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<html xmlns="http://www.w3.org/TR/REC-html40">
<!-- Mirrored from www.cs.cmu.edu/~nlao/ by HTTrack Website Copier/3.x [XR&CO'2014], Fri, 02 Feb 2018 05:01:57 GMT -->
<head>
<meta HTTP-EQUIV="content-type" CONTENT="text/html; charset=UTF-8"></meta>
<title>Ni Lao's Homepage</title>
<style>
img {
max-width: 100% !important;
max-height: 100% !important;
}
</style>
</head>
<body lang=EN-US link=blue vlink=purple style='tab-interval:.5in'>
<div class=Section1>
<img border="0" src="images/mtv-birds.jpeg" align="center" >
<h2>
Ni Lao (<span lang="ZH-CN" >劳逆</span>)
<img border="0" src="images/gmail.png" width="159" height="29">
</h2>
<p >I work on
<a href="http://arxiv.org/pdf/1406.7445v1">machine learning</a>,
<a href="publication/2010/2010.ML.PRA.pdf">information retrieval</a>, and
<a href="publication/2012/2012.emnlp.paper.pdf"> natural language processing</a>.
Previously I have studied a wide range of topics such as
<a href="publication/older/Chapter_18.pdf"> robotic soccer</a>,
<a href="publication/older/p375-yuan.pdf"> computer system diagnosis</a>,
<a href="https://www.microsoft.com/en-us/research/publication/object-level-vertical-search/"> product search</a>, and
<a href="publication/2009/2009-NTCIR7-IR4QA-LaoN.pdf"> question answering</a>.
Now I am interested in
<a href="publication/2017-nsm-acl.pdf"> learning to control machines</a>, and
<a href="publication/2015-KB-workshop.pdf"> learning to create machines</a>. </p>
I graduated from <a href="http://www.lti.cs.cmu.edu/">Language
Technologies Institute</a>, <a href="http://www.cs.cmu.edu/">School of Computer
Science</a> at <a href="http://www.cmu.edu/">Carnegie Mellon University</a>.
My thesis was advised by professor <a href="http://www.cs.cmu.edu/~wcohen/">William
W. Cohen</a>.
I worked at <a href="http://research.google.com/">Google</a> and <a href="https://machinelearning.apple.com/">Apple</a> on language understanding and question answering.
I was the chief scientist at <a href="https://saymosaic.com/">SayMosaic</a>.
Now I work at Google on large pretrained models.
</p>
<p >My <a href="doc/cv.2024.pdf">CV</a>,
<a href="doc/RS.pdf">research statement</a>,
and <a href="publication/2012/thesis.pdf">thesis</a>.
My collection of <a href="fun/index.html"> interesting stuff</a></span>
<!--and <a href="http://nilao1980.spaces.live.com/">MSNSpace </a></p>-->
</p>
<p >
I TAed classes <a href="http://malt.ml.cmu.edu/mw/index.php/Machine_Learning_with_Large_Datasets_10-605_in_Spring_2012">
Machine Learning with Large Datasets, 2012</a> and <a href="http://www.cs.cmu.edu/~epxing/Class/10701/">
Machine Learning, 2010</a>.
</p>
<p> My
<a href="https://twitter.com/lao_ni"> Twitter</a>,
<a href="https://www.linkedin.com/in/ni-lao/"> LinkedIn</a>,
<a href="http://medium.com/@ni-lao/"> Medium</a>,
and <a href="https://www.facebook.com/noon99"> Facebook</a>.
