Yexiang Xue's Personal Website

[J20] Maxwell Jacobson and Yexiang Xue.
Integrating Spatial Reasoning into Neural Generative Models for Design Production.
To appear in the Artificial Intelligence journal, 2024. [ArXiv preprint][slides]

[C64] Nan Jiang, Md Nasim, Yexiang Xue.
Vertical Symbolic Regression via Deep Policy Gradient.
In the Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI), 2024. [Acceptance rate 14%] [PDF]

[C63] Nan Jiang, Jinzhao Li, and Yexiang Xue.
A Tighter Convergence Proof of Reverse Experience Replay.
In the Proceedings of the First Reinforcement Learning Conference (RLC), 2024. [PDF]

[C62] Md Masudur Rahman, Yexiang Xue.
Natural Language-based State Representation in Deep Reinforcement Learning.
In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Findings, 2024. [PDF]

[C61] Jinzhao Li, Nan Jiang and Yexiang Xue.
Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration with Provable Guarantees.
In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024. [Acceptance rate 23.75%] [ArXiv] [YouTube]

[C60] Nan Jiang and Yexiang Xue.
Racing Control Variable Genetic Programming for Symbolic Regression.
In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024. [Acceptance rate 23.75%] [pdf]

[C59] Md Nasim and Yexiang Xue.
Efficient Learning of PDEs via Taylor Expansion and Sparse Decomposition into Value and Fourier Domains.
In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024. [Acceptance rate 23.75%] [pdf][YouTube]

[C58] Md Nasim, Anter El-Azab, Xinghang Zhang, and Yexiang Xue.
End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning.
In Proceedings of the Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24), 2024. [Acceptance rate 24%] [pdf][YouTube]

[C57] Nan Jiang and Yexiang Xue.
Symbolic Regression via Control Variable Genetic Programming.
In Proceedings of the 2022 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2023. [Acceptance rate 24%] [pdf][slides]

[C56] Jinzhao Li, Daniel Fink, Christopher Wood, Carla P. Gomes and Yexiang Xue.
Provable Optimization of Quantal Response Leader-Follower Games with Exponentially Large Action Spaces.
In Proc. of The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023. [Acceptance rate 23.3%] [pdf]

[C55] Nan Jiang, Yi Gu, Yexiang Xue.
Learning Markov Random Fields for Combinatorial Structures via Sampling through Lovasz Local Lemma.
In Proc. Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023. [Acceptance rate 19.6%] [pdf]

[J19] Xinwei Zhang, Mohamed El Masry, Daniela Chanci Arrubla, Maria Romeo Tricas, Surya C. Gnyawali, Gayle Gordillo, Yexiang Xue, Chandan K Sen, Juan Wachs.
Autonomous Multi-modality Burn Wound Characterization using Artificial Intelligence. In Military Medicine, 2023.

[J18] Glebys Gonzalez, Mythra Balakuntala, Mridul Agarwal, Tomas Low, Bruce Knoth, Andrew W Kirkpatrick, Jessica McKee, Gregory Hager, Vaneet Aggarwal, Yexiang Xue, Richard Voyles, Juan Wachs.
ASAP: A Semi-Autonomous Precise System for Telesurgery during Communication Delays.
In IEEE Transactions on Medical Robotics and Bionics, 2023. [website]

[J17] Md Nasim, A.R.G. Sreekar, Tongjun Niu, Cuncai Fan, Zhongxia Shang, Jin Li, Haiyan Wang, Anter El-Azab, Yexiang Xue*, and Xinghang Zhang*.
Unraveling the size fluctuation and shrinkage of nanovoids during in situ radiation of Cu by automatic pattern recognition and phase field simulation.
In Journal of Nuclear Materials (JNM), 2022.[ScienceDirect]

[J16] Nan Jiang, Maosen Zhang, Willem-Jan van Hoeve, Yexiang Xue.
Constraint Reasoning Embedded Structured Prediction.
In Journal of Machine Learning Research (JMLR), 23(345):1--40, 2022.[JMLR website][slides]

[J15] Hantao Shu, Fan Ding, Jingtian Zhou, Yexiang Xue, Dan Zhao, Jianyang Zeng, Jianzhu Ma.
Boosting single-cell gene regulatory network reconstruction via bulk-cell transcriptomic data.
In Briefings in Bioinformatics, Volume 23, Issue 5, 2022.[website]

