Andrew Ng

Home

Publications

Research

My Group

Courses

Contact

Publications

Convolutional-Recursive Deep Learning for 3D Object Classification. Richard Socher, Brody Huval, Bharath Bhat, Christopher D. Manning and Andrew Y. Ng In NIPS 2012.

Semantic Compositionality through Recursive Matrix-Vector Spaces. Richard Socher, Brody Huval, Christopher D. Manning and Andrew Y. Ng In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2012.

Improving Word Representations via Global Context and Multiple Word Prototypes. Eric H. Huang, Richard Socher, Christopher D. Manning and Andrew Y. Ng In Association for Computational Linguistics Conference (ACL), 2012

Large Scale Distributed Deep Networks. J. Dean, G.S. Corrado, R. Monga, K. Chen, M. Devin, Q.V. Le, M.Z. Mao, M.A. Ranzato, A. Senior, P. Tucker, K. Yang, A. Y. Ng. In NIPS 2012.

Recurrent Neural Networks for Noise Reduction in Robust ASR. A.L. Maas, Q.V. Le, T.M. O'Neil, O. Vinyals, P. Nguyen, and A.Y. Ng. In Interspeech 2012.

Word-level Acoustic Modeling with Convolutional Vector Regression. Andrew L. Maas, Stephen D. Miller, Tyler M. O'Neil, Andrew Y. Ng, and Patrick Nguyen. (2012). In ICML 2012 Representation Learning Workshop.

Emergence of Object-Selective Features in Unsupervised Feature Learning. Adam Coates, Andrej Karpathy, and Andrew Y. Ng. In NIPS 2012.

Deep Learning of Invariant Features via Simulated Fixations in Video. Will Y. Zou, Shenghuo Zhu, Andrew Y. Ng, Kai Yu. In NIPS 2012.

Learning Feature Representations with K-means. Adam Coates and Andrew Y. Ng. In Neural Networks: Tricks of the Trade, Reloaded, Springer LNCS, 2012.

new Building High-Level Features using Large Scale Unsupervised Learning. Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeffrey Dean and Andrew Y. Ng. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, 2012. [pdf]

new Semantic Compositionality through Recursive Matrix-Vector Spaces, Richard Socher, Brody Huval, Christopher D. Manning and Andrew Y. Ng. In Conference on Empirical Methods in Natural Language Processing (EMNLP 2012). [pdf]

new End-to-End Text Recognition with Convolutional Neural Networks. Tao Wang, David J. Wu, Adam Coates and Andrew Y. Ng. In Proceedings of the Twenty-First International Conference on Pattern Recognition (ICPR). 2012. [pdf]

new Selecting Receptive Fields in Deep Networks. Adam Coates and Andrew Y. Ng. In NIPS*2011. [pdf]

new ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning. Quoc V. Le, Alex Karpenko, Jiquan Ngiam and Andrew Y. Ng. In NIPS*2011. [pdf]

new Sparse Filtering, Jiquan Ngiam, Pangwei Koh, Zhenghao Chen, Sonia Bhaskar and Andrew Y. Ng. In NIPS*2011. [pdf]

new Unsupervised learning models of primary cortical receptive fields and receptive field plasticity, Andrew Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh and Andrew Y. Ng. In NIPS*2011. [pdf]

new Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection. Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, and Christopher D. Manning In NIPS*2011. [pdf]

new Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions. Richard Socher, Jeffrey Pennington, Eric Huang, Andrew Y. Ng, and Christopher D. Manning In Conference on Empirical Methods in Natural Language Processing (EMNLP 2011). [pdf]

new Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning, Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David Wu and Andrew Y. Ng In Proceedings of the 11th International Conference on Document Analysis and Recognition (ICDAR 2011), 2011. [pdf] Best student paper award

new Parsing natural scenes and natural language with recursive neural networks, Richard Socher, Cliff Lin, Andrew Y. Ng and Christopher Manning. In Proceedings of the Twenty-Eighth International Conference on Machine Learning, 2011. [pdf] Distinguished application paper award.