</p>
<h2>Selected Publications</h2>
<p> <i> (Patents) <a href="https://patents.google.com/?inventor=Ni+Lao"> Google Patents </a> </i> </p>
<p> Cheng HE, Ni Lao, Xiuqi Tan, Sumang Liu, Method and apparatus for searching historical data, US20190370398A1, 2019 </p>
<p> Ni Lao, Chen Liang, Quoc V Le, John Blitzer,
Neural question answering system,
US20190130251A1. 2019 </p>
<p> Ni Lao, Lukasz Mieczyslaw Kaiser, Nitin Gupta, Afroz Mohiuddin, Preyas Popat,
Answer to question neural networks,
US20180114108A1, WO2018097907A1, DE202017106363U1, GB2557014A. 2018 </p>
<p> Ni Lao, Jiazhong NIE, Fan Yang,
Natural language processing with an n-gram machine,
US Patent App. 16/069,781, WO2019083519A1, 2017 </p>
<p> A Subramanya, F Pereira, N Lao, J Blitzer, R Gupta,
Querying a data graph using natural language queries
US Patent 10,810,193 </p>
<p> Kevyn B Collins-thompson, Ni Lao,
Context-Aware Query Alteration,
US Patent App. 13/043,500, 2012 </p>
<p> <i> (Peer reviewed papers)
<a href="https://www.semanticscholar.org/author/N.-Lao/1914797"> Semantic Scholar </a> ,
<a href="http://scholar.google.com/citations?user=hxEoGAMAAAAJ"> Google Scholar </a> ,
<a href="http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/l/Lao:Ni.html"> DBLP </a>
</i> </p>
<p> Gengchen Mai, Krzysztof Janowicz, Rui Zhu, Ling Cai, Ni Lao,
<a href="https://agile-giss.copernicus.org/articles/2/8/2021/">
Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions
</a>,
AGILE: GIScience Series 2, 1-21, 2021.
</p>
<p> Gengchen Mai, Krzysztof Janowicz, Ling Cai, Rui Zhu, Bo Yan, Blake Regalia, Bo Yan, Meilin Shi, Ni Lao,
<a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12629">
SE‐KGE: A location‐aware Knowledge Graph Embedding model for Geographic Question Answering and Spatial Semantic Lifting
</a>,
in Transactions in GIS, 2020.
</p>
<p> Gengchen Mai, Krzysztof Janowicz, Bo Yan, Rui Zhu, Ling Cai, Ni Lao,
<a href="https://openreview.net/forum?id=rJljdh4KDH">
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells </a>,
in ICLR 2020.
<a href="publication/2020iclr-space2vec.pdf"> slides </a>,
<a href="https://www.youtube.com/watch?v=7hTtBXqea1E"> spotlight presentation </a>,
<a href="https://github.com/gengchenmai/space2vec"> github </a>
<br> <b>Notes:</b> Symbolic representations are efficient and accurate, and that is how mammals represent positions and locations.
</p>
<p>
Mai et al, Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online, In: Proceedings of AGILE 2020, Jun. 16 - 19, 2020, Chania, Crete, Greece.
</p>
<p> Gengchen Mai, Krzysztof Janowicz, Bo Yan, Rui Zhu, Ling Cai, Ni Lao.
<a href="https://arxiv.org/abs/1910.00084">
Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs</a>,
In the 10th ACM International Conference on Knowledge Capture (K-CAP 2019)
</p>
<p> Felix Wu, Boyi Li, Lequn Wang, Ni Lao, John Blitzer, Kilian Q. Weinberger,
<a href="https://arxiv.org/abs/1909.13128">
Integrated Triaging for Fast Reading Comprehension</a>,
Preprint, 2019.
</p>
<p> Jacob Biloki, Chen Liang, Ni Lao,
<a href="https://openreview.net/pdf?id=HJxPAFgEON">
Neural Program Planner for Structured Predictions</a>,
In ICLR 2019, Workshop on Deep Reinforcement Learning Meets Structured Prediction.
</p>
<p> Felix Wu, Boyi Li, Lequn Wang, Ni Lao, John Blitzer, Kilian Q. Weinberger,
<a href="https://arxiv.org/abs/1902.11291">
FastFusionNet: New State-of-the-Art for DAWNBench SQuAD</a>,
Technical Report, 2019
</p>
<p> Gengchen Mai, Krzysztof Janowicz and Cheng He, Sumang Liu, Ni Lao.
<a href="https://arxiv.org/abs/1810.02802">
POIReviewQA: A Semantically Enriched POI Retrieval and Question Answering Dataset</a>,
In 12th Workshop on Geographic Information Retrieval (GIR 2018)
</p>
<p> Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc Le, Ni Lao,
<a href="https://arxiv.org/abs/1807.02322">
Memory Augmented Policy Optimization for Program Synthesis with Generalization </a>,
In NIPS 2018
<a href="https://medium.com/@noon99/do-androids-dream-of-great-success-757a3fc8ff6b"> Medium </a>
<br> <b>Notes:</b> Animals dream of memory traces of high emotional/motivational values.