[J14] Tongjun Niu, Sreekar Rayaprolu, Zhongxia Shang, Tianyi Sun, Cuncai Fan, Yifan Zhang, Chao Shen, Md Nasim, Wei-ying Chen, Meimei Li, Yexiang Xue, Haiyan Wang, Anter El-Azab, Xinghang Zhang.
In situ study on heavy ion irradiation induced microstructure evolution in single crystal Cu with nanovoids at elevated temperature.
In Materials Today Communications, Volume 33, 2022.[website]

[C54] Maxwell Jacobson, Case Wright, Nan Jiang, Gustavo Rodriguez-Rivera, Yexiang Xue.
Task Detection in Continual Learning via Familiarity Autoencoders.
In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2022. [pdf]

[C53] Md Masudur Rahman, Yexiang Xue.
Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning.
In Proceedings of the 2022 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2022. [Acceptance rate: 26%] [pdf]

[C52] Md Masudur Rahman, Yexiang Xue.
Bootstrap Advantage Estimation for Policy Optimization in Reinforcement Learning.
In Proceedings of IEEE 2022 International Conference on Machine Learning and Applications (ICMLA), 2022.

[C51] Md Nasim, Xinghang Zhang, Anter El-Azab, Yexiang Xue.
Efficient Learning of Sparse and Decomposable PDEs using Random Projection.
In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022. [Acceptance rate: 32.3%] [pdf]

[C50] Fan Ding, Yexiang Xue.
X-MEN: Guaranteed XOR-Maximum Entropy Constrained Inverse Reinforcement Learning.
In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022. [Acceptance rate: 32.3%] [pdf][supplementary materials]

[C49] Maxwell Jacobson, Daniela Arrubla, Maria Romeo Tricas, Mohammed El Masry, Surya Gnyawali, Gayle Gordillo, Yexiang Xue, Chandan Sen, Juan Wachs.
Autonomous Multi-modality Burn Wound Characterization using Artificial Intelligence.
In Military Health System Research Symposium (MHSRS), 2022.

[C48] Glebys Gonzalez, Mythra Balakuntala, Mridul Agarwal, Md Masudur Rahman, Thomas Low, Vaneet Aggarwal, Yexiang Xue, Richard Voyles, Juan Wachs.
ASAP: A Semi-Autonomous Precise robotic framework for remote surgery under delays.
In Military Health System Research Symposium (MHSRS), 2022.

[J13] Alexander S. Flecker, Qinru Shi, Rafael M. Almeida, Hector Angarita, Jonathan M. Gomes-Selman, Roosevelt García-Villacorta, Suresh A. Sethi, Steven A.Thomas, N. LeRoy Poff, Bruce R. Forsberg, Sebastian A. Heilpern, Stephen K. Hamilton, Jorge D. Abad, Elizabeth P. Anderson, Nathan Barros, Isabel Carolina Bernal, Richard Bernstein, Carlos M. Cañas, Olivier Dangles, Andrea C. Encalada, Ayan S. Fleischmann, Michael Goulding, Jonathan Higgins, Céline Jézéquel, Erin I. Larson, Peter B. McIntyre, John M. Melack, Mariana Montoya, Thierry Oberdorff, Rodrigo Paiva, Guillaume Perez, Brendan H. Rappazzo, Scott Steinschneider, Sandra Torres, Mariana Varese, M.Todd Walter, Xiaojian Wu, Yexiang Xue, Xavier E. Zapata-Ríos, Carla P. Gomes.
Reducing Adverse Impacts of Amazon Hydropower Expansion.
In Science, Vol 375, Issue 6582, pp. 753-760. [website] [perspective]

[C47] Nan Jiang, Chen Luo, Vihan Lakshman, Yesh Dattatreya, Yexiang Xue.
Massive Text Normalization via an Efficient Randomized Algorithm.
In Proceedings of 2022 ACM The Web Conference (WWW), 2022. [Acceptance rate: 17.7%] [pdf]

[C46] Chonghao Sima, Yexiang Xue.
LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning.
In Proceedings of 35th Conference on Neural Information Processing Systems (NeurIPS), 2021. [Acceptance rate: 26%] [pdf]

[C45] Yexiang Xue, Md Nasim, Maosen Zhang, Cuncai Fan, Xinghang Zhang, Anter El-Azab.
Physics Knowledge Discovery via Neural Differential Equation Embedding.
In Proceedings of 2021 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2021. [Acceptance rate: 29%] [pdf]

