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D-Index & Metrics

Computer Science

D-Index
37
Citations
4583
World Ranking
10918
National Ranking
4538

Overview

Adam R. Klivans is affiliated with The University of Texas at Austin in the United States. Their research spans primarily the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Molecular Biology, Electrical and Electronic Engineering, Computational Theory and Mathematics, and Computer Vision and Pattern Recognition.

Their work covers diverse topics, with notable contributions to Machine Learning and Algorithms, Adversarial Robustness in Machine Learning, Neural Networks and Applications, Domain Adaptation and Few-Shot Learning, Machine Learning and Data Classification, Bioinformatics and Genomic Networks, and RNA and protein synthesis mechanisms.

Adam R. Klivans has a significant publication record with thirty papers in arXiv (Cornell University), four in bioRxiv (Cold Spring Harbor Laboratory), along with publications in Nature Communications, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, and Leibniz-Zentrum für Informatik (Schloss Dagstuhl).

Recent papers include the following:

  • Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations, 2024, Nature Communications
  • Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection, 2020, arXiv (Cornell University)
  • Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent, 2020, arXiv (Cornell University)
  • Predicting a Protein's Stability under a Million Mutations, 2023, arXiv (Cornell University)
  • Statistical-Query Lower Bounds via Functional Gradients, 2020, arXiv (Cornell University)

Frequent coauthors of Adam R. Klivans include:

  • Aravind Gollakota
  • Konstantinos Stavropoulos
  • Arsen Vasilyan
  • Daniel J. Diaz
  • Surbhi Goel

Best Publications

  • Graph Nonisomorphism Has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses

    Adam R. Klivans;Dieter van Melkebeek

  • Agnostically Learning Halfspaces

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

  • Cryptographic hardness for learning intersections of halfspaces

    Adam R. Klivans;Alexander A. Sherstov

  • Learning intersections and thresholds of halfspaces

    A. R. Klivans;R. O'Donnell;Rocco A. Servedio

  • Learning DNF in time 2 õ ( n 1/3 )

    Adam R. Klivans;Rocco A. Servedio

  • Randomness efficient identity testing of multivariate polynomials

    Adam R. Klivans;Daniel Spielman

  • Agnostically learning halfspaces

    A.T. Kalai;A.R. Klivans;Yishay Mansour;R.A. Servedio

  • Learning DNF in time

    Adam R. Klivans;Rocco Servedio

  • Learning Halfspaces with Malicious Noise

    Adam R. Klivans;Philip M. Long;Rocco A. Servedio

  • Learning Geometric Concepts via Gaussian Surface Area

    A.R. Klivans;R. O'Donnell;R.A. Servedio

  • Efficient Algorithms for Outlier-Robust Regression

    Adam R. Klivans;Pravesh K. Kothari;Raghu Meka

  • Reliably Learning the ReLU in Polynomial Time

    Surbhi Goel;Varun Kanade;Adam R. Klivans;Justin Thaler

  • Learning Graphical Models Using Multiplicative Weights

    Adam Klivans;Raghu Meka

  • List-decoding reed-muller codes over small fields

    Parikshit Gopalan;Adam R. Klivans;David Zuckerman

  • Learnability beyond AC0

    Jeffrey C. Jackson;Adam R. Klivans;Rocco A. Servedio

  • An FPTAS for #Knapsack and Related Counting Problems

    Parikshit Gopalan;Adam Klivans;Raghu Meka;Daniel tefankovic

  • Boosting and Hard-Core Set Construction

    Adam R. Klivans;Rocco A. Servedio

  • Agnostically learning decision trees

    Parikshit Gopalan;Adam Tauman Kalai;Adam R. Klivans

  • Learnability and automatizability

    M. Alekhnovich;M. Braverman;V. Feldman;A.R. Klivans

  • Toward Attribute Efficient Learning of Decision Lists and Parities

    Adam R. Klivans;Rocco A. Servedio

  • Learning One Convolutional Layer with Overlapping Patches

    Surbhi Goel;Adam R. Klivans;Raghu Meka

Frequent Co-Authors

Rocco A. Servedio
Rocco A. Servedio Columbia University
Lance Fortnow
Lance Fortnow Illinois Institute of Technology
Alexandros G. Dimakis
Alexandros G. Dimakis The University of Texas at Austin
Adam Tauman Kalai
Adam Tauman Kalai Microsoft (United States)
Ilias Diakonikolas
Ilias Diakonikolas University of Wisconsin–Madison
Philip M. Long
Philip M. Long Google (United States)
Daniel M. Kane
Daniel M. Kane University of California, San Diego
Toniann Pitassi
Toniann Pitassi Columbia University
Daniel A. Spielman
Daniel A. Spielman Yale University
Mark Braverman
Mark Braverman Princeton University

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