The optimal experience replay strategy in RL is to balance good and bad experiences focusing on the more supprising ones.
</p>
<p> T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, B. Yang, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, J. Welling,
<a href="https://dl.acm.org/citation.cfm?id=3191513">
Never-Ending Learning</a>,
Communications of the ACM, 2018
</p>
<p> Juanzi Li, Ming Zhou, Guilin Qi, Ni Lao, Tong Ruan, Jianfeng Du,
<a href="http://www.springer.com/gp/book/9789811073588">
Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence</a>,
Communications in Computer and Information Science, Springer, 2017
</p>
<p>Fan Yang, Jiazhong Nie, William W. Cohen, Ni Lao,
<a href="https://arxiv.org/pdf/1711.06744">Learning to Organize Knowledge with N-Gram Machines </a>,
ICLR 2018 Workshop.
<a href="publication/2018-ngm-iclr-poster.pdf">poster</a>
<a href="http://www.akbc.ws/2017/">AKBC</a>
<a href="publication/2017-ngm-akbc-poster.pdf">best poster award</a>,
<!--
<a href="https://sites.google.com/view/ciai2017/schedule">CIAI</a>
<a href="publication/2017-ngm-ciai-poster.pdf">poster</a>
-->
<br> <b>Notes:</b> Towards indexing meaning in text with a symbolic open-domain schema.
</p>
<p>Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, Ni Lao,
<a href="publication/2017-nsm-acl.pdf"> <b>Neural Symbolic Machines</b>:
Learning Semantic Parsers on Freebase with Weak Supervision </a>, ACL 2017.
<a href="publication/2016-nsm-poster.pdf">poster</a> <a href="publication/2016-nsm.pdf">slides</a> <a href="publication/2017-nsm-acl-slides.pdf">slides</a>
<br> <b>Notes:</b> We want a symbolic machine, which is good at large scale KG computation, to be controlled by a neural net, which is good at learning from data.
</p>
<p>Felix Wu, Ni Lao, John Blitzer, Guandao Yang, and Kilian Weinberger,
<a href="https://arxiv.org/abs/1711.04352"> Fast Reading Comprehension with Convnets </a>,
Techincal Report, 2017. </p>
<p>Ni Lao, Einat Minkov and William Cohen, <a href="publication/2015.acl.pdf"> Learning relational features with backward random walks </a>, ACL 2015. <a href="publication/2015.acl.poster.pdf">poster</a></p>
<p> William Yang Wang, Kathryn Mazaitis, Ni Lao, Tom M. Mitchell, William W. Cohen,
<a href="http://www.cs.cmu.edu/~yww/papers/ProPPR_MLJ_sub.pdf">
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic</a>,
Machine Learning Journal (MLJ 2015), Springer.</p>
<p> T. Mitchell, W. Cohen, E. Hruscha, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner,B. Kisiel,J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohammad, N. Nakashole, E. Platanios,A. Ritter, M. Samadi, B. Settles, R.Wang, D.Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, J.Welling (2015):
<a href="http://www.cs.cmu.edu/~wcohen/postscript/aaai-2015.pdf">
Never-Ending Learning</a> in AAAI-2015. </p>
<p> Ni Lao, Jun Zhu: <a href="http://arxiv.org/pdf/1406.7445v1"> Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields</a>.
CoRR abs/1406.7445 (2014) <a href="code/trmrf-src-20151206.jar">
code</a>
<br> <b>Notes:</b> Evaluate exponentially many CRF features efficiently by only considering big gradients, which is very sparse.
</p>
<p> Xin Luna Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, <b>Ni Lao</b>,
Kevin Murphyy, Thomas Strohmann, Shaohua Sun, Wei Zhang
<a href="publication/2014.kdd.pdf">
Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion</a>.
KDD, 2014
<br> <b>Notes:</b> auto-matic KG construcion at Google scale.
