World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
25663
World Ranking
5198
National Ranking
238

Research.com Recognitions

  • 2019 - Fellow of Alfred P. Sloan Foundation

Overview

Moritz Hardt is affiliated with the Max Planck Institute for Intelligent Systems in Germany, focusing primarily on computer science. Their research spans various subfields including artificial intelligence, management science and operations research, safety research, economics and econometrics, and statistics and probability.

The scientist's work covers a range of main topics, notably:

  • Advanced Bandit Algorithms Research
  • Ethics and Social Impacts of AI
  • Explainable Artificial Intelligence (XAI)
  • Adversarial Robustness in Machine Learning
  • Reinforcement Learning in Robotics
  • Machine Learning and Data Classification
  • Domain Adaptation and Few-Shot Learning

Moritz Hardt has contributed papers to several publication venues, with a strong presence on arXiv (Cornell University), and additional publications in the 2022 ACM Conference on Fairness, Accountability, and Transparency, Communications of the ACM, Proceedings of the National Academy of Sciences, and the SSRN Electronic Journal. Key recent papers include:

  • Understanding deep learning (still) requires rethinking generalization, 2021, Communications of the ACM
  • Algorithmic amplification of politics on Twitter, 2021, Proceedings of the National Academy of Sciences
  • Stochastic Optimization for Performative Prediction, 2020, arXiv (Cornell University)
  • Difficult Lessons on Social Prediction from Wisconsin Public Schools, 2023, arXiv (Cornell University)
  • Questioning the Survey Responses of Large Language Models, 2023, arXiv (Cornell University)

The scientist frequently collaborates with a number of coauthors, including:

  • Celestine Mendler-Dünner
  • Rediet Abebe
  • Ricardo Dominguez-Olmedo
  • John A. Miller
  • Ludwig Schmidt

In recognition of their contributions, Moritz Hardt was named a Fellow of the Alfred P. Sloan Foundation in 2019.

Best Publications

  • Understanding deep learning (still) requires rethinking generalization

    Chiyuan Zhang;Samy Bengio;Moritz Hardt;Benjamin Recht

  • Fairness through awareness

    Cynthia Dwork;Moritz Hardt;Toniann Pitassi;Omer Reingold

  • Understanding deep learning requires rethinking generalization.

    Chiyuan Zhang;Samy Bengio;Moritz Hardt;Benjamin Recht

  • Equality of opportunity in supervised learning

    Moritz Hardt;Eric Price;Nathan Srebro

  • Sanity Checks for Saliency Maps

    Julius Adebayo;Justin Gilmer;Michael Christoph Muelly;Ian Goodfellow

  • Train faster, generalize better: stability of stochastic gradient descent

    Moritz Hardt;Benjamin Recht;Yoram Singer

  • Avoiding Discrimination through Causal Reasoning

    Niki Kilbertus;Mateo Rojas-Carulla;Giambattista Parascandolo;Moritz Hardt

  • On the geometry of differential privacy

    Moritz Hardt;Kunal Talwar

  • A Simple and Practical Algorithm for Differentially Private Data Release

    Moritz Hardt;Katrina Ligett;Frank Mcsherry

  • A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis

    Moritz Hardt;Guy N. Rothblum

  • The reusable holdout: Preserving validity in adaptive data analysis

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Preserving Statistical Validity in Adaptive Data Analysis

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Delayed Impact of Fair Machine Learning.

    Lydia T. Liu;Sarah Dean;Esther Rolf;Max Simchowitz

  • Measuring the predictability of life outcomes with a scientific mass collaboration.

    Matthew J Salganik;Ian Lundberg;Alexander T Kindel;Caitlin E Ahearn

  • Understanding Alternating Minimization for Matrix Completion

    Moritz Hardt

  • Identity Matters in Deep Learning

    Moritz Hardt;Tengyu Ma

  • Privately Releasing Conjunctions and the Statistical Query Barrier

    Anupam Gupta;Moritz Hardt;Aaron Roth;Jonathan R. Ullman

  • Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

    Yu Sun;Xiaolong Wang;Zhuang Liu;John Miller

  • Strategic Classification

    Moritz Hardt;Nimrod Megiddo;Christos Papadimitriou;Mary Wootters

  • Gradient Descent Learns Linear Dynamical Systems

    Moritz Hardt;Tengyu Ma;Benjamin Recht

  • A System for Massively Parallel Hyperparameter Tuning

    Liam Li;Kevin G. Jamieson;Afshin Rostamizadeh;Ekaterina Gonina

  • A System for Massively Parallel Hyperparameter Tuning

    Liam Li;Kevin Jamieson;Afshin Rostamizadeh;Ekaterina Gonina

  • Performative Prediction

    Juan C. Perdomo;Tijana Zrnic;Celestine Mendler-Dünner;Moritz Hardt

Frequent Co-Authors

Aaron Roth
Aaron Roth University of Pennsylvania
Benjamin Recht
Benjamin Recht University of California, Berkeley
Vitaly Feldman
Vitaly Feldman Apple (United States)
Omer Reingold
Omer Reingold Stanford University
Toniann Pitassi
Toniann Pitassi Columbia University
Cynthia Dwork
Cynthia Dwork Harvard University
Boaz Barak
Boaz Barak Harvard University
David Steurer
David Steurer ETH Zurich
Jonathan Ullman
Jonathan Ullman Northeastern University
Samy Bengio
Samy Bengio Apple (United States)

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