Bio


Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). 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. 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).

Academic Appointments


Professional Education


  • BS, MIT (2004)
  • MEng, MIT (2005)
  • PhD, UC Berkeley (2011)

2022-23 Courses


Stanford Advisees


All Publications


  • Stronger data poisoning attacks break data sanitization defenses MACHINE LEARNING Koh, P., Steinhardt, J., Liang, P. 2021
  • WILDS: A Benchmark of in-the-Wild Distribution Shifts Koh, P., Sagawa, S., Marklund, H., Xie, S., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R., Gao, I., Lee, T., David, E., Stavness, I., Guo, W., Earnshaw, B. A., Haque, I. S., Beery, S., Leskovec, J., Kundaje, A., Pierson, E., Levine, S., Finn, C., Liang, P., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021
  • Beyond IID: Three Levels of Generalization for Question Answering on Knowledge Bases Gu, Y., Kase, S., Vanni, M. T., Sadler, B. M., Liang, P., Yan, X., Su, Y., ACM ASSOC COMPUTING MACHINERY. 2021: 3477-3488
  • Prefix-Tuning: Optimizing Continuous Prompts for Generation Li, X., Liang, P., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2021: 4582-4597
  • Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices Liu, E., Raghunathan, A., Liang, P., Finn, C., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021
  • Just Train Twice: Improving Group Robustness without Training Group Information Liu, E., Haghgoo, B., Chen, A. S., Raghunathan, A., Koh, P., Sagawa, S., Liang, P., Finn, C., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021
  • Catformer: Designing Stable Transformers via Sensitivity Analysis Davis, J., Gu, A., Choromanski, K., Dao, T., Re, C., Finn, C., Liang, P., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021
  • Robust Encodings: A Framework for Combating Adversarial Typos Jones, E., Jia, R., Raghunathan, A., Liang, P., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 2752–65
  • Concept Bottleneck Models Koh, P., Nguyen, T., Tang, Y., Mussmann, S., Pierson, E., Kim, B., Liang, P., Daume, H., Singh, A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020
  • Understanding Self-Training for Gradual Domain Adaptation Kumar, A., Ma, T., Liang, P., Daume, H., Singh, A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020
  • Feature Noise Induces Loss Discrepancy Across Groups Khani, F., Liang, P., Daume, H., Singh, A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020
  • A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree Li, R., Liang, P., Mussmann, S., ACM ASSOC COMPUTING MACHINERY. 2020: 102–21
  • Enabling Language Models to Fill in the Blanks Donahue, C., Lee, M., Liang, P., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 2492–2501
  • ExpBERT: Representation Engineering with Natural Language Explanations Murty, S., Koh, P., Liang, P., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 2106–13
  • Pretraining deep learning molecular representations for property prediction Liu, B., Hu, W., Leskovec, J., Liang, P., Pande, V. AMER CHEMICAL SOC. 2019
  • Inferring Multidimensional Rates of Aging from Cross-Sectional Data. Proceedings of machine learning research Pierson, E., Koh, P. W., Hashimoto, T., Koller, D., Leskovec, J., Eriksson, N., Liang, P. 2019; 89: 97–107

    Abstract

    Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained to be order-isomorphic, the model family is identifiable solely from cross-sectional data provided the distribution of time-independent variation is known. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors.