</p>
<p><b>Ni Lao</b>, Amarnag Subramanya, Fernando Pereira, William W. Cohen
<a href="publication/2012/2012.emnlp.paper.pdf">
Reading The Web with Learned Syntactic-Semantic Inference Rules</a>.
EMNLP, 2012
<br> <b>Notes:</b> read the web.
</p>
<p><b>Ni Lao</b>, William W. Cohen,
<a href="publication/2012/2012.dils.pdf">
Personalized Reading Recommendations for Saccharomyces Genome Database</a>.
DILS, 2012 <a href="publication/2012/2012.dils.poster.portrat.pdf"> poster</a> </p>
<p><b>Ni Lao</b>, Tom Mitchell, William W. Cohen,
<a href="publication/2011/2011.emnlp.paper.pdf">
Random Walk Inference and Learning in A Large Scale Knowledge Base</a>.
EMNLP, 2011 <a href="publication/2011/2011.emnlp.slides.pdf"> slides</a>
<a href="publication/2011/2011.emnlp.poster.pdf"> poster</a>
<a href="data/publish.amt.labels.tar.tar"> AMT labels of 16 relations </a>
<a href="data/publish.distant.supervision.tar.tar"> Distant Supervision labels of 96 relations</a>
<br> <b>Notes:</b> To learn effectively we need to first set a budget for every direction/branch we try.
</p>
<p>Jun Zhu, <b>Ni Lao</b>, Ning Chen, Eric P. Xing
<a href="publication/2011/2011.kdd.topic.pdf">
Conditional Topical Coding: an Efficient Topic Model Conditioned on Rich Features</a>.
KDD, 2011 </p>
<p><b>Ni Lao</b>, William W. Cohen,
<a href="publication/2010/2010.ML.PRA.pdf">
Relational retrieval using a combination of path-constrained random walks</a>
Machine Learning, 2010, Volume 81, Number 1, Pages 53-67 (ECML, 2010
<a href="publication/2010/2010.ECML.slides.pdf"> slides</a>
<a href="publication/2010/2010.ECML.poster.pdf"> poster</a> )</p>
<p><b>Ni Lao</b>, Jun Zhu, Liu Liu, Yandong Liu, William W. Cohen,
<a href="publication/2010/2010.NIPS.CVI.pdf">
Efficient Relational Learning with Hidden Variable Detection</a>.
NIPS, 2010 <a href="publication/2010/2010.NIPS.CVI.poster.pdf"> poster</a>
<br> <b>Notes:</b> Where do new concepts come from? They come from the estimated gain of data likelihood.
</p>
<p><b>Ni Lao</b>, William W. Cohen,
<a href="publication/2010/2010.KDD.PRA.pdf">
Fast Query Execution for Retrieval Models based on Path Constrained Random Walks</a>.
KDD, 2010
<br> <b>Notes:</b> Randomness is your friend for both efficiency and quality.
</p>
<p>Jun Zhu, <b>Ni Lao</b>, E. P. Xing,
<a href="publication/2010/2010.KDD.GLight.pdf">
Grafting-Light: Fast, Incremental Feature Selection and Structure Learning of Markov Random Fields</a>.
KDD, 2010
<br> <b>Notes:</b> Where do new features come from? They come from the estimated gain of data likelihood.
</p>
<p><b>Lao, Ni</b>, Hideki Shima, Teruko Mitamura and Eric Nyberg. 2008.
<a href="publication/2009/2009-NTCIR7-IR4QA-LaoN.pdf">
Query Expansion and Machine Translation for Robust Cross-Lingual Information Retrieval</a>
, in Proceedings of NTCIR-7 Workshop, Japan.</p>
<p>Shima, Hideki, <b>Ni Lao</b>, Eric Nyberg and Teruko Mitamura. 2008.
<a href="publication/2009/2009-NTCIR7-CCLQA-ShimaH.pdf">
Complex Cross-lingual Question Answering as Sequential Classification and Multi-Document Summarization Task</a>
, in Proceedings of NTCIR-7 Workshop, Japan.</p>
<p>W. Zuo, <b>N. Lao</b>, Y. Geng, and K. Ma. 2008.
