Phoenix Young Musicians Competition (2000). His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. Jonathan Berant, Andrew Chou, Roy Frostig, Percy Liang. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Machine learning is facing a robustness crisis. Percy Liang's 133 research works with 5,234 citations and 3,995 reads, including: Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning Percy LIANG of Stanford University, CA (SU) | Read 30 publications | Contact Percy LIANG Get Stanford HAI updates delivered directly to your inbox. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … Most of our recent papers have been published on CodaLab as executable Stanford University Professor Percy Liang discusses the challenges of conversational AI and the latest leading-edge efforts to enable people to speak naturally with computers. The problem is perhaps fundamental to learning: fitting huge low bias models can easily overfit the superficial statistics of the […] Year; Squad: 100,000+ questions for machine comprehension of text. The Stanford AI Lab is dynamic and community-oriented, providing many opportunities for research collaboration and innovation. Statistical Machine Learning Group Optional line 2 - add two-line-signature to body class to display Names. ... Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang International Conference of Machine Learning (ICML) 2018 and distributionally robust optimization to ensure the fairness of machine learning models over time (ICML 2018). (ICLR 2019). we showed that state-of-the-art systems, Matthew Lamm mlamm@stanford.edu. Percy Liang. Also check us … Share. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Percy Liang. Cited by. We've worked on using influence functions to understand black-box models (ICML 2017), This grant was recommended to enable Professor Liang to spend significant time engaging in our process to determine whether to provide his … from MIT, 2004; Ph.D. from UC Berkeley, 2011). I broadly identify with the His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014). Percy Liang. Recent Posts. Sham Kakade's statistical learning theory course. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Jeannette Bohg Learning Dependency-Based Compositional Semantics Semantic Representations for Textual Inference Workshop – Mar. surprising accuracy despite not being told explicitly what a SAT problem is Stanford University Stanford, CA 94305 Percy Liang Department of Computer Science Stanford University Stanford, CA 94305 Abstract In user-facing applications, displaying calibrated confidence measures— probabilities that correspond to true frequency—can be as … Cited by. Abstract: Natural language promises to be the ultimate interface for interacting with computers, allowing users to effortlessly tap into the wealth … Percy Liang Computer Science Stanford University pliang@cs.stanford.edu Abstract Our goal is to learn a semantic parser that maps natural language utterances into ex-ecutable programs when only indirect su-pervision is available: examples are la-beled with the correct execution result, Association for Computational Linguistics (ACL), 2014. Stanford / Autumn 2018-2019 Announcements. Communication: We will use Piazza for all communications, and will send out an access code through Canvas. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … In the Autumn of 2015, I was the head TA for CS221, Stanford's introductory artificial intelligence class, taught by Percy Liang. I'm a 5th-year PhD student in the Stanford Linguistics Department and a member of the Stanford NLP Group.I work with Chris Manning and Dan Jurafsky. Empirical Methods in Natural Language Processing (EMNLP), 2013. Megha Srivastava, Tatsunori Hashimoto, Percy Liang International Conference of Machine Learning (ICML) 2020 Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning Megha Srivastava, Hoda Heidari, Andreas Krause Knowledge Discovery and Data Mining (KDD) 2019 Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. September 30, 2020 - 10:00am to 11:00am. We are actively looking for contributors, Title. Computers can do a lot, but tapping into their full power requires the rather non-trivial ability to program. I will start my PhD in Computer Science at Stanford in Fall 2020, supported by the NSF GRFP Fellowship (2018-2023). of an experiment from raw data to final results. ‎Show Behind The Tech with Kevin Scott, Ep Percy Liang: Stanford University Professor, technologist, and researcher in AI - Mar 19, 2020 ‎We talk with Stanford University Professor Percy Liang. They discuss the challenges of conversational AI and the latest leading-edge efforts to … Stanford, CA 94305. a platform that allows researchers to run and manage their experiments by maintaining the full provenance from MIT, 2004; Ph.D. from UC Berkeley, 2011). Percy Liang (Stanford University): Pushing the Limits of Machine Learning. ... Pang Wei Koh, Percy Liang. ... Percy Liang's course notes from previous offerings of this course. Stanford University natural language processing (ACL, NAACL, EMNLP) communities. Percy Liang Stanford University. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … Sort. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. I was a section leader for Stanford's CS106A (Introduction to Programming) class in the Winter of 2012. Given society's increasing reliance on machine learning, Statistical Machine Learning Group Optional line 2 - add two-line-signature to body class to display Understanding Black-box Predictions via Influence Functions. Stanford University School of Engineering 626,652 views 1:11:41 Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction and Logistics - Duration: 1:07:52. Authors: Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang Download PDF Abstract: We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. Semantic Parsing on Freebase from Question-Answer Pairs. from MIT, 2004; Ph.D. from UC Berkeley, 2011). We encourage all students to use Piazza, either through public or private posts. Faculty & Research Scientists. CS229T/STAT231: Statistical Learning Theory (Winter 2016) Percy Liang Last updated Wed Apr 20 2016 01:36 These lecture notes will be updated periodically as the course goes on. Statistical Learning Theory (CS229T/STATS231). Articles Cited by. Faculty & Research Scientists. Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. are easily fooled by distracting sentences in a way that no human would be (EMNLP 2017). Update: 2020-03-19. His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Fellowships: - NSF Graduate Research Fellowship (2012-2015) - Stanford Math+X Fellowship (2012-2013) - Greylock X Fellow (2017) 2007 - 2011: B.S. with people and improve over time through interaction. Semantic Parsing for Natural Language Interfaces - Percy Liang, PhD . His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … Percy Liang Michael Jordan Statistical and computational concerns have motivated parameter estimators based on various forms of likelihood, e.g., joint, condi- tional, and pseudolikelihood. from MIT, 2004; Ph.D. from UC Berkeley, 2011). The Open Philanthropy Project recommended a grant of $1,337,600 over four years (from July 2017 to July 2021) to Stanford University to support research by Professor Percy Liang and three graduate students on AI safety and alignment. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University Lecture 2: Machine Learning 1 – Linear Classifiers, SGD | Stanford CS221: AI (Autumn 2019) Topics: Linear classification, Loss minimization, Stochastic gradient descent Stanford University Professor Percy Liang discusses the challenges of conversational AI and the latest leading-edge efforts to enable people to speak naturally with computers. Research Groups. 210 Panama Street Sort by citations Sort by year Sort by title. The funds will be split approximately evenly across the … Martin Wainwright's statistical learning theory course. I am currently a postdoctoral scholar advised by Percy Liang in the Stanford department of computer science. SAIL is committed to advancing knowledge and fostering learning in an atmosphere of discovery and creativity. International Conference … Cited by. Dorsa Sadigh and Chelsea Finn Win the Best Paper Award at CORL 2020; Chirpy Cardinal Wins Second Place in the Alexa Prize; Chelsea Finn and Jiajun Wu Receive Samsung AI Researcher of the Year Awards Search Search. Percy Liang named Sloan Research Fellow . from MIT, 2004; Ph.D. from UC Berkeley, 2011). I led a team of 18 TAs for a class with 550 enrolled students. Also check us … of changing data distributions and adversaries. Peter Bartlett's statistical learning theory course. Percy Liang. Cited by. machine learning natural language processing. Despite reaching human-level performance on a wide range of benchmarks, state-of-the-art systems can be easily fooled by seemingly small perturbations that don’t affect humans. Title. Names. it is critical to build tools to make machine learning more reliable in the wild. Stanford University Mina Lee Stanford University {cdonahue,minalee,pliang}@cs.stanford.edu Percy Liang Stanford University Abstract We present a simple approach for text infill-ing, the task of predicting missing spans of text at any position in a document. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Previously, I acquired my PhD from UC San Diego where I was jointly advised by Julian McAuley (computer science) and Miller Puckette (music).. My research sits at the intersection of machine learning, music, audio, and interaction. Semantic Parsing via Paraphrasing. Amita Kamath Robin Jia Percy Liang Computer Science Department, Stanford University fkamatha, robinjia, pliangg@cs.stanford.edu Abstract To avoid giving wrong answers, question an-swering (QA) models need to know when to abstain from answering. A while back, I wrote a friendly introduction to natural language interfaces (XRDS magazine 2014) Gill Bejerano. They discuss the challenges of conversational AI and the latest leading-edge efforts to enable people to speak naturally with computers. and a slightly more technical survey article on executable semantic parsing (CACM 2016). I also received my B.S. Autumn 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, 2017-18, 2018-19: Winter 2012-13, 2013-14, 2014-15: 2015-16: Presidential Early Career Award for Scientists and Engineers (2019), Microsoft Research Faculty Fellowship (2014), Graduate fellowships: NSF, NDSEG, GAANN, Siebel Scholar, Programming contests: E-mail: robinjia at stanford dot edu I am a sixth-year Ph.D. student in Computer Science at Stanford University, advised by Percy Liang.I am interested in building natural language understanding systems that are robust when given unexpected inputs. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. I am a Computer Science Ph.D. student studying Machine Learning at We have been developing CodaLab Worksheets, Finally, I am a strong proponent of efficient and reproducible research. Jeannette Bohg Cordura Hall semidefinite programming to provide certificates a neural network is safe from a class of adversaries (NeurIPS 2018), Percy Liang's course notes from previous offerings of this course. Enter email addresses associated with all of your current and historical institutional affiliations, as well as all your previous publications, and the Toronto Paper Matching System. Percy Liang Computer Science Stanford University pliang@cs.stanford.edu Abstract Our goal is to learn a semantic parser that maps natural language utterances into ex-ecutable programs when only indirect su-pervision is available: examples are la-beled with the correct execution result, For example, on the SQuAD reading comprehension dataset we created (EMNLP 2016), Dorsa Sadigh and Chelsea Finn Win the Best Paper Award at CORL 2020; Chirpy Cardinal Wins Second Place in the Alexa Prize; Chelsea Finn and Jiajun Wu Receive Samsung AI Researcher of the Year Awards Skip slideshow. Ph.D. in Statistics at Stanford University Advisor: Percy Liang. Associate Professor of Computer Science, Stanford University. Feb 24 2015. Ph.D. Committee: Percy Liang, Wing Hung Wong, Chris Manning, and Lester Mackey. I'm currently visiting CoAStaL, the NLP group at University of Copenhagen.. My area of research is Natural Language Processing. Description. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. While infill-ing could enable rich functionality especially Stanford Report. Unsupervised Transformation Learning via Convex Relaxations, Tatsunori B. Hashimoto, John C. Duchi, Percy Liang. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Traditionally, semantic parsers are trained primarily from text paired with knowledge base information. papers. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … Articles Cited by. Associate Professor of Computer Science, Stanford University. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. Sort by citations Sort by year Sort by title. Percy Liang, Alexandre Bouchard-Côté, Dan Klein, Ben Taskar. Our work spans the spectrum from answering deep, foundational questions in the theory of machine learning to building practical large-scale machine learning algorithms which are widely used in industry. Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. Sang Michael Xie's Homepage. The Open Philanthropy Project recommended a planning grant of $25,000 to Professor Percy Liang at Stanford University. Moreover, users of-ten ask questions that diverge from the model’s Percy Liang Stanford University pliang@cs.stanford.edu Abstract A central challenge in semantic parsing is handling the myriad ways in which knowl-edge base predicates can be expressed. Yuchen Zhang, Percy Liang, Moses Charikar. Don’t miss out. ... Percy Liang (Preferred) Suggest Name; Emails. Gill Bejerano. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. in Mathematics at Duke University Minor in Biology The Stanford Statistical Machine Learning Group at Stanford is a unique blend of faculty, students, and post-docs spanning AI, systems, theory, and statistics. I'm interested in building systems that learn to translate natural language descriptions (e.g., in English or Chinese) into programs (e.g., in Python or C++). One can also use natural language to describe classifiers directly rather than requiring labeled data (ACL 2018). Sort. Percy Liang's 133 research works with 5,234 citations and 3,995 reads, including: Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning The Stanford Statistical Machine Learning Group at Stanford is a unique blend of faculty, students, and post-docs spanning AI, systems, theory, and statistics. One idea we've explored is to "naturalize" a programming language gradually into a natural language (ACL 2017). Of efficient and reproducible research at University of Copenhagen.. My area of research natural. Can do a lot, but tapping into their full power requires rather! | contact percy Liang is an Assistant Professor of Computer Science at Stanford University Stanford CA! Idea we 've explored is to `` naturalize '' a Programming language gradually into a natural language ( )... To `` naturalize '' a Programming language gradually into a natural language to describe classifiers rather. 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