2024
J. Wang, Y. V. Pant, L. Zhao, M. Antkiewicz and K. Czarnecki, "Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles," in IEEE Transactions on Intelligent Transportation Systems.
2023
Lee, J., Sedwards, S., & Czarnecki, K. (2023). Uniformly Constrained Reinforcement Learning. Accepted for Publication in Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS): Special Issue on Multi-Objective Decision Making (MODeM).
2022
Queiroz, R. (2022). Scenario Modeling and Execution for Simulation Testing of Automated-Driving Systems. Retrieved from http://hdl.handle.net/10012/18825 (Original work published 2022)
Larter, S. (2022). A Hierarchical Pedestrian Behaviour Model to Reproduce Realistic Human Behaviour in a Traffic Environment. Retrieved from http://hdl.handle.net/10012/18094 (Original work published 2022)
Gu, S. (2022). XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection. Retrieved from http://hdl.handle.net/10012/17899 (Original work published 2022)
Kahn, M., Sarkar, A., & Czarnecki, K. (2022). I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames.. I Know You Can’t See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames. Presented at the. Retrieved from https://arxiv.org/abs/2109.09807
Pitropov, M. (2022). LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection. Retrieved from http://hdl.handle.net/10012/18062 (Original work published 2022)
Sarkar, A. (2022). Empirical Game Theoretic Models for Autonomous Driving: Methods and Applications. Retrieved from http://hdl.handle.net/10012/18751 (Original work published 2022)
Nguyen, V. D. (2022). Out-of-Distribution Detection for LiDAR-based 3D Object Detection. Retrieved from http://hdl.handle.net/10012/17902 (Original work published 2022)
Sarkar, A., Larson, K., & Czarnecki, K. (2022). Generalized dynamic cognitive hierarchy models for strategic driving behavior.. Generalized Dynamic Cognitive Hierarchy Models for Strategic Driving Behavior. Presented at the. Retrieved from https://arxiv.org/abs/2109.09861
Bouchard, F. ed\ eric, Sedwards, S., & Czarnecki, K. (2022). A Rule-Based Behaviour Planner for Autonomous Driving. Berlin (virtual): Springer.
Larter, S., Queiroz, R., Sedwards, S., Sarkar, A., & Czarnecki, K. (2022). A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation. Aachen, Germany: IEEE. https://doi.org/10.1109/IV51971.2022.9827035 (Original work published 2022)
2021
Kahn, M. (2021). Dynamic-Occlusion-Aware Risk Identification for Autonomous Vehicles Using Hypergames. Retrieved from http://hdl.handle.net/10012/17774 (Original work published 2021)
Sarkar, A., & Czarnecki, K. (2021). Solution Concepts in Hierarchical Games Under Bounded Rationality With Applications to Autonomous Driving.. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16715
Ernst, G., Sedwards, S., Zhang, Z., & Hasuo, I. (2021). Falsification of Hybrid Systems Using Adaptive Probabilistic Search. ACM Transactions on Modeling and Computer Simulation (TOMACS), 31, 1-22. https://doi.org/10.1145/3459605
Sarkar, A., Larson, K., & Czarnecki, K. (2021). A taxonomy of strategic human interactions in traffic conflicts. A Taxonomy of Strategic Human Interactions in Traffic Conflicts. Presented at the. Retrieved from https://arxiv.org/abs/2109.13367
Abdelzad, V., Lee, J., Sedwards, S., Soltani, S., & Czarnecki, K. (2021). Non-divergent Imitation for Verification of Complex Learned Controllers. Shenzhen, China (virtual): IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533410
Balakrishnan, A., Lee, J., Gaurav, A., Czarnecki, K., & Sedwards, S. (2021). Transfer Reinforcement Learning for Autonomous Driving: From WiseMove to WiseSim. ACM Transactions on Modeling and Computer Simulation, 31, Article No. 15, pp 1~26. https://doi.org/10.1145/3449356 (Original work published 2021)
Lee, J., & Sutton, R. S. (2021). Policy iterations for reinforcement learning problems in continuous time and space \textemdash Fundamental theory and methods. Automatica, 126, 109421, 15 pages. https://doi.org/10.1016/j.automatica.2020.109421 (Original work published 2021)
Lee, S., Lee, J., & Hasuo, I. (2021). Predictive PER: Balancing Priority and Diversity Towards Stable Deep Reinforcement Learning. Shenzhen, China (virtual): IEEE. https://doi.org/10.1109/IJCNN52387.2021.9534243
Lee, J., Sedwards, S., & Czarnecki, K. (2021). Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning. Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning. Presented at the. Online at http://modem2021.cs.nuigalway.ie/. Retrieved from https://arxiv.org/abs/2201.07958
2020
Balakrishnan, A (2020). Closing the Modelling Gap: Transfer Learning from a Low-Fidelity Simulator for Autonomous Driving. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15570 (Original work published 2020)
Valov, P (2020). Transferring Pareto Frontiers across Heterogeneous Hardware Environments. Waterloo. Retrieved from http://hdl.handle.net/10012/16295 (Original work published 2020)
Lee, S, Lee, J, & Hasuo, I (2020). Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement Learning. Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement Learning. Presented at the. Retrieved from https://sites.google.com/view/deep-rl-workshop-neurips2020/home
Budde, C, D\textquoterightArgenio, P, Hartmanns, A, & Sedwards, S (2020). An Efficient Statistical Model Checker for Nondeterminism and Rare Events. International Journal on Software Tools For Technology Transfer, Special Issue TACAS 2018.
Chen, W. T. (2020). Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active Learning. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15537 (Original work published 2020)
Ilievski, M (2020). WiseBench: A Motion Planning Benchmarking Framework for Autonomous Vehicles. Waterloo. Retrieved from http://hdl.handle.net/10012/16422 (Original work published 2020)
Salay, R, Czarnecki, K, Alvarez, I, Elli, M. S., Sedwards, S, & Weast, J (2020). PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML. PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety With ML. Presented at the. New York: CEUR.
Bouchard, F. ed\ eric. (2020). Expert System and a Rule Set Development Method for Urban Behaviour Planning. Retrieved from http://hdl.handle.net/10012/15864 (Original work published 2020)
Denouden, T (2020). An Application of Out-of-Distribution Detection for Two-Stage Object Detection Networks. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15646 (Original work published 2020)
Gaurav, A (2020). Safety-Oriented Stability Biases for Continual Learning. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15579 (Original work published 2020)
Jhunjhunwala, A, Lee, J, Sedwards, S, Abdelzad, V, & Czarnecki, K (2020). Improved Policy Extraction via Online Q-Value Distillation. Improved Policy Extraction via Online Q-Value Distillation. Presented at the. Glasgow: IEEE.
Chen, H, Cohen, R, Dautenhahn, K, Law, E, & Czarnecki, K (2020). Autonomous Vehicle Visual Signals for Pedestrians: Experiments and Design Recommendations. Autonomous Vehicle Visual Signals for Pedestrians: Experiments and Design Recommendations. Presented at the. Retrieved from https://arxiv.org/abs/2010.05115
Gaurav, A, Vernekar, S, Lee, J, Sedwards, S, Abdelzad, V, & Czarnecki, K (2020). Simple Continual Learning Strategies for Safer Classifers. Simple Continual Learning Strategies for Safer Classifers. Presented at the. CEUR. Retrieved from http://ceur-ws.org/Vol-2560/paper6.pdf (Original work published 2020)
Dillen, N, Ilievski, M, Law, E, Nacke, L. E., Czarnecki, K, & Schneider, O (2020). Keep Calm and Ride Along: Passenger Comfort and Anxiety as Physiological Responses to Autonomous Driving Styles. Keep Calm and Ride Along: Passenger Comfort and Anxiety As Physiological Responses to Autonomous Driving Styles. Presented at the. ACM. https://doi.org/10.1145/3313831.3376247 (Original work published 2020)
Chen, H (2020). Autonomous Vehicles with Visual Signals for Pedestrians: Experiments and Design Recommendations. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15534 (Original work published 2020)
Vernekar, S (2020). Training Reject-Classifiers for Out-of-distribution Detection via Explicit Boundary Sample Generation. Waterloo. Retrieved from http://hdl.handle.net/10012/15582 (Original work published 2020)
Antkiewicz, M, Kahn, M, Ala, M, Czarnecki, K, Wells, P, Acharya, A, & Beiker, S (2020). Modes of Automated Driving System Scenario Testing: Experience Report and Recommendations. SAE Int. J. Adv. & Curr. Prac. In Mobility, 2, 2248-2266. https://doi.org/10.4271/2020-01-1204 (Original work published 2020)
2019
Sarkar, A., & Czarnecki, K. (2019). A behavior driven approach for sampling rare event situations for autonomous vehicles. A Behavior Driven Approach for Sampling Rare Event Situations for Autonomous Vehicles. Presented at the. Retrieved from https://ieeexplore.ieee.org/abstract/document/8967715
Khan, S. (2019). Towards Synthetic Dataset Generation for Semantic Segmentation Networks. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15128 (Original work published 2019)
Khan, S., Phan, B. T., Salay, R., & Czarnecki, K. (2019). ProcSy: Procedural Synthetic Dataset Generation Towards Influence Factor Studies Of Semantic Segmentation Networks. ProcSy: Procedural Synthetic Dataset Generation Towards Influence Factor Studies Of Semantic Segmentation Networks. Presented at the. Long Beach, California, USA: IEEE. Retrieved from https://bit.ly/2G3MA1P (Original work published 2019)
Li, C. (2019). Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/14697 (Original work published 2019)
Deng, J. (2019). MLOD: A multi-view 3D object detection based on robust feature fusion method. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15086 (Original work published 2019)
Ilievski, M., Sedwards, S., Gaurav, A., Balakrishnan, A., Sarkar, A., Lee, J., Bouchard, F. ed\ eric, De Iaco, R., & Czarnecki, K. (2019). Design Space of Behaviour Planning for Autonomous Driving. Waterloo. Retrieved from https://arxiv.org/abs/1908.07931 (Original work published 2019)
Li, C., & Czarnecki, K. (2019). Urban Driving with Multi-Objective Deep Reinforcement Learning. Urban Driving With Multi-Objective Deep Reinforcement Learning. Presented at the. Montreal: IFAAMAS.
Li, C., & Czarnecki, K. (2019). Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective. Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective. Presented at the. Montreal.
Chao, E. (2019). Autonomous Driving: Mapping and Behavior Planning for Crosswalks. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15121 (Original work published 2019)
De Iaco, R. (2019). Motion Planning and Safety for Autonomous Driving. Waterloo. Retrieved from http://hdl.handle.net/10012/15303 (Original work published 2019)
Vernekar, S., Gaurav, A., Denouden, T., Phan, B. T., Abdelzad, V., Salay, R., & Czarnecki, K. (2019). Analysis of Confident-Classifiers for Out-of-Distribution Detection. Analysis of Confident-Classifiers for Out-of-Distribution Detection. Presented at the. Retrieved from https://drive.google.com/uc?export=download\&id=1AY0zFvQ_u1UmGcn0bHs1XCWX9QncaqvT (Original work published 2019)
Jhunjhunwala, A. (2019). Policy Extraction via Online Q-Value Distillation. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/14963 (Original work published 2019)
Hurl, B. (2019). Local and Cooperative Autonomous Vehicle Perception from Synthetic Datasets. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15118 (Original work published 2019)
Sarkar, A., & Czarnecki, K. (2019). A behavior driven approach for sampling rare event situations for autonomous vehicles. A Behavior Driven Approach for Sampling Rare Event Situations for Autonomous Vehicles. Presented at the. Retrieved from https://ieeexplore.ieee.org/abstract/document/8967715
Khan, S. (2019). Towards Synthetic Dataset Generation for Semantic Segmentation Networks. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15128 (Original work published 2019)
Li, C. (2019). Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/14697 (Original work published 2019)
Khan, S., Phan, B. T., Salay, R., & Czarnecki, K. (2019). ProcSy: Procedural Synthetic Dataset Generation Towards Influence Factor Studies Of Semantic Segmentation Networks. ProcSy: Procedural Synthetic Dataset Generation Towards Influence Factor Studies Of Semantic Segmentation Networks. Presented at the. Long Beach, California, USA: IEEE. Retrieved from https://bit.ly/2G3MA1P (Original work published 2019)
Deng, J. (2019). MLOD: A multi-view 3D object detection based on robust feature fusion method. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15086 (Original work published 2019)
Ilievski, M., Sedwards, S., Gaurav, A., Balakrishnan, A., Sarkar, A., Lee, J., Bouchard, F. ed\ eric, De Iaco, R., & Czarnecki, K. (2019). Design Space of Behaviour Planning for Autonomous Driving. Waterloo. Retrieved from https://arxiv.org/abs/1908.07931 (Original work published 2019)
Li, C., & Czarnecki, K. (2019). Urban Driving with Multi-Objective Deep Reinforcement Learning. Urban Driving With Multi-Objective Deep Reinforcement Learning. Presented at the. Montreal: IFAAMAS.
Babaee, R., Ganesh, V., & Sedwards, S. (2019). Accelerated Learning of Predictive Runtime Monitors for Rare Failure. Porto, Portugal: Springer.
De Iaco, R., Smith, S. L., & Czarnecki, K. (2019). Learning a Lattice Planner Control Set for Autonomous Vehicles. Learning a Lattice Planner Control Set for Autonomous Vehicles. Presented at the. Paris, France. https://doi.org/10.1109/IVS.2019.8813797
Angus, M. (2019). Towards Pixel-Level OOD Detection for Semantic Segmentation. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15004 (Original work published 2019)
Phan, B. T., Khan, S., Salay, R., & Czarnecki, K. (2019). Bayesian Uncertainty Quantification with Synthetic Data. Bayesian Uncertainty Quantification With Synthetic Data. Presented at the. Turku, Finland: SAFECOMP. Retrieved from https://www.waise.org/ (Original work published 2019)
Jhunjhunwala, A. (2019). Policy Extraction via Online Q-Value Distillation. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/14963 (Original work published 2019)
Vernekar, S., Gaurav, A., Denouden, T., Phan, B. T., Abdelzad, V., Salay, R., & Czarnecki, K. (2019). Analysis of Confident-Classifiers for Out-of-Distribution Detection. Analysis of Confident-Classifiers for Out-of-Distribution Detection. Presented at the. Retrieved from https://drive.google.com/uc?export=download\&id=1AY0zFvQ_u1UmGcn0bHs1XCWX9QncaqvT (Original work published 2019)
Hurl, B. (2019). Local and Cooperative Autonomous Vehicle Perception from Synthetic Datasets. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15118 (Original work published 2019)
2018
Zhang, Z., Ernst, G., Hasuo, I., & Sedwards, S. (2018). Time-Staging Enhancement of Hybrid System Falsification. Porto, Portugal: IEEE. Retrieved from https://ieeexplore.ieee.org/abstract/document/8429475
D\textquoterightArgenio, P., Hartmanns, A., & Sedwards, S. (2018). Lightweight Statistical Model Checking in Nondeterministic Continuous Time. Limassol, Cyprus: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-030-03421-4_22
Phan, B. T., Salay, R., Czarnecki, K., Abdelzad, V., Denouden, T., & Vernekar, S. (2018). Calibrating Uncertainties in Object Localization Task. Calibrating Uncertainties in Object Localization Task. Presented at the. (Original work published 2018)
Czarnecki, K., & Salay, R. (2018). Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving. Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving. Presented at the. Väster\r as, Sweden: Springer. (Original work published 2018)
Liang, J. H. (2018). Machine Learning for SAT Solvers. Waterloo, ON, Canada. Retrieved from http://hdl.handle.net/10012/14207 (Original work published 2018)
Juodisius, P., Sarkar, A., Mukkamala, R. R., Antkiewicz, M., Czarnecki, K., & asowski, A. W. (2018). Clafer: Lightweight Modeling of Structure and Behaviour. The Art, Science, and Engineering of Programming Journal, 3. https://doi.org/10.22152/programming-journal.org/2019/3/2 (Original work published 2018)
Zhang, Z., Ernst, G., Sedwards, S., Arcani, P., & Hasuo, I. (2018). Two-Layered Falsification of Hybrid Systems Guided by Monte Carlo Tree Search. IEEE TCAD. Torino, Italy: IEEE. Retrieved from https://ieeexplore.ieee.org/document/8418450
Colwell, I. (2018). Runtime Restriction of the Operational Design Domain: A Safety Concept for Automated Vehicles. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/13398 (Original work published 2018)
Zayan, D., Sarkar, A., Antkiewicz, M., Maciel, R. S. P., & Czarnecki, K. (2018). Example-driven modeling: on effects of using examples on structural model comprehension, what makes them useful, and how to create them. https://doi.org/10.1007/s10270-017-0652-3 (Original work published 2018)
Zulkoski, E. (2018). Understanding and Enhancing CDCL-based SAT Solvers. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/13525 (Original work published 2018)
Budde, C., D\textquoterightArgenio, P., Hartmanns, A., & Sedwards, S. (2018). A Statistical Model Checker for Nondeterminism and Rare Events. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-89963-3_20
Given-Wilson, T., Legay, A., Sedwards, S., & Zendra, O. (2018). Group abstraction for assisted navigation of social activities in intelligent environments. Springer Journal of Reliable Intelligent Environments, 4, 107\textendash120. Retrieved from https://link.springer.com/article/10.1007/s40860-018-0058-1
Colwell, I., Phan, B. T., Saleem, S., Salay, R., & Czarnecki, K. (2018). An Automated Vehicle Safety Concept Based on Runtime Restriction of the Operational Design Domain. An Automated Vehicle Safety Concept Based on Runtime Restriction of the Operational Design Domain. Presented at the.
Angus, M., ElBalkini, M., Khan, S., Harakeh, A., Andrienko, O., Reading, C., Czarnecki, K., & Waslander, S. (2018). Unlimited Road-scene Synthetic Annotation (URSA) Dataset. Unlimited Road-Scene Synthetic Annotation (URSA) Dataset. Presented at the. Maui, Hawaii, USA: IEEE. Retrieved from https://arxiv.org/abs/1807.06056 (Original work published 2018)
Passos, L., Queiroz, R., Mukelabai, M., Berger, T., Apel, S., Czarnecki, K., & Padilla, J. A. (2018). A Study of Feature Scattering in the Linux Kernel. IEEE Transactions on Software Engineering.
D\textquoterightArgenio, P., Gerhold, M., Hartmanns, A., & Sedwards, S. (2018). A Hierarchy of Scheduler Classes for Stochastic Automata. Thessaloniki, Greece: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-89366-2_21
Kido, K., Sedwards, S., & Hasuo, I. (2018). Bounding Errors Due to Switching Delays in Incrementally Stable Switched Systems. Oxford, United Kingdom: Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S2405896318311583
Chandail, R. (2018). Vision Augmented State Estimation with Fault Tolerance. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/13291 (Original work published 2018)
2017
Sarkar, A., Czarnecki, K., Angus, M., Li, C., & Waslander, S. (2017). Trajectory prediction of traffic agents at urban intersections through learned interactions.. Trajectory Prediction of Traffic Agents at Urban Intersections through Learned Interactions. Presented at the. Retrieved from https://ieeexplore.ieee.org/document/8317731
Larson, K., Peled, D., & Sedwards, S. (2017). Memory-Efficient Tactics for Randomized LTL Model Checking. Heidelberg, Germany: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-72308-2_10
Valov, P., Petkovich, J.-C., Guo, J., Fischmeister, S., & Czarnecki, K. (2017). Transferring Performance Prediction Models Across Different Hardware Platforms. Transferring Performance Prediction Models Across Different Hardware Platforms. Presented at the. https://doi.org/10.1145/3030207.3030216 (Original work published 2017)
Chauchan, M., Pellizzoni, R., & Czarnecki, K. (2017). Modeling the Effects of AUTOSAR Overheads on Application Timing and Schedulability. Modeling the Effects of AUTOSAR Overheads on Application Timing and Schedulability. Presented at the. Retrieved from https://dac.com/2017/accepted-papers (Original work published 2017)
