World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
12086
World Ranking
6086
National Ranking
2741

Research.com Recognitions

  • 2008 - Fellow of Alfred P. Sloan Foundation

Overview

Adam Tauman Kalai is affiliated with Microsoft in the United States. Their research contributions span multiple areas within computer science, with a significant focus on artificial intelligence and machine learning.

Their publication record includes 46 works in computer science, with 34 specifically focused on artificial intelligence. Other subfields include sociology and political science, electrical and electronic engineering, computer vision and pattern recognition, and computer science applications.

The research topics covered by Kalai include:

  • Topic Modeling
  • Machine Learning and Algorithms
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Data Classification
  • Natural Language Processing Techniques
  • Ferroelectric and Negative Capacitance Devices
  • Ethics and Social Impacts of AI

Frequent collaborators in their work include Lester Mackey, Vikas Garg, Varun Kanade, Myra Cheng, and Maria De-Arteaga.

Kalai's work has been published predominantly in venues such as arXiv (Cornell University), with 23 publications, as well as contributions to the Leibniz-Zentrum für Informatik (Schloss Dagstuhl), Data Mining and Knowledge Discovery, and Games and Economic Behavior.

Recent selected papers include:

  • Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies (2022), arXiv (Cornell University)
  • Textbooks Are All You Need (2023), arXiv (Cornell University)
  • OpenAI o1 System Card (2024), arXiv (Cornell University)
  • Do Language Models Know When They're Hallucinating References? (2023), arXiv (Cornell University)
  • Language Models Can Teach Themselves to Program Better (2022), arXiv (Cornell University)

In recognition of their work, Kalai was named a Fellow of the Alfred P. Sloan Foundation in 2008.

Best Publications

  • Man is to computer programmer as woman is to homemaker? debiasing word embeddings

    Tolga Bolukbasi;Kai-Wei Chang;James Zou;Venkatesh Saligrama

  • Noise-tolerant learning, the parity problem, and the statistical query model

    Avrim Blum;Adam Kalai;Hal Wasserman

  • Online convex optimization in the bandit setting: gradient descent without a gradient

    Abraham D. Flaxman;Adam Tauman Kalai;H. Brendan McMahan

  • Efficient algorithms for online decision problems

    Adam Kalai;Santosh Vempala

  • Beating the hold-out: bounds for K-fold and progressive cross-validation

    Avrim Blum;Adam Kalai;John Langford

  • Agnostically Learning Halfspaces

    Adam Tauman Kalai;Adam R. Klivans;Yishay Mansour;Rocco A. Servedio

  • Trust-based recommendation systems: an axiomatic approach

    Reid Andersen;Christian Borgs;Jennifer Chayes;Uriel Feige

  • Logarithmic regret algorithms for online convex optimization

    Elad Hazan;Adam Kalai;Satyen Kale;Amit Agarwal

  • Universal portfolios with and without transaction costs

    Avrim Blum;Adam Kalai

  • Efficiently learning mixtures of two Gaussians

    Adam Tauman Kalai;Ankur Moitra;Gregory Valiant

  • Adaptively Learning the Crowd Kernel

    Omer Tamuz;Ce Liu;Serge Belongie;Ohad Shamir

  • Analysis of Perceptron-Based Active Learning

    Sanjoy Dasgupta;Adam Tauman Kalai;Claire Monteleoni

  • Efficient algorithms for universal portfolios

    Adam Kalai;Santosh Vempala

  • Playing Games with Approximation Algorithms

    Sham M. Kakade;Adam Tauman Kalai;Katrina Ligett

  • Adaptively Learning the Crowd Kernel

    Omer Tamuz;Omer Tamuz;Ce Liu;Ohad Shamir;Adam Kalai

  • Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression

    Sham M Kakade;Varun Kanade;Ohad Shamir;Adam Kalai

  • Decoupled classifiers for fair and efficient machine learning

    Cynthia Dwork;Nicole Immorlica;Adam Tauman Kalai;Mark D. M. Leiserson

  • Simulated Annealing for Convex Optimization

    Adam Tauman Kalai;Santosh Vempala

  • A Machine Learning Framework for Programming by Example

    Aditya Menon;Omer Tamuz;Sumit Gulwani;Butler Lampson

  • The myth of the Folk Theorem

    Christian Borgs;Jennifer T. Chayes;Nicole Immorlica;Adam Tauman Kalai

  • Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression

    Sham Kakade;Adam Tauman Kalai;Varun Kanade;Ohad Shamir

Frequent Co-Authors

Jennifer Chayes
Jennifer Chayes University of California, Berkeley
Christian Borgs
Christian Borgs University of California, Berkeley
Avrim Blum
Avrim Blum Toyota Technological Institute at Chicago
Nicole Immorlica
Nicole Immorlica Microsoft (United States)
Kai-Wei Chang
Kai-Wei Chang University of California, Los Angeles
Santosh Vempala
Santosh Vempala Georgia Institute of Technology
James Zou
James Zou Stanford University
Moshe Tennenholtz
Moshe Tennenholtz Technion – Israel Institute of Technology
Butler W. Lampson
Butler W. Lampson Microsoft (United States)
Sumit Gulwani
Sumit Gulwani Microsoft (United States)

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