[C44] Mridul Agarwal, Glebys Gonzalez, Mythra Varun Balakuntala Srinivasa Murthy, Md Masudur Rahman, Vaneet Aggarwal, Yexiang Xue, Juan Wachs, Richard Voyles.
Dexterous Skill Transfer between Surgical Procedures for Teleoperated Robotic Surgery.
In Proceedings of the 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2021. [pdf]

[C43] Md Masudur Rahman, Richard Voyles, Juan Wachs, Yexiang Xue.
Sequential Prediction with Logic Constraints for Surgical Robotic Activity Recognition.
In Proceedings of the 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2021. [pdf]

[C42] Fan Ding, Yexiang Xue.
XOR-SGD: Provable Convex Stochastic Optimization for Decision-making under Uncertainty.
In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021. [Acceptance rate: 26.5%] [pdf]

[C41] Fan Ding, Nan Jiang, Jianzhu Ma, Jian Peng, Jinbo Xu, Yexiang Xue.
PALM: Probabilistic Area Loss Minimization for Protein Sequence Alignment.
In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021. [Acceptance rate: 26.5%] [pdf]

[C40] Fan Ding, Jianzhu Ma, Jinbo Xu, Yexiang Xue.
XOR-CD: Linearly Convergent Constrained Structure Generation.
In Proceedings of the Thirty-eighth International Conference on Machine Learning (ICML), 2021. [Acceptance rate: 21.5%] [pdf]

[C39] Glebys Gonzalez, Mythra V. Agarwal, Mridul, Balakuntala, Md Masudur Rahman, Upinder Kaur, Richard M. Voyles, Vaneet Aggarwal, Yexiang Xue, Juan Wachs.
DESERTS: Delay-Tolerant Semi-Autonomous Robot Teleoperation for Surgery.
In Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021. [pdf]

[J12] Tongjun Niu, Md Nasim, Annadanam Rayaprolu Goutham Sreekar, Cuncai Fan, Jin Li, Zhongxia Shang, Yexiang Xue, Anter El-Azab, Haiyan Wang, Xinghang Zhang.
Recent studies on void shrinkage in metallic materials subjected to in situ heavy ion irradiations.
In The Journal of The Minerals, Metals & Materials Society (JOM), 2020. [springer]

[J11] Md Masudur Rahman, Mythra V. Balakuntala, Glebys Gonzalez, Mridul Agarwal, Upinder Kaur, Vishnunandan L. N. Venkatesh, Natalia Sanchez-Tamayo, Yexiang Xue, Richard M. Voyles, Vaneet Aggarwal, Juan Wachs.
SARTRES: a semi-autonomous robot teleoperation environment for surgery.
In the journal of Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Nov 2020. [website]

[J10] Glebys T. Gonzalez, Upinder Kaur, Md Masudur Rahman, Vishnunandan Venkatesh, Natalia Sanchez, Gregory Hager, Yexiang Xue, Richard Voyles, Juan Wachs.
From the DESK (Dexterous Surgical Skill) to the Battlefield - A Robotics Exploratory Study.
In MHSRS Journal (Military Medicine), 2020.

[C38] Maosen Zhang, Nan Jiang, Lei Li, Yexiang Xue.
Constraint Satisfaction Driven Natural Language Generation: A Tree Search Embedded MCMC Approach.
In the Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020. [pdf]

[J9] Beichen Lyu, Stuart Smith, Yexiang Xue, Katherine Rainey, Keith Cherkauer.
An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems.
In the Transactions of the American Society of Agricultural and Biological Engineers (ASABE). 2020. [website]

[C37] Pramith Devulapalli, Bistra Dilkina, Yexiang Xue.
Embedding Conjugate Gradient in Learning Random Walks for Landscape Connectivity Modeling in Conservation.
In the Proceedings of the Twenty-ninth International Joint Conference on Artificial Intelligence (IJCAI), 2020. [Acceptance rate: 12.7%]. [pdf]

[C36] Fan Ding, Hanjing Wang, Ashish Sabharwal, Yexiang Xue.
Towards Efficient Discrete Integration via Adaptive Quantile Queries.
In Proc. of the 24th European Conference on Artificial Intelligence (ECAI). 2020. [Acceptance rate: 26.8%]. [pdf]

