Generalization in Planning

Overview

Humans are good at solving sequential decision-making problems, generalizing from a few examples, and learning skills that can be transferred to solve unseen problems. However, these problems remain long-standing open problems in AI.

This workshop will feature a synthesis of the best ideas on the topic from multiple highly active research communities. On the one hand, recent advances in deep-reinforcement learning have led to data-driven methods that provide strong short-horizon reasoning and planning, with open problems regarding sample efficiency, generalizability and transferability. On the other hand, advances and open questions in the AI planning community have been complementary, featuring robust analytical methods that provide sample-efficient generalizability and transferability for long-horizon sequential decision making, with open problems in short-horizon control and in the design and modeling of representations.

We welcome submissions addressing the problem of generalizable and transferable learning in all forms of sequential decision making. This event represents the seventh edition of the recurring GenPlan series of Workshops.

Call for Papers

The workshop will focus on research related to all aspects of learning, generalization, and transfer in sequential decision-making (SDM). This topic features technical problems that are of interest not only in multiple sub-fields of AI research (including reinforcement learning, automated planning, and learning for knowledge representation) but also in other fields of research, including formal methods and program synthesis. We will welcome submissions that address formal as well as empirical issues on topics such as:

• Formulations of generalized SDM problems.
• Representations, learning, and synthesis for generalized plans and policies.
• Learning for transfer and generalization in reinforcement learning.
• Learning and representing hierarchical policies and behaviors for SDM.
• Learning and synthesis of generalizable solutions for SDM problem classes.
• Learning paradigms, representations, and algorithms for transferring learned knowledge and solutions to new SDM problems.
• Learning and representing generalized Q/V functions and heuristics for plan and policy generalization.
• Learning high-level models and hierarchical solutions for generalizable SDM.
• Neuro-symbolic approaches for generalization and transfer in SDM.
• Few-shot learning and transfer for SDM.
• Meta-learning for generalizable policies.
• Learning for program synthesis.
• Learning domain control knowledge and partial policies.
• Generalization and transfer in robot planning problems.
• Representation of solution structures that enable generalization and transfer.

Submission Guidelines

Submissions can describe either work in progress or mature work that would be of interest to researchers working on generalization in planning. We also welcome “highlights” papers summarizing and highlighting results from multiple recent papers by the authors. Preference will be given to new work (including highlights) and work in progress rather than exact resubmissions of previously published work.

Submissions of papers being reviewed at other venues (AAAI, ICRA, ICLR, AAMAS, CVPR, etc.) are welcome since GenPlan is a non-archival venue and we will not require a transfer of copyright. If such papers are currently under blind review, please anonymize the submission.

Two types of papers can be submitted:

• full technical papers with the length of up to 9 pages + references
• short papers with the length between 3 and 5 pages + references

Submissions may use as many pages of appendices (after the references) as they wish, but the reviewers are not required to read the appendix. Submissions should use the NeurIPS paper format. The papers should adhere to the NeurIPS Code of Conduct and NeurIPS policy on using LLMs for writing in their paper. Papers can be submitted via OpenReview at https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/GenPlan.

