Andrew Y. Ng

Computer Science Department, Stanford University

	2013
i8		Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng: Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors. CoRR abs/1301.3618 (2013)
i7		Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng: Zero-Shot Learning Through Cross-Modal Transfer. CoRR abs/1301.3666 (2013)
i6		Andrew Y. Ng, Michael I. Jordan: PEGASUS: A Policy Search Method for Large MDPs and POMDPs. CoRR abs/1301.3878 (2013)
i5		Michael Kearns, Yishay Mansour, Andrew Y. Ng: An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. CoRR abs/1302.1552 (2013)
	2012
c146		Eric H. Huang, Richard Socher, Christopher D. Manning, Andrew Y. Ng: Improving Word Representations via Global Context and Multiple Word Prototypes. ACL (1) 2012: 873-882
c145		Richard Socher, Brody Huval, Christopher D. Manning, Andrew Y. Ng: Semantic Compositionality through Recursive Matrix-Vector Spaces. EMNLP-CoNLL 2012: 1201-1211
c144		Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Greg Corrado, Kai Chen, Jeffrey Dean, Andrew Y. Ng: Building high-level features using large scale unsupervised learning. ICML 2012
c143		Tao Wang, David J. Wu, Adam Coates, Andrew Y. Ng: End-to-end text recognition with convolutional neural networks. ICPR 2012: 3304-3308
c142		Andrew L. Maas, Quoc V. Le, Tyler M. O'Neil, Oriol Vinyals, Patrick Nguyen, Andrew Y. Ng: Recurrent Neural Networks for Noise Reduction in Robust ASR. INTERSPEECH 2012
c141		Richard Socher, Brody Huval, Bharath Putta Bath, Christopher D. Manning, Andrew Y. Ng: Convolutional-Recursive Deep Learning for 3D Object Classification. NIPS 2012: 665-673
c140		Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, Andrew W. Senior, Paul A. Tucker, Ke Yang, Andrew Y. Ng: Large Scale Distributed Deep Networks. NIPS 2012: 1232-1240
c139		Adam Coates, Andrej Karpathy, Andrew Y. Ng: Emergence of Object-Selective Features in Unsupervised Feature Learning. NIPS 2012: 2690-2698
c138		Will Y. Zou, Andrew Y. Ng, Shenghuo Zhu, Kai Yu: Deep Learning of Invariant Features via Simulated Fixations in Video. NIPS 2012: 3212-3220
i4		Jeff Bilmes, Andrew Y. Ng: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (2009). CoRR abs/1206.3959 (2012)
i3		Roger Grosse, Rajat Raina, Helen Kwong, Andrew Y. Ng: Shift-Invariance Sparse Coding for Audio Classification. CoRR abs/1206.5241 (2012)
i2		Pieter Abbeel, Daphne Koller, Andrew Y. Ng: Learning Factor Graphs in Polynomial Time & Sample Complexity. CoRR abs/1207.1366 (2012)
	2011
j17		Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng: Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun. ACM 54(10): 95-103 (2011)
j16		J. Zico Kolter, Andrew Y. Ng: The Stanford LittleDog: A learning and rapid replanning approach to quadruped locomotion. I. J. Robotic Res. 30(2): 150-174 (2011)
j15		Adam Coates, Andrew Y. Ng, Honglak Lee: An Analysis of Single-Layer Networks in Unsupervised Feature Learning. Journal of Machine Learning Research - Proceedings Track 15: 215-223 (2011)
c137		Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, Christopher Potts: Learning Word Vectors for Sentiment Analysis. ACL 2011: 142-150
c136		Quoc V. Le, Will Y. Zou, Serena Y. Yeung, Andrew Y. Ng: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. CVPR 2011: 3361-3368
c135		Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, Christopher D. Manning: Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions. EMNLP 2011: 151-161