[C35] Fan Ding, Yexiang Xue.
Contrastive Divergence Learning with Chained Belief Propagation.
In Proc. of the 10th International Conference on Probabilistic Graphical Models (PGM). 2020. [pdf]

[C34] Glebys Gonzalez, Md Masudur Rahman, Mridul Agarwal, Mythra Balakuntala, Vishnu Venkatesh, Vaneet Aggarwal, Yexiang Xue, Richard Voyles, Gregory Hager, MAJ Andrew W Kirkpatrick, MAJ Steve Overholser, Juan Wachs.
ASTRO: A Semi-Autonomous Telemedicine Robot for Operative Surgery.
In Military Health System Research Symposium (MHSRS), 2020.

[J8] Carla Gomes, Thomas Dietterich, Christopher Barrett, Jon Conrad, Bistra Dilkina, Stefano Ermon, Fei Fang, Andrew Farnsworth, Alan Fern, Xiaoli Fern, Daniel Fink, Douglas Fisher, Alexander Flecker, Daniel Freund, Angela Fuller, John Gregoire, John Hopcroft, Steve Kelling, Zico Kolter, Warren Powell, Nicole Sintov, John Selker, Bart Selman, Daniel Sheldon, David Shmoys, Milind Tambe, Weng-Keen Wong, Christopher Wood, Xiaojian Wu, Yexiang Xue, Amulya Yadav, Abdul-Aziz Yakubu, Mary Lou Zeeman.
Computational Sustainability: Computing for a Better World and a Sustainable Future.
In Communications of ACM (CACM). September 2019, Vol. 62 No. 9, Pages 56-65. [full text] Cover story of CACM!

[J7] Rafael M. Almeida, Qinru Shi, Jonathan M. Gomes-Selman, Xiaojian Wu, Yexiang Xue, Hector Angarita, Nathan Barros, Bruce R. Forsberg, Roosevelt García-Villacorta, Stephen K. Hamilton, John M. Melack, Mariana Montoya, Guillaume Perez, Suresh A. Sethi, Carla P. Gomes, Alexander S. Flecker.
Reducing Greenhouse Gas Emissions of Amazon Hydropower with Strategic Dam Planning.
In Nature Communications. 2019. [website]

[J6] Carla P. Gomes, Junwen Bai, Yexiang Xue, Johan Björck, Brendan Rappazzo, Sebastian Ament, Richard Bernstein, Shufeng Kong, Santosh K Suram, Robert Bruce van Dover, John M Gregoire.
Multi-Agent Generative AI for Automated Mapping of Materials' Crystal Structures.
In the Materials Research Society (MRS) Communications, 2019.

[J5] Yexiang Xue and Carla P. Gomes.
Engaging Citizen Scientists in Data Collection for Conservation.
Book chapter in Artificial Intelligence and Conservation, Cambridge Press, 2019.

[C33] Yexiang Xue, Willem-Jan van Hoeve.
Embedding Decision Diagrams into Generative Adversarial Networks.
In Proc. of the Sixteenth International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2019. [Acceptance rate: 44.7%] [springer]

[C32] Jinning Li and Yexiang Xue.
Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks.
In the Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI), 2019. [Acceptance rate: 13.7%] [pdf]

[C31] Anmol Kabra, Yexiang Xue, and Carla P. Gomes.
GPU-accelerated principal-agent game for scalable citizen science.
In the Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS), 2019. [acm DL]

[C30] Beichen Lyu, Stuart D Smith, Yexiang Xue, Keith Cherkauer.
Deriving Vegetation Indices from High-throughput Images by Using Unmanned Aerial Systems in Soybean Breeding.
In the Proceedings of American Society of Agricutural and Biological Engineers (ASABE) Annual International Meeting, 2019. [ASABE website]

[C29] Md Masudur Rahman, Natalia Sanchez-Tamayo, Glebys Gonzalez, Mridul Agarwal, Vaneet Aggarwal, Richard M. Voyles, Yexiang Xue, and Juan Wachs.
Transferring Dexterous Surgical Skill Knowledge between Robots for Semi-autonomous Teleoperation.
In the proceedings of the 28th IEEE International Conference on Robot & Human Interactive Communication (ROMAN), 2019. [pdf]

