| The hitchhiker’s guide to testing statistical significance in natural language processing R Dror, G Baumer, S Shlomov, R Reichart Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 246 | 2018 |
| Do not have enough data? Deep learning to the rescue! A Anaby-Tavor, B Carmeli, E Goldbraich, A Kantor, G Kour, S Shlomov, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7383-7390, 2020 | 128 | 2020 |
| Deep dominance-how to properly compare deep neural models R Dror, S Shlomov, R Reichart Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 42 | 2019 |
| Statistical significance testing for natural language processing R Dror, L Peled-Cohen, S Shlomov, R Reichart Synthesis Lectures on Human Language Technologies 13 (2), 1-116, 2020 | 30 | 2020 |
| Not enough data A Anaby-Tavor, B Carmeli, E Goldbraich, A Kantor, G Kour, S Shlomov, ... Deep learning to the rescue, 1911 | 6 | 1911 |
| Robust non-Bayesian social learning I Arieli, Y Babichenko, S Shlomov Proceedings of the 2019 ACM Conference on Economics and Computation, 549-550, 2019 | 4 | 2019 |
| We've had this conversation before: A Novel Approach to Measuring Dialog Similarity O Lavi, E Rabinovich, S Shlomov, D Boaz, I Ronen, A Anaby-Tavor arXiv preprint arXiv:2110.05780, 2021 | 1 | 2021 |
| Virtually additive learning I Arieli, Y Babichenko, S Shlomov Journal of Economic Theory 197, 105322, 2021 | 1 | 2021 |
| Pareto-efficient probabilistic solutions A Kantor, M Masin, S Shlomov, R Dror US Patent App. 15/905,988, 2019 | 1 | 2019 |
| Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestration S Zeltyn, S Shlomov, A Yaeli, A Oved International Joint Conference on Artificial Intelligence, 2022 | | 2022 |
| Understanding the Properties of Generated Corpora N Zwerdling, S Shlomov, E Goldbraich, G Kour, B Carmeli, N Tepper, ... arXiv preprint arXiv:2206.11219, 2022 | | 2022 |
| Language-model-based data augmentation method for textual classification tasks with little data A Kantor, AA Tavor, B Carmeli, E Goldbraich, G Kour, S Shlomov, ... US Patent App. 16/870,917, 2021 | | 2021 |
| Robust learning in social networks via matrix scaling Y Babichenko, S Shlomov Operations Research Letters 49 (5), 720-727, 2021 | | 2021 |
| Phase Transitions in Kyle's Model with Market Maker Profit Incentives CA Lehalle, E Neuman, S Shlomov arXiv preprint arXiv:2103.04481, 2021 | | 2021 |
| General Contact Process with Rapid Stirring L Mytnik, S Shlomov ALEA 18, 17-33, 2021 | | 2021 |
| Statistical Significance Tests R Dror, L Peled-Cohen, S Shlomov, R Reichart Statistical Significance Testing for Natural Language Processing, 9-21, 2020 | | 2020 |
| Open Questions and Challenges R Dror, L Peled-Cohen, S Shlomov, R Reichart Statistical Significance Testing for Natural Language Processing, 75-77, 2020 | | 2020 |
| Deep Significance R Dror, L Peled-Cohen, S Shlomov, R Reichart Statistical Significance Testing for Natural Language Processing, 35-50, 2020 | | 2020 |
| Statistical Hypothesis Testing R Dror, L Peled-Cohen, S Shlomov, R Reichart Statistical Significance Testing for Natural Language Processing, 3-7, 2020 | | 2020 |
| Replicability Analysis R Dror, L Peled-Cohen, S Shlomov, R Reichart Statistical Significance Testing for Natural Language Processing, 51-73, 2020 | | 2020 |
Rotem DrorUniversity of PennsylvaniaVerified email at seas.upenn.edu