    View details for PubMedID 31538144

  • SPoC: Search-based Pseudocode to Code Kulal, S., Pasupat, P., Chandra, K., Lee, M., Padon, O., Aiken, A., Liang, P., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
  • Inferring Multidimensional Rates of Aging from Cross-Sectional Data Pierson, E., Koh, P., Hashimoto, T., Koller, D., Leskovec, J., Eriksson, N., Liang, P., Chaudhuri, K., Sugiyama, M. MICROTOME PUBLISHING. 2019: 97–107
  • Defending against Whitebox Adversarial Attacks via Randomized Discretization Zhang, Y., Liang, P., Chaudhuri, K., Sugiyama, M. MICROTOME PUBLISHING. 2019: 684–93
  • On the Accuracy of Influence Functions for Measuring Group Effects Koh, P., Ang, K., Teo, H. K., Liang, P., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
  • Verified Uncertainty Calibration Kumar, A., Liang, P., Ma, T., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
  • Unlabeled Data Improves Adversarial Robustness Carmon, Y., Raghunathan, A., Schmidt, L., Liang, P., Duchi, J. C., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
  • Training Classifiers with Natural Language Explanations. Proceedings of the conference. Association for Computational Linguistics. Meeting Hancock, B., Bringmann, M., Varma, P., Liang, P., Wang, S., Re, C. 2018; 2018: 1884–95

    Abstract

    Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100* faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.

    View details for PubMedID 31130772

  • Active Learning of Points-To Specifications Bastani, O., Sharma, R., Aiken, A., Liang, P. ASSOC COMPUTING MACHINERY. 2018: 678–92
  • A Retrieve-and-Edit Framework for Predicting Structured Outputs Hashimoto, T. B., Guu, K., Oren, Y., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • Generalized Binary Search For Split-Neighborly Problems Mussmann, S., Liang, P., Storkey, A., PerezCruz, F. MICROTOME PUBLISHING. 2018
  • Know What You Don't Know: Unanswerable Questions for SQuAD Rajpurkar, P., Jia, R., Liang, P., Gurevych, Miyao, Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 784–89
  • Training Classifiers with Natural Language Explanations Hancock, B., Varma, P., Wang, S., Bringmann, M., Liang, P., Re, C., Gurevych, Miyao, Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 1884–95
  • Prediction with a Short Memory Sharan, V., Kakade, S., Liang, P., Valiant, G., Diakonikolas, Kempe, D., Henzinger, M. ASSOC COMPUTING MACHINERY. 2018: 1074–87
  • Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss Mussmann, S., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • Semidefinite relaxations for certifying robustness to adversarial examples Raghunathan, A., Steinhardt, J., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • The price of debiasing automatic metrics in natural language evaluation Chaganty, A., Mussmann, S., Liang, P., Gurevych, Miyao, Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 643–53
  • Synthesizing Program Input Grammars Bastani, O., Sharma, R., Aiken, A., Liang, P. ASSOC COMPUTING MACHINERY. 2017: 95–110
  • Naturalizing a Programming Language via Interactive Learning Wang, S. I., Ginn, S., Liang, P., Manning, C. D., Barzilay, R., Kan, M. Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2017: 929–38
  • From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood Guu, K., Pasupat, P., Liu, E., Liang, P., Barzilay, R., Kan, M. Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2017: 1051-1062
  • Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings He, H., Balakrishnan, A., Eric, M., Liang, P., Barzilay, R., Kan, M. Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2017: 1766-1776
  • Certified Defenses for Data Poisoning Attacks Steinhardt, J., Koh, P., Liang, P., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
  • Learning Overcomplete HMMs Sharan, V., Kakade, S., Liang, P., Valiant, G., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
  • Unsupervised Transformation Learning via Convex Relaxations Hashimoto, T. B., Duchi, J. C., Liang, P., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
  • Learning Executable Semantic Parsers for Natural Language Understanding COMMUNICATIONS OF THE ACM Liang, P. 2016; 59 (9): 68-76