[C28] Naveen Madapana, Md Masudur Rahman, Natalia Sanchez-Tamayo, Mythra V. Balakuntala, Glebys Gonzalez, Jyothsna Padmakumar Bindu, L. N. Vishnunandan Venkatesh, Xingguang Zhang, Juan Barragan Noguera, Thomas Low, Richard M. Voyles, Yexiang Xue, and Juan Wachs
DESK: A Robotic Activity Dataset for Dexterous Surgical Skills Transfer to Medical Robots.
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. [pdf]

[C27] Junwen Bai, Zihang Lai, Runzhe Yang, Yexiang Xue, John Gregoire, Carla P. Gomes.
Imitation Refinement For X-Ray Diffraction Signal Processing.
In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019. [Acceptance rate: 46.5%]

[C26] Yexiang Xue, Yang Yuan, Zhitian Xu, and Ashish Sabharwal.
Expanding Holographic Embeddings for Knowledge Completion.
In Proc. of the Thirty-second Conference on Neural Information Processing Systems (NeurIPS), 2018. [pdf] [poster] [YouTube]

[C25] Ashish Sabharwal, and Yexiang Xue.
Adaptive Stratified Sampling for Precision-Recall Estimation.
In Proc. of the Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2018. [pdf]

[C24] Di Chen, Yexiang Xue, and Carla Gomes.
End-to-End Learning for the Deep Multivariate Probit Model.
In Proc. of the 35th International Conference on Machine Learning (ICML), 2018. [pdf]

[C23] Jonathan Gomes-Selman, Qinru Shi, Yexiang Xue, Roosevelt Garcia-Villacorta, Alexander Flecker and Carla Gomes.
Boosting Efficiency for Computing the Pareto Frontier on Tree Structured Networks.
In Proc. 15th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2018. [pdf]

[J4] Junwen Bai, Yexiang Xue, Johan Bjorck, Ronan Le Bras, Brendan Rappazzo, Richard Bernstein, Santosh K. Suram, Robert Bruce van Dover, John M. Gregoire, Carla P. Gomes.
Phase Mapper: Accelerating Materials Discovery with AI.
In AI Magazine, Vol. 39, No 1. 2018. [paper]

[C22] Yexiang Xue*, Luming Tang*, Di Chen, Carla P. Gomes.
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder.
In Proc. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. [pdf]
* indicates equal contribution.

[C21] Xiaojian Wu, Jonathan Gomes-Selman, Qinru Shi, Yexiang Xue, Roosevelt Garcia-Villacorta,
Elizabeth Anderson, Suresh Sethi, Scott Steinchneider, Alexander Flecker, Carla P. Gomes.
Efficiently Approximating the Pareto Frontier: Hydropower Dam Placement in the Amazon Basin.
In Proc. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. [pdf]

[C20] Johan Bjorck, Yiwei Bai, Xiaojian Wu, Yexiang Xue, Mark Whitmore, Carla P. Gomes.
Scalable Relaxations of Sparse Packing Constraints: Optimal Biocontrol in Predator-Prey Networks.
In Proc. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. [pdf]

[C19] Nathan Jensen, Russell Toth, Yexiang Xue, Richard Bernstein, Eddy Chebelyon, Andrew Mude, Christopher B. Barrett, Carla Gomes.
Don't Follow the Crowd: Incentives for Directed Spatial Sampling.
In Agricultural and Applied Economics Association (AAEA), 2017. [pdf]

[C18] Yexiang Xue*, Xiaojian Wu*, Bart Selman, and Carla P. Gomes.
XOR-Sampling for Network Design with Correlated Stochastic Events.
In Proc. 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017. [pdf]
* indicates equal contribution.

[C17] Di Chen, Yexiang Xue, Daniel Fink, Shuo Chen, and Carla P. Gomes.
Deep Multi-species Embedding.
In Proc. 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017. [pdf]

[C16] Yexiang Xue, Junwen Bai, Ronan Le Bras, Brendan Rappazzo, Richard Bernstein, Johan Bjorck, Liane Longpre, Santosh K. Suram, Robert B. van Dover, John Gregoire, and Carla Gomes.
Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery.
In Proc. 29th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), 2017. [pdf][video 1][video 2][video 3]
IAAI Innovative Application Award

[C15] Yexiang Xue, Xiaojian Wu, Dana Morin, Bistra Dilkina, Angela Fuller, J. Andrew Royle, and Carla Gomes.
Dynamic Optimization of Landscape Connectivity Embedding Spatial-Capture-Recapture Information.
In Proc. 31th AAAI Conference on Artificial Intelligence (AAAI), 2017. [pdf] [supplementary materials]

[J3] Santosh K. Suram, Yexiang Xue, Junwen Bai, Ronan LeBras, Brendan H Rappazzo, Richard Bernstein, Johan Bjorck, Lan Zhou, R. Bruce van Dover, Carla P. Gomes, and John M. Gregoire.
Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System.
In American Chemical Society Combinatorial Science, Dec, 2016. [DOI][pdf][video 1][video 2][video 3]
Editor's choice and the cover story!

