegating Data Collection in Decentralized Machine Learning
by: Ananthakrishnan, Nivasini, et al.
Published: (2023)

On Surjectivity of Neural Networks: Can you elicit any behavior from your model?
by: Jiang, Haozhe, et al.
Published: (2025)

Panprediction: Optimal Predictions for Any Downstream Task and Loss
by: Balakrishnan, Sivaraman, et al.
Published: (2025)

Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
by: Ananthakrishnan, Nivasini, et al.
Published: (2024)

Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?
by: Gölz, Paul, et al.
Published: (2025)

Algorithmic Content Selection and the Impact of User Disengagement
by: Calvano, Emilio, et al.
Published: (2024)

Distribution-Free Fair Federated Learning with Small Samples
by: Yin, Qichuan, et al.
Published: (2024)

Truthfulness of Calibration Measures
by: Haghtalab, Nika, et al.
Published: (2024)

Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Data
by: Ozeki, Ren, et al.
Published: (2024)

Learning in Stackelberg Games with Non-myopic Agents
by: Haghtalab, Nika, et al.
Published: (2022)

A Collectivist, Economic Perspective on AI
by: Jordan, Michael I.
Published: (2025)

Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry
by: Jagadeesan, Meena, et al.
Published: (2024)

Smooth Nash Equilibria: Algorithms and Complexity
by: Daskalakis, Constantinos, et al.
Published: (2023)

Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
by: Bothmann, Ludwig, et al.
Published: (2023)

Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
by: Halawi, Danny, et al.
Published: (2024)

Quality-Diversity Generative Sampling for Learning with Synthetic Data
by: Chang, Allen, et al.
Published: (2023)

Water Demand Forecasting of District Metered Areas through Learned Consumer Representations
by: Ramachandran, Adithya, et al.
Published: (2025)

Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization
by: Gong, Ziyu, et al.
Published: (2025)

Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
by: Wang, Ziming, et al.
Published: (2025)

Reconciling Predictive Multiplicity in Practice
by: Behzad, Tina, et al.
Published: (2025)

Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality
by: Zanna, Khadija, et al.
Published: (2024)

Understanding the Transit Gap: A Comparative Study of On-Demand Bus Services and Urban Climate Resilience in South End, Charlotte, NC and Avondale, Chattanooga, TN
by: Hosseini, Sanaz Sadat, et al.
Published: (2024)

From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning
by: Qiu, Eric S., et al.
Published: (2026)

Non-myopic Matching and Rebalancing in Large-Scale On-Demand Ride-Pooling Systems Using Simulation-Informed Reinforcement Learning
by: Namdarpour, Farnoosh, et al.
Published: (2025)

Model Multiplicity and Predictive Arbitrariness in Recidivism Risk Assessment
by: Singh, Ashwin, et al.
Published: (2026)

A Sequentially Fair Mechanism for Multiple Sensitive Attributes
by: Hu, François, et al.
Published: (2023)

Optimal Decision Making Under Strategic Behavior
by: Tsirtsis, Stratis, et al.
Published: (2019)

Reconsidering Fairness Through Unawareness From the Perspective of Model Multiplicity
by: Höltgen, Benedikt, et al.
Published: (2025)

Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set
by: Dai, Gordon, et al.
Published: (2025)

Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
by: Aden-Ali, Ishaq, et al.
Published: (2026)

Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks
by: He, Erhu, et al.
Published: (2024)

Fair Sampling in Diffusion Models through Switching Mechanism
by: Choi, Yujin, et al.
Published: (2024)

DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning
by: Tepeli, Yasin I., et al.
Published: (2024)

Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank
by: Scarlatos, Alexander, et al.
Published: (2024)