Michael P. Kim

Orcid: 0009-0002-2928-5006

Affiliations:
  • University of California, Berkeley, Miller Institute for Basic Research in Science, CA, USA
  • Stanford University, CA, USA (PhD 2020)


According to our database1, Michael P. Kim authored at least 24 papers between 2015 and 2023.

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Bibliography

2023
Characterizing notions of omniprediction via multicalibration.
CoRR, 2023

Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Making Decisions Under Outcome Performativity.
Proceedings of the 14th Innovations in Theoretical Computer Science Conference, 2023

Loss Minimization Through the Lens Of Outcome Indistinguishability.
Proceedings of the 14th Innovations in Theoretical Computer Science Conference, 2023

Is Your Model Predicting the Past?
Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023

2022
Backward baselines: Is your model predicting the past?
CoRR, 2022

Planting Undetectable Backdoors in Machine Learning Models.
CoRR, 2022

Planting Undetectable Backdoors in Machine Learning Models : [Extended Abstract].
Proceedings of the 63rd IEEE Annual Symposium on Foundations of Computer Science, 2022

Low-Degree Multicalibration.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
A complexity-theoretic perspective on fairness.
PhD thesis, 2020

Outcome Indistinguishability.
Electron. Colloquium Comput. Complex., 2020

Preference-Informed Fairness.
Proceedings of the 11th Innovations in Theoretical Computer Science Conference, 2020

A Distributional Framework For Data Valuation.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Learning from Outcomes: Evidence-Based Rankings.
Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

Tracking and Improving Information in the Service of Fairness.
Proceedings of the 2019 ACM Conference on Economics and Computation, 2019

Multiaccuracy: Black-Box Post-Processing for Fairness in Classification.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018
Fairness Through Computationally-Bounded Awareness.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Multicalibration: Calibration for the (Computationally-Identifiable) Masses.
Proceedings of the 35th International Conference on Machine Learning, 2018

On Estimating Edit Distance: Alignment, Dimension Reduction, and Embeddings.
Proceedings of the 45th International Colloquium on Automata, Languages, and Programming, 2018

2017
Who Can Win a Single-Elimination Tournament?
SIAM J. Discret. Math., 2017

Calibration for the (Computationally-Identifiable) Masses.
CoRR, 2017

2015
Fixing Tournaments for Kings, Chokers, and More.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015


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