[C14] Junwen Bai, Johan Bjorck, Yexiang Xue, Santosh K. Suram, John Gregoire, and Carla Gomes.
Relaxation Methods for Constrained Matrix Factorization Problems: Solving the Phase Mapping Problem in Materials Discovery.
To appear in the Fourteenth International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR), 2017.

[C13] Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman.
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
In the Proceedings of the 29th Annual Conference on Neural Information Processing Systems (NIPS), 2016. [pdf] [spotlight video]

[C12] Yexiang Xue, Ian Davies, Daniel Fink, Christopher Wood, Carla P. Gomes.
Behavior Identification in Two-stage Games for Incentivizing Citizen Science Exploration
In the Proceedings of the 22nd International Principles and Practice of Constraint Programming (CP), 2016. [pdf][video]
** Click [here] to participate in the fun Avicaching Game!

[C11] Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes and Bart Selman.
Variable Elimination in the Fourier Domain
In the Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016. [pdf][supplementary materials][video in Simons Institute]

[C10] Yexiang Xue, Ian Davies, Daniel Fink, Christopher Wood, Carla P. Gomes.
Avicaching: A Two Stage Game for Bias Reduction in Citizen Science
In the Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016. [pdf][supplementary materials][video]
** Click [here] to participate in the fun Avicaching Game!

[C9] Yexiang Xue, Stefano Ermon, Carla P. Gomes, Bart Selman.
Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem: Application to Materials Discovery.
In the Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), 2015. [pdf] [supplementary materials][video]

[C8] Stefano Ermon, Yexiang Xue, Russell Toth, Bistra Dilkina, Richard Bernstein, Theodoros Damoulas, Patrick Clark, Steve DeGloria, Andrew Mude, Christopher Barrett, and Carla Gomes.
Learning Large Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa.
In Proc. 29th AAAI Conference on Artificial Intelligence (AAAI), 2015. [pdf]

[C7] Yilun Wang, Yu Zheng, and Yexiang Xue.
Travel Time Estimation of a Path using Sparse Trajectories.
In the Proceeding of the 20th SIGKDD conference on Knowledge Discovery and Data Mining (KDD), 2014. [pdf]

[C6] Ronan Le Bras, Yexiang Xue, Richard Bernstein, Carla P. Gomes, Bart Selman.
A Human Computation Framework for Boosting Combinatorial Solvers.
In Second AAAI Conference on Human Computation and CrowdSourcing (HComp), 2014. [pdf]

[C5] Yexiang Xue, Bistra Dilkina, Theodoros Damoulas, Daniel Fink, Carla P. Gomes and Steve Kelling.
Improving Your Chances: Boosting Citizen Science Discovery.
In First AAAI Conference on Human Computation and CrowdSourcing (HComp), 2013. [pdf] [hot spot list] [species list].

[J2] Stefano Ermon, Yexiang Xue, Carla Gomes, and Bart Selman.
Learning Policies For Battery Usage Optimization in Electric Vehicles.
In Machine Learning (ML), 2013. [online version]

[C4] Ronan Le Bras, Bistra Dilkina, Yexiang Xue, Carla P. Gomes, Kevin S. McKelvey, Claire Montgomery and Michael K. Schwartz.
Robust Network Design for Multispecies Conservation.
In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2013. [pdf]

[C3] Bistra Dilkina, Katherine Lai, Ronan Le Bras, Yexiang Xue, Carla P. Gomes, Ashish Sabharwal, Jordan Suter, Kevin S. McKelvey, Michael K. Schwartz and Claire Montgomery.
Large Landscape Conservation - Synthetic and Real-World Datasets.
In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2013. [pdf]