    View details for DOI 10.1145/2866568

    View details for Web of Science ID 000382671100026

  • Learning Language Games through Interaction Wang, S. I., Liang, P., Manning, C. D., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 2368–78
  • How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions Chaganty, A., Liang, P., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 578-587
  • Data Recombination for Neural Semantic Parsing Jia, R., Liang, P., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 12-22
  • Unsupervised Risk Estimation Using Only Conditional Independence Structure Steinhardt, J., Liang, P., Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2016
  • Simpler Context-Dependent Logical Forms via Model Projections Long, R., Pasupat, P., Liang, P., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 1456-1465
  • Inferring Logical Forms From Denotations Pasupat, P., Liang, P., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 23-32
  • Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings Khani, F., Rinard, M., Liang, P., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 952-962
  • Data Augmentation via Levy Processes PERTURBATIONS, OPTIMIZATION, AND STATISTICS Wager, S., Fithian, W., Liang, P., Hazan, T., Papandreou, G., Tarlow, D. 2016: 343-373
  • Bringing Machine Learning and Compositional Semantics Together ANNUAL REVIEW OF LINGUISTICS, VOL 1 Liang, P., Potts, C. 2015; 1: 355-376
  • Learning Where to Sample in Structured Prediction Shi, T., Steinhardt, J., Liang, P., Lebanon, G., Vishwanathan, S. V. MICROTOME PUBLISHING. 2015: 875-884
  • Environment-Driven Lexicon Induction for High-Level Instructions Misra, D. K., Tao, K., Liang, P., Saxena, A., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 992-1002
  • Building a Semantic Parser Overnight Wang, Y., Berant, J., Liang, P., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 1332-1342
  • Compositional Semantic Parsing on Semi-Structured Tables Pasupat, P., Liang, P., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 1470-1480
  • Learning with Relaxed Supervision Steinhardt, J., Liang, P., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015
  • Calibrated Structured Prediction Kuleshov, V., Liang, P., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015
  • Estimating Mixture Models via Mixtures of Polynomials Wang, S. I., Chaganty, A., Liang, P., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015
  • Tensor Factorization via Matrix Factorization Kuleshov, V., Chaganty, A., Liang, P., Lebanon, G., Vishwanathan, S. V. MICROTOME PUBLISHING. 2015: 507-516
  • On-the-Job Learning with Bayesian Decision Theory Werling, K., Chaganty, A., Liang, P., Manning, C. D., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015
  • Linking People in Videos with "Their" Names Using Coreference Resolution 13th European Conference on Computer Vision (ECCV) Ramanathan, V., Joulin, A., Liang, P., Li Fei-Fei, F. F. SPRINGER INT PUBLISHING AG. 2014: 95–110
  • Zero-shot Entity Extraction from Web Pages Pasupat, P., Liang, P., Toutanova, K., Wu, H. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2014: 391-401
  • Semantic Parsing via Paraphrasing Berant, J., Liang, P., Toutanova, K., Wu, H. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2014: 1415-1425
  • Altitude Training: Strong Bounds for Single-Layer Dropout Wager, S., Fithian, W., Wang, S., Liang, P., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2014
  • Simple MAP Inference via Low-Rank Relaxations Frostig, R., Wang, S., Liang, P., Manning, C. D., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2014
  • Dropout training as adaptive regularization. Wager, S., Wang, S., Liang, P. 2013
  • Video Event Understanding using Natural Language Descriptions IEEE International Conference on Computer Vision (ICCV) Ramanathan, V., Liang, P., Li Fei-Fei, F. F. IEEE. 2013: 905–912
  • A Data Driven Approach for Algebraic Loop Invariants 22nd European Symposium on Programming (ESOP) Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, A. V. SPRINGER-VERLAG BERLIN. 2013: 574–592
  • A data driven approach for algebraic loop invariants. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, Aditya, V. 2013
  • Spectral experts for estimating mixtures of linear regressions. Chaganty, A., Liang, P. 2013
  • Video event understanding using natural language descriptions. Ramanathan, V., Liang, P., Fei-Fei, L. 2013
  • Feature noising for log-linear structured prediction. Wang, S., Wang, M., Wager, S., Liang, P., Manning, C. 2013
  • Semantic parsing on Freebase from question-answer pairs. Berant, J., Chou, A., Frostig, R., Liang, P. 2013
  • Genome Editing of Human Embryonic Stem Cells and Induced Pluripotent Stem Cells With Zinc Finger Nucleases for Cellular Imaging CIRCULATION RESEARCH Wang, Y., Zhang, W. Y., Hu, S., Lan, F., Lee, A. S., Huber, B., Lisowski, L., Liang, P., Huang, M., de Almeida, P. E., Won, J. H., Sun, N., Robbins, R. C., Kay, M. A., Urnov, F. D., Wu, J. C. 2012; 111 (12): 1494-?