new The importance of encoding versus training with sparse coding and vector quantization, Adam Coates and Andrew Y. Ng. In Proceedings of the Twenty-Eighth International Conference on Machine Learning, 2011. [pdf]

new On optimization methods for deep learning, Quoc V. Le, Jiquan Ngiam, Adam Coates, Abhik Lahiri, Bobby Prochnow and Andrew Y. Ng. In Proceedings of the Twenty-Eighth International Conference on Machine Learning, 2011. [pdf]

new Learning deep energy models, Jiquan Ngiam, Zhenghao Chen, Pangwei Koh and Andrew Y. Ng. In Proceedings of the Twenty-Eighth International Conference on Machine Learning, 2011. [pdf]

new Multimodal deep learning, Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee and Andrew Y. Ng. In Proceedings of the Twenty-Eighth International Conference on Machine Learning, 2011. [pdf]
A preliminary version had also appeared in the NIPS*2010 Workshop on Deep Learning and Unsupervised Feature Learning. [pdf]

new On random weights and unsupervised feature learning, Andrew Saxe, Pangwei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh and Andrew Y. Ng. In Proceedings of the Twenty-Eighth International Conference on Machine Learning, 2011. [pdf, code]
A preliminary version had also appeared in the NIPS*2010 Workshop on Deep Learning and Unsupervised Feature Learning. [pdf, supplementary material]

new Learning hierarchical spatio-temporal features for action recognition with independent subspace analysis, Quoc V. Le, Will Zou, Serena Yeung and Andrew Y. Ng. In Computer Vision and Pattern Recognition (CVPR), 2011. [pdf, appendix, code, features]

new An Analysis of Single-Layer Networks in Unsupervised Feature Learning, Adam Coates, Honglak Lee and Andrew Ng. In AISTATS 14, 2011. [pdf, code, STL-10 dataset]
A preliminary version had also appeared in the NIPS*2010 Workshop on Deep Learning and Unsupervised Feature Learning. [pdf]

new Learning Word Vectors for Sentiment Analysis, Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. In 49th Annual Meeting of the Association for Computational Linguistics (ACL), 2011. [pdf, data]

new A Low-cost Compliant 7-DOF Robotic Manipulator. Morgan Quigley, Alan Asbeck and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2011. [pdf]

new Grasping with Application to an Autonomous Checkout Robot. Ellen Klingbeil, Deepak Drao, Blake Carpenter, Varun Ganapathi, Oussama Khatib, Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2011. [pdf]

new Autonomous Sign Reading for Semantic Mapping. Carl Case, Bipin Suresh, Adam Coates and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2011. [pdf]

Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks Richard Socher, Christopher Manning and Andrew Ng. In NIPS*2010 Workshop on Deep Learning and Unsupervised Feature Learning. [pdf]

A Probabilistic Model for Semantic Word Vectors Andrew Maas and Andrew Ng. In NIPS*2010 Workshop on Deep Learning and Unsupervised Feature Learning. [pdf]

Tiled Convolutional Neural Networks, Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pangwei Koh and Andrew Y. Ng. In NIPS*2010. [pdf, visualizations]

Energy Disaggregation via Discriminative Sparse Coding, J. Zico Kolter and Andrew Y. Ng. In NIPS*2010. [pdf]

Autonomous Helicopter Aerobatics through Apprenticeship Learning, Pieter Abbeel, Adam Coates and Andrew Y. Ng. In International Journal of Robotics Research (IJRR), 2010. [pdf]

Autonomous Operation of Novel Elevators for Robot Navigation, Ellen Klingbeil, Blake Carpenter, Olga Russakovsky and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2010. [pdf]

Learning to grasp objects with multiple contact points, Quoc Le, David Kamm and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2010. [pdf]

Multi-Camera Object Detection for Robotics, Adam Coates and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2010. [pdf]

A Probabilistic Approach to Mixed Open-loop and Closed-loop Control, with Application to Extreme Autonomous Driving, J. Zico Kolter, Christian Plagemann, David T. Jackson, Andrew Y. Ng and Sebastian Thrun. In International Conference on Robotics and Automation (ICRA), 2010. [pdf, videos]

