Yexiang Xue's Personal Website

Research Highlights

		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].

Publications

Conference Papers & Journal Articles

[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]