Institution profile

University of California, San Diego

Academic institutionnorthamerica · us
Official website
Research library2,054linked papers
Opportunities0open roles
Selected work

Representative Papers

Testing Piketty's Hypothesis on the Drivers of Income Inequality: Evidence from Panel VARs with Heterogeneous Dynamics

Aug 01, 2016Social Science Research Network

This study empirically tests Piketty’s central hypothesis that the “r−g gap”—the difference between the rate of return on capital (r) and the economic growth rate (g)—drives rising income inequality and capital’s share of national income. Using panel data from 19 advanced economies over 1980–2015, we estimate a heterogeneous dynamic panel structural vector autoregression (Panel SVAR) model—the first systematic causal identification of r−g’s effects on inequality and capital share. We conduct robustness checks across multiple operationalizations of r−g. Contrary to Piketty’s prediction, we find no statistically significant evidence that an expanding r−g gap increases either top income inequality or capital’s share of national income; results hold across all specifications. By providing the first formal econometric validation—rather than mere descriptive correlation—this work overcomes a longstanding methodological gap in the literature and establishes a new empirical benchmark for analyzing the drivers of inequality dynamics.

29 citations2 influentialRead paper

Federated Large Language Models: Current Progress and Future Directions

Sep 24, 2024arXiv.org

To address the convergence difficulties and high communication overhead of large language models (LLMs) in federated learning (FL) caused by data heterogeneity, this paper introduces FedLLM—the first unified analytical framework for LLMs in FL. It systematically surveys two dominant paradigms: federated fine-tuning and federated prompt learning, while rigorously analyzing core challenges including data heterogeneity, communication efficiency, and privacy preservation. The work identifies promising future directions—namely, federated pre-training and LLM-augmented FL—and fills a critical gap in systematic literature review. A multidimensional taxonomy and evaluation framework is established to clarify key technical bottlenecks. Integrating insights from FL, LLM adaptation, prompt engineering, distributed optimization, and privacy-preserving computation, this study delivers a practical, robust, and privacy-aware methodology for deploying LLMs in real-world federated settings. (149 words)

16 citations1 influentialRead paper

Treatment Allocation under Uncertain Costs

Mar 20, 2021

This paper addresses the problem of optimal treatment allocation under a budget constraint when treatment costs vary heterogeneously with covariates. We propose a threshold rule based on a priority score and establish, for the first time, a theoretical link between optimal allocation under uncertain costs and instrumental variable (IV) estimation of heterogeneous treatment effects. We rigorously derive the optimal threshold structure and prove its learnability. Our method integrates randomized controlled trial data, priority score modeling, threshold-based decision making, and an IV estimation framework. Empirically, the approach significantly outperforms standard benchmarks across multiple evaluation metrics, achieving maximal social value or firm profit within budget constraints. It provides a new paradigm—statistically rigorous yet practically implementable—for applications including scarce healthcare resource allocation and dynamic pricing.

14 citations1 influentialRead paper

Learning to Discover at Test Time

Jan 22, 2026

This work proposes TTT-Discover, a novel approach that introduces test-time training (TTT) to scientific discovery by leveraging online reinforcement learning to optimize large language models during inference. Unlike conventional AI methods that rely solely on the generalization of pretrained models and struggle to autonomously identify optimal solutions at test time, TTT-Discover focuses on generating a single high-quality solution rather than improving average performance. Built upon the open-source model OpenAI gpt-oss-120b and augmented with a customized search subroutine and the Tinker API, the method achieves highly efficient and low-cost optimization. It sets new state-of-the-art results across diverse domains—including mathematical theorem proving, GPU kernel design, algorithmic competitions, and single-cell denoising—with all findings validated by domain experts or competition organizers at a cost of only a few hundred dollars per task.

11 citations1 influentialRead paper
Recent publications

Latest Papers