Grasping Novel Objects with Depth Segmentation, Deepak Rao, Quoc V. Le, Thanathorn Phoka, Morgan Quigley, Attawith Sudsand and Andrew Y. Ng. In Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2010. [pdf]

Low-cost Accelerometers for Robotic Manipulator Perception Morgan Quigley, Reuben Brewer, Sai P. Soundararaj, Vijay Pradeep, Quoc V. Le and Andrew Y. Ng. In Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2010. [pdf]

A Steiner tree approach to object detection, Olga Russakovsky and Andrew Y. Ng. In Computer Vision and Pattern Recognition (CVPR), 2010. [pdf]

Measuring invariances in deep networks, Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee and Andrew Y. Ng. In NIPS*2009. [pdf]

Unsupervised feature learning for audio classification using convolutional deep belief networks, Honglak Lee, Yan Largman, Peter Pham and Andrew Y. Ng. In NIPS*2009. [pdf]

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng. In Proceedings of the Twenty-Sixth International Conference on Machine Learning, 2009. [pdf] Best paper award: Best application paper.

Large-scale Deep Unsupervised Learning using Graphics Processors, Rajat Raina, Anand Madhavan and Andrew Y. Ng. In Proceedings of the Twenty-Sixth International Conference on Machine Learning, 2009. [pdf]

A majorization-minimization algorithm for (multiple) hyperparameter learning, Chuan Sheng Foo, Chuong Do and Andrew Y. Ng. In Proceedings of the Twenty-Sixth International Conference on Machine Learning, 2009. [pdf]

Regularization and Feature Selection in Least-Squares Temporal Difference Learning, J. Zico Kolter and Andrew Y. Ng. In Proceedings of the Twenty-Sixth International Conference on Machine Learning, 2009. [pdf] An extended version of the paper is also available. [pdf]

Near-Bayesian Exploration in Polynomial Time, J. Zico Kolter and Andrew Y. Ng. In Proceedings of the Twenty-Sixth International Conference on Machine Learning, 2009. [pdf] An extended version of the paper is also available. [pdf]

Policy Search via the Signed Derivative, J. Zico Kolter and Andrew Y. Ng. In Proceedings of Robotics: Science and Systems (RSS), 2009. [pdf]

Joint calibration of multiple sensors, Quoc Le and Andrew Y. Ng. In Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2009. [pdf]

Scalable Learning for Object Detection with GPU Hardware, Adam Coates, Paul Baumstarck, Quoc Le, and Andrew Y. Ng. In Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2009. [pdf]

Exponential Family Sparse Coding with Application to Self-taught Learning, Honglak Lee, Rajat Raina, Alex Teichman and Andrew Y. Ng. In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09), 2009. [pdf]

Apprenticeship learning for helicopter control, Adam Coates, Pieter Abbeel and Andrew Y. Ng. In Communications of the ACM, Volume 52, 2009. [pdf]

ROS: an open-source Robot Operating System, Morgan Quigley, Brian Gerkey, Ken Conley, Josh Faust, Tully Foote, Jeremy Leibs, Eric Berger, Rob Wheeler, and Andrew Y. Ng. In Proceedings of the Open-Source Software workshop at the International Conference on Robotics and Automation (ICRA), 2009. [pdf]

High-Accuracy 3D Sensing for Mobile Manipulation: Improving Object Detection and Door Opening, Morgan Quigley, Siddharth Batra, Stephen Gould, Ellen Klingbeil, Quoc Le, Ashley Wellman and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2009. [pdf]

Stereo Vision and Terrain Modeling for Quadruped Robots, J. Zico Kolter, Youngjun Kim and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2009. [pdf]

Task-Space Trajectories via Cubic Spline Optimization, J. Zico Kolter and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2009. [pdf]

Learning Sound Location from a Single Microphone, Ashutosh Saxena and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2009. [pdf]

Learning 3-D Object Orientation from Images, Ashutosh Saxena, Justin Driemeyer and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2009. [pdf] (Preliminary version previously presented in the NIPS workshop on Robotic Challenges for Machine Learning, 2007.)