Important Dates

Invited Talks

Giuseppe De Giacomo University of Oxford	Logic, Automata, and Games in Linear Temporal Logics on Finite Traces Temporal logics on finite traces (LTLf, LDLf, PPLTL, etc.) are increasingly attracting the interest of the scientific community. These logics are variants of temporal logics used for specifying dynamic properties in Formal Methods, but focussing on finite though unbounded traces. They are becoming popular in several areas, including AI planning for expressing temporally extended goals, reactive synthesis for automatically synthesizing interactive programs, reinforcement learning for expressing non-Markovian rewards and dynamics, and Business Process Modeling for declaratively specifying processes. These logics can express general safety and guarantee (reachability) properties, though they cannot talk about the behaviors at the infinitum as more traditional temporal logics on infinite traces. The key characteristic of these logics is that they can be reduced to equivalent regular automata, and in turn, automata, once determinized, into two-player games on graphs. This gives them unprecedented computational effectiveness and scalability. In this talk, we will look at these logics, their corresponding automata, and resulting games, and show their relevance in service composition. In particular, we show how they can be used for automatically synthesizing orchestrators for advanced forms of goal-oriented synthesis. Slides Bio: Giuseppe De Giacomo is a Professor of Computer Science at the Department of Computer Science of the University of Oxford. He has been previously a Professor at the Department of Computer, Control, and Management Engineering of the University of Roma "La Sapienza". His research activity concerns theoretical, methodological, and practical aspects in different areas of AI and CS, most prominently Knowledge Representation, Reasoning about Actions, Generalized Planning, Autonomous Agents, Reactive Synthesis and Verification, Service Composition, Business Process Modeling, and Data Management and Integration. He is an AAAI Fellow, ACM Fellow, and EurAI Fellow. He received an ERC Advanced Grant for the project WhiteMech: White-box Self Programming Mechanisms (2019-2024). He was the Program Chair of ECAI 2020 and KR 2014. He is on the Board of EurAI. He chairs the steering committee of the new EurAI yearly summer school ESSAI.
Hector Geffner RWTH Aachen University	Learning General Policies and Sketches Recent progress in deep learning and deep reinforcement learning (DRL) has been truly remarkable, yet two problems remain: structural policy generalization and policy reuse. The first is about getting policies that generalize in a reliable way; the second is about getting policies that can be reused and combined in a flexible, goal-oriented manner. The two problems are studied in DRL but only experimentally, and the results are not clear and crisp. In our work, we have tackled these problems in a slightly different way, developing languages for expressing general policies, and methods for learning them using combinatorial and DRL approaches. We have also developed languages for expressing and learning general subgoal structures (sketches) and hierarchical polices which are based on the notion of planning width. In the talk, I'll present the main ideas and results. This is joint work with Blai Bonet, Simon Ståhlberg, Dominik Drexler, and other members of the RLeap team. Slides Bio: Hector Geffner is an Alexander von Humboldt Professor at the RWTH Aachen University, Germany and a Guest Wallenberg Professor at Linköping University, Sweden. Before joining RWTH, he was an ICREA Research Professor at the Universitat Pompeu Fabra in Barcelona, Spain. Hector obtained a Ph.D. in Computer Science at UCLA and then worked at the IBM T.J. Watson Research Center in NY, and at the Universidad Simon Bolivar in Caracas. Distinctions for his work and the work of his team include the 1990 ACM Dissertation Award and three ICAPS Influential Paper Awards. Hector leads a project on representation learning for acting and planning (RLeap) funded by an ERC grant and is currently hiring.
Roberta Raileanu FAIR, Meta AI	In-Context Learning of Sequential Decision-Making Tasks Training autonomous agents that can learn new tasks from only a handful of demonstrations is a long-standing problem in machine learning. Recently, transformers have been shown to learn new language or vision tasks without any weight updates from only a few examples, also referred to as in-context learning. However, the sequential decision-making setting poses additional challenges having a lower tolerance for errors since the environment's stochasticity or the agent's actions can lead to unseen, and sometimes unrecoverable, states. In this talk, I will show that naively applying transformers to this setting does not enable in-context learning of new tasks. I will then show how different design choices such as the model size, data diversity, environment stochasticity, and trajectory burstiness, affect in-context learning of sequential decision-making tasks. Finally, I will show that by training on large diverse offline datasets, transformers are able to learn entirely new tasks with unseen states, actions, dynamics, and rewards, using only a handful of demonstrations and no weight updates. I will end my talk with a discussion of the limitations of offline learning approaches in sequential decision-making and some directions for future work. Slides Bio: Roberta Raileanu is a Research Scientist at Meta and an Honoray Lecturer at UCL. Her research focuses on designing machine learning algorithms that can make robust sequential decisions in complex environments. In particular, Roberta works in the area of deep reinforcement learning, with a focus on generalization, adaptation, continual, and open-ended learning. Roberta holds a PhD in Computer Science from NYU and a B.A. in Astrophysics from Princeton University.
Peter Stone The University of Texas at Austin and Sony AI	Causal Dynamics Learning for Task-Independent State Abstraction Learning dynamics models accurately is an important goal for Model-Based Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics model which is vulnerable to spurious correlations and therefore generalizes poorly to unseen states. In this paper, we introduce Causal Dynamics Learning for Task-Independent State Abstraction (CDL), which first learns a theoretically proved causal dynamics model that removes unnecessary dependencies between state variables and the action, thus generalizing well to unseen states. A state abstraction can then be derived from the learned dynamics, which not only improves sample efficiency but also applies to a wider range of tasks than existing state abstraction methods. Evaluated on two simulated environments and downstream tasks, both the dynamics model and policies learned by the proposed method generalize well to unseen states and the derived state abstraction improves sample efficiency compared to learning without it. Slides Bio: Peter Stone holds the Truchard Foundation Chair in Computer Science at the University of Texas at Austin. He is Associate Chair of the Computer Science Department, as well as Director of Texas Robotics. In 2013 he was awarded the University of Texas System Regents' Outstanding Teaching Award and in 2014 he was inducted into the UT Austin Academy of Distinguished Teachers, earning him the title of University Distinguished Teaching Professor. Professor Stone's research interests in Artificial Intelligence include machine learning (especially reinforcement learning), multiagent systems, and robotics. Professor Stone received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. He is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, IEEE Fellow, AAAS Fellow, ACM Fellow, Fulbright Scholar, and 2004 ONR Young Investigator. In 2007 he received the prestigious IJCAI Computers and Thought Award, given biannually to the top AI researcher under the age of 35, and in 2016 he was awarded the ACM/SIGAI Autonomous Agents Research Award. Professor Stone co-founded Cogitai, Inc., a startup company focused on continual learning, in 2015, and currently serves as Executive Director of Sony AI America.
Amy Zhang The University of Texas at Austin and Meta AI	Value-Based Abstractions for Planning As reinforcement learning continues to advance, the integration of efficient planning algorithms with powerful representation learning becomes crucial for solving long-horizon tasks. We address key challenges in planning, reward learning, and representation learning through the objective of learning value-based abstractions. We explore this idea via goal-conditioned reinforcement learning to learn generalizable value functions and action-free pre-training. By leveraging self-supervised reinforcement learning and efficient planning algorithms, these approaches collectively contribute to the advancement of decision-making systems capable of learning and adapting to diverse tasks in real-world environments. Slides Bio: Amy Zhang is an assistant professor at UT Austin in the Chandra Family Department of Electrical and Computer Engineering and visiting faculty at Meta AI - FAIR. Her work focuses on improving generalization in reinforcement learning through bridging theory and practice in learning and utilizing structure in real world problems. Previously, she was a postdoctoral fellow at UC Berkeley and obtained her PhD from McGill University and the Mila Institute.