    Abstract

    Molecular imaging has proven to be a vital tool in the characterization of stem cell behavior in vivo. However, the integration of reporter genes has typically relied on random integration, a method that is associated with unwanted insertional mutagenesis and positional effects on transgene expression.To address this barrier, we used genome editing with zinc finger nuclease (ZFN) technology to integrate reporter genes into a safe harbor gene locus (PPP1R12C, also known as AAVS1) in the genome of human embryonic stem cells and human induced pluripotent stem cells for molecular imaging.We used ZFN technology to integrate a construct containing monomeric red fluorescent protein, firefly luciferase, and herpes simplex virus thymidine kinase reporter genes driven by a constitutive ubiquitin promoter into a safe harbor locus for fluorescence imaging, bioluminescence imaging, and positron emission tomography imaging, respectively. High efficiency of ZFN-mediated targeted integration was achieved in both human embryonic stem cells and induced pluripotent stem cells. ZFN-edited cells maintained both pluripotency and long-term reporter gene expression. Functionally, we successfully tracked the survival of ZFN-edited human embryonic stem cells and their differentiated cardiomyocytes and endothelial cells in murine models, demonstrating the use of ZFN-edited cells for preclinical studies in regenerative medicine.Our study demonstrates a novel application of ZFN technology to the targeted genetic engineering of human pluripotent stem cells and their progeny for molecular imaging in vitro and in vivo.

    View details for DOI 10.1161/CIRCRESAHA.112.274969

    View details for Web of Science ID 000311994700042

    View details for PubMedID 22967807

    View details for PubMedCentralID PMC3518748

  • Induced Pluripotent Stem Cells as a Disease Modeling and Drug Screening Platform JOURNAL OF CARDIOVASCULAR PHARMACOLOGY Ebert, A. D., Liang, P., Wu, J. C. 2012; 60 (4): 408-416

    Abstract

    Induced pluripotent stem cells (iPSCs) hold great hopes for therapeutic application in various diseases. Although ongoing research is dedicated to achieving clinical translation of iPSCs, further understanding of the mechanisms that underlie complex pathogenic conditions is required. Compared with other classical models for studying diseases, iPSCs provide considerable advantages. A newly emerging application of iPSCs is in vitro disease modeling, which can significantly improve the never-ending search for new pharmacological cures. Here, we will discuss current efforts to create iPSC-dependent patient-specific disease models. Furthermore, we will review the use of iPSCs for development and testing of new therapeutic agents and the implications for high-throughput drug screening.