Reactive Grasping using Optical Proximity Sensors, Kaijen Hsiao, Paul Nangeroni, Manfred Huber, Ashutosh Saxena and Andrew Y. Ng. In International Conference on Robotics and Automation (ICRA), 2009. [pdf]

Autonomous Autorotation of an RC Helicopter, Pieter Abbeel, Adam Coates, Timothy Hunter and Andrew Y. Ng. In 11th International Symposium on Experimental Robotics (ISER), 2008. [pdf, supplementary material]

Apprenticeship Learning for Motion Planning with Application to Parking Lot Navigation, Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng and Sebastian Thrun. In Proceedings of the International Conference on Intellegent Robots and Systems (IROS), 2008. [pdf]

Space-indexed Dynamic Programming: Learning to Follow Trajectories, J. Zico Kolter, Adam Coates, Andrew Y. Ng, Yi Gu, and Charles DuHadway. In Proceedings of the Twenty-fifth International Conference on Machine Learning, 2008. [ps, pdf]

Learning for Control from Multiple Demonstrations, Adam Coates, Pieter Abbeel and Andrew Y. Ng. In Proceedings of the Twenty-fifth International Conference on Machine Learning, 2008. [ps, pdf, supplementary material] Best paper award: Best application paper.

Integrating visual and range data for robotic object detection, Stephen Gould, Paul Baumstarck, Morgan Quigley, Andrew Y. Ng and Daphne Koller. In ECCV workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications (M2SFA2), 2008. [pdf]

Make3D: Learning 3-D Scene Structure from a Single Still Image, Ashutosh Saxena, Min Sun, Andrew Y. Ng. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2008. [ps, pdf]

Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks, Rion Snow, Brendan O'Connor, Daniel Jurafsky and Andrew Y. Ng. In Proceedings of EMNLP 2008. [pdf]

Learning to Open New Doors, Ellen Klingbeil, Ashutosh Saxena, Andrew Y. Ng. In Robotics Science and Systems (RSS) workshop on Robot Manipulation, 2008. [pdf]

Make3D: Depth Perception from a Single Still Image, Ashutosh Saxena, Min Sun, and Andrew Y. Ng. In AAAI (Nectar Track), 2008. [pdf]

A Fast Data Collection and Augmentation Procedure for Object Recognition, Benjaminn Sapp, Ashutosh Saxena, and Andrew Y. Ng. In AAAI, 2008. [pdf]

Learning grasp strategies with partial shape information, Ashutosh Saxena, Lawson Wong, and Andrew Y. Ng. In AAAI, 2008. [pdf]

A Complete Control Architecture for Quadruped Locomotion Over Rough Terrain, J. Zico Kolter, Mike Rodgers and Andrew Y. Ng. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2008. [pdf]

Robotic Grasping of Novel Objects using Vision, Ashutosh Saxena, Justin Driemeyer, and Andrew Y. Ng. In International Journal of Robotics Research (IJRR), 2008. [pdf]

Learning 3-D Scene Structure from a Single Still Image, Ashutosh Saxena, Min Sun, and Andrew Y. Ng. In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007. [ps, pdf] Best paper award.

3-D Reconstruction from Sparse Views using Monocular Vision , Ashutosh Saxena, Min Sun, and Andrew Y. Ng. In ICCV workshop on Virtual Representations and Modeling of Large-scale environments (VRML), 2007. [ps, pdf]

A Vision-based System for Grasping Novel Objects in Cluttered Environments, Ashutosh Saxena, Lawson Wong, Morgan Quigley and Andrew Y. Ng. In Proceedings of the International Symposium on Robotics Research (ISRR), 2007. [ps,pdf]

3-D depth reconstruction from a single still image, Ashutosh Saxena, Sung H. Chung, and Andrew Y. Ng. In the International Journal of Computer Vision (IJCV), 2007. [ps,pdf]

Hierarchical Apprenticeship Learning with Applications to Quadruped Locomotion, J. Zico Kolter, Pieter Abbeel, and Andrew Y. Ng. In NIPS*2007. [ps, pdf]

Sparse deep belief net model for visual area V2, Honglak Lee, Ekanadham Chaitanya, and Andrew Y. Ng. In NIPS*2007. [ps, pdf]

Efficient multiple hyperparameter learning for log-linear models, Chuong Do, Chuan-Sheng Foo, Andrew Y. Ng. In NIPS*2007. [ps, pdf]

Learning omnidirectional path following using dimensionality reduction, J. Zico Kolter and Andrew Y. Ng. In Proceedings of Robotics: Science and Systems, 2007. [ps, pdf]

Shift-Invariant Sparse Coding for Audio Classification, Roger Grosse, Rajat Raina, Helen Kwong and Andrew Y. Ng. In Proceedings of the Twenty-third Conference on Uncertainty in Artificial Intelligence, 2007. [ps, pdf, code]

Learning to merge word senses, Rion Snow, Sushant Prakash, Dan Jurafsky and Andrew Y. Ng. In Proceedings of EMNLP 2007. [ps, pdf]

Self-taught learning: Transfer learning from unlabeled data, Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer and Andrew Y. Ng. In Proceedings of the Twenty-fourth International Conference on Machine Learning, 2007. [ps, pdf]

Portable GNSS Baseband Logging, Morgan Quigley, Pieter Abbeel, Dave S. De Lorenzo, Yi Gu, Sara Bolouki, Dennis Akos, and Andrew Y. Ng. In Institute of Navigation (ION) GNSS Conference, 2007. [ps, pdf coming soon]

Robotic Grasping of Novel Objects, Ashutosh Saxena, Justin Driemeyer, Justin Kearns and Andrew Y. Ng. In NIPS 19, 2007. [ps, pdf]

An Application of Reinforcement Learning to Aerobatic Helicopter Flight, Pieter Abbeel, Adam Coates, Morgan Quigley and Andrew Y. Ng. In NIPS 19, 2007. [ps, pdf, videos]

Efficient sparse coding algorithms. Honglak Lee, Alexis Battle, Raina Rajat and Andrew Y. Ng. In NIPS 19, 2007. [ps, pdf, code]

Map-Reduce for Machine Learning on Multicore. Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary Bradski, Andrew Y. Ng and Kunle Olukotun. In NIPS 19, 2007. [ps, pdf]

Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video Stephen Gould, Joakim Arfvidsson, Adrian Kaehler, Benjamin Sapp, Marius Meissner, Gary Bradski, Paul Baumstarck, Sukwon Chung and Andrew Y. Ng. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07), 2007. [ps, pdf]

Probabilistic Mobile Manipulation in Dynamic Environments, with Application to Opening Doors, Anya Petrovskaya and Andrew Y. Ng. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07), 2007. [ps, pdf]

A Factor Graph Model for Software Bug Finding, Ted Kremenek, Andrew Y. Ng and Dawson Engler. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07), 2007. [ps, pdf]

Depth Estimation using Monocular and Stereo Cues, Ashutosh Saxena, Jamie Schulte and Andrew Y. Ng. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07), 2007. [pdf]

Learning to grasp novel objects using vision, Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Chioma Osondu, and Andrew Y. Ng. In International Symposium on Experimental Robotics (ISER) 2006. [ps, pdf]

Have we met? MDP based speaker ID for robot dialogue, Filip Krsmanovic, Curtis Spencer, Daniel Jurafsky and Andrew Y. Ng. In Proceedings of the Ninth International Conference on Spoken Language Processing (InterSpeech--ICSLP), 2006. [pdf]

Semantic taxonomy induction from heterogenous evidence, Rion Snow, Dan Jurafsky and Andrew Y. Ng. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics (ACL), 2006. [ps, pdf] Best paper award.

Using inaccurate models in reinforcement learning, Pieter Abbeel, Morgan Quigley and Andrew Y. Ng. In Proceedings of the Twenty-third International Conference on Machine Learning, 2006. [ps, pdf] An extended version of the paper is also available. [ps, pdf]

Transfer learning by constructing informative priors, Rajat Raina, Andrew Y. Ng and Daphne Koller. In Proceedings of the Twenty-third International Conference on Machine Learning, 2006. [ps, pdf] An earlier version had also been presented at the NIPS 2005 Workshop on Inductive Transfer.

From uncertainty to belief: Inferring the specification within, Ted Kremenek, Paul Twohey, Godmar Back, Andrew Y. Ng and Dawson Engler. In Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI) , 2006. [ps, pdf]

Efficient L1 Regularized Logistic Regression. Su-In Lee, Honglak Lee, Pieter Abbeel and Andrew Y. Ng. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06), 2006. [ps, pdf, code]

Solving the problem of cascading errors: Approximate Bayesian inference for linguistic annotation pipelines, Jenny Finkel, Chris Manning and Andrew Y. Ng. In Proceedings of EMNLP 2006. [pdf]

Quadruped robot obstacle negotiation via reinforcement learning, Honglak Lee, Yirong Shen, Chih-Han Yu, Gurjeet Singh, and Andrew Y. Ng. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2006. [ps, pdf]

Bayesian estimation for autonomous object manipulation based on tactile sensors, Anya Petrovskaya, Oussama Khatib, Sebastian Thrun, and Andrew Y. Ng. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2006. [ps, pdf]

Learning Factor Graphs in Polynomial Time and Sample Complexity, Pieter Abbeel, Daphne Koller, Andrew Y. Ng In Journal of Machine Learning Research, 7:1743-1788, 2006. [ps, pdf]

A dynamic Bayesian network model for autonomous 3d reconstruction from a single indoor image, Erick Delage, Honglak Lee and Andrew Y. Ng. In CVPR 2006. [ps, pdf]

groupTime: Preference-Based Group Scheduling, Mike Brzozowski, Kendra Carattini, Scott R. Klemmer, Patrick Mihelich, Jiang Hu, Andrew Y. Ng. In CHI 2006. [ps, pdf]

Contextual search and name disambiguation in email using graphs, Einat Minkov, William Cohen and Andrew Y. Ng. In Proceedings of the Twenty-ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006. [ps, pdf]

Learning Depth from Single Monocular Images, Ashutosh Saxena, Sung Chung, and Andrew Y. Ng. In NIPS 18, 2006. [ps, pdf]

Learning vehicular dynamics, with application to modeling helicopters, Pieter Abbeel, Varun Ganapathi and Andrew Y. Ng. In NIPS 18, 2006. [ps, pdf]

On Local Rewards and the Scalability of Distributed Reinforcement Learning, J. Andrew Bagnell and Andrew Y. Ng. In NIPS 18, 2006. [ps, pdf]

Transfer learning for text classification, Chuong Do and Andrew Y. Ng. In NIPS 18, 2006. [ps, pdf]

Fast Gaussian Process Regression using KD-trees, Yirong Shen, Andrew Y. Ng and Matthias Seeger. In NIPS 18, 2006. [ps, pdf]

Automatic single-image 3d reconstructions of indoor Manhattan world scenes, Erick Delage, Honglak Lee and Andrew Y. Ng. In Proceedings of the International Symposium on Robotics Research (ISRR), 2005. [ps, pdf]

Robust Textual Inference via Graph Matching, Aria Haghighi, Andrew Y. Ng and Chris Manning. In Proceedings of the Human Language Technology Conference/Empirical Methods in Natural Language Processing (HLT-EMNLP), 2005. [ps, pdf]

High-speed obstacle avoidance using monocular vision and reinforcement learning, Jeff Michels, Ashutosh Saxena and Andrew Y. Ng. In Proceedings of the Twenty-second International Conference on Machine Learning, 2005. [ps, pdf]

Exploration and apprenticeship learning in reinforcement learning, Pieter Abbeel and Andrew Y. Ng. In Proceedings of the Twenty-second International Conference on Machine Learning, 2005. [ps, pdf]

Robust textual inference via learning and abductive reasoning, Rajat Raina, Andrew Y. Ng and Chris Manning. In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI), 2005. [ps, pdf]

Spam deobfuscation using a hidden Markov model, Honglak Lee and and Andrew Y. Ng. In Proceedings of the Second Conference on Email and Anti-Spam, 2005. [ps, pdf] Best student paper award.

