Institution profile

Stanford University

Academic institutionnorthamerica · us
Official website
Research library4,139linked papers
Opportunities0open roles
Selected work

Representative Papers

Using a market economy to provision compute resources across planet-wide clusters

May 23, 20092009 IEEE International Symposium on Parallel & Distributed Processing

To address resource supply-demand imbalances—manifesting as shortages and surpluses—across globally distributed heterogeneous computing clusters, this paper proposes a resource rationing mechanism grounded in real-world market economics. Methodologically, it introduces a periodic simulated-clock auction framework integrating utilization-driven reserve-price setting, long-term resource quota modeling, and supply-demand equilibrium pricing, enabling dynamic price signals to guide users’ autonomous job placement decisions. Its key contribution lies in being the first to systematically embed microeconomic market mechanisms into large-scale distributed resource allocation, replacing static quota or immediate-scheduling paradigms. Evaluated on the Google experimental market, the mechanism significantly incentivizes user migration toward underutilized clusters: resource utilization variance decreases by 32%, and shortage rate drops by 41%. These results empirically validate that price-based incentives can effectively drive system-level behavioral optimization and achieve global resource equilibrium.

64 citations2 influentialRead paper

Inequality's Economic and Social Roots: The Role of Social Networks and Homophily

Mar 20, 2021Social Science Research Network

This study examines how social network homophily exacerbates multidimensional inequality—in education, employment, health, income, and wealth—and impedes economic mobility through networked transmission of information, opportunities, and behaviors. Moving beyond conventional individual-capability frameworks, it pioneers an integrated approach bridging network science and institutional economics, proposing a “policy cocktail” framework that jointly targets structural economic institutions and relational social ties. Methodologically, the study combines social network analysis, dynamic homophily modeling, cross-domain inequality inference, and policy counterfactual simulation. Results demonstrate that network segregation exerts a stronger influence on intergenerational immobility than individual endowments; structural and relational interventions yield synergistic effects unattainable through isolated measures. The findings provide a theoretically grounded, operationally tractable foundation for designing multidimensional, targeted inclusive policies—offering both conceptual leverage and actionable intervention pathways to mitigate entrenched inequality and restore mobility.

37 citations1 influentialRead paper

A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot

Jul 26, 2023arXiv.org

To address the limitation of conventional generative models (e.g., GANs, diffusion models) — their reliance on large-scale labeled data — in data-scarce domains such as medical imaging and remote sensing, this paper proposes a unified framework termed “Generative Modeling under Data Constraints” (GM-DC). We systematically establish a two-dimensional taxonomy: (i) task dimension—encompassing low-data, few-shot, and zero-shot settings; and (ii) methodological dimension—integrating transfer learning, meta-learning, prompt engineering, and multi-paradigm fusion. This work is the first to uncover cross-paradigm adaptation principles and synergistic mechanisms under data constraints. The survey comprehensively analyzes lightweight designs and knowledge transfer strategies for mainstream architectures—including VAEs, GANs, and diffusion models—and identifies critical research gaps while charting emerging trends. As the inaugural holistic GM-DC survey, it is accompanied by an open-source platform for continuous resource updates, providing both theoretical foundations and practical guidance for data-efficient generative modeling.

28 citationsRead paper

Learning from a Biased Sample

Sep 05, 2022arXiv.org

To address degraded generalization performance at deployment caused by demographic representation imbalance in training data, this paper proposes a conditional Γ-bias sampling model that characterizes constrained conditional distributional shifts between test and training distributions. Building upon this model, distributionally robust optimization is formulated as a tractable augmented convex risk minimization problem. For the first time, sieve theory is integrated to establish statistical consistency guarantees. Furthermore, an end-to-end deep learning algorithm is designed, incorporating a custom robust loss function and the Rockafellar–Uryasev representation of Conditional Value-at-Risk (CVaR). Empirical evaluation on mental health score prediction and ICU length-of-stay forecasting demonstrates that the proposed method significantly improves out-of-distribution robustness, consistently outperforming standard empirical risk minimization and existing bias-correction approaches.

20 citations1 influentialRead paper

Nonstationary Bandit Learning via Predictive Sampling

May 04, 2022International Conference on Artificial Intelligence and Statistics

This paper addresses the failure of Thompson sampling to maintain effective exploration in non-stationary multi-armed bandits due to its neglect of information timeliness. We propose Predictive Sampling, the first method to explicitly incorporate timeliness modeling into the Bayesian decision framework. It achieves adaptive exploration prioritization via dynamic prior updating and timeliness-weighted sampling. Theoretically, we derive the first Bayesian regret upper bound applicable to non-stationary environments and prove its boundedness. Computationally, we design a scalable approximate posterior inference mechanism. Experiments across diverse non-stationary settings—including abrupt and gradual distributional shifts—demonstrate that Predictive Sampling significantly outperforms classical Thompson sampling. The algorithm exhibits strong convergence properties, robustness to environmental dynamics, and practical scalability, making it suitable for real-world deployment.

19 citations3 influentialRead paper
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