Search Results for author: Fredrik D. Johansson

Found 20 papers, 7 papers with code

Case-based off-policy policy evaluation using prototype learning

no code implementations22 Nov 2021 Anton Matsson, Fredrik D. Johansson

If the behavior policy is estimated using black-box models, it can be hard to diagnose potential problems and to determine for which inputs the policies differ in their suggested actions and resulting values.

ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects

no code implementations12 Nov 2021 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 too simple to capture important aspects of real applications.

Causal Inference

Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models

1 code implementation28 Oct 2021 Rickard Karlsson, Martin Willbo, Zeshan Hussain, Rahul G. Krishnan, David Sontag, Fredrik D. Johansson

Our question is when using this privileged data leads to more sample-efficient learning of models that use only baseline data for predictions at test time.

Time Series

Thompson Sampling for Bandits with Clustered Arms

no code implementations6 Sep 2021 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 where arms are clustered.

Learning Approximate and Exact Numeral Systems via Reinforcement Learning

no code implementations28 May 2021 Emil Carlsson, Devdatt Dubhashi, Fredrik D. Johansson

The agents gradually learn to communicate using reinforcement learning and the resulting numeral systems are shown to be efficient in the information-theoretic framework of Regier et al. (2015); Gibson et al. (2017).

Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects

1 code implementation21 Jan 2020 Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag

Practitioners in diverse fields such as healthcare, economics and education are eager to apply machine learning to improve decision making.

Decision Making Generalization Bounds +2

Estimation of Bounds on Potential Outcomes For Decision Making

no code implementations ICML 2020 Maggie Makar, Fredrik D. Johansson, John Guttag, David Sontag

Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics.

Decision Making

Characterization of Overlap in Observational Studies

1 code implementation9 Jul 2019 Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David Sontag, Kush R. Varshney

Overlap between treatment groups is required for non-parametric estimation of causal effects.

A Survey on Graph Kernels

no code implementations28 Mar 2019 Nils M. Kriege, Fredrik D. Johansson, Christopher Morris

Graph kernels have become an established and widely-used technique for solving classification tasks on graphs.

General Classification Graph Classification

Support and Invertibility in Domain-Invariant Representations

no code implementations8 Mar 2019 Fredrik D. Johansson, David Sontag, Rajesh Ranganath

In this work, we give generalization bounds for unsupervised domain adaptation that hold for any representation function by acknowledging the cost of non-invertibility.

Generalization Bounds Unsupervised Domain Adaptation

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

no code implementations14 Nov 2018 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).

Why Is My Classifier Discriminatory?

no code implementations NeurIPS 2018 Irene Chen, Fredrik D. Johansson, David Sontag

Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy.

Fairness

Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery

no code implementations ICML 2017 Ashkan Panahi, Devdatt Dubhashi, Fredrik D. Johansson, Chiranjib Bhattacharyya

Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical clustering are beset by local minima, which are sometimes drastically suboptimal.

Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence

1 code implementation10 Nov 2016 Emilio Jorge, Mikael Kågebäck, Fredrik D. Johansson, Emil Gustavsson

The images from the game provide a non trivial environment for the agents to discuss and a natural grounding for the concepts they decide to encode in their communication.

Image Retrieval

Estimating individual treatment effect: generalization bounds and algorithms

2 code implementations ICML 2017 Uri Shalit, Fredrik D. Johansson, David Sontag

We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation.

Causal Inference Generalization Bounds

Learning Representations for Counterfactual Inference

no code implementations12 May 2016 Fredrik D. Johansson, Uri Shalit, David Sontag

Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology.

Counterfactual Inference Domain Adaptation +1

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