Dear all, We are going to have 2 AI seminars in the coming week. The first talk will be on *"Rigorous Experimentation For Reinforcement Learning" *by Scott Jordan and is scheduled to be on October 10th (Today), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. This is an *in-person* event and will be held at *KEAR 305* *Rigorous Experimentation For Reinforcement Learning* Scott Jordan Postdoc University of Alberta *Abstract:* Scientific fields make advancements by leveraging the knowledge created by others to push the boundary of understanding. The primary tool in many fields for generating knowledge is empirical experimentation. Although common, generating accurate knowledge from empirical experiments is often challenging due to inherent randomness in execution and confounding variables that can obscure the correct interpretation of the results. As such, researchers must hold themselves and others to a high degree of rigor when designing experiments. Unfortunately, most reinforcement learning (RL) experiments lack this rigor, making the knowledge generated from experiments dubious. This dissertation proposes methods to address central issues in RL experimentation. Evaluating the performance of an RL algorithm is the most common type of experiment in RL literature. Most performance evaluations are often incapable of answering a specific research question and produce misleading results. Thus, the first issue we address is how to create a performance evaluation procedure that holds up to scientific standards. Despite the prevalence of performance evaluation, these types of experiments produce limited knowledge, e.g., they can only show how well an algorithm worked and not why, and they require significant amounts of time and computational resources. As an alternative, this dissertation proposes that scientific testing, the process of conducting carefully controlled experiments designed to further the knowledge and understanding of how an algorithm works, should be the primary form of experimentation. Lastly, this dissertation provides a case study using policy gradient methods, showing how scientific testing can replace performance evaluation as the primary form of experimentation. As a result, this dissertation can motivate others in the field to adopt more rigorous experimental practices. *Speaker Bio:* Scott Jordan got his Batchelor's degree from Oregon State in 2015 while working with Tom Dietterich. He recently got his Ph.D. from the University of Massachusetts and is now a Postdoc at the University of Alberta working with Martha White. His research focuses on reinforcement learning with the goal of understanding the properties necessary for scalable and effective sequential decision-making, with works published in ICML, NeurIPS, and other ML venues. His dissertation addresses issues with poor experimentation practices common in reinforcement learning research. This will be followed by a talk on Friday, October 14th, 2022. The second talk will be on *"Causal Inference and Data Fusion" *by Elias Bareinboim and is scheduled to be on October 14th (Friday), 1-2 PM PST. It will be followed by a 30-minute Q&A session by the graduate students. This is an *in-person* event and will be held at *KEC 1001* *Causal Inference and Data Fusion* Elias Bareinboim Associate Professor, Department of Computer Science Director, Causal Artificial Intelligence Laboratory Columbia University *Abstract:* Causal inference is usually dichotomized into two categories, experimental (Fisher, Cox, Cochran) and observational (Neyman, Rubin, Robins, Dawid, Pearl) which, by and large, are studied separately. Understanding reality is more demanding. Experimental and observational studies are but two extremes of a rich spectrum of research designs that generate the bulk of the data available in practical, large-scale situations. In typical medical explorations, for example, data from multiple observations and experiments are collected, coming from distinct experimental setups, different sampling conditions, and heterogeneous populations. In this talk, I will introduce the data-fusion problem, which is concerned with piecing together multiple datasets collected under heterogeneous conditions (to be defined) so as to obtain valid answers to queries of interest. The availability of multiple heterogeneous datasets presents new opportunities to causal analysts since the knowledge that can be acquired from combined data would not be possible from any individual source alone. However, the biases that emerge in heterogeneous environments require new analytical tools. Some of these biases, including confounding, sampling selection, and cross-population biases, have been addressed in isolation, largely in restricted parametric models. I will present my work on a general, non-parametric framework for handling these biases and, ultimately, a theoretical solution to the problem of fusion in causal inference tasks. Suggested readings: 1. E. Bareinboim and J. Pearl. Causal inference and the Data-Fusion Problem. <http://https//www.pnas.org/content/113/27/7345> Proceedings of the National Academy of Sciences, 113(27): 7345-7352, 2016. 2. E. Bareinboim, J. Correa, D. Ibeling, T. Icard. On Pearl’s Hierarchy and the Foundations of Causal Inference <https://causalai.net/r60.pdf>. In “Probabilistic and Causal Inference: The Works of Judea Pearl”, In Probabilistic and Causal Inference: The Works of Judea Pearl (ACM, Special Turing Series), pp. 507-556, 2022. 3. K. Xia, K. Lee, Y. Bengio, E. Bareinboim. The Causal-Neural Connection: Expressiveness, Learnability, and Inference <https://causalai.net/r80.pdf>. In Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS), 2021. *Speaker Bio:* Elias Bareinboim is an associate professor in the Department of Computer Science and the director of the Causal Artificial Intelligence (CausalAI) Laboratory at Columbia University. His research focuses on causal and counterfactual inference and their applications to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. His work was the first to propose a general solution to the problem of ``data-fusion,'' providing practical methods for combining datasets generated under different experimental conditions and plagued with various biases. More recently, Bareinboim has been exploring the intersection of causal inference with decision-making (including reinforcement learning) and explainability (including fairness analysis). Bareinboim received his Ph.D. from the University of California, Los Angeles, where he was advised by Judea Pearl. Bareinboim was named one of ``AI's 10 to Watch'' by IEEE, and is a recipient of the NSF CAREER Award, the ONR Young Investigator Award, the Dan David Prize Scholarship, the 2014 AAAI Outstanding Paper Award, and the 2019 UAI Best Paper Award. *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.