Formal Verification of the IEEE 802.1D Spanning Tree Protocol Using Extended Rebeca. - Hossein Hojjat; Hootan Nakhost; Marjan Sirjani (FSEN, 2005)

Integration of Symmetry and Macro-operators in Planning. - Amirali Houshmandan; Gholamreza Ghassem-Sani; Hootan Nakhost (MICAI, 2007)

Integrating Module Checking and Deduction in a Formal Proof for the Perlman Spanning Tree Protocol (STP). - Hossein Hojjat; Hootan Nakhost; Marjan Sirjani (J. Univers. Comput. Sci., 2007)

Monte-Carlo Exploration for Deterministic Planning. - Hootan Nakhost; Martin Müller 003 (IJCAI, 2009)

Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement. - Hootan Nakhost; Martin Müller 003 (ICAPS, 2010)

Improving Local Search for Resource-Constrained Planning. - Hootan Nakhost; Jörg Hoffmann 001; Martin Müller 003 (SOCS, 2010)

A Local Monte Carlo Tree Search Approach in Deterministic Planning. - Fan Xie 001; Hootan Nakhost; Martin Müller 003 (AAAI, 2011)

Resource-Constrained Planning: A Monte Carlo Random Walk Approach. - Hootan Nakhost; Jörg Hoffmann 001; Martin Müller 003 (ICAPS, 2012)

Planning Via Random Walk-Driven Local Search. - Fan Xie 001; Hootan Nakhost; Martin Müller 003 (ICAPS, 2012)

ArvandHerd: Parallel Planning with a Portfolio. - Richard Anthony Valenzano; Hootan Nakhost; Martin Müller 003; Jonathan Schaeffer 001; Nathan R. Sturtevant (ECAI, 2012)

A Theoretical Framework for Studying Random Walk Planning. - Hootan Nakhost; Martin Müller 003 (SOCS, 2012)

Towards a Second Generation Random Walk Planner: An Experimental Exploration. - Hootan Nakhost; Martin Müller 003 (IJCAI, 2013)

Towards a theory of random walk planning: Regress factors, fair homogeneous graphs and extensions. - Hootan Nakhost; Martin Müller 003 (AI Commun., 2014)

Interpretable Sequence Learning for Covid-19 Forecasting. - Sercan Ömer Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh 005; Leyou Zhang; Martin Nikoltchev; Yash Sonthalia; Hootan Nakhost; et al. (NeurIPS, 2020)

Interpretable Sequence Learning for COVID-19 Forecasting. - Sercan Ömer Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh 005; Leyou Zhang; Nate Yoder; Martin Nikoltchev; Yash Sonthalia; et al. (CoRR, 2020)

Learning and Evaluating a Differentially Private Pre-trained Language Model. - Shlomo Hoory; Amir Feder; Avichai Tendler; Sofia Erell; Alon Peled-Cohen; Itay Laish; Hootan Nakhost; Uri Stemmer; Ayelet Benjamini; Avinatan Hassidim; Yossi Matias (EMNLP (Findings), 2021)

Controlling Commercial Cooling Systems Using Reinforcement Learning. - Jerry Luo; Cosmin Paduraru; Octavian Voicu; Yuri Chervonyi; Scott Munns; Jerry Li; Crystal Qian; Praneet Dutta; Jared Quincy Davis; Ningjia Wu; Xingwei Yang; Chu-Ming Chang; et al. (CoRR, 2022)

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. - Cheng-Yu Hsieh; Chun-Liang Li; Chih-Kuan Yeh; Hootan Nakhost; Yasuhisa Fujii; Alex Ratner; Ranjay Krishna; Chen-Yu Lee; Tomas Pfister (ACL (Findings), 2023)

SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data. - Ruoxi Sun 002; Sercan Ö. Arik; Rajarishi Sinha; Hootan Nakhost; Hanjun Dai; Pengcheng Yin; Tomas Pfister (EMNLP (Findings), 2023)

Universal Self-Adaptive Prompting. - Xingchen Wan; Ruoxi Sun 002; Hootan Nakhost; Hanjun Dai; Julian Eisenschlos; Sercan Ö. Arik; Tomas Pfister (EMNLP, 2023)

SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL. - Ruoxi Sun 002; Sercan Ö. Arik; Hootan Nakhost; Hanjun Dai; Rajarishi Sinha; Pengcheng Yin; Tomas Pfister (CoRR, 2023)

TextGenSHAP: Scalable Post-Hoc Explanations in Text Generation with Long Documents. - James Enouen; Hootan Nakhost; Sayna Ebrahimi; Sercan Ö. Arik; Yan Liu 002; Tomas Pfister (ACL (Findings), 2024)

Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization. - Xingchen Wan; Ruoxi Sun 002; Hootan Nakhost; Sercan Ö. Arik (NeurIPS, 2024)

SQL-PaLM: Improved large language model adaptation for Text-to-SQL. - Ruoxi Sun 002; Sercan Ö. Arik; Alexandre Muzio; Lesly Miculicich; Satya Kesav Gundabathula; Pengcheng Yin; Hanjun Dai; Hootan Nakhost; Rajarishi Sinha; Zifeng Wang 002; Tomas Pfister (Trans. Mach. Learn. Res., 2024)

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation. - Xingchen Wan; Han Zhou 010; Ruoxi Sun 002; Hootan Nakhost; Ke Jiang; Sercan Ö. Arik (CoRR, 2025)

PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving. - Mihir Parmar; Xin Liu; Palash Goyal; Yanfei Chen; Long T. Le; Swaroop Mishra; Hossein Mobahi; Jindong Gu; Zifeng Wang 002; Hootan Nakhost; Chitta Baral; Chen-Yu Lee; et al. (CoRR, 2025)