c134		Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David J. Wu, Andrew Y. Ng: Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning. ICDAR 2011: 440-445
c133		Richard Socher, Cliff Chiung-Yu Lin, Andrew Y. Ng, Christopher D. Manning: Parsing Natural Scenes and Natural Language with Recursive Neural Networks. ICML 2011: 129-136
c132		Quoc V. Le, Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow, Andrew Y. Ng: On optimization methods for deep learning. ICML 2011: 265-272
c131		Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, Andrew Y. Ng: Multimodal Deep Learning. ICML 2011: 689-696
c130		Adam Coates, Andrew Y. Ng: The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization. ICML 2011: 921-928
c129		Andrew M. Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Y. Ng: On Random Weights and Unsupervised Feature Learning. ICML 2011: 1089-1096
c128		Jiquan Ngiam, Zhenghao Chen, Pang Wei Koh, Andrew Y. Ng: Learning Deep Energy Models. ICML 2011: 1105-1112
c127		Ellen Klingbeil, Deepak Rao, Blake Carpenter, Varun Ganapathi, Andrew Y. Ng, Oussama Khatib: Grasping with application to an autonomous checkout robot. ICRA 2011: 2837-2844
c126		Carl Case, Bipin Suresh, Adam Coates, Andrew Y. Ng: Autonomous sign reading for semantic mapping. ICRA 2011: 3297-3303
c125		Morgan Quigley, Alan T. Asbeck, Andrew Y. Ng: A low-cost compliant 7-DOF robotic manipulator. ICRA 2011: 6051-6058
c124		Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, Christopher D. Manning: Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection. NIPS 2011: 801-809
c123		Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng: ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning. NIPS 2011: 1017-1025
c122		Jiquan Ngiam, Pang Wei Koh, Zhenghao Chen, Sonia A. Bhaskar, Andrew Y. Ng: Sparse Filtering. NIPS 2011: 1125-1133
c121		Andrew M. Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Y. Ng: Unsupervised learning models of primary cortical receptive fields and receptive field plasticity. NIPS 2011: 1971-1979
c120		Adam Coates, Andrew Y. Ng: Selecting Receptive Fields in Deep Networks. NIPS 2011: 2528-2536
i1		Quoc V. Le, Rajat Monga, Matthieu Devin, Greg Corrado, Kai Chen, Marc'Aurelio Ranzato, Jeffrey Dean, Andrew Y. Ng: Building high-level features using large scale unsupervised learning. CoRR abs/1112.6209 (2011)
	2010
j14		Pieter Abbeel, Adam Coates, Andrew Y. Ng: Autonomous Helicopter Aerobatics through Apprenticeship Learning. I. J. Robotic Res. 29(13): 1608-1639 (2010)
c119		Morgan Quigley, Alan T. Asbeck, Andrew Y. Ng: Low-Cost Manipulation Powered by ROS. Enabling Intelligence through Middleware 2010
c118		Olga Russakovsky, Andrew Y. Ng: A Steiner tree approach to efficient object detection. CVPR 2010: 1070-1077
c117		Adam Coates, Andrew Y. Ng: Multi-camera object detection for robotics. ICRA 2010: 412-419
c116		Ellen Klingbeil, Blake Carpenter, Olga Russakovsky, Andrew Y. Ng: Autonomous operation of novel elevators for robot navigation. ICRA 2010: 751-758
c115		J. Zico Kolter, Christian Plagemann, David T. Jackson, Andrew Y. Ng, Sebastian Thrun: A probabilistic approach to mixed open-loop and closed-loop control, with application to extreme autonomous driving. ICRA 2010: 839-845
c114		Quoc V. Le, David Kamm, A. F. Kara, Andrew Y. Ng: Learning to grasp objects with multiple contact points. ICRA 2010: 5062-5069
c113		Deepak Rao, Quoc V. Le, Thanathorn Phoka, Morgan Quigley, Attawith Sudsang, Andrew Y. Ng: Grasping novel objects with depth segmentation. IROS 2010: 2578-2585