<a href="publication/2008/2008JPE-GeoSVM.pdf">
GeoSVM: an efficient and effective tool to predict species' potential distributions</a>.
Journal of Plant Ecology, 1(2): 143-145.
<br> <b>Notes:</b> Genetic Algorithm sucks. SVM (and max entropy) models rocks.
</p>
<p>Yiming Yang,Abhimanyu Lad, <b>Ni Lao</b>, Abhay Harpale, Bryan Kisiel, Monica Rogati,
<a href="publication/2007/yang-sigir07.pdf">
Utility-based information distillation over temporally sequenced documents</a>,
SIGIR, pp. 31-38, 2007.
<br> <b>Notes:</b> simulated user experience from logs.
</p>
<p>Chun Yuan; <b>Ni Lao</b>; Ji-Rong Wen; Jiwei Li; Zheng Zhang; Yi-Min Wang; Wei-Ying Ma,
<a href="publication/older/p375-yuan.pdf">
Automated Known Problem Diagnosis with Event Traces, </a>
EuroSys, 2006.
<br> <b>Notes:</b> PC Genomics.
</p>
<p><b>Ni Lao</b>, Ji-Rong Wen, Wei-Ying Ma, Yi-Min Wang,
<a href="publication/older/ni_LISA.pdf">
Combine High Level Symptom and Low Level State Information for Configuration Fault Diagnosis</a>,
LISA, 2004.</p>
<p>Ji-Rong Wen, <b>Ni Lao</b>, Wei-Ying Ma,
<a href="publication/older/p255-Wen.pdf">
Probabilistic Model for Contextual Retrieval</a>,
SIGIR, 2004.
<br> <b>Notes:</b> you can find/create great data in an industry environment.
</p>
<p>Archana Ganapathi, Yi-Min Wang, <b>Ni</b> <b>Lao</b>, Ji-Rong Wen,
<a href="publication/older/ganapathia_DSN_registry.pdf">
Why PCs Are Fragile and What We Can Do About It: A Study of Windows Registry Problems</a>,
Dependable System and Network (DSN), 2004.</p>
<p>Jinyi Yao, <b>Lao Ni</b>, Fan Yang, Yunpeng Cai, Zengqi Sun,
<a href="publication/older/Chapter_18.pdf">
Technical Solutions of TsinghuAeolus for Robotic Soccer</a>.
Robocup 2003: 205-213,RoboCup, pp. 205-213, 2003
<br> <b>Notes:</b> a two-time RoboCup world champion used dynamic programming and geometry (instead of statistical machine learning).
</p>
<h2> Manuscript and Presentations</h2>
<p><b>Ni Lao</b>, <a href="publication/2021-Understand-and-Organize.pdf"> Learning to Understand Questions and Organize Knowledge </a>. 2021.
In this talk I first introduce previous work in query understanding -- more specifically weakly supervised semantic parsing and its related issues such as 1) symbolic representations for efficient inference; 2) unbiased low-variance gradient estimation with experience replays; 3) sequence reranking model design and training.
Then I discuss preliminary work in document understanding, which aims to achieve generalizability, scalability and accountability beyond the current large sequence models.
</p>
<p><b>Ni Lao</b>, <a href="https://medium.com/syncedreview/google-open-sources-lingvo-framework-for-sequence-to-sequence-modeling-c62d5e52a774"> A Review of Google's Lingvo </a>. 2019.
By invitation of Synced I wrote a review for Google's Lingvo framework.
I sincerely thank for the help from Patrick Nguyen and Esther Lee.
