Alex Clinton — accepted AI-conference papers

2025

A Cramér–von Mises Approach to Incentivizing Truthful Data Sharing

Alex Clinton, Thomas Zeng, Yiding Chen, Jerry Zhu, Kirthevasan Kandasamy

NeurIPS 2025poster

Modern data marketplaces and data sharing consortia increasingly rely on incentive mechanisms to encourage agents to contribute data. However, schemes that reward agents based on the quantity of submitted data are vulnerable to manipulation, as agents may submit fabricated or low-quality data to inf…

Cited by 0 Source ↗Scholar ↗

2025

Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution

Alex Clinton, Yiding Chen, Jerry Zhu, Kirthevasan Kandasamy

ICML 2025poster

We study a collaborative learning problem where $m$ agents aim to estimate a vector $\mu =(\mu_1,\ldots,\mu_d)\in \mathbb{R}^d$ by sampling from associated univariate normal distributions $(\mathcal{N}(\mu_k, \sigma^2))\_{k\in[d]}$. Agent $i$ incurs a cost $c_{i,k}$ to sample from $\mathcal{N}(\mu_k…

Cited by 0 Source ↗PDF ↗Scholar ↗