| 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 | 69 | 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 | 68 | 2016 |
| Algorithm configuration landscapes: More benign than expected? Y Pushak, H Hoos International Conference on Parallel Problem Solving from Nature, 271-283, 2018 | 53 | 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 | 20 | 2020 |
| AutoML Loss Landscapes Y Pushak, HH Hoos ACM Transactions on Evolutionary Learning and Optimization (TELO), 2022 | 17 | 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 |
| Generalized expectation maximization F Schmidt, Y Pushak, S Wray US Patent App. 16/935,313, 2022 | 3 | 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 | 3 | 2022 |
| Fast, approximate conditional distribution sampling Y Pushak, T Hetherington, KR Nia, Z Zohrevand, S Jinturkar, N Agarwal US Patent 11,687,540, 2023 | 2 | 2023 |
| 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 | 2 | 2022 |
| 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 | 2 | 2020 |
| 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 | 1 | 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 | 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 |
| Expert-optimal correlation: contamination factor identification for unsupervised anomaly detection Y Pushak, C Le Clei, F Zogaj, HF Moghadam, S Hong, H Chafi US Patent App. 18/075,824, 2024 | | 2024 |
| Unify95: meta-learning contamination thresholds from unified anomaly scores Y Pushak, HF Moghadam, A Yakovlev, IIRD Hopkins US Patent App. 17/994,530, 2024 | | 2024 |
| Learning hyper-parameter scaling models for unsupervised anomaly detection F Zogaj, Y Pushak, HF Moghadam, S Hong, H Chafi US Patent App. 18/075,784, 2024 | | 2024 |
| CHROMOSOME REPRESENTATION LEARNING IN EVOLUTIONARY OPTIMIZATION TO EXPLOIT THE STRUCTURE OF ALGORITHM CONFIGURATION Y Pushak, M Owhadi Kareshk, H Fathi Moghadam, S Hong, H Chafi US Patent App. 17/900,779, 2024 | | 2024 |
| Fast and accurate anomaly detection explanations with forward-backward feature importance A Seyfi, Y Pushak, S Hong, HF Moghadam, H Chafi US Patent App. 17/992,743, 2023 | | 2023 |
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
