Johansson, Fredrik D.

22 publications

ICML 2025 Prediction Models That Learn to Avoid Missing Values Lena Stempfle, Anton Matsson, Newton Mwai, Fredrik D. Johansson

NeurIPS 2024 Active Preference Learning for Ordering Items In- and Out-of-Sample Herman Bergström, Emil Carlsson, Devdatt Dubhashi, Fredrik D. Johansson

ICMLW 2024 Batched Fixed-Confidence Pure Exploration for Bandits with Switching Constraints Newton Mwai, Milad Malekipirbazari, Fredrik D. Johansson

ICMLW 2024 Identifiable Latent Bandits: Combining Observational Data and Exploration for Personalized Healthcare Ahmet Zahid Balcıoğlu, Emil Carlsson, Fredrik D. Johansson

NeurIPS 2024 IncomeSCM: From Tabular Data Set to Time-Series Simulator and Causal Estimation Benchmark Fredrik D. Johansson

TMLR 2024 Unsupervised Domain Adaptation by Learning Using Privileged Information Adam Breitholtz, Anton Matsson, Fredrik D. Johansson

TMLR 2023 Fast Treatment Personalization with Latent Bandits in Fixed-Confidence Pure Exploration Newton Mwai Kinyanjui, Emil Carlsson, Fredrik D. Johansson

ICMLW 2023 Learning Replacement Variables in Interpretable Rule-Based Models Lena Stempfle, Fredrik D. Johansson

TMLR 2023 Off-Policy Evaluation with Out-of-Sample Guarantees Sofia Ek, Dave Zachariah, Fredrik D. Johansson, Peter Stoica

AAAI 2023 Sharing Pattern Submodels for Prediction with Missing Values Lena Stempfle, Ashkan Panahi, Fredrik D. Johansson

IJCAI 2023 Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa Markus B. Pettersson, Mohammad Kakooei, Julia Ortheden, Fredrik D. Johansson, Adel Daoud

CHIL 2022 ADCB: An Alzheimer’s Disease Simulator for Benchmarking Observational Estimators of Causal Effects Newton Mwai Kinyanjui, Fredrik D Johansson

UAI 2022 Case-Based Off-Policy Evaluation Using Prototype Learning Anton Matsson, Fredrik D. Johansson

NeurIPS 2022 Efficient Learning of Nonlinear Prediction Models with Time-Series Privileged Information Bastian Jung, Fredrik D Johansson

JMLR 2022 Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag

IJCAI 2021 Thompson Sampling for Bandits with Clustered Arms Emil Carlsson, Devdatt P. Dubhashi, Fredrik D. Johansson

NeurIPS 2020 Learning to Search Efficiently for Causally Near-Optimal Treatments Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D Johansson

AISTATS 2019 Support and Invertibility in Domain-Invariant Representations Fredrik D. Johansson, David Sontag, Rajesh Ranganath

NeurIPS 2018 Why Is My Classifier Discriminatory? Irene Chen, Fredrik D Johansson, David Sontag

ICML 2017 Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery Ashkan Panahi, Devdatt Dubhashi, Fredrik D. Johansson, Chiranjib Bhattacharyya

ICML 2017 Estimating Individual Treatment Effect: Generalization Bounds and Algorithms Uri Shalit, Fredrik D. Johansson, David Sontag

NeurIPS 2015 Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization Fredrik D Johansson, Ankani Chattoraj, Chiranjib Bhattacharyya, Devdatt Dubhashi