Despite the progress, several key challenges limit the applicability and scalability of deep RL algorithms. Biostatistics. You also must indicate on each homework with whom you have collaborated. Learn more. If applicable, live demonstrations of your software are highly encouraged. Abstract / PDF [990K] Aging Gracefully with Geriatrix: A File System Aging Tool. PSB (Pacific Symposium on Bicomputing) 2019 accepted, Liu, X., Wang, H., Ye, W., & Xing, EP. A recently developed class of models, called Contextual Explanation Networks (CENs) [1], provides a flexible way to achieve this: on a high level, it allows to learn families of contextualized simple/interpretable models (e.g., sparse linear), called explanations, which are ‘glued’ together via deep neural networks. The notes must be detailed and thorough, and must be submitted through a pull request on GitHub within 1 week after the lecture. 9.1 - 9.2), Jordan Textbook, Ch. Acquired the knowledge of Machine Learning, especially in the field of computer vision and statistic machine learning, also object detection and segmentation. Sparse inverse covariance estimation with the graphical lasso. IEEE VTC 2018 Accpeted. Carnegie Mellon University Pittsburgh, PA Thesis Committee Eric P. Xing, Chair Jaime Carbonell Tom Mitchell Dan Roth ... names) and all the members of the Sailing lab. they're used to log you in. Recently [2] proposed a smooth characterization of directed acyclic graphs, enabling continuous optimization for Bayesian network structure learning, similar to graphical lasso for Markov networks. We use essential cookies to perform essential website functions, e.g. Focus on knowledge of analyzing genes, especially in the field of correcting the confoundering factors among the high dimension heterogeneous data. Intern at Toutiao AI-Lab,Beijing, China, NOv 2017 – Current, Focus on surveillance video parsing in computer vision, Research Intern Multiplex Confounding Factor Correction for Genomic Association Mapping with Squared Sparse Linear Mixed Model. We ask that you retain any copyright notices, and include a written notice indicating the source of any materials you use. Top 6 in China competition of Imagine Cup 2017, Interdisciplinary Contest In Modeling Meritorious Winner. Probabilistic Graphical Models 10-708 • Spring 2019 • Carnegie Mellon University. Below are a few interesting topics that have been developed very actively in recently years and worth some explorations in the class: The goal of AutoML is to make machine learning more accessible by automatically generating a data analysis pipeline that can include data pre-processing, feature selection, and feature engineering methods along with machine learning methods and parameter settings that are optimized for your data. For example, efficient exploration and long-term credit assignment remain core problems, especially in settings with sparse or delayed rewards. Is building a contextual model for the join distribution over X, C, Y the right way to go? The idea is inspired by a recent technique used in model-based reinforcement learning [3]: Given a sentence in the source language and a pre-trained target LM, generate a sequence of words in the target language by starting from a random sequence and iteratively refining it to increase its likelihood under the given LM. 10-708 - Probabilistic Graphical Models - Carnegie Mellon University - Spring 2019 Probabilistic Graphical Models. PetuumMed: algorithms and system for EHR-based medical decision support Eric Xing Petuum Inc. Carnegie Mellon University SAILING LAB Laboratory for Statistical Artificial InteLigence & … 9.3-9.5), Ch. For example, when we learn a generative model for shapes, it would be ideal if each latent variables would correspond to the shapes pose, shadow, rotations, lighting etc. All project teams will present their work at the end of the semester. Join them to grow your own development teams, manage permissions, and collaborate on projects. This topic will allow us to explore different directions in large-scale machine learning to address the aforementioned problems: Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. If you feel that we have made a mistake in grading your homework, please submit a regrading request on Gradescope and we will consider your request. Estimating Bayesian network structure from data is one of the fundamental problems in graphical models. These advances have especially benefited vision-based RL problems and robotic manipulation methods. K. Zhang, B. Schöklopf, P. Spirtes, and C. Glymour. I’d be happy to share more specific project ideas and advise students. We ask that you retain any copyright notices, and include a written notice indicating the source of any materials you use. Disentangled representation learning involves learning a set of latent variables that each capture individual factors of variation in the data. However, is it possible to integrate such