Program

Video Recordings now available on the NeurIPS Workshop Page

Accepted Papers

• GOOSE: Learning Domain-Independent Heuristics
  Dillon Ze Chen, Felipe Trevizan, and Sylvie Thiébaux
• Contextual Pre-Planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
  Guy Azran, Mohamad Hosein Danesh, Stefano V Albrecht, and Sarah Keren
• Graph Neural Networks and Graph Kernels For Learning Heuristics: Is there a difference?
  Dillon Ze Chen, Felipe Trevizan, and Sylvie Thiébaux
• Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
  Lukas Schäfer, Filippos Christianos, Amos Storkey, and Stefano V Albrecht
• Conservative World Models
  Scott Jeen, Tom Bewley, and Jonathan Cullen
• Quantized Local Independence Discovery for Fine-Grained Causal Dynamics Learning in Reinforcement Learning
  Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi, Byoung-Tak Zhang, and Sanghack Lee
• Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce
  Omkar Shelke, Pranavi Pathakota, Anandsingh Chauhan, Hardik Meisheri, Harshad Khadilkar, and Balaraman Ravindran
• Learning Safe Action Models with Partial Observability
  Brendan Juba, Hai Le, and Roni Stern
• Learning How to Create Generalizable Hierarchies for Robot Planning
  Naman Shah and Siddharth Srivastava
• Massively Scalable Inverse Reinforcement Learning for Route Optimization
  Matt Barnes^*, Matthew Abueg^*, Oliver Lange, Matt Deeds, Jason Trader, Denali Molitor, Markus Wulfmeier, and Shawn O'Banion
• Towards General-Purpose In-Context Learning Agents
  Louis Kirsch, James Harrison, C. Freeman, Jascha Sohl-Dickstein, and Jürgen Schmidhuber
• Learning Interactive Real-World Simulators
  Sherry Yang, Yilun Du, Seyed Kamyar Seyed Ghasemipour, Jonathan Tompson, Dale Schuurmans, and Pieter Abbeel
• Learning Generalizable Symbolic Options for Transfer in Reinforcement Learning
  Rashmeet Kaur Nayyar, Shivanshu Verma, and Siddharth Srivastava
• Value Iteration with Value of Information Networks
  Samantha Johnson, Michael Buice, and Koosha Khalvati
• Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines
  Yu-An Lin, Chen-Tao Lee, Guan-Ting Liu, Pu-Jen Cheng, and Shao-Hua Sun
• Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures
  Jung-Chun Liu, Chi-Hsien Chang, Shao-Hua Sun, and Tian-Li Yu
• Leveraging Behavioral Cloning for Representation Alignment in Cross-Domain Policy Transfer
  Hayato Watahiki, Ryo Iwase, Ryosuke Unno, and Yoshimasa Tsuruoka
• COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
  Xiyao Wang, Ruijie Zheng, Yanchao Sun, Ruonan Jia, Wichayaporn Wongkamjan, Huazhe Xu, and Furong Huang
• Explore to Generalize in Zero-Shot RL
  Ev Zisselman, Itai Lavie, Daniel Soudry, and Aviv Tamar
• Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models
  Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Rich Walke, Chelsea Finn, Aviral Kumar, and Sergey Levine
• Towards More Likely Models for AI Planning
  Turgay Caglar, Sirine Belhaj, Tathagata Chakraborti, Michael Katz, and Sarath Sreedharan
• Contrastive Abstraction for Reinforcement Learning
  Vihang Patil, Markus Hofmarcher, Elisabeth Rumetshofer, and Sepp Hochreiter
• Robust Driving Across Scenarios via Multi-residual Task Learning
  Vindula Jayawardana, Sirui Li, Cathy Wu, Yashar Farid, and Kentaro Oguchi
• Relating Goal and Environmental Complexity for Improved Task Transfer: Initial Results
  Sunandita Patra, Paul Rademacher, Kristen Jacobson, Kyle Hassold, Onur Kulaksızoğlu, Laura Hiatt, Mark Roberts, and Dana Nau
• Learning Abstract World Models for Value-preserving Planning with Options
  Rafael Rodriguez-Sanchez and George Konidaris
• Uncertainty-Aware Action Repeating Options
  Joongkyu Lee, Seung Joon Park, Yunhao Tang, and Min-hwan Oh
• Contrastive Representations Make Planning Easy
  Benjamin Eysenbach, Vivek Myers, Sergey Levine, and Ruslan Salakhutdinov
• POMRL: No-Regret Learning-to-Plan with Increasing Horizons
  Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, and Tom Zahavy
• Exploiting Contextual Structure to Generate Useful Auxiliary Tasks
