Publications
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.
To appear in International Conference on Robotics and Automation (ICRA), 2009. [pdf coming soon]
Stereo Vision and Terrain Modeling for Quadruped Robots,
J. Zico Kolter, Youngjun Kim and Andrew Y. Ng.
To appear in International Conference on Robotics and Automation (ICRA), 2009. [pdf coming soon]
Task-Space Trajectories via Cubic Spline Optimization,
J. Zico Kolter and Andrew Y. Ng.
To appear in International Conference on Robotics and Automation (ICRA), 2009. [pdf coming soon]
Learning Sound Location from a Single Microphone,
Ashutosh Saxena and Andrew Y. Ng.
To appear 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.
To appear 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.
To appear 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]
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. [ps, 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]
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 Eigth Annual ACM Conference on Computational Learning Theory, 1995. [ps, pdf].