[C2] Yexiang Xue, Arthur Choi, and Adnan Darwiche.
Basing Decisions on Sentences in Decision Diagrams.
In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2012. [pdf]

[C1] Stefano Ermon, Yexiang Xue, Carla Gomes, and Bart Selman.
Learning Policies For Battery Usage Optimization in Electric Vehicles.
In In Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD),2012 [pdf]

[J1] Arthur Choi, Yexiang Xue, and Adnan Darwiche.
Same-Decision Probability: A Confidence Measure for Threshold-Based Decisions.
In the International Journal of Approximate Reasoning (IJAR), Vol. 53, No. 9, 2012. [pdf]

Research Highlights

		AI-driven Scientific Discovery We propose an end-to-end framework to learn physics models in the form of Partial Differential Equations (PDEs) directly from the experiment data. We scale up learning first-principle models harnessing randomized algorithms, exploiting the fact that the temporal evolutions of many physical systems often consist of gradually changing updates across wide areas in addition to a few rapid updates concentrated in a small set of “interfacial” regions. The development of AI-driven scientific discovery approaches was motivated by the real-world application of learning the physics model of nano-scale crystalline defects in materials under extreme conditions. Papers: [C45][C46][C51][J14][J17][C57][slides].
		Constraint Reasoning Embedded in Machine Learning We propose COnstraint REasoning embedded structured learning (CORE), a scalable constraint reasoning and machine learning integrated approach for learning over structured domains. We embed decision diagrams, a popular constraint reasoning tool, as a fully-differentiable module into deep learning models. In data-driven operational research and program synthesis from the natural language, the structures generated with CORE satisfy 100% of the constraints when using exact decision diagrams. In addition, CORE boosts learning performance by reducing the modeling space via constraint satisfaction. CORE also generates designs which satisfy complex user specifications as well as meet aethetics, utility and convenience requirements. Papers: [C33] [C38][J16][Preprint][slides]
		Dimensionality Reduction with Complex Constraints for Scientific Discovery: Application to High-throughput Materials Discovery In this research, we solve the phase-map identification problem to determine the crystal structure of materials based on high-energy synchrotron-based X-ray diffraction (XRD) data. Our AI solution tightly integrates machine learning, automated reasoning, as well as crowdsourcing and human computation. Since our AI platform has been deployed at the Department of Energy's Joint Center for Artificial Photosynthesis (JCAP), thousands of X-ray diffraction patterns have been processed and the results yield the discovery of new materials for energy applications. Our scientific discovery is featured as editor's choice and the cover story in American Chemical Society Combinatorial Science, and received the IAAI Innovative Application Award. Papers: [C16] [J3] [C14] [C9] [C6].

		Avicaching: a Two Stage Game for Bias Reduction in Citizen Science In this research, we introduce Avicaching as a game theoretic solution to address the data bias problem in citizen science. Avicaching is a novel two-stage game, in which the organizer, a citizen-science program, incentivizes the agents, the citizen scientists, to visit under-sampled locations. We provide OPTIKA, a novel way of encoding this two-stage game as a single optimization problem, cleverly embedding (an approximation of) the agents' problems into the organizer's problem. When implemented in the eBird citizen science program, our Avicaching game shifted 19% birding effort from traditional hotspots to undersampled locations in 3 counties in upstate New York. Our story is featured in NSF news. [video][Avicaching Website] Papers: [C10] [C12] [C5].

		Solving Marginal MAP problems with NP Oracles and XOR Constraints We solve the Marginal Maximum A Posteriori (Marginal MAP) problem, arising naturally from many applications at the intersection of decision-making and machine learning. Marginal MAP problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting). Our solution embeds the intractable counting subproblem as queries to NP oracles, subject to additional XOR constraints, leading to a constant factor approximation algorithm to solve the Marginal MAP problem. Our approach has been applied in probabilistic reasoning (Upper-left in the picture on the left), machine learning (Upper-right), information cascade and network design (Bottom). [spotlight video] Papers: [C13] [C15].

		Compact Knowledge Representation in the Fourier Domain In this research, we present a compact representation of high dimensional knowledge based on discrete Fourier representations, complementing the classical approach based on conditional independence. We show a large class of probabilistic graphical models have a compact Fourier representation. We demonstrate the significance of this representation by applying it to the variable elimination algorithm. We show that a simple algorithm with a new representation leads to competitive scores on UAI inference challenge instances. Paper: [C11].