  Benedict Quartey, Ankit Shah, and George Konidaris
• Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels
  Thomas Jiralerspong^*, Flemming Kondrup^*, Doina Precup, and Khimya Khetarpal
• Inverse Reinforcement Learning with Multiple Planning Horizons
  Jiayu Yao, Finale Doshi-Velez, and Barbara Engelhardt
• A Theoretical Explanation of Deep RL Performance in Stochastic Environments
  Cassidy Laidlaw, Banghua Zhu, Stuart Russell, and Anca Dragan
• Learning Discrete Models for Classical Planning Problems
  Forest Agostinelli and Misagh Soltani
• Simple Data Sharing for Multi-Tasked Goal-Oriented Problems
  Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, and Ching-An Cheng
• Plansformer: Generating Symbolic Plans using Transformers
  Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi, Lior Horesh, Biplav Srivastava, Francesco Fabiano, and Andrea Loreggia
• Stochastic Safe Action Model Learning
  Zihao Deng and Brendan Juba
• Understanding Representations Pretrained with Auxiliary Losses for Embodied Agent Planning
  Yuxuan Li and Luca Weihs
• Agent-Centric State Discovery for Finite-Memory POMDPs
  Lili Wu, Ben Evans, Riashat Islam, Raihan Seraj, Yonathan Efroni, and Alex Lamb
• MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning
  Arundhati Banerjee, Soham Phade, Stefano Ermon, and Stephan Zheng
• Learning Generalizable Visual Task Through Interaction
  Weiwei Gu, Anant Sah, and Nakul Gopalan
• Modeling Boundedly Rational Agents with Latent Inference Budgets
  Athul Jacob, Abhishek Gupta, and Jacob Andreas
• Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation
  Adam Sigal, Hsiu-Chin Lin, and AJung Moon
• Hierarchical Reinforcement Learning with AI Planning Models
  Junkyu Lee, Michael Katz, Don Joven Agravante, Miao Liu, Geraud Nangue Tasse, Tim Klinger, and Shirin Sohrabi
• Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning
  David Yunis, Justin Jung, Falcon Dai, and Matthew Walter
• Non-adaptive Online Finetuning for Offline Reinforcement Learning
  Audrey Huang, Mohammad Ghavamzadeh, Nan Jiang, and Marek Petrik
• Robustness and Regularization in Reinforcement Learning
  Esther Derman, Yevgeniy Men, Matthieu Geist, and Shie Mannor
• Work-in-Progress: Using Symbolic Planning with Deep RL to Improve Learning
  Tianpei Yang, Srijita Das, Christabel Wayllace, and Matthew Taylor
• Inductive Generalization in Reinforcement Learning from Specifications
  Rohit Kushwah, Vignesh Subramanian, Suguman Bansal, and Subhajit Roy
• Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI
  Emily Jin^*, Jiaheng Hu^*, Zhuoyi Huang, Ruohan Zhang, Jiajun Wu, Li Fei-Fei, and Roberto Martín-Martín
• Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Stochastic Settings
  Rushang Karia^*, Pulkit Verma^*, Gaurav Vipat, and Siddharth Srivastava
• PADDLE: Logic Program Guided Policy Reuse in Deep Reinforcement Learning
  Hao Zhang, Tianpei Yang, Yan Zheng, Jianye Hao, and Matthew Taylor
• RL³: Boosting Meta Reinforcement Learning via RL inside RL²
  Abhinav Bhatia, Samer Nashed, and Shlomo Zilberstein
• Reasoning with Language Model is Planning with World Model
  Shibo Hao, Yi Gu, Haodi Ma, Joshua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu
• Targeted Uncertainty Reduction in Robust MDPs
  Uri Gadot, Kaixin Wang, Esther Derman, Navdeep Kumar, Kfir Levy, and Shie Mannor
• Normalization Enhances Generalization in Visual Reinforcement Learning
  Lu Li, Jiafei Lyu, Guozheng Ma, Zilin Wang, Zhenjie Yang, Xiu Li, and Zhiheng Li
• A Study of Generalization in Offline Reinforcement Learning
  Ishita Mediratta, Qingfei You, Minqi Jiang, and Roberta Raileanu
• General and Reusable Indexical Policies and Sketches
  Blai Bonet, Dominik Drexler, and Hector Geffner
• Learning AI-System Capabilities under Stochasticity
  Pulkit Verma^*, Rushang Karia^*, Gaurav Vipat, Anmol Gupta, and Siddharth Srivastava
• Reinforcement Learning with Augmentation Invariant Representation: A Non-contrastive Approach
  Nasik Muhammad Nafi and William Hsu