c112		Ellen Klingbeil, Ashutosh Saxena, Andrew Y. Ng: Learning to open new doors. IROS 2010: 2751-2757
c111		Morgan Quigley, Reuben D. Brewer, Sai Prashanth Soundararaj, Vijay Pradeep, Quoc V. Le, Andrew Y. Ng: Low-cost accelerometers for robotic manipulator perception. IROS 2010: 6168-6174
c110		J. Zico Kolter, Siddharth Batra, Andrew Y. Ng: Energy Disaggregation via Discriminative Sparse Coding. NIPS 2010: 1153-1161
c109		Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Jin hao Chia, Pang Wei Koh, Andrew Y. Ng: Tiled convolutional neural networks. NIPS 2010: 1279-1287
r2		Adam Coates, Pieter Abbeel, Andrew Y. Ng: Autonomous Helicopter Flight Using Reinforcement Learning. Encyclopedia of Machine Learning 2010: 53-61
r1		Pieter Abbeel, Andrew Y. Ng: Inverse Reinforcement Learning. Encyclopedia of Machine Learning 2010: 554-558
	2009
j13		Jan Peters, Andrew Y. Ng: Guest editorial: Special issue on robot learning, Part A. Auton. Robots 27(1): 1-2 (2009)
j12		Jan Peters, Andrew Y. Ng: Guest editorial: Special issue on robot learning, Part B. Auton. Robots 27(2): 91-92 (2009)
j11		Adam Coates, Pieter Abbeel, Andrew Y. Ng: Apprenticeship learning for helicopter control. Commun. ACM 52(7): 97-105 (2009)
j10		Ashutosh Saxena, Min Sun, Andrew Y. Ng: Make3D: Learning 3D Scene Structure from a Single Still Image. IEEE Trans. Pattern Anal. Mach. Intell. 31(5): 824-840 (2009)
c108		Chuan-Sheng Foo, Chuong B. Do, Andrew Y. Ng: A majorization-minimization algorithm for (multiple) hyperparameter learning. ICML 2009: 41
c107		J. Zico Kolter, Andrew Y. Ng: Near-Bayesian exploration in polynomial time. ICML 2009: 65
c106		J. Zico Kolter, Andrew Y. Ng: Regularization and feature selection in least-squares temporal difference learning. ICML 2009: 66
c105		Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. ICML 2009: 77
c104		Rajat Raina, Anand Madhavan, Andrew Y. Ng: Large-scale deep unsupervised learning using graphics processors. ICML 2009: 110
c103		Ashutosh Saxena, Justin Driemeyer, Andrew Y. Ng: Learning 3-D object orientation from images. ICRA 2009: 794-800
c102		J. Zico Kolter, Youngjun Kim, Andrew Y. Ng: Stereo vision and terrain modeling for quadruped robots. ICRA 2009: 1557-1564
c101		J. Zico Kolter, Andrew Y. Ng: Task-space trajectories via cubic spline optimization. ICRA 2009: 1675-1682
c100		Ashutosh Saxena, Andrew Y. Ng: Learning sound location from a single microphone. ICRA 2009: 1737-1742
c99		Kaijen Hsiao, Paul Nangeroni, Manfred Huber, Ashutosh Saxena, Andrew Y. Ng: Reactive grasping using optical proximity sensors. ICRA 2009: 2098-2105
c98		Morgan Quigley, Siddharth Batra, Stephen Gould, Ellen Klingbeil, Quoc V. Le, Ashley Wellman, Andrew Y. Ng: High-accuracy 3D sensing for mobile manipulation: Improving object detection and door opening. ICRA 2009: 2816-2822
c97		Honglak Lee, Rajat Raina, Alex Teichman, Andrew Y. Ng: Exponential Family Sparse Coding with Application to Self-taught Learning. IJCAI 2009: 1113-1119
c96		Quoc V. Le, Andrew Y. Ng: Joint calibration of multiple sensors. IROS 2009: 3651-3658
c95		Adam Coates, Paul Baumstarck, Quoc V. Le, Andrew Y. Ng: Scalable learning for object detection with GPU hardware. IROS 2009: 4287-4293
c94		Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, Andrew Y. Ng: Measuring Invariances in Deep Networks. NIPS 2009: 646-654
c93		Honglak Lee, Peter T. Pham, Yan Largman, Andrew Y. Ng: Unsupervised feature learning for audio classification using convolutional deep belief networks. NIPS 2009: 1096-1104
