389 citations · 1.1k across the 25 of their papers we have counts for
26 papers
MotherTree: Meta-learning on synthetic data improves decision tree training
Ziyuan Wang, Fredrik D. Johansson
Conventional decision tree algorithms produce effective, transparent models that can be audited, communicated, and deployed independently of the training data, but require learning…
Practicality of generalization guarantees for unsupervised domain adaptation with neural networks
Adam Breitholtz, Fredrik D. Johansson
Understanding generalization is crucial to confidently engineer and deploy machine learning models, especially when deployment implies a shift in the data domain. For such domain a…
Unsupervised domain adaptation by learning using privileged information
Adam Breitholtz, Anton Matsson, Fredrik D. Johansson
Successful unsupervised domain adaptation is guaranteed only under strong assumptions such as covariate shift and overlap between input domains. The latter is often violated in hig…
Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Observational studies require adjustment for confounding factors that are correlated with both the treatment and outcome. In the setting where the observed variables are tabular qu…
Off-Policy Evaluation with Out-of-Sample Guarantees
Sofia Ek, Dave Zachariah, Fredrik D. Johansson +1
We consider the problem of evaluating the performance of a decision policy using past observational data. The outcome of a policy is measured in terms of a loss (aka. disutility or…
Efficient learning of nonlinear prediction models with time-series privileged information
Bastian Jung, Fredrik D Johansson
In domains where sample sizes are limited, efficient learning algorithms are critical. Learning using privileged information (LuPI) offers increased sample efficiency by allowing p…
Sharing pattern submodels for prediction with missing values
Lena Stempfle, Ashkan Panahi, Fredrik D. Johansson
Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. When variables are missing in recurring patter…
Image-based Treatment Effect Heterogeneity
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Randomized controlled trials (RCTs) are considered the gold standard for estimating the average treatment effect (ATE) of interventions. One use of RCTs is to study the causes of g…
Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the eff…
Case-based off-policy policy evaluation using prototype learning
Anton Matsson, Fredrik D. Johansson
Importance sampling (IS) is often used to perform off-policy policy evaluation but is prone to several issues, especially when the behavior policy is unknown and must be estimated…
ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects
Newton Mwai Kinyanjui, Fredrik D. Johansson
Simulators make unique benchmarks for causal effect estimation since they do not rely on unverifiable assumptions or the ability to intervene on real-world systems, but are often t…
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
Rickard K. A. Karlsson, Martin Willbo, Zeshan Hussain +3
We study prediction of future outcomes with supervised models that use privileged information during learning. The privileged information comprises samples of time series observed…
Thompson Sampling for Bandits with Clustered Arms
Emil Carlsson, Devdatt Dubhashi, Fredrik D. Johansson
We propose algorithms based on a multi-level Thompson sampling scheme, for the stochastic multi-armed bandit and its contextual variant with linear expected rewards, in the setting…
Learning to search efficiently for causally near-optimal treatments
Samuel Håkansson, Viktor Lindblom, Omer Gottesman +1
Finding an effective medical treatment often requires a search by trial and error. Making this search more efficient by minimizing the number of unnecessary trials could lower both…
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
Fredrik D. Johansson, Uri Shalit, Nathan Kallus +1
Practitioners in diverse fields such as healthcare, economics and education are eager to apply machine learning to improve decision making. The cost and impracticality of performin…
Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar, Fredrik D. Johansson, John Guttag +1
Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often…
Characterization of Overlap in Observational Studies
Michael Oberst, Fredrik D. Johansson, Dennis Wei +4
Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate th…
A Survey on Graph Kernels
Nils M. Kriege, Fredrik D. Johansson, Christopher Morris
Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-b…
Support and Invertibility in Domain-Invariant Representations
Fredrik D. Johansson, David Sontag, Rajesh Ranganath
Learning domain-invariant representations has become a popular approach to unsupervised domain adaptation and is often justified by invoking a particular suite of theoretical resul…
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…
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)…
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…
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…
Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D. Johansson, David Sontag
There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal…