| A bi-objective optimization framework for three-dimensional road alignment design D Hirpa, W Hare, Y Lucet, Y Pushak, S Tesfamariam Transportation Research Part C: Emerging Technologies 65, 61-78, 2016 | 72 | 2016 |
| Multiple-path selection for new highway alignments using discrete algorithms Y Pushak, W Hare, Y Lucet European Journal of Operational Research 248 (2), 415-427, 2016 | 72 | 2016 |
| Algorithm configuration landscapes: More benign than expected? Y Pushak, H Hoos International Conference on Parallel Problem Solving from Nature, 271-283, 2018 | 55 | 2018 |
| Golden parameter search: exploiting structure to quickly configure parameters in parallel Y Pushak, HH Hoos Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 245-253, 2020 | 23 | 2020 |
| AutoML Loss Landscapes Y Pushak, HH Hoos ACM Transactions on Evolutionary Learning and Optimization (TELO), 2022 | 22 | 2022 |
| Dataset-free, approximate marginal perturbation-based feature attributions Z Zohrevand, Y Pushak, T Hetherington, KR Nia, S Jinturkar, N Agarwal US Patent App. 17/232,671, 2022 | 5 | 2022 |
| Local Permutation Importance: A Stable, Linear-TIme Local Machine Learning Feature Attributor Y Pushak, Z Zohrevand, T Hetherington, KR Nia, S Jinturkar, N Agarwal US Patent App. 17/319,729, 2022 | 4 | 2022 |
| Post-hoc explanation of machine learning models using generative adversarial networks KR Nia, T Hetherington, Z Zohrevand, Y Pushak, S Jinturkar, N Agarwal US Patent App. 17/131,387, 2022 | 4 | 2022 |
| Generalized expectation maximization F Schmidt, Y Pushak, S Wray US Patent App. 16/935,313, 2022 | 4 | 2022 |
| N-1 Experts: Unsupervised Anomaly Detection Model Selection C Le Clei, Y Pushak, F Zogaj, MO Kareshk, Z Zohrevand, R Harlow, ... First Conference on Automated Machine Learning (Late-Breaking Workshop), 2022 | 4 | 2022 |
| Advanced statistical analysis of empirical performance scaling Y Pushak, HH Hoos Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 236-244, 2020 | 4 | 2020 |
| Fast, approximate conditional distribution sampling Y Pushak, T Hetherington, KR Nia, Z Zohrevand, S Jinturkar, N Agarwal US Patent 11,687,540, 2023 | 3 | 2023 |
| Empirical scaling analyzer: An automated system for empirical analysis of performance scaling Y Pushak, Z Mu, HH Hoos AI Communications 33 (2), 93-111, 2020 | 3 | 2020 |
| Unify95: meta-learning contamination thresholds from unified anomaly scores Y Pushak, HF Moghadam, A Yakovlev, IIRD Hopkins US Patent App. 17/994,530, 2024 | 1 | 2024 |
| Global, model-agnostic machine learning explanation technique for textual data Z Zohrevand, T Hetherington, KR Nia, Y Pushak, S Jinturkar, N Agarwal US Patent 11,720,751, 2023 | 1 | 2023 |
| Efficient and accurate regional explanation technique for nlp models Z Zohrevand, T Hetherington, KR Nia, Y Pushak, S Jinturkar, N Agarwal US Patent App. 17/212,163, 2022 | 1 | 2022 |
| Algorithm configuration landscapes: analysis and exploitation Y Pushak University of British Columbia, 2022 | 1 | 2022 |
| Road design optimization with a surrogate function Y Pushak | 1 | 2015 |
| MULTIPLIER TUNING POSTPROCESSING FOR MACHINE LEARNING BIAS MITIGATION M Godbout, Y Pushak, H Fathi Moghadam, S Hong, H Chafi US Patent App. 18/529,300, 2024 | | 2024 |
| ACCELERATING AUTOMATED ALGORITHM CONFIGURATION USING HISTORICAL PERFORMANCE DATA Y Pushak, M Mahdavi, A Asgari Khoshouyeh, A Seyfi, Z Zohrevand, ... US Patent App. 18/202,472, 2024 | | 2024 |
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaEmail yang diverifikasi di cs.rwth-aachen.de