c92		J. Zico Kolter, Andrew Y. Ng: Policy search via the signed derivative. Robotics: Science and Systems 2009
c91		Savil Srivastava, Ashutosh Saxena, Christian Theobalt, Sebastian Thrun, Andrew Y. Ng: i23 - Rapid Interactive 3D Reconstruction from a Single Image. VMV 2009: 19-28
e1		Jeff Bilmes, Andrew Y. Ng (Eds.): UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009. AUAI Press 2009
	2008
j9		Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng: 3-D Depth Reconstruction from a Single Still Image. International Journal of Computer Vision 76(1): 53-69 (2008)
j8		Ashutosh Saxena, Justin Driemeyer, Andrew Y. Ng: Robotic Grasping of Novel Objects using Vision. I. J. Robotic Res. 27(2): 157-173 (2008)
c90		Benjamin Sapp, Ashutosh Saxena, Andrew Y. Ng: A Fast Data Collection and Augmentation Procedure for Object Recognition. AAAI 2008: 1402-1408
c89		Ashutosh Saxena, Lawson L. S. Wong, Andrew Y. Ng: Learning Grasp Strategies with Partial Shape Information. AAAI 2008: 1491-1494
c88		Ashutosh Saxena, Min Sun, Andrew Y. Ng: Make3D: Depth Perception from a Single Still Image. AAAI 2008: 1571-1576
c87		Rion Snow, Brendan O'Connor, Daniel Jurafsky, Andrew Y. Ng: Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks. EMNLP 2008: 254-263
c86		Adam Coates, Pieter Abbeel, Andrew Y. Ng: Learning for control from multiple demonstrations. ICML 2008: 144-151
c85		J. Zico Kolter, Adam Coates, Andrew Y. Ng, Yi Gu, Charles DuHadway: Space-indexed dynamic programming: learning to follow trajectories. ICML 2008: 488-495
c84		J. Zico Kolter, Mike P. Rodgers, Andrew Y. Ng: A control architecture for quadruped locomotion over rough terrain. ICRA 2008: 811-818
c83		Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng, Sebastian Thrun: Apprenticeship learning for motion planning with application to parking lot navigation. IROS 2008: 1083-1090
c82		Pieter Abbeel, Adam Coates, Timothy Hunter, Andrew Y. Ng: Autonomous Autorotation of an RC Helicopter. ISER 2008: 385-394
	2007
j7		Masayoshi Matsuoka, Alan Chen, Surya P. N. Singh, Adam Coates, Andrew Y. Ng, Sebastian Thrun: Autonomous Helicopter Tracking and Localization Using a Self-surveying Camera Array. I. J. Robotic Res. 26(2): 205-215 (2007)
c81		Rion Snow, Sushant Prakash, Daniel Jurafsky, Andrew Y. Ng: Learning to Merge Word Senses. EMNLP-CoNLL 2007: 1005-1014
c80		Ashutosh Saxena, Min Sun, Andrew Y. Ng: Learning 3-D Scene Structure from a Single Still Image. ICCV 2007: 1-8
c79		Ashutosh Saxena, Min Sun, Andrew Y. Ng: 3-D Reconstruction from Sparse Views using Monocular Vision. ICCV 2007: 1-8
c78		Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, Andrew Y. Ng: Self-taught learning: transfer learning from unlabeled data. ICML 2007: 759-766
c77		Stephen Gould, Joakim Arfvidsson, Adrian Kaehler, Benjamin Sapp, Marius Messner, Gary R. Bradski, Paul Baumstarck, Sukwon Chung, Andrew Y. Ng: Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video. IJCAI 2007: 2115-2121
c76		Anna Petrovskaya, Andrew Y. Ng: Probabilistic Mobile Manipulation in Dynamic Environments, with Application to Opening Doors. IJCAI 2007: 2178-2184
c75		Ashutosh Saxena, Jamie Schulte, Andrew Y. Ng: Depth Estimation Using Monocular and Stereo Cues. IJCAI 2007: 2197-2203
c74		Ted Kremenek, Andrew Y. Ng, Dawson R. Engler: A Factor Graph Model for Software Bug Finding. IJCAI 2007: 2510-2516
c73		Ashutosh Saxena, Lawson L. S. Wong, Morgan Quigley, Andrew Y. Ng: A Vision-Based System for Grasping Novel Objects in Cluttered Environments. ISRR 2007: 337-348