</p>
<p><b>Ni Lao</b>, <a href="publication/2019-Lecun-Cake.pdf"> Yann LeCun's Cake Explained </a>. 2019. This essay was based on <a href="https://www.youtube.com/watch?v=o9rxofxDf-I"> an interview</a> I got from Robin.ly</p>
<p><b>Ni Lao</b>, <a href="publication/201901.jiangmen.tutorial.pdf"> Weakly Supervised
Natural Language Understanding </a>. JiangMen tutorial, 2019. <a href="https://www.youtube.com/watch?v=EzZu7LgKQaU"> video</a> </p>
<p><b>Ni Lao</b>, <a href="publication/2018.aifrontiers.tutorial.pdf"> Weakly Supervised
Natural Language Understanding </a>. AIFrontiers tutorial, 2018. </p>
<p><b>Ni Lao</b>, <a href="publication/2018-Androids-Dream.pdf"> Do Androids Dream of Great Success? </a>. 2018. </p>
<p><b>Ni Lao</b>, <a href="publication/2017-NSLU-stanford.pdf"> Neural Symbolic Language Understanding </a>. 2017. </p>
<p><b>Ni Lao</b>, <a href="publication/2017-text-gen.pdf"> Text Generation Survey </a>. 2017. </p>
<p><b>Ni Lao, Xipeng Qiu</b>, <a href="publication/2017-ccks.pdf"> Knowledge Acquisition </a>. 2017. </p>
<p><b>Ni Lao</b>, <a href="publication/2016-nips.pdf"> NIPS 2016 Overview </a>. 2016. </p>
<p><b>Ni Lao</b>, <a href="publication/2016-nsm.pdf"> Neural Symbolic Machines </a>. 2016. </p>
<p><b>Ni Lao</b>, <a href="publication/2015-KB-workshop.pdf"> New Development in Knowledge Acquisition, Inference, and Applications </a>. 2015. (This is a lecture at <a href="http://www.ccf.org.cn/sites/ccf/xhdtnry.jsp?contentId=2897719127940">CCF ADL65<a>. I added my take on the relationship between connectionism and symbolism, which seems to be an important issue at the moment.)
</p>
<!--
<p><b>Ni Lao</b>, <a href="unpublished/2012.elements.pdf"> Elements of Intelligence</a>. 2012. </p>
-->
<p><b>Ni Lao</b>, <a href="unpublished/2012.elephant-ai.pdf"> Elephant and AI </a>. LTI Colloquium Report, Spring 2012. </p>
<p><b>Ni Lao</b>, <a href="unpublished/2012.verbal.programming.pdf"> Programming by Demonstrations and Verbal Commands</a>. LTI Colloquium Report, Spring 2012 </p>
<p><b>Ni Lao</b>, <a href="unpublished/2011.semantics.pdf"> Beyond Shallow Semantics</a>. LTI Colloquium Report, Fall 2011. </p>
<p><b>Ni Lao</b>, <a href="unpublished/2011.ccg.fractal.pdf"> CCG, Fractal, and Emergence</a>. LTI Colloquium Report, Spring 2011. </p>
<p><b>Ni Lao</b>, <a href="unpublished/2011.RL.unknown.pdf"> Reinforcement Learning In An Unknown Domain </a> (slides). 2011. </p>
<!--<p><b>Ni Lao</b>, <a href="unpublished/2010/2010.fall.personal.assistant.pdf"> Relation Machines</a>. LTI Colloquium Report, Fall 2010. </p>
-->
<p><b>Ni Lao</b>, <a href="unpublished/2010.spring.POM.pdf"> Probablistic Ontology Model</a>. LTI Colloquium Report Fall 2010. </p>
<p><b>Ni Lao</b>, <a href="unpublished/2009.split.emit.pdf"> Split-Emit Process for Natural Language Generation</a>. Advanced NLP seminar 2009. </p>
<p><b>Ni Lao</b>, Jun Zhu, <a href="unpublished/2009.CFI.pdf"> Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields </a>. 2009. </p>
<p><b>Ni Lao</b>, T. Mitamura, E. Nyberg, <a href="unpublished/2009.CSRL.pdf"> Tree Representations for Chinese Semantic Role Labeling</a>. 2009. </p>
<p><b>Ni Lao</b>, <a href="unpublished/2007.read.the.web.pdf"> Read The Web </a>(slides). Advanced IR seminar 2007. </p>
