Lawrence D. W. Schmidt

About

Lawrence D. W. Schmidt is the Victor J. Menezes (1972) Career Development Associate Professor of Finance at MIT Sloan. He is an applied economist working at the intersection of finance and macro-labor, with research that has developed new insights into the risk exposures and decision-making processes of households, institutional investors, and financial intermediaries. His work has deepened understanding of asset prices, financial policy, and the workings of the real economy through two interrelated strands: one studying fundamental risk factors impacting human capital and the causes and consequences of imperfect risk-sharing in labor and financial markets, and the other focusing on the drivers of financial markets by examining individual decision-making, strategic complementarities, and information processing frictions. Schmidt specializes in leveraging large, detailed microeconomic datasets, advanced econometric methods, and textual analysis tools to analyze the dynamics of financial markets and economic welfare. His research has been published in leading journals such as the American Economic Review, the Journal of Finance, and the Review of Financial Studies, and has received multiple awards including the 2015 AQR Top Finance Graduate Award and the 2024 Dimensional Fund Advisors First Prize Award for the best capital markets paper. His current research emphasizes the role of risky human capital in asset markets and the real economy, particularly how new technologies…

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Data Mining
• Algorithm
• Engineering

Selected publications

• Measuring Robustness to Natural Distribution Shifts in Image Classification

  arXiv (Cornell University) · 2020 · 170 citations

  Senior authorCorresponding

  We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets. Most research on robustness focuses on synthetic image perturbations (noise, simulated weather artifacts, adversarial examples, etc.), which leaves open how robustness on synthetic distribution shift relates to distribution shift arising in real data. Informed by an evaluation of 204 ImageNet models in 213 different test conditions, we find that there is often little to no transfer…

  PublisherOA PDFDOI

• Neural Kernels Without Tangents

  arXiv (Cornell University) · 2020-03-04 · 35 citations

  preprintOpen access

  We investigate the connections between neural networks and simple building blocks in kernel space. In particular, using well established feature space tools such as direct sum, averaging, and moment lifting, we present an algebra for creating "compositional" kernels from bags of features. We show that these operations correspond to many of the building blocks of "neural tangent kernels (NTK)". Experimentally, we show that there is a correlation in test error between neural network architectures…

  PublisherOA PDFDOI

• Objaverse-XL: A Universe of 10M+ 3D Objects

  2023-01-01 · 31 citations

  article
  PublisherDOI

• The Effect of Natural Distribution Shift on Question Answering Models

  arXiv (Cornell University) · 2020-04-29 · 22 citations

  preprintOpen accessSenior author

  We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data. Our first test set is from the original Wikipedia domain and measures the extent to which existing systems overfit the original test set. Despite several years of heavy test set re-use, we find no evidence of adaptive overfitting. The remaining three test sets are constructed from New York Times articles, Reddit posts, and Amazon produc…

  PublisherOA PDFDOI

• Benchmarking Distribution Shift in Tabular Data with TableShift

  2023-01-01 · 6 citations

  articleSenior author
  PublisherDOI

Frequent coauthors

• Vaishaal Shankar

  29 shared

• Benjamin Recht

  University of California, Berkeley

  22 shared

• Piotr Indyk

  Moscow Institute of Thermal Technology

  20 shared

• Aleksander Ma̧dry

  15 shared

• Rebecca Roelofs

  Google (United States)

  14 shared

• Chinmay Hegde

  13 shared

• Moritz Hardt

  11 shared

• Ilias Diakonikolas

  10 shared

Labs

• MIT SloanPI

Awards & honors

• 2024 Dimensional Fund Advisors First Prize Award for the bes…

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