Guo, J., Blais, E., Czarnecki, K., & van Beek, P. (2017). A Worst-Case Analysis of Constraint-Based Algorithms for Exact Multi-objective Combinatorial Optimization. A Worst-Case Analysis of Constraint-Based Algorithms for Exact Multi-Objective Combinatorial Optimization. Presented at the. https://doi.org/10.1007/978-3-319-57351-9_16 (Original work published 2017)
Queiroz, R., Passos, L., Valente, M. T., Hunsen, C., Apel, S., & Czarnecki, K. (2017). The shape of feature code: an analysis of twenty C-preprocessor-based systems. Software \& Systems Modeling, 16, 96. https://doi.org/10.1007/s10270-015-0483-z (Original work published 2017)
Zulkoski, E., Bright, C., Heinle, A., Kotsireas, I., Czarnecki, K., & Ganesh, V. (2017). Combining SAT Solvers with Computer Algebra Systems to Verify Combinatorial Conjectures. Journal of Automated Reasoning, 58, 339. https://doi.org/10.1007/s10817-016-9396-y (Original work published 2011)
Kido, K., Sedwards, S., & Hasuo, I. (2017). Switching Delays and the Skorokhod Distance in Incrementally Stable Switched Systems. Seoul, South Korea: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-030-17910-6_9
Hartmanns, A., Sedwards, S., & D\textquoterightArgenio, P. (2017). Efficient Simulation-based Verification of Probabilistic Timed Automata. Efficient Simulation-Based Verification of Probabilistic Timed Automata, 1419\textendash1430. Las Vegas, USA: IEEE. https://doi.org/10.1109/WSC.2017.8247885
Ross, J., Murashkin, A., Liang, J. H., Antkiewicz, M., & Czarnecki, K. (2017). Synthesis and Exploration of Multi-Level, Multi-Perspective Architectures of Automotive Embedded Systems. Software and Systems Modeling. https://doi.org/10.1007/s10270-017-0592-y
Queiroz, R., Passos, L., Valente, M. T., Hunsen, C., Apel, S., & Czarnecki, K. (2017). The shape of feature code: an analysis of twenty C-preprocessor-based systems. Software \& Systems Modeling, 16, 96. https://doi.org/10.1007/s10270-015-0483-z (Original work published 2017)
2016
Khalilov, E., Ross, J., Antkiewicz, M., Völter, M., & Czarnecki, K. (2016). Modeling and Optimizing Automotive Electric/Electronic (E/E) Architectures: Towards Making Clafer Accessible to Practitioners. Modeling and Optimizing Automotive Electric Electronic (E E) Architectures: Towards Making Clafer Accessible to Practitioners. Presented at the. https://doi.org/10.1007/978-3-319-47169-3_37 (Original work published 2016)
Liang, J. H., Ganesh, V., Poupart, P., & Czarnecki, K. (2016). Exponential Recency Weighted Average Branching Heuristic for SAT Solvers. Exponential Recency Weighted Average Branching Heuristic for SAT Solvers. Presented at the. Retrieved from http://dl.acm.org/citation.cfm?id=3016100.3016385 (Original work published 2016)
Zulkoski, E., Ganesh, V., & Czarnecki, K. (2016). MathCheck: A Math Assistant via a Combination of Computer Algebra Systems and SAT Solvers. MathCheck: A Math Assistant via a Combination of Computer Algebra Systems and SAT Solvers. Presented at the. AAAI Press. (Original work published 2016)
Sarkar, A. (2016). Meta-learning Performance Prediction of Highly Configurable Systems: A Cost-oriented Approach. Waterloo. Retrieved from http://hdl.handle.net/10012/10406 (Original work published 2016)
2015
Sarkar, A. ., Guo, J. ., Siegmund, N. ., Apel, S. ., & Czarnecki, K. . (2015). Cost-efficient sampling for performance prediction of configurable systems. Cost-Efficient Sampling for Performance Prediction of Configurable Systems. Presented at the. Retrieved from https://ieeexplore.ieee.org/document/7372023