c72		Chuong B. Do, Chuan-Sheng Foo, Andrew Y. Ng: Efficient multiple hyperparameter learning for log-linear models. NIPS 2007
c71		J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng: Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion. NIPS 2007
c70		Honglak Lee, Chaitanya Ekanadham, Andrew Y. Ng: Sparse deep belief net model for visual area V2. NIPS 2007
c69		J. Zico Kolter, Andrew Y. Ng: Learning omnidirectional path following using dimensionality reduction. Robotics: Science and Systems 2007
c68		Roger Grosse, Rajat Raina, Helen Kwong, Andrew Y. Ng: Shift-Invariance Sparse Coding for Audio Classification. UAI 2007: 149-158
	2006
j6		Pieter Abbeel, Daphne Koller, Andrew Y. Ng: Learning Factor Graphs in Polynomial Time and Sample Complexity. Journal of Machine Learning Research 7: 1743-1788 (2006)
c67		Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. Ng: Efficient L1 Regularized Logistic Regression. AAAI 2006: 401-408
c66		Rion Snow, Daniel Jurafsky, Andrew Y. Ng: Semantic Taxonomy Induction from Heterogenous Evidence. ACL 2006
c65		Andrew Y. Ng: Reinforcement Learning and Apprenticeship Learning for Robotic Control. ALT 2006: 29-31
c64		Mike Brzozowski, Kendra Carattini, Scott R. Klemmer, Patrick Mihelich, Jiang Hu, Andrew Y. Ng: groupTime: preference based group scheduling. CHI 2006: 1047-1056
c63		Erick Delage, Honglak Lee, Andrew Y. Ng: A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image. CVPR (2) 2006: 2418-2428
c62		Andrew Y. Ng: Reinforcement Learning and Apprenticeship Learning for Robotic Control. Discovery Science 2006: 14
c61		Jenny Rose Finkel, Christopher D. Manning, Andrew Y. Ng: Solving the Problem of Cascading Errors: Approximate Bayesian Inference for Linguistic Annotation Pipelines. EMNLP 2006: 618-626
c60		Pieter Abbeel, Morgan Quigley, Andrew Y. Ng: Using inaccurate models in reinforcement learning. ICML 2006: 1-8
c59		Rajat Raina, Andrew Y. Ng, Daphne Koller: Constructing informative priors using transfer learning. ICML 2006: 713-720
c58		Anna Petrovskaya, Oussama Khatib, Sebastian Thrun, Andrew Y. Ng: Bayesian Estimation for Autonomous Object Manipulation based on Tactile Sensors. ICRA 2006: 707-714
c57		Honglak Lee, Yirong Shen, Chih-Han Yu, Gurjeet Singh, Andrew Y. Ng: Quadruped Robot Obstacle Negotiation via Reinforcement Learning. ICRA 2006: 3003-3010
c56		Filip Krsmanovic, Curtis Spencer, Daniel Jurafsky, Andrew Y. Ng: Have we met? MDP based speaker ID for robot dialogue. INTERSPEECH 2006
c55		Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Chioma Osondu, Andrew Y. Ng: Learning to Grasp Novel Objects Using Vision. ISER 2006: 33-42
c54		Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng: An Application of Reinforcement Learning to Aerobatic Helicopter Flight. NIPS 2006: 1-8
c53		Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary R. Bradski, Andrew Y. Ng, Kunle Olukotun: Map-Reduce for Machine Learning on Multicore. NIPS 2006: 281-288
c52		Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y. Ng: Efficient sparse coding algorithms. NIPS 2006: 801-808
c51		Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y. Ng: Robotic Grasping of Novel Objects. NIPS 2006: 1209-1216
c50		Ted Kremenek, Paul Twohey, Godmar Back, Andrew Y. Ng, Dawson R. Engler: From Uncertainty to Belief: Inferring the Specification Within. OSDI 2006: 161-176