<p><b>Ni Lao</b>, <a href="unpublished/2007.SchemaExtractionModel.pdf"> Schema Extraction Model </a>. Advanced IR seminar 2007. </p>
<p><b>Ni Lao</b>, <a href="unpublished/2007.Knowledge.Acquisition.pdf"> Knowledge Acquisition From Text--A Survey </a> Statistical NLP class 2007. </p>
<p> </p>
<h2>Thesis</h2>
<p>PhD thesis, 2012. <a href="publication/2012/thesis.pdf">
Efficient Random Walk Inference with Knowledge Bases</a>
(<a href="publication/2012/defense.pdf">slides</a>). Carnegie Mellon University</p>
<p>Master thesis, 2006. <a href="publication/older/master.thesis.pdf">
Data Mining Problems in Automatic Computer Diagnosis</a>. Tsinghua University</p>
<p>Bachelor thesis, 2003. <a href="publication/older/bachelor.thesis.pdf">
Mining Spatial-Temporal Data Using Constructive Induction</a>. Tsinghua University</p>
<h2>Code</h2>
<p>2012, <a href="code/2014.pra.zip">Path Ranking Algorithm</a> A system for
relational retrieval on heterogamous graphs (<a href="https://github.com/noon99jaki/pra"> github </a>) </p>
<p>2006, <a href="code/geoSVM.rar">geoSVM</a> A predictive system for modeling species potential distributions
based on SVM. See details at <a href="http://www.unm.edu/~wyzuo/GEO.htm"> Wenyun's page</a> </p>
<p>Data Sets</p>
<p>2012, <a href="data/NELL165.zip">NELL v165</a> NELL Knowledge graph in both triple format and PRA format</p>
<p>2010, <a href="data/2010.yeast2.gz">yeast2</a> updated yeast data with extra information about Mesh heading, chemicals and affiliations etc. (321K entities and 6.1M links)</p>
<p>2010, <a href="data/2010.fly.gz">fly</a> a biological literature graph with 770K entities and 3.5M links </p>
<p>2010, <a href="data/2010.fly.gz">yeast</a> a biological literature graph with 164K entities and 2.8M links</p>
<h2>Acdemic Services</h2>
<p> 2023: ACL*, EMNLP*, ICML, IJGIS, Neurips*, TALLIP, Computers & Security (*Area chair for large language models and reasoning)</p>
<p> 2022: ACL, CoNLL, EMNLP, ICLR, KDD, Neurips, TGIS </p>
<p> 2021: ACL, AAAI, CoNLL, EMNLP, ICLR, NAACL, SIGIR, TALLIP, GeoAI, NLP4ConvAI </p>
<p> 2020: ACL, AAAI, COLING, CoNLL, EACL, EMNLP, ICLR, ICML, IJCAI, Neurips, SIGIR, TALLIP, TKDE </p>
<p> I co-organize the Deep RL Meets Structured Prediction workshop 2019
<a href="https://iclr.cc/Conferences/2019/Schedule?showEvent=630"> ICLR page </a>,
<a href="https://sites.google.com/view/iclr2019-drlstructpred"> homepage </a>,
<a href="publication/2019.DRL4SP.intro.pdf"> intro slides</a>
</p>
<p> 2019: ACL, AAAI, CCL, CoNLL, EMNLP, ICLR, IJCAI, NAACL, SIGIR, TKDE </p>
<p> 2018: ACL, CCKS, COLING, EMNLP, NAACL, NLPCC, NIPS, SIGIR </p>
<p> 2017: ACL, CCKS, EMNLP, IJCAI, IJCNLP, SIGIR, TKDE, WSDM, Google Research Grants </p>
<p> 2016: CIKM, COLING, IJCAI, NAACL, TKDE, WWW, Google Research Grants </p>
<p> 2015: CIKM, ICML, IJCAI, MLJ, NIPS, TKDE </p>
<p> Since 2012 I have being the manager of the
<a href="https://groups.google.com/forum/#!categories/ml-news"> Machine Learning News<a> Google Group,
which seems to be quite popular in academia.
<p>
<!--
<p> 2014: CIKM, KDD </p>
<p> 2013: CIKM, Google Research Grants </p>
<p> 2012: JCST, KDD, Neurocomputing J., SIGIR, TKDD, PKDD </p>
<p> 2011: NIPS, T. Fuzzy Systems </p>
<p> 2010: CIKM </p>
-->
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