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Fredrik D. Johansson

26 papers hereh-index 204.6k citations55 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author5
  • middle author9
  • last author9

Across the 24 of 26 papers where every author was matched, so the position is known.

fields
  • cs.LG17
  • stat.ML7
  • cs.DS1
  • stat.AP1
same name
  • Fredrik D. Johansson — 6 papers, h 3
  • Fredrik D. Johansson — 4 papers, h 3
  • Fredrik D. Johansson — 3 papers, h 0
  • Fredrik D. Johansson — 3 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152026
most citedA Survey on Graph Kernels

389 citations · 1.1k across the 25 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

stat.AP2018

Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions

Fredrik D. Johansson

We study heterogeneity in the effect of a mindset intervention on student-level performance through an observational dataset from the National Study of Learning Mindsets (NSLM). Ou…

cs.LG2018★ 83 cited

Evaluating Reinforcement Learning Algorithms in Observational Health Settings

Omer Gottesman, Fredrik Johansson, Joshua Meier +16

Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare. Reinforcement learning (RL)…

stat.ML2018★ 153 cited

Why Is My Classifier Discriminatory?

Irene Chen, Fredrik D. Johansson, David Sontag

Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this…

stat.ML2018★ 35 cited

Learning Weighted Representations for Generalization Across Designs

Fredrik D. Johansson, Nathan Kallus, Uri Shalit +1

Predictive models that generalize well under distributional shift are often desirable and sometimes crucial to building robust and reliable machine learning applications. We focus…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.