Learning factor graphs in polynomial time & sample complexity, Pieter Abbeel, Daphne Koller and Andrew Y. Ng. In Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence, 2005. [ps, pdf]

Discriminative Learning of Markov Random Fields for Segmentation of 3D Range Data, Drago Anguelov, Ben Taskar, Vasco Chatalbashev, Daphne Koller, Dinkar Gupta, Geremy Heitz and Andrew Y. Ng. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), 2005. [ps, pdf]

Discriminative training of Kalman filters, Pieter Abbeel, Adam Coates, Mike Montemerlo, Andrew Y. Ng and Sebastian Thrun. In Proceedings of Robotics: Science and Systems, 2005. [ps, [pdf]

Autonomous Helicopter Tracking and Localization Using a Self-Calibrating Camera Array, Masa Matsuoka, Surya Singh, Alan Chen, Adam Coates, Andrew Y. Ng and Sebastian Thrun. In Proceedings of the Fifth International Conference on Field Service Robotics, 2005. [ps, pdf]

Stable adaptive control with online learning, Andrew Y. Ng and H. Jin Kim. In NIPS 17, 2005. [ps, pdf]

Learning syntactic patterns for automatic hypernym discovery, Rion Snow, Dan Jurafsky and Andrew Y. Ng. In NIPS 17, 2005. [ps, pdf]

Online bounds for Bayesian algorithms, Sham Kakade and Andrew Y. Ng. In NIPS 17, 2005. [ps, pdf]

Learning first order Markov models for control, Pieter Abbeel and Andrew Y. Ng. In NIPS 17, 2005. [ps, pdf]

Inverted autonomous helicopter flight via reinforcement learning, Andrew Y. Ng, Adam Coates, Mark Diel, Varun Ganapathi, Jamie Schulte, Ben Tse, Eric Berger and Eric Liang. In International Symposium on Experimental Robotics, 2004. [ps, pdf]

Apprenticeship learning via inverse reinforcement learning, Pieter Abbeel and Andrew Y. Ng. In Proceedings of the Twenty-first International Conference on Machine Learning, 2004. [ps, pdf]

Feature selection, L1 vs. L2 regularization, and rotational invariance, Andrew Y. Ng. In Proceedings of the Twenty-first International Conference on Machine Learning, 2004. [ps, pdf]

Learning random walk models for inducing word dependency probabilities, Kristina Toutanova, Christopher Manning and Andrew Y. Ng. In Proceedings of the Twenty-first International Conference on Machine Learning, 2004. [ps, pdf]

Online learning of pseudo-metrics, Shai Shalev-Shwartz, Yoram Singer and Andrew Y. Ng. In Proceedings of the Twenty-first International Conference on Machine Learning, 2004. [ps, pdf]

Policy search by dynamic programming, J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng and Jeff Schneider, In NIPS 16, 2004. [ps, pdf]

Classification with Hybrid Generative/Discriminative Models, Rajat Raina, Yirong Shen, Andrew Y. Ng and Andrew McCallum, In NIPS 16, 2004. [ps, pdf]

Latent Dirichlet Allocation, David Blei, Andrew Y. Ng and Michael Jordan. Journal of Machine Learning Research, 3:993-1022, 2003. [ps, pdf]

Distance metric learning, with application to clustering with side-information, Eric Xing, Andrew Y. Ng, Michael Jordan, and Stuart Russell. In NIPS 15, 2003. [ps, pdf]

On Discriminative vs. Generative Classifiers: A comparison of logistic regression and Naive Bayes, Andrew Y. Ng and Michael Jordan. In NIPS 14, 2002. [ps, pdf]

On Spectral Clustering: Analysis and an algorithm, Andrew Y. Ng, Michael Jordan, and Yair Weiss. In NIPS 14, 2002. [ps, pdf]

Latent Dirichlet Allocation, David Blei, Andrew Y. Ng, and Michael Jordan. In NIPS 14, 2002. [ps, pdf]

Link analysis, eigenvectors, and stability, Andrew Y. Ng, Alice X. Zheng and Michael Jordan. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), 2001. [ps, pdf]

Stable algorithms for link analysis, Andrew Y. Ng, Alice X. Zheng and Michael Jordan. In Proceedings of the Twenty-fourth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2001. [ps, pdf]

Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection, Andrew Y. Ng and Michael Jordan. In Proceedings of the Eighteenth International Conference on Machine Learning, 2001. [ps, pdf]

Data-Intensive Question Answering. Eric Brill, Jimmy Lin, Michele Banko, Susan Dumais, and Andrew Y. Ng. In TREC-10, 2001. [pdf]

PEGASUS: A policy search method for large MDPs and POMDPs, Andrew Y. Ng and Michael Jordan. In Uncertainty in Artificial Intelligence, Proceedings of the Sixteenth Conference, 2000. [ps, pdf]

Algorithms for inverse reinforcement learning, Andrew Y. Ng and Stuart Russell. In Proceedings of the Seventeenth International Conference on Machine Learning, 2000. [ps, pdf]

Approximate inference algorithms for two-layer Bayesian networks, Andrew Y. Ng and Michael Jordan. In NIPS 12, 2000. [ps, pdf]

Policy search via density estimation, Andrew Y. Ng, Ronald Parr and Daphne Koller. In NIPS 12, 2000. [ps, pdf]

Approximate planning in large POMDPs via reusable trajectories, Michael Kearns, Yishay Mansour and Andrew Y. Ng. In NIPS 12, 2000. [ps, pdf]. A long version is also available. [ps, pdf]

Policy invariance under reward transformations: Theory and application to reward shaping, Andrew Y. Ng, Daishi Harada and Stuart Russell. In Proceedings of the Sixteenth International Conference on Machine Learning, 1999. [ps, pdf]

A sparse sampling algorithm for near-optimal planning in large Markov decision processes, Michael Kearns, Yishay Mansour and Andrew Y. Ng. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), 1999. [ps, pdf]. Long version to appear in Machine Learning.

On Feature Selection: Learning with Exponentially many Irrelevant Features as Training Examples, Andrew Y. Ng. In Proceedings of the Fifteenth International Conference on Machine Learning, 1998. [ps, pdf]

Applying Online-search to Reinforcement Learning, Scott Davies, Andrew Y. Ng and Andrew Moore. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), 1998. [ps, pdf]. An earlier version had also been presented at the Workshop on Reinforcement Learning at ICML97, 1997. [ps, pdf]

Improving Text Classification by Shrinkage in a Hierarchy of Classes, Andrew McCallum, Roni Rosenfeld, Tom Mitchell and Andrew Y. Ng in Proceedings of the Fifteenth International Conference on Machine Learning, 1998. [ps, pdf]

Preventing "Overfitting" of Cross-Validation data, Andrew Y. Ng, in Proceedings of the Fourteenth International Conference on Machine Learning, 1997. [ps, pdf]

An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering, Michael Kearns, Yishay Mansour and Andrew Y. Ng, in Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence, 1997. [ps, pdf]. Also a book chapter in Learning in Graphical Models, Ed. Michael Jordan, 1998.

An Experimental and Theoretical Comparison of Model Selection Methods, Michael Kearns, Yishay Mansour, Andrew Y. Ng and Dana Ron, in Machine Learning 27(1), pp. 7-50, 1997. [pdf]. A shorter version had also appeard in Proceedings of the Eighth Annual ACM Conference on Computational Learning Theory, 1995. [ps, pdf].