Cover Image

A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Clinton, Alex, Zeng, Thomas, Chen, Yiding, Zhu, Xiaojin, Kandasamy, Kirthevasan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution
by: Clinton, Alex, et al.
Published: (2024)

Incentivizing Truthful Data Contributions in a Marketplace for Mean Estimation
by: Chen, Keran, et al.
Published: (2025)

Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning
by: Huh, Joon Suk, et al.
Published: (2024)

Constrained Best Arm Identification with Tests for Feasibility
by: Cai, Ting, et al.
Published: (2025)

Balancing Performance and Costs in Best Arm Identification
by: Harding, Michael O., et al.
Published: (2025)

Learning to Price Homogeneous Data
by: Chen, Keran, et al.
Published: (2024)

Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget
by: Harding, Michael O., et al.
Published: (2026)

Bandit Profit-maximization for Targeted Marketing
by: Huh, Joon Suk, et al.
Published: (2024)

Bernstein-von Mises for Adaptively Collected Data
by: Du, Kevin, et al.
Published: (2025)

TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning
by: Wei, Zhepei, et al.
Published: (2025)

Incentivizing Time-Aware Fairness in Data Sharing
by: Chen, Jiangwei, et al.
Published: (2025)

Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning
by: Sim, Rachael Hwee Ling, et al.
Published: (2026)

Incentivized Truthful Communication for Federated Bandits
by: Wei, Zhepei, et al.
Published: (2024)

Incentivizing Truthful Language Models via Peer Elicitation Games
by: Chen, Baiting, et al.
Published: (2025)

Pairwise Exchanges of Freely Replicable Goods with Negative Externalities
by: Yang, Shangyuan, et al.
Published: (2026)

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning
by: Chakarov, Dimitar, et al.
Published: (2024)

A solution for the mean parametrization of the von Mises-Fisher distribution
by: Nonnenmacher, Marcel, et al.
Published: (2024)

Bayesian Circular Regression with von Mises Quasi-Processes
by: Cohen, Yarden, et al.
Published: (2024)

Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior
by: Jaberzadeh, Amir, et al.
Published: (2023)

Strategy-robust Online Learning in Contextual Pricing
by: Huh, Joon Suk, et al.
Published: (2025)

Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling
by: Bendada, Walid, et al.
Published: (2025)

Adaptive von Mises-Fisher Likelihood Loss for Supervised Deep Time Series Hashing
by: Perez, Juan Manuel, et al.
Published: (2025)

Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees
by: Chen, Ziqin, et al.
Published: (2026)

Perturbation-Induced Linearization: Constructing Unlearnable Data with Solely Linear Classifiers
by: Liu, Jinlin, et al.
Published: (2026)

Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometry
by: You, Kisung, et al.
Published: (2025)

DINO as a von Mises-Fisher mixture model
by: Govindarajan, Hariprasath, et al.
Published: (2024)

Convergence Of Consistency Model With Multistep Sampling Under General Data Assumptions
by: Chen, Yiding, et al.
Published: (2025)

A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning
by: Zhang, Xiaojin, et al.
Published: (2024)

Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach
by: Hu, Gang, et al.
Published: (2024)

The Poisson Midpoint Method for Langevin Dynamics: Provably Efficient Discretization for Diffusion Models
by: Kandasamy, Saravanan, et al.
Published: (2024)

MaxMin-L2-SVC-NCH: A Novel Approach for Support Vector Classifier Training and Parameter Selection
by: Luo, Linkai, et al.
Published: (2023)

Detecting Blinks in Healthy and Parkinson's EEG: A Deep Learning Perspective
by: Lensky, Artem, et al.
Published: (2025)

Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory
by: Zhang, Xiaojin, et al.
Published: (2024)

Distributional Reinforcement Learning via the Cramér Distance
by: Aziz, Vanya, et al.
Published: (2026)

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
by: Zhang, Xiaojin, et al.
Published: (2023)

Sobre el estadístico de Cramér-von Mises
by: Pablo Martínez-Camblor
Published: (2012)

Pruning Increases Orderedness in Recurrent Computation
by: Song, Yiding
Published: (2025)

Bridging Privacy and Robustness for Trustworthy Machine Learning
by: Zhang, Xiaojin, et al.
Published: (2024)

Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale
by: Jesson, Andrew, et al.
Published: (2024)

Model Capacity Determines Grokking through Competing Memorisation and Generalisation Speeds
by: Song, Yiding, et al.
Published: (2026)