World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
23405
World Ranking
4184
National Ranking
1973

Overview

J. Zico Kolter is affiliated with Carnegie Mellon University in the United States and has contributed extensively to the field of computer science with a focus on artificial intelligence and machine learning. Their research spans several key topics, including adversarial robustness in machine learning, anomaly detection techniques and applications, domain adaptation and few-shot learning, topic modeling, machine learning and data classification, model reduction and neural networks, and natural language processing techniques.

Their recent publications demonstrate a strong presence in top-tier venues and preprint archives. Notable papers include:

  • Fast is better than free: Revisiting adversarial training (2020), published in arXiv (Cornell University)
  • Universal and Transferable Adversarial Attacks on Aligned Language Models (2023), published in arXiv (Cornell University)
  • Machine Learning for Sustainable Energy Systems (2021), published in Annual Review of Environment and Resources
  • DC3: A learning method for optimization with hard constraints (2021), published in arXiv (Cornell University)
  • Representation Engineering: A Top-Down Approach to AI Transparency (2023), published in arXiv (Cornell University)

Kolter's frequent coauthors include Yiding Jiang, Priya L. Donti, Shaojie Bai, Matt Fredrikson, and Aditi Raghunathan, reflecting collaborative work across multiple projects and research areas.

The major venues where they have published most frequently include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Annual Review of Environment and Resources
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • PLoS ONE

Kolter's work intersects fields such as:

  • Computer Science

With a detailed focus on subfields including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Statistical and Nonlinear Physics
  • Molecular Biology

The emphasis on adversarial robustness and related machine learning techniques is evident in the number of publications. Their research covers both theoretical and applied aspects of AI, including optimization methods and transparency in machine learning models.

Best Publications

  • An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

    Shaojie Bai;J. Zico Kolter;Vladlen Koltun

  • Towards fully autonomous driving: Systems and algorithms

    Jesse Levinson;Jake Askeland;Jan Becker;Jennifer Dolson

  • Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts

    J. Zico Kolter;Marcus A. Maloof

  • Multimodal Transformer for Unaligned Multimodal Language Sequences

    Yao-Hung Hubert Tsai;Shaojie Bai;Paul Pu Liang;J. Zico Kolter;J. Zico Kolter

  • Provable defenses against adversarial examples via the convex outer adversarial polytope

    Eric Wong;J. Zico Kolter

  • Certified Adversarial Robustness via Randomized Smoothing

    Jeremy M. Cohen;Elan Rosenfeld;J. Zico Kolter

  • Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation

    J. Zico Kolter;Tommi S. Jaakkola

  • Learning to Detect and Classify Malicious Executables in the Wild

    J. Zico Kolter;Marcus A. Maloof

  • Provable defenses against adversarial examples via the convex outer adversarial polytope

    J. Zico Kolter;Eric Wong

  • Fast is better than free: Revisiting adversarial training

    Eric Wong;Leslie Rice;J. Zico Kolter

  • Learning to detect malicious executables in the wild

    Unknown

  • OptNet: differentiable optimization as a layer in neural networks

    Brandon Amos;J. Zico Kolter

  • Scaling provable adversarial defenses

    Eric Wong;Frank R. Schmidt;Jan Hendrik Metzen;J. Zico Kolter

  • Differentiable Convex Optimization Layers

    Akshay Agrawal;Brandon Amos;Shane T. Barratt;Stephen P. Boyd

  • Near-Bayesian exploration in polynomial time

    J. Zico Kolter;Andrew Y. Ng

  • Deep Equilibrium Models

    Shaojie Bai;J. Zico Kolter;Vladlen Koltun

  • End-to-End Differentiable Physics for Learning and Control

    Filipe de Avila Belbute-Peres;Kevin A. Smith;Kelsey R. Allen;Josh Tenenbaum

  • Using additive expert ensembles to cope with concept drift

    Unknown

  • Gradient descent GAN optimization is locally stable

    Vaishnavh Nagarajan;J. Zico Kolter

  • Regularization and feature selection in least-squares temporal difference learning

    J. Zico Kolter;Andrew Y. Ng

  • Task-based End-to-end Model Learning in Stochastic Optimization

    Priya L. Donti;Brandon Amos;J. Zico Kolter

  • Differentiable MPC for End-to-end Planning and Control

    Brandon Amos;Ivan Dario Jimenez Rodriguez;Jacob Sacks;Byron Boots

Frequent Co-Authors

Vladlen Koltun
Vladlen Koltun Apple (United States)
Pradeep Ravikumar
Pradeep Ravikumar Carnegie Mellon University
Andrew Y. Ng
Andrew Y. Ng Stanford University
Huan Zhang
Huan Zhang University of California, Los Angeles
Ruslan Salakhutdinov
Ruslan Salakhutdinov Carnegie Mellon University
Saurabh Kumar Garg
Saurabh Kumar Garg University of Tasmania
Zachary C. Lipton
Zachary C. Lipton Carnegie Mellon University
Stephen Boyd
Stephen Boyd Stanford University
Ashish Kapoor
Ashish Kapoor Microsoft (United States)
Yuanzhi Li
Yuanzhi Li Carnegie Mellon University

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