c49		Einat Minkov, William W. Cohen, Andrew Y. Ng: Contextual search and name disambiguation in email using graphs. SIGIR 2006: 27-34
	2005
j5		David Heckerman, Tom Berson, Joshua Goodman, Andrew Y. Ng: The First Conference on E-mail and Anti-Spam. AI Magazine 26(1): 96 (2005)
c48		Rajat Raina, Andrew Y. Ng, Christopher D. Manning: Robust Textual Inference Via Learning and Abductive Reasoning. AAAI 2005: 1099-1105
c47		Honglak Lee, Andrew Y. Ng: Spam Deobfuscation using a Hidden Markov Model. CEAS 2005
c46		Dragomir Anguelov, Benjamin Taskar, Vassil Chatalbashev, Daphne Koller, Dinkar Gupta, Geremy Heitz, Andrew Y. Ng: Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data. CVPR (2) 2005: 169-176
c45		Masayoshi Matsuoka, Alan Chen, Surya P. N. Singh, Adam Coates, Andrew Y. Ng, Sebastian Thrun: Autonomous Helicopter Tracking and Localization Using a Self-Surveying Camera Array. FSR 2005: 19-30
c44		Pieter Abbeel, Andrew Y. Ng: Exploration and apprenticeship learning in reinforcement learning. ICML 2005: 1-8
c43		Jeff Michels, Ashutosh Saxena, Andrew Y. Ng: High speed obstacle avoidance using monocular vision and reinforcement learning. ICML 2005: 593-600
c42		Erick Delage, Honglak Lee, Andrew Y. Ng: Automatic Single-Image 3d Reconstructions of Indoor Manhattan World Scenes. ISRR 2005: 305-321
c41		Aria Haghighi, Andrew Y. Ng, Christopher D. Manning: Robust Textual Inference via Graph Matching. HLT/EMNLP 2005
c40		Pieter Abbeel, Varun Ganapathi, Andrew Y. Ng: Learning vehicular dynamics, with application to modeling helicopters. NIPS 2005
c39		J. Andrew Bagnell, Andrew Y. Ng: On Local Rewards and Scaling Distributed Reinforcement Learning. NIPS 2005
c38		Chuong B. Do, Andrew Y. Ng: Transfer learning for text classification. NIPS 2005
c37		Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng: Learning Depth from Single Monocular Images. NIPS 2005
c36		Yirong Shen, Andrew Y. Ng, Matthias Seeger: Fast Gaussian Process Regression using KD-Trees. NIPS 2005
c35		Pieter Abbeel, Adam Coates, Michael Montemerlo, Andrew Y. Ng, Sebastian Thrun: Discriminative Training of Kalman Filters. Robotics: Science and Systems 2005: 289-296
c34		Pieter Abbeel, Daphne Koller, Andrew Y. Ng: Learning Factor Graphs in Polynomial Time & Sample Complexity. UAI 2005: 1-9
	2004
j4		Sebastian Thrun, Yufeng Liu, Daphne Koller, Andrew Y. Ng, Zoubin Ghahramani, Hugh F. Durrant-Whyte: Simultaneous Localization and Mapping with Sparse Extended Information Filters. I. J. Robotic Res. 23(7-8): 693-716 (2004)
c33		Pieter Abbeel, Andrew Y. Ng: Apprenticeship learning via inverse reinforcement learning. ICML 2004
c32		Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng: Online and batch learning of pseudo-metrics. ICML 2004
c31		Kristina Toutanova, Christopher D. Manning, Andrew Y. Ng: Learning random walk models for inducing word dependency distributions. ICML 2004
c30		Andrew Y. Ng, Adam Coates, Mark Diel, Varun Ganapathi, Jamie Schulte, Ben Tse, Eric Berger, Eric Liang: Autonomous Inverted Helicopter Flight via Reinforcement Learning. ISER 2004: 363-372
c29		Pieter Abbeel, Andrew Y. Ng: Learning first-order Markov models for control. NIPS 2004
c28		Sham M. Kakade, Andrew Y. Ng: Online Bounds for Bayesian Algorithms. NIPS 2004
c27		Andrew Y. Ng, H. Jin Kim: Stable adaptive control with online learning. NIPS 2004
c26		Rion Snow, Daniel Jurafsky, Andrew Y. Ng: Learning Syntactic Patterns for Automatic Hypernym Discovery. NIPS 2004
	2003
j3		David M. Blei, Andrew Y. Ng, Michael I. Jordan: Latent Dirichlet Allocation. Journal of Machine Learning Research 3: 993-1022 (2003)
