Intent-aware temporal query modeling for keyword suggestion. - Fredrik Johansson 002; Tobias Färdig; Vinay Jethava; Svetoslav Marinov (PIKM, 2012)

Entity disambiguation in anonymized graphs using graph kernels. - Linus Hermansson; Tommi Kerola; Fredrik Johansson 002; Vinay Jethava; Devdatt P. Dubhashi (CIKM, 2013)

DLOREAN: Dynamic Location-Aware Reconstruction of Multiway Networks. - Fredrik Johansson 002; Vinay Jethava; Devdatt P. Dubhashi (ICDM Workshops, 2013)

Global graph kernels using geometric embeddings. - Fredrik D. Johansson; Vinay Jethava; Devdatt P. Dubhashi; Chiranjib Bhattacharyya (ICML, 2014)

Generalized Shortest Path Kernel on Graphs. - Linus Hermansson; Fredrik D. Johansson; Osamu Watanabe 001 (Discovery Science, 2015)

Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings. - Fredrik D. Johansson; Devdatt P. Dubhashi (KDD, 2015)

Classifying Large Graphs with Differential Privacy. - Fredrik D. Johansson; Otto Frost; Carl Retzner; Devdatt P. Dubhashi (MDAI, 2015)

Neural context embeddings for automatic discovery of word senses. - Mikael Kågebäck; Fredrik D. Johansson; Richard Johansson; Devdatt P. Dubhashi (VS@HLT-NAACL, 2015)

Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization. - Fredrik D. Johansson; Ankani Chattoraj; Chiranjib Bhattacharyya; Devdatt P. Dubhashi (NIPS, 2015)

Visions and open challenges for a knowledge-based culturomics. - Nina Tahmasebi; Lars Borin; Gabriele Capannini; Devdatt P. Dubhashi; Peter Exner; Markus Forsberg; Gerhard Gossen; Fredrik D. Johansson; Richard Johansson; Mikael Kågebäck; Olof Mogren; Pierre Nugues; et al. (Int. J. Digit. Libr., 2015)

Learning Representations for Counterfactual Inference. - Fredrik D. Johansson; Uri Shalit; David A. Sontag (ICML, 2016)

Bounding and Minimizing Counterfactual Error. - Uri Shalit; Fredrik D. Johansson; David A. Sontag (CoRR, 2016)

Learning with Geometric Embeddings of Graphs. - Fredrik Johansson 002 (2016)

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

Estimating individual treatment effect: generalization bounds and algorithms. - Uri Shalit; Fredrik D. Johansson; David A. Sontag (ICML, 2017)

Why Is My Classifier Discriminatory? - Irene Y. Chen; Fredrik D. Johansson; David A. Sontag (NeurIPS, 2018)

Evaluating Reinforcement Learning Algorithms in Observational Health Settings. - Omer Gottesman; Fredrik D. Johansson; Joshua Meier; Jack Dent; Donghun Lee; Srivatsan Srinivasan; Linying Zhang; Yi Ding; David Wihl; Xuefeng Peng; Jiayu Yao; Isaac Lage; et al. (CoRR, 2018)

Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions. - Fredrik D. Johansson (CoRR, 2018)

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

Estimation of Utility-Maximizing Bounds on Potential Outcomes. - Maggie Makar; Fredrik D. Johansson; John V. Guttag; David A. Sontag (CoRR, 2019)

Characterization of Overlap in Observational Studies. - Michael Oberst; Fredrik D. Johansson; Dennis Wei; Tian Gao; Gabriel A. Brat; David A. Sontag; Kush R. Varshney (AISTATS, 2020)

Estimation of Bounds on Potential Outcomes For Decision Making. - Maggie Makar; Fredrik D. Johansson; John V. Guttag; David A. Sontag (ICML, 2020)

Learning to search efficiently for causally near-optimal treatments. - Samuel Håkansson; Viktor Lindblom; Omer Gottesman; Fredrik D. Johansson (NeurIPS, 2020)

A survey on graph kernels. - Nils M. Kriege; Fredrik D. Johansson; Christopher Morris 001 (Appl. Netw. Sci., 2020)

Learning Approximate and Exact Numeral Systems via Reinforcement Learning. - Emil Carlsson; Devdatt P. Dubhashi; Fredrik D. Johansson (CogSci, 2021)

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

ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects. - Newton Mwai Kinyanjui; Fredrik D. Johansson (CoRR, 2021)

Case-based off-policy policy evaluation using prototype learning. - Anton Matsson; Fredrik D. Johansson (CoRR, 2021)

