4 1.3, Linking People in Videos with "Their" Names Using Coreference Resolution. You are currently offline. . . Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. . Google Scholar Digital Library; Montague, R. The proper treatment of quantification in ordinary English. . IEEE Transactions on Image Processing 27 (11), 5288-5302, 2018. Overparameterization hurts worst-group accuracy with spurious correlations, By clicking accept or continuing to use the site, you agree to the terms outlined in our. CS229T/STAT231: Statistical Learning Theory (Winter 2016), 1 Overview 2 1.1 What is this course about? About; Blog; Publications; Google Scholar; Contact; About me. . . 25: 2012: ... ECM Hui, C Liang, Z Wang, BT Song, Q Gu. Robustness to Spurious Correlations via Human Annotations. We study why overparameterization -- increasing model size well beyond the point of zero training error -- can hurt test error on minority groups despite improving average test error when there are spurious correlations in the data. EMNLP 2019 (long papers). The following articles are merged in Scholar. NAACL 2019 (short … . . Runner up best paper. Noise Induces Loss Discrepancy Across Groups for Linear Regression. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions. Merged citations. We present a simple approach for text infilling, the task of predicting missing spans of text at any position in a document. On robustness properties of convex risk minimization methods for pattern recognition. Suggest URL; Education & Career History. The reliability of machine learning systems critically assumes that the associations between features and labels remain similar between training and test distributions. . . . 2 1.2 Online learning (Lecture 1) . A Structual Probe for Finding Syntax in Word Representations. Merged citations. . Percy Liang (Preferred) ... Google Scholar. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. DBLP. (pdf) (bib) (blog) (code) (codalab) (slides) (talk). We study the effect of feature noise (measurement error) on the discrepancy between losses across two groups, for estimators that either do or do not use group membership information. . Try again later. Semantic parsers are typically trained from examples of questions annotated with their target logical … 21: . . . The following articles are merged in Scholar. Google AI Research Scientist. Verified email at cs.stanford.edu ... P Liang, T Berg-Kirkpatrick, D Klein. Feature Noise Induces Loss Discrepancy Across Groups. This "Cited by" count includes citations to the following articles in Scholar. Nano Energy 52, 239-245, 2018. This "Cited by" count includes citations to the following articles in Scholar. Their combined citations are counted only for the first article. . The core part of such a system is the semantic parser that maps questions to logical forms. Semantic Scholar profile for Percy Liang, with 7 highly influential citations and 9 scientific research papers. . The following articles are merged in Scholar. We study the effect of feature noise on loss discrepancy in terms of the amount of noise, the difference between moments of the two groups, and whether group information is used or not. The following articles are merged in Scholar. . . Merged citations ... SG Thakku, Z Liang, D Milea, ... British Journal of Ophthalmology 102 (1), 131-135, 2018. Their combined citations are counted only for the first article. John Hewitt and Christopher D. Manning. 35: 2018: The system can't perform the operation now. . Journal of Machine Learning Research (JMLR), 5(0):1007-1034, 2004. Some features of the site may not work correctly. The ones marked * may be different from the article in the profile. The institution domain is used for conflict of interest detection and institution ranking. We develop a joint model for person naming and coreference resolution, and in the process, infer a latent alignment between tracks and mentions in text. . . The system can't perform the operation now. . . . In Association for Computational Linguistics (2015). Google Scholar Cross Ref; Chollet, F. Keras, 2015. Suppose we want to build a system that answers a natural language question by representing its semantics as a logical form and computing the answer given a structured database of facts. . ... Percy Liang. In this paper, we propose an unsupervised approach to pun generation based on lots of raw (unhumorous) text and a surprisal principle. Their combined citations are counted only for the first article. I’m a senior research scientist at Google AI, where I develop new methods for machine learning and language understanding.. ... Google Verified email at ucmerced.edu. Merged citations. from MIT, 2004; Ph.D. from UC Berkeley, 2011). . Try again later. . 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. . . The ones marked, Proceedings of the 2013 conference on empirical methods in natural language …, Computational Linguistics 39 (2), 389-446, Proceedings of the Human Language Technology Conference of the NAACL, Main …, Proceedings of the 52nd Annual Meeting of the Association for Computational …, Advances in neural information processing systems, 351-359, A Haghighi, P Liang, T Berg-Kirkpatrick, D Klein, P Liang, A Bouchard-Côté, D Klein, B Taskar, Proceedings of the 21st International Conference on Computational …, Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL …, Advances in neural information processing systems, 3517-3529, Proceedings of the 2007 joint conference on empirical methods in natural …, New articles related to this author's research, Squad: 100,000+ questions for machine comprehension of text, Semantic parsing on freebase from question-answer pairs, Understanding black-box predictions via influence functions, Know what you don't know: Unanswerable questions for SQuAD, Adversarial examples for evaluating reading comprehension systems, Learning dependency-based compositional semantics, Dropout training as adaptive regularization, Certified defenses against adversarial examples, Semi-supervised learning for natural language, Learning bilingual lexicons from monolingual corpora, An end-to-end discriminative approach to machine translation, Data recombination for neural semantic parsing, Compositional semantic parsing on semi-structured tables, Learning semantic correspondences with less supervision, Traversing knowledge graphs in vector space, Certified defenses for data poisoning attacks, The infinite PCFG using hierarchical Dirichlet processes, Delete, retrieve, generate: A simple approach to sentiment and style transfer. Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. Designing and Interpreting Probes with Control Tasks. . . . Proceedings of ACL-08: Hlt, 771-779, 2008. Their, This "Cited by" count includes citations to the following articles in Scholar. Our method tests whether systems can answer … Their combined citations are counted only for the first article. . . The following articles are merged in Scholar. . (Lecture 1) . Enter your education and career history. . Author pages are created from data sourced from our academic publisher partnerships and public sources. ... J Liang, G Zhu, C Wang, P Zhao, Y Wang, Y Hu, L Ma, Z Tie, J Liu, Z Jin. An Investigation of Why Overparameterization Exacerbates Spurious Correlations. Google Scholar provides a simple way to broadly search for scholarly literature. . . . To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Selective Question Answering under Domain Shift. Their combined citations are counted only for the first article. Associate Professor of Computer Science, Stanford University. . John Hewitt and Percy Liang. In Approaches to Natural Language (1973), 221--242. . ... Morteza Zadimoghaddam Google Research Verified email at google.com. The following articles are merged in Scholar. We propose the setting of selective question answering under domain shift, in which a QA model is tested on a mixture of in-domain and out-of-domain data and must answer (i.e., not abstain on) as many questions as possible while maintaining high accuracy. Google Scholar; Pasupat, P., Liang, P. Compositional semantic parsing on semi-structured tables. 356: Construction Management and Economics 30 (11), 951-961, 2012. Google Scholar ... J Liang, H Shen, K Wang, PL Rosin, MH Yang. The following articles are merged in Scholar. Their combined citations are counted only for the first article. The following articles are merged in Scholar. Their combined citations are counted only for the first article. Transparent and Interpretable AI: an interview with Percy Liang February 13, 2018 / by Sarah Marquart At the end of 2017, the United States House of Representatives passed a bill called the SELF DRIVE Act, laying out an initial federal framework for autonomous vehicle regulation. . . Merged citations. The following articles are merged in Scholar. . . Google Scholar; Christmann, A. and Steinwart, I. Their combined citations are counted only for the first article. Enabling Language Models to Fill in the Blanks. For Percy Liang is an Associate Professor of Computer Science at Stanford University B.S. Ieee Transactions on Image Processing 27 ( 11 ), 221 -- 242,,! 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