Related Workshops

This workshop builds upon GenPlan workshop series.

• Workshop on Generalization in Planning at IJCAI-ECAI 2022
• Workshop on Generalization in Planning at IJCAI 2021
• Workshop on Generalization in Planning at AAAI 2020
• Workshop on Reasoning about Actions and Processes: Highlights of Recent Advances at ICAPS 2019
• Workshop on Reasoning about Actions and Processes: Highlights of Recent Advances at KR 2018
• Workshop on Generalized Planning at ICAPS 2017
• Workshop on Generalized Planning at AAAI 2011
• Workshop on Generalized Planning: Macros, Loops, Domain Control at ICAPS 2009

Committees

Organizing Committee

Pulkit Verma

Arizona State University, USA

Siddharth Srivastava

Arizona State University, USA

Aviv Tamar

Technion - Israel Institute for Technology, Israel

Felipe Trevizan

Australian National University, Australia

Advisory Board

• Blai Bonet, Universitat Pompeu Fabra, Spain, and Universidad Simón Bolívar, Venezuela.
• Giuseppe De Giacomo, University of Oxford, United Kingdom.
• Hector Geffner, RWTH Aachen University, Germany and Linköping University, Sweden.
• Anders Jonsson, Universitat Pompeu Fabra, Spain.
• Sheila McIlraith, The University of Toronto, Canada.
• Siddharth Srivastava, Arizona State University, USA.
• Peter Stone, The University of Texas at Austin, USA and Sony AI.
• Sylvie Thiébaux, Australian National University, Australia and Université de Toulouse, France.
• Shlomo Zilberstein, The University of Massachusetts Amherst.