models into the CEN framework and make them trainable end-to-end? Applications of ML in the healthcare domain may significantly benefit from such models. You will be allowed 6 total homework late days without penalty for the entire semester. Your class project is an opportunity for you to explore an interesting problem in the context of a real-world data sets. Welcome to the website of the Integrated Innovation Institute at Carnegie Mellon University. CVPR 2018 Workshop Accpeted. © Copyright 2020 Carnegie Mellon University. We appreciate everyone being actively involved in the class! The class project will be carried out in groups of 3 or 4 people, and has four main parts: a proposal, a midway report, a final report, and a poster/oral presentation. There is a number of interesting directions one could take CEN further. We plan to collaborate with faculty at community colleges on offering a variety of hands-on project-based courses as part of community college course offerings. If you are interested in a particular project, please contact the respective contact person to get further ideas or details. You can explore new applications of deep generative models, improve the theoretical understanding and empirical optimization of deep generative models, design metrics for improved evaluation of deep generative models, and other new directions. The original paper [1] developed CEN with only linear explanations (for scalar and structured output spaces). © Copyright 2020 Carnegie Mellon University. Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning. The allowed late days are counted by day (i.e., each new late day starts at 12:00 am ET). In Advances in Neural Information Processing Systems. DAGs with NO TEARS: Continuous Optimization for Structure Learning. CEN for few-shot learning and/or meta-learning. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). Is it possible to use other types of simple models? Once the allowed late days are exceeded, the penalty is 50% per late day conted by hour (i.e., 2.0833% per hour). The grading breakdown is as follows: Note that this class does not have any tests or exams. – Intuition on why should it be better than the state of the art The probabilistic graphical models framework provides an unified view for this wide range of problems, enables efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. 40% for clear and concise description of proposed method, 40% for literature survey that covers at least 4 relevant papers, 20% for introduction and literature survey, 20% for the design of upcoming experiments and revised plan of activities (in an appendix, please show the old and new activity plans), 10% for data collection and preliminary results, Introduction: problem definition and motivation, Background & Related Work: background info and literature survey, Methods July 2020 - New Ph.D. student Ryan Bowman joins the lab. Description of potential data sets to use for the experiments. A policy \pi maps each state-action pair (s, a) \in \mathcal{S} \times \mathcal{A} to the probability \pi(s, a) of taking action a when in state s. The agent’s goal is to learn a policy that maximizes its cumulative discounted reward \mathbb{E}_\pi\left[\sum_t \gamma^t r_t \right]. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. 8, 9 (Sec. Everlasting Iatric Researcher (Eir): Identifying the Article and Reading for Genetic Association Knowledge. Mummy-papa, you have been a constant source of inspi- You signed in with another tab or window. Methods Accepted (impact factor 3.782), Ye, W. Liu, X. Wang, H. & Xing, EP. Note that even though you can use datasets you have used before, you cannot use work that you started prior to this class as your project. This should be approximately half a page long. The project is an integral part of this class, and is designed to be as similar as possible to researching and writing a conference-style paper. Friedman, J., Hastie, T., & Tibshirani, R. (2008). Projects should be done in teams of three students. Recently we have witnessed very active development toward a few directions such as hyperparameter searching, neural architecture search, etc. © Copyright 2020 Carnegie Mellon University. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. – Description of your testbed and a list of questions your experiments are designed to answer whether information about pose, shadow, rotations are given or not), design metrics for improved evaluation of disentanglement in models, as well as new applications of disentangled representation learning to improve performance on NLP, vision, and multimodal tasks. Once those days are used, you will be penalized according to the following policy: You must turn in at least 3 of the 4 assignments, even if for zero credit, in order to pass the course. Sailing Lab, Carnegie Mellon University, Pittsburgh, July 2017 – Sept 2017.

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