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Teaching

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Published in *NeurIPS 2019*, 2019

Real-world applications often combine learning and optimization problems on graphs. For instance, our objective may be to cluster the graph in order to detect meaningful communities (or solve other common graph optimization problems such as facility location, maxcut, and so on). However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization. Standard approaches treat learning and optimization entirely separately, while recent machine learning work aims to predict the optimal solution directly from the inputs. Here, we propose an alternative decision-focused learning approach that integrates a differentiable proxy for common graph optimization problems as a layer in learned systems. The main idea is to learn a representation that maps the original optimization problem onto a simpler proxy problem that can be efficiently differentiated through. Experimental results show that our ClusterNet system outperforms both pure end-to-end approaches (that directly predict the optimal solution) and standard approaches that entirely separate learning and optimization.

Recommended citation: Wilder, B., Ewing, E., Dilkina, B., & Tambe, M. (2019). End to end learning and optimization on graphs. arXiv preprint arXiv:1905.13732. __https://arxiv.org/abs/1905.13732__

Published in *AAAI 2020*, 2020

Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary. Motivated by green security, where the defender may only observe an adversary’s response to defense on a limited set of targets, we study the problem of defending against the same adversary on a larger set of targets from the same distribution. We give a theoretical justification for why standard two-stage learning approaches, where a model of the adversary is trained for predictive accuracy and then optimized against, may fail to maximize the defender’s expected utility in this setting. We develop a decision-focused learning approach, where the adversary behavior model is optimized for decision quality, and show empirically that it achieves higher defender expected utility than the two-stage approach when there is limited training data and a large number of target features.

Recommended citation: Perrault, A., Wilder, B., Ewing, E., Mate, A., Dilkina, B., & Tambe, M. (2019). Decision-Focused Learning of Adversary Behavior in Security Games. AAAI2020 __https://arxiv.org/abs/1903.00958__

Published in *AAMAS 2021*, 2021

Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be NP-Hard for both make-span and total arrival time minimization. While many algorithms have been developed to solve MAPF problems, there is no dominating optimal MAPF algorithm that works well in all types of problems and no standard guidelines for when to use which algorithm. In this work, we develop the deep convolutional network MAPFAST (Multi-Agent Path Finding Algorithm SelecTor), which takes a MAPF problem instance and attempts to select the fastest algorithm to use from a portfolio of algorithms. We improve the performance of our model by including single-agent shortest paths in the instance embedding given to our model and by utilizing supplemental loss functions in addition to a classification loss. We evaluate our model on a large and diverse dataset of MAPF instances, showing that it outperforms all individual algorithms in its portfolio as well as the state-of-the-art optimal MAPF algorithm selector. We also provide an analysis of algorithm behavior in our dataset to gain a deeper understanding of optimal MAPF algorithms’ strengths and weaknesses to help other researchers leverage different heuristics in algorithm designs.

Recommended citation: Ren, Jingyao, et al. "MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings." AAMAS 2021 __https://arxiv.org/abs/2102.12461__

High School Mentoring, *SHINE at USC*, 1900

I worked with the Summer High School Intensive Next generation Engineering (SHINE) program at USC for two years. The program takes high school students and matches them with PhD student mentors, who guide them in research projects. I mentored 3 students over the two years on MAPF related projects, including looking for reinforcement learning based solutions to MAPF. We covered a wide range of mathematical concepts required to understand and implement RL-based algorithms and then they implemented their own algorithms, trained agents, and assessed performance. In addition to learning about robotics, AI, and ML, students also learned how to conduct research, including how to read papers, how to ask scientific questions, and how to present meaningful results.

Undergraduate Mentoring, *USC*, 1900

I mentored an undergraduate student on a research project investigating Quality Diversity (QD) for developing robust control policies for multi-robot systems. We sought to use QD methods to train a diverse set of control policies that would perform well under a number of different starting conditions. Her final presentation won a Best Presentation award at USC’s Summer Research Symposium.

High School Teaching, *Brown University*, 1900