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Hootan Nakhost

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
2023 · Cheng-Yu Hsieh, Chun-Liang Li, et al. · 312
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Monte-Carlo exploration for deterministic planning
2009 · Hootan Nakhost, Martin Müller · 78
Resource-Constrained Planning: A Monte Carlo Random Walk Approach
2012 · Hootan Nakhost, Joerg Hoffmann, et al. · Proceedings of the International Conference on Automated Planning and Scheduling · 55
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Interpretable Sequence Learning for COVID-19 Forecasting
2020 · Arik, Sercan O., Li, Chun-Liang, et al. · arXiv (Cornell University) · 52
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Learning and Evaluating a Differentially Private Pre-trained Language Model
2021 · Shlomo Hoory, Amir Feder, et al. · 43
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Interpretable sequence learning for COVID-19 forecasting
2020 · Sercan Ö. Arık, Chun‐Liang Li, et al. · neural information processing systems · 34
Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement
2010 · Hootan Nakhost, Martin Müller · Proceedings of the International Conference on Automated Planning and Scheduling · 34
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SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)
2023 · Sun, Ruoxi, Arik, Sercan Ö., et al. · arXiv (Cornell University) · 31
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ArvandHerd: Parallel Planning with a Portfolio
2012 · Valenzano Richard, Nakhost Hootan, et al. · Frontiers in artificial intelligence and applications · 28
Controlling Commercial Cooling Systems Using Reinforcement Learning
2022 · Luo, Jerry, Paduraru, Cosmin, et al. · arXiv (Cornell University) · 25
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SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data
2023 · Ruoxi Sun, Sercan Arik, et al. · 18
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Planning Via Random Walk-Driven Local Search
2012 · Fan Xie, Hootan Nakhost, et al. · Proceedings of the International Conference on Automated Planning and Scheduling · 18
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Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
2023 · Hsieh, Cheng-Yu, Li, Chun-Liang, et al. · arXiv (Cornell University) · 16
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Improving Local Search for Resource-Constrained Planning
2010 · Mueller, Martin, Hoffman, Joerg, et al. · 12
Learning and Evaluating a Differentially Private Pre-trained Language Model
2021 · Shlomo Hoory, Amir Feder, et al. · 11
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Improving Local Search for Resource-Constrained Planning
2010 · Hootan Nakhost, Jörg Hoffmann, et al. · Proceedings of the International Symposium on Combinatorial Search · 8
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Towards a second generation random walk planner: an experimental exploration
2013 · Hootan Nakhost, Martin Müller · 7
Formal Verification of the IEEE 802.1D Spanning Tree Protocol Using Extended R ebeca
2006 · Hossein Hojjat, Hootan Nakhost, et al. · Electronic Notes in Theoretical Computer Science · 7
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PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving
2025 · Mihir Parmar, Xin Liu, et al. · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 6
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Universal Self-Adaptive Prompting
2023 · Xingchen Wan, Ruoxi Sun, et al. · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 6
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A Theoretical Framework for Studying Random Walk Planning
2021 · Hootan Nakhost, Martin Mueller · Proceedings of the International Symposium on Combinatorial Search · 4
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TextGenSHAP: Scalable Post-Hoc Explanations in Text Generation with Long Documents
2024 · James Enouen, Hootan Nakhost, et al. · 3
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A Local Monte Carlo Tree Search Approach in Deterministic Planning
2011 · Fan Xie, Hootan Nakhost, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 3
Towards a theory of random walk planning: Regress factors, fair homogeneous graphs and extensions
2014 · Hootan Nakhost, Martin Müller · AI Communications · 2
Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement - Extended Version
2010 · Nakhost, Hootan, Müller, Martin · ERA: Education and Research Archive (University of Alberta) · 2
Works count: 44
Citation count: 820
H-index: 13
I10-index: 15
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