Dave Zachariah
Universitetslektor vid Institutionen för informationsteknologi; Systemteknik
- E-post:
- dave.zachariah@it.uu.se
- Besöksadress:
- Hus 10, Lägerhyddsvägen 1
- Postadress:
- Box 337
751 05 UPPSALA
- Akademiska meriter:
- Docent
- CV:
- Ladda ned CV
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Kort presentation
Associate professor at the division of Systems and Control and coordinator of the Machine Learning Arena. Research interests include: statistical machine learning & signal processing; decision theory & causal inference; localization & sensor fusion.
Paper preprints available on arXiv
Reproducible research accessible on github
Publikationer
Senaste publikationer
- Off-Policy Evaluation with Out-of-Sample Guarantees (2023)
- Regularized Linear Regression via Covariance Fitting (2023)
- Analysis of the Minimum-Norm Least-Squares Estimator and Its Double-Descent Behavior [Lecture Notes] (2023)
- Online Learning for Prediction via Covariance Fitting (2023)
- Min-Max Probe Placement and Extended Relaxation Estimation Method for Processing Blade Tip Timing Signals (2023)
Alla publikationer
Artiklar
- Off-Policy Evaluation with Out-of-Sample Guarantees (2023)
- Regularized Linear Regression via Covariance Fitting (2023)
- Analysis of the Minimum-Norm Least-Squares Estimator and Its Double-Descent Behavior [Lecture Notes] (2023)
- Online Learning for Prediction via Covariance Fitting (2023)
- Min-Max Probe Placement and Extended Relaxation Estimation Method for Processing Blade Tip Timing Signals (2023)
- A shift from the problematic of "transformation" (2022)
- Scalable Belief Updating for Urban Air Quality Modeling and Prediction (2021)
- Robust localization in wireless networks from corrupted signals (2021)
- Robust Prediction When Features are Missing (2020)
- Robust Risk Minimization for Statistical Learning From Corrupted Data (2020)
- Classical labor values (2020)
- Data consistency approach to model validation (2019)
- Data Consistency Approach to Model Validation (2019)
- Learning sparse graphs for prediction of multivariate data processes (2019)
- Effect Inference From Two-Group Data With Sampling Bias (2019)
- Comparison of two hyperparameter-free sparse signal processing methods for direction-of-arrival tracking in the HF97 ocean acoustic experiment (2018)
- Identification of cascade water tanks using a PWARX model (2018)
- Recursive nonlinear-system identification using latent variables (2018)
- Scalable and Passive Wireless Network Clock Synchronization in LOS Environments (2017)
- Comments on “Enhanced PUMA for Direction-of-Arrival Estimation and Its Performance Analysis” (2017)
- A multicomponent T2 relaxometry algorithm for myelin water imaging of the brain (2016)
- Recursive identification method for piecewise ARX models (2016)
- Joint ranging and clock parameter estimation by wireless round trip time measurements (2015)
- Cramér–Rao bound analog of Bayes' rule (2015)
- Online hyperparameter-free sparse estimation method (2015)
- Weighted SPICE (2014)
- Schedule-based sequential localization in asynchronous wireless networks (2014)
- Estimation for the linear model with uncertain covariance matrices (2014)
- Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging (2013)
- Composite Gaussian Processes: Scalable Computation and Performance Analysis
- Scalable Belief Updating forUrban Air Quality Modeling and Prediction
- Semi-Supervised Learning of Classierswhen Labels are Missing at Random
- Robust learning in heterogeneous contexts
- Calibration tests beyond classification
- CalibrationAnalysis.jl
Konferenser
- Learning Pareto-Efficient Decisions with Confidence (2022)
- Advanced Processing of a Blade Vibratory Response Obtained With Tip Timing Method Using Hyperparameter-Free Sparse Estimation Method (2021)
- Inference of Causal Effects when Control Variables are Unknown (2020)
- Approximate Gaussian Process Regression and Performance Analysis Using Composite Likelihood (2020)
- Learning Robust Decision Policies from Observational Data (2020)
- A latent variable approach to heat load prediction in thermal grids (2020)
- Flexible Models for Smart Maintenance (2019)
- Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding (2019)
- Prediction of Spatial Point Processes (2019)
- Calibration tests in multi-class classification (2019)
- Identification of nonlinear feedback mechanisms operating in closed loop using inertial sensors (2018)
- Learning localized spatio-temporal models from streaming data (2018)
- How consistent is my model with the data? (2018)
- Regularized parametric system identification (2018)
- Model-robust counterfactual prediction method (2018)
- Prediction Performance After Learning in Gaussian Process Regression (2017)
- Recursive nonlinear system identification using latent variables (2016)
- Prediction performance after learning in Gaussian process regression (2016)
- Online prediction of spatial fields for radio-frequency communication (2016)
- Minimum sidelobe beampattern design for MIMO radar systems (2014)
- Gridless compressive-sensing methods for frequency estimation (2014)
- Enhanced Capon beamformer using regularized covariance matching (2013)