    View details for DOI 10.1097/FJC.0b013e318247f642

    View details for Web of Science ID 000309977900012

    View details for PubMedID 22240913

    View details for PubMedCentralID PMC3343213

  • Modeling Pathogenesis in Familial Hypertrophic Cardiomyopathy Using Patient-Specific Induced Pluripotent Stem Cells Basic Cardiovascular Sciences Scientific Session Lan, F., Lee, A., Liang, P., Navarrete, E., Wang, L., Leng, H., Sanchez, V., Yen, M., Wang, Y., Nguyen, P., Sun, N., Abilez, O., Lewis, R., Yamaguchi, Y., Ashley, E., Bers, D., Robbins, R., Longaker, M., Wu, J. LIPPINCOTT WILLIAMS & WILKINS. 2012
  • Identifiability and unmixing of latent parse trees. Hsu, D., Kakade, Sham, M., Liang, P. 2012
  • Learning dependency-based compositional semantics. Liang, P., Jordan, Michael, I., Klein, D. 2011
  • Learning minimal abstractions. Liang, P., Tripp, O., Naik, M. 2011
  • Scaling up abstraction refinement via pruning. Liang, P., Naik, M. 2011
  • A dynamic evaluation of static heap abstractions. Liang, P., Tripp, O., Naik, M., Sagiv, M. 2010
  • Learning programs: a hierarchical Bayesian approach. Liang, P., Jordan, Michael, I., Klein, D. 2010
  • A game-theoretic approach to generating spatial descriptions. Golland, D., Liang, P., Klein, D. 2010
  • Type-based MCMC. Liang, P., Jordan, Michael, I., Klein, D. 2010
  • A simple domain-independent probabilistic approach to generation. Angeli, G., Liang, P., Klein, D. 2010
  • On the interaction between norm and dimensionality: multiple regimes in learning. Liang, P., Srebro, N. 2010
  • Learning from measurements in exponential families. Liang, P., Jordan, Michael, I., Klein, D. 2009
  • Learning semantic correspondences with less supervision. Liang, P., Jordan, Michael, I., Klein, D. 2009
  • Probabilistic grammars and hierarchical Dirichlet processes. The Oxford Handbook of Applied Bayesian Analysis Liang, P., Jordan, Michael, I., Klein, D. 2009
  • Online EM for unsupervised models. Liang, P., Klein, D. 2009
  • Asymptotically optimal regularization in smooth parametric models. Liang, P., Bach, F., Bouchard, G., Jordan, Michael, I. 2009
  • Optimal team size and monitoring in organizations ACCOUNTING REVIEW Liang, P. J., Rajan, M. V., Ray, K. 2008; 83 (3): 789-822
  • Analyzing the errors of unsupervised learning. Liang, P., Klein, D. 2008
  • Learning bilingual lexicons from monolingual corpora. Haghighi, A., Liang, P., Berg-Kirkpatrick, T., Klein, D. 2008
  • An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. Liang, P., Jordan, Michael, I. 2008
  • A probabilistic approach to language change. Bouchard-Côté, A., Liang, P., Griffiths, T., Klein, D. 2008
  • Agreement-based learning. Liang, P., Klein, D., Jordan, Michael, I. 2008
  • Structure compilation: trading structure for features. Liang, P., Daume, H., Klein, D. 2008
  • A permutation-augmented sampler for Dirichlet process mixture models. Liang, P., Jordan, Michael, I., Taskar, B. 2007
  • Structured Bayesian nonparametric models with variational inference (tutorial). Liang, P., Klein, D. 2007
  • The infinite PCFG using hierarchical Dirichlet processes. Liang, P., Petrov, S., Jordan, Michael, I., Klein, D. 2007
  • A probabilistic approach to diachronic phonology. Bouchard-Côté, A., Liang, P., Griffiths, T., Klein, D. 2007
  • Alignment by agreement. Liang, P., Taskar, B., Klein, D. 2006
  • An end-to-end discriminative approach to machine translation. Liang, P., Bouchard-Côté, A., Klein, D., Taskar, B. 2006
  • A data structure for maintaining acyclicity in hypergraphs. Massachusetts Institute of Technology Technical Report Liang, P., Srebro, N. 2005
  • Efficient geometric algorithms for parsing in two dimensions. Liang, P., Narasimhan, M., Shilman, M., Viola, P. 2005
  • Linear programming in bounded tree-width Markov networks. Liang, P., Srebro, N. 2005
  • Methods and experiments with bounded tree-width Markov networks. Massachusetts Institute of Technology Technical Report Liang, P., Srebro, N. 2004
  • How much of a hypertree can be captured by windmills? Massachusetts Institute of Technology Technical Report Liang, P., Srebro, N. 2003
  • INTERFEROMETRIC STUDIES OF THE JOVIAN ATMOSPHERIC PROBE FIELD Liang, P. Y., Prakash, S. G., Bershader, D. AMER INST PHYSICS. 1980: 1093–94
  • Saponins and sapogenins. III. The sapogenins obtained from chlorogalum pomeridianum JOURNAL OF THE AMERICAN CHEMICAL SOCIETY Liang, P., Noller, C. R. 1935; 57 (1): 525-527