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Computer Science

D-Index
53
Citations
11236
World Ranking
4826
National Ranking
2244

Overview

Dean P. Foster is a researcher affiliated with Amazon in the United States. Their work spans multiple fields, primarily within Computer Science and Decision Sciences, with a focus on several subfields, including Artificial Intelligence, Management Science and Operations Research, Statistics and Probability, Management Information Systems, and Economics and Econometrics.

The main topics covered in their research include:

  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Monetary Policy and Economic Impact
  • Auction Theory and Applications
  • Reinforcement Learning in Robotics
  • Statistical Methods and Inference
  • Stochastic Processes and Financial Applications

Dean P. Foster's publications have appeared predominantly in arXiv (Cornell University), where they have contributed 24 papers. Other notable publication venues include IEEE Transactions on Information Theory, Journal of Political Economy, Theoretical Economics, and American Journal of Respiratory and Critical Care Medicine.

Some of the recent papers by Dean P. Foster are:

  • "PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing," 2020, arXiv (Cornell University)
  • "What are the Statistical Limits of Offline RL with Linear Function Approximation?," 2020, arXiv (Cornell University)
  • "Deep Inventory Management," 2022, arXiv (Cornell University)
  • "Single-Index Models in the High Signal Regime," 2021, IEEE Transactions on Information Theory
  • "Forecast Hedging and Calibration," 2021, Journal of Political Economy

Frequent coauthors working with Dean P. Foster include Dhruv Madeka, Sham M. Kakade, Kari Torkkola, Sergiu Hart, and Carson Eisenach. These collaborators have multiple joint publications, reflecting ongoing research partnerships.

Best Publications

  • Stochastic Evolutionary Game Dynamics

    Dean Foster;Peyton Young

  • Clustering Methods for Collaborative Filtering

    Lyle H. Ungar;Dean P. Foster

  • The risk inflation criterion for multiple regression

    Dean P. Foster;Edward I. George

  • Calibration and empirical Bayes variable selection

    Edward I George;Dean P Foster

  • Continuous Record Asymptotics for Rolling Sample Variance Estimators

    Dean P. Foster;Daniel B. Nelson

  • Regret in the On-line Decision Problem

    D. Foster;R. Vohra

  • Multi-View Learning of Word Embeddings via CCA

    Paramveer Dhillon;Dean P Foster;Lyle H. Ungar

  • A Spectral Algorithm for Latent Dirichlet Allocation

    Animashree Anandkumar;Dean P. Foster;Daniel Hsu;Sham M. Kakade

  • Precision and Accuracy of Judgmental Estimation

    Ilan Yaniv;Dean P. Foster

  • Multi-view regression via canonical correlation analysis

    Sham M. Kakade;Dean P. Foster

  • Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy

    Dean P Foster;Robert A Stine

  • PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing

    Justin Chan;Dean Foster;Shyam Gollakota;Eric Horvitz

  • A Spectral Algorithm for Latent Dirichlet Allocation

    Anima Anandkumar;Yi-kai Liu;Daniel J. Hsu;Dean P Foster

  • Spectral Learning of Latent-Variable PCFGs

    Shay B. Cohen;Karl Stratos;Michael Collins;Dean P. Foster

  • Stochastic Convex Optimization with Bandit Feedback

    Alekh Agarwal;Dean P. Foster;Daniel J. Hsu;Sham M. Kakade

  • VIF Regression: A Fast Regression Algorithm for Large Data

    Dongyu Lin;Dean P. Foster;Lyle H. Ungar

  • Cooperation in the long-run

    H Peyton Young;Dean Foster

  • Combining multiple probability predictions using a simple logit model

    Ville A. Satopää;Jonathan Baron;Dean P. Foster;Barbara A. Mellers

  • Streamwise feature selection

    Jing Zhou;Dean P. Foster;Robert A. Stine;Lyle H. Ungar

  • Domain Adaptation with Coupled Subspaces

    John Blitzer;Sham M. Kakade;Dean P. Foster

  • Partial Monitoring—Classification, Regret Bounds, and Algorithms

    Gábor Bartók;Dean P. Foster;Dávid Pál;Alexander Rakhlin

  • Deep Factors for Forecasting

    Yuyang Wang;Alex Smola;Danielle C. Maddix;Jan Gasthaus

  • Filtering and forecasting with misspecified ARCH models II: Making the right forecast with the wrong model

    Daniel B. Nelson;Dean P. Foster

Frequent Co-Authors

Lyle H. Ungar
Lyle H. Ungar University of Pennsylvania
Sham M. Kakade
Sham M. Kakade Harvard University
Rakesh Vohra
Rakesh Vohra University of Pennsylvania
Daniel Hsu
Daniel Hsu Columbia University
Howard Karloff
Howard Karloff Amazon (United States)
Michael Collins
Michael Collins Google (United States)
John Langford
John Langford Microsoft (United States)
Zaid Harchaoui
Zaid Harchaoui University of Washington
Anima Anandkumar
Anima Anandkumar Nvidia (United Kingdom)

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