Institution profile

London School of Economics

Academic institutioneurope · gb
Official website
Research library256linked papers
Opportunities0open roles
Selected work

Representative Papers

The Drift Burst Hypothesis

Sep 27, 2016Journal of Econometrics

This study investigates the existence, market prevalence, and underlying mechanisms of transient, localized “drift bursts” in financial asset prices. To this end, we incorporate drift bursts into a continuous-time Itô semimartingale framework and develop a theoretical model under no-arbitrage conditions, alongside a nonparametric test statistic designed to reliably detect such events from high-frequency data contaminated by noise. Our work is the first to formally model drift bursts as a regular feature of financial markets, uncovering their intrinsic links to liquidity shocks and price reversals. Empirical analysis reveals that drift bursts are pervasive across equity, bond, foreign exchange, and commodity markets, occurring on average once per week; notably, negative bursts accompanied by high trading volume are more likely to trigger significant price reversals.

64 citations10 influentialRead paper

Approximating Nash Social Welfare by Matching and Local Search

Nov 07, 2022Symposium on the Theory of Computing

This paper studies Nash social welfare (NSW) maximization under submodular utilities, addressing both symmetric and weighted (asymmetric) settings, while simultaneously pursuing approximation efficiency and fairness—specifically EFX. We propose the first deterministic algorithmic framework that integrates bipartite matching with local search. For the symmetric case, it achieves a $(4+varepsilon)$-approximation to optimal NSW, drastically improving upon the previous best ratio of 380; for the weighted case, it attains a $(omega+2+varepsilon)$-approximation, where $omega$ is the largest weight ratio. Crucially, it is the first polynomial-time algorithm to simultaneously guarantee $12$-EFX fairness and $(8+varepsilon)$-NSW approximation—breaking the prior barrier that precluded constant-factor NSW approximations under EFX. Our core innovation lies in unifying matching structures with submodular optimization, leveraging a weighted geometric mean objective to jointly approximate efficiency and fairness.

18 citations5 influentialRead paper

Towards Open Diversity-Aware Social Interactions

Feb 17, 2025arXiv.org

In the digital era, the rapid proliferation of diverse populations, perspectives, and knowledge lacks corresponding adaptive mechanisms, leading to superficial social relationships and intensified echo chambers. Method: This study proposes and implements the “We Internet” platform, introducing— for the first time—the Diversity-Aware AI framework, which integrates sociology, ethics, and artificial intelligence. It establishes multidimensional modeling and representation learning methods for social diversity and designs a human-AI collaborative, ethics-driven algorithmic architecture with interpretable matching guidance. Contribution/Results: Empirical validation demonstrates that the framework significantly enhances cross-group understanding, mitigates filter bubbles, and deepens collaborative engagement. It provides both a theoretical foundation and an implementable paradigm for open, inclusive, and trustworthy social AI systems.

3 citationsRead paper

A complete characterization of testable hypotheses

Jan 08, 2026

This study addresses the long-standing problem of characterizing the existence of nontrivial (strictly unbiased) hypothesis tests in the absence of a common dominating measure. By examining the closure of the convex hull of a set of probability measures within the space of bounded finitely additive measures, and leveraging topological separation properties under the total variation distance, the authors establish necessary and sufficient conditions for testability without requiring a reference control measure. This work completes the theoretical program initiated by Le Cam, providing the first complete characterization of testability in full generality. Illustrative examples further highlight the essential roles played by measure-theoretic and convex-analytic considerations in this foundational result.

2 citations1 influentialRead paper

Detecting LLM-Generated Text with Performance Guarantees

Jan 10, 2026arXiv.org

The proliferation of highly realistic text generated by large language models has intensified risks related to misinformation and academic misconduct, underscoring the urgent need for reliable detection methods. This work proposes an online classifier that distinguishes human- from model-generated text without relying on watermarks or prior knowledge of the generative model, operating efficiently on CPU alone. By integrating statistical learning with computationally efficient feature modeling, the method introduces, for the first time, controllable statistical inference guarantees that rigorously bound Type I error while achieving high statistical power, superior classification accuracy, and strong computational efficiency. Empirical evaluations demonstrate that the proposed detector significantly outperforms existing approaches across multiple benchmarks.

2 citationsRead paper
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