Using time-series privileged information for provably efficient learning of prediction models. - Rickard K. A. Karlsson; Martin Willbo; Zeshan M. Hussain; Rahul G. Krishnan; David A. Sontag; Fredrik Johansson 002 (AISTATS, 2022)

ADCB: An Alzheimer's disease simulator for benchmarking observational estimators of causal effects. - Newton Mwai Kinyanjui; Fredrik D. Johansson (CHIL, 2022)

Efficient learning of nonlinear prediction models with time-series privileged information. - Bastian Jung; Fredrik D. Johansson (NeurIPS, 2022)

Case-based off-policy evaluation using prototype learning. - Anton Matsson; Fredrik D. Johansson (UAI, 2022)

Using satellites and artificial intelligence to measure health and material-living standards in India. - Adel Daoud; Felipe Jordan; Makkunda Sharma; Fredrik D. Johansson; Devdatt P. Dubhashi; Sourabh Bikash Paul; Subhashis Banerjee (CoRR, 2022)

Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa. - Connor T. Jerzak; Fredrik Johansson 002; Adel Daoud (CoRR, 2022)

Sharing pattern submodels for prediction with missing values. - Lena Stempfle; Fredrik D. Johansson (CoRR, 2022)

Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. - Fredrik D. Johansson; Uri Shalit; Nathan Kallus; David A. Sontag (J. Mach. Learn. Res., 2022)

Practicality of generalization guarantees for unsupervised domain adaptation with neural networks. - Adam Breitholtz; Fredrik D. Johansson (Trans. Mach. Learn. Res., 2022)

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

Image-based Treatment Effect Heterogeneity. - Connor Thomas Jerzak; Fredrik Daniel Johansson; Adel Daoud (CLeaR, 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 (IJCAI, 2023)

Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities. - Connor T. Jerzak; Fredrik D. Johansson; Adel Daoud (CoRR, 2023)

Off-Policy Evaluation with Out-of-Sample Guarantees. - Sofia Ek; Dave Zachariah; Fredrik D. Johansson; Peter Stoica (Trans. Mach. Learn. Res., 2023)

Fast Treatment Personalization with Latent Bandits in Fixed-Confidence Pure Exploration. - Newton Mwai Kinyanjui; Emil Carlsson; Fredrik D. Johansson (Trans. Mach. Learn. Res., 2023)

Pure Exploration in Bandits with Linear Constraints. - Emil Carlsson; Debabrota Basu; Fredrik D. Johansson; Devdatt P. Dubhashi (AISTATS, 2024)

MINTY: Rule-based models that minimize the need for imputing features with missing values. - Lena Stempfle; Fredrik D. Johansson (AISTATS, 2024)

How Should We Represent History in Interpretable Models of Clinical Policies? - Anton Matsson; Lena Stempfle; Yaochen Rao; Zachary R. Margolin; Heather J. Litman; Fredrik D. Johansson (ML4H@NeurIPS, 2024)

Active preference learning for ordering items in- and out-of-sample. - Herman Bergström; Emil Carlsson; Devdatt P. Dubhashi; Fredrik D. Johansson (NeurIPS, 2024)

IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark. - Fredrik D. Johansson (NeurIPS, 2024)

Identifiable latent bandits: Combining observational data and exploration for personalized healthcare. - Ahmet Zahid Balcioglu; Emil Carlsson; Fredrik D. Johansson (CoRR, 2024)

Overcoming label shift in targeted federated learning. - Edvin Listo Zec; Adam Breitholtz; Fredrik D. Johansson (CoRR, 2024)

Expert Study on Interpretable Machine Learning Models with Missing Data. - Lena Stempfle; Arthur James; Julie Josse; Tobias Gauss; Fredrik D. Johansson (CoRR, 2024)

Unsupervised Domain Adaptation by Learning Using Privileged Information. - Adam Breitholtz; Anton Matsson; Fredrik D. Johansson (Trans. Mach. Learn. Res., 2024)

Prediction Models That Learn to Avoid Missing Values. - Lena Stempfle; Anton Matsson; Newton Mwai Kinyanjui; Fredrik D. Johansson (CoRR, 2025)

Pragmatic Policy Development via Interpretable Behavior Cloning. - Anton Matsson; Yaochen Rao; Heather J. Litman; Fredrik D. Johansson (CoRR, 2025)

Latent Preference Bandits. - Newton Mwai Kinyanjui; Emil Carlsson; Fredrik D. Johansson (CoRR, 2025)