Program Committee

• Cameron Allen, University of California Berkeley, USA.
• Daman Arora, Microsoft Research, India.
• Siddhant Bhambri, Arizona State University, USA.
• Abhinav Bhatia, University of Massachusetts Amherst, USA.
• Blai Bonet, Universitat Pompeu Fabra, Spain, and Universidad Simón Bolívar, Venezuela.
• Rohan Chitnis, Meta Reality Labs, USA.
• Dillon Chen, Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), France.
• Roberto Cipollone, Sapienza University of Rome, Italy.
• Marek Cygan, University of Warsaw, Poland.
• Mehdi Dadvar, Arizona State University, USA.
• Tal Daniel, Technion - Israel Institute of Technology, Israel.
• Khen Elimelech, Rice University, USA.
• Zeyu Feng, National University of Singapore, Singapore.
• Haotian Fu, Brown University, USA.
• Raquel Fuentetaja, Universidad Carlos III de Madrid, Spain.
• Priyanka Golia, CISPA Helmholtz Center for Information Security, Germany.
• Sachin Grover, Palo Alto Research Center, USA.
• Kshitij Gupta, Mila - Quebec AI Institute, Canada.
• Akkamahadevi Hanni, Arizona State University, USA.
• Jiaheng Hu, The University of Texas at Austin, USA.
• Shashi Shekhar Jha, Indian Institute of Technology Ropar, India.
• Yuqian Jiang, The University of Texas at Austin, USA.
• Sergio Jiménez Celorrio, Universitat Politècnica de València, Spain.
• Anders Jonsson, Universitat Pompeu Fabra, Spain.
• Tom Jurgenson, Technion - Israel Institute of Technology, Israel.
• Rushang Karia, Arizona State University, USA.
• Erez Karpas, Technion - Israel Institute of Technology, Israel.
• Khimya Khetarpal, Google DeepMind and Mila - Quebec AI Institute, Canada.
• Harsha Kokel, IBM Research, USA.
• Orr Krupnik, Technion - Israel Institute of Technology, Israel.
• Kimin Lee, Korea Advanced Institute of Science & Technology, South Korea.
• Gabriel Paludo Licks, Sapienza University of Rome, Italy.
• Man Luo, Mayo Clinic Department of AI and Informatics, USA.
• Reuth Mirsky, Bar Ilan University, Israel.
• Samer Nashed, University of Massachusetts Amherst, USA.
• Rashmeet Kaur Nayyar, Arizona State University, USA.
• Eva Onaindia, Universitat Politècnica de València, Spain.
• Ron Petrick, Heriot-Watt University, United Kingdom.
• Zohar Rimon, Technion - Israel Institute of Technology, Israel.
• Max Rudolph, The University of Texas at Austin, USA.
• Jyothir S V, New York University, USA.
• Javier Segovia-Aguas, Universitat Pompeu Fabra, Spain.
• Naman Shah, Arizona State University, USA.
• Wiliam Shen, Massachusetts Institute of Technology, USA.
• Harshit Sikchi, The University of Texas at Austin, USA.
• Tom Silver, Massachusetts Institute of Technology, USA.
• Simon Ståhlberg, Linköping University, Sweden.
• Utkarsh Soni, Arizona State University, USA.
• Roni Stern, Palo Alto Research Center, USA and Ben Gurion University of the Negev, Israel.
• Matan Sudry, Technion - Israel Institute of Technology, Israel.
• Matthew E. Taylor, University of Alberta, Canada.
• Sam Toyer, University of California Berkeley, USA.
• Elena Umili, Sapienza University of Rome, Italy.
• Karthik Valmeekam, Arizona State University, USA.
• Mudit Verma, Arizona State University, USA.
• Kevin Vora, Arizona State University, USA.
• Zizhao Wang, The University of Texas at Austin, USA.
• Haoran Xu, The University of Texas at Austin, USA.
• Shentao Yang, The University of Texas at Austin, USA.
• Ev Zisselman, Technion - Israel Institute of Technology, Israel.