c25		J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng, Jeff G. Schneider: Policy Search by Dynamic Programming. NIPS 2003
c24		Andrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry: Autonomous Helicopter Flight via Reinforcement Learning. NIPS 2003
c23		Rajat Raina, Yirong Shen, Andrew Y. Ng, Andrew McCallum: Classification with Hybrid Generative/Discriminative Models. NIPS 2003
	2002
j2		Michael J. Kearns, Yishay Mansour, Andrew Y. Ng: A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. Machine Learning 49(2-3): 193-208 (2002)
c22		Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart J. Russell: Distance Metric Learning with Application to Clustering with Side-Information. NIPS 2002: 505-512
c21		Susan T. Dumais, Michele Banko, Eric Brill, Jimmy J. Lin, Andrew Y. Ng: Web question answering: is more always better?. SIGIR 2002: 291-298
	2001
c20		Andrew Y. Ng, Michael I. Jordan: Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. ICML 2001: 377-384
c19		Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan: Link Analysis, Eigenvectors and Stability. IJCAI 2001: 903-910
c18		David M. Blei, Andrew Y. Ng, Michael I. Jordan: Latent Dirichlet Allocation. NIPS 2001: 601-608
c17		Andrew Y. Ng, Michael I. Jordan: On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. NIPS 2001: 841-848
c16		Andrew Y. Ng, Michael I. Jordan, Yair Weiss: On Spectral Clustering: Analysis and an algorithm. NIPS 2001: 849-856
c15		Alice X. Zheng, Andrew Y. Ng, Michael I. Jordan: Stable Algorithms for Link Analysis. SIGIR 2001: 258-266
c14		Eric Brill, Jimmy J. Lin, Michele Banko, Susan T. Dumais, Andrew Y. Ng: Data-Intensive Question Answering. TREC 2001
	2000
c13		Andrew Y. Ng, Stuart J. Russell: Algorithms for Inverse Reinforcement Learning. ICML 2000: 663-670
c12		Andrew Y. Ng, Michael I. Jordan: PEGASUS: A policy search method for large MDPs and POMDPs. UAI 2000: 406-415
	1999
c11		Andrew Y. Ng, Daishi Harada, Stuart J. Russell: Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping. ICML 1999: 278-287
c10		Michael J. Kearns, Yishay Mansour, Andrew Y. Ng: A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. IJCAI 1999: 1324-1231
c9		Andrew Y. Ng, Michael I. Jordan: Approximate Inference A lgorithms for Two-Layer Bayesian Networks. NIPS 1999: 533-539
c8		Michael J. Kearns, Yishay Mansour, Andrew Y. Ng: Approximate Planning in Large POMDPs via Reusable Trajectories. NIPS 1999: 1001-1007
c7		Andrew Y. Ng, Ronald Parr, Daphne Koller: Policy Search via Density Estimation. NIPS 1999: 1022-1028
	1998
c6		Scott Davies, Andrew Y. Ng, Andrew W. Moore: Applying Online Search Techniques to Continuous-State Reinforcement Learning. AAAI/IAAI 1998: 753-760
c5		Andrew McCallum, Ronald Rosenfeld, Tom M. Mitchell, Andrew Y. Ng: Improving Text Classification by Shrinkage in a Hierarchy of Classes. ICML 1998: 359-367
c4		Andrew Y. Ng: On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples. ICML 1998: 404-412
	1997
j1		Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron: An Experimental and Theoretical Comparison of Model Selection Methods. Machine Learning 27(1): 7-50 (1997)
c3		Andrew Y. Ng: Preventing "Overfitting" of Cross-Validation Data. ICML 1997: 245-253
c2		Michael J. Kearns, Yishay Mansour, Andrew Y. Ng: An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. UAI 1997: 282-293
	1995
c1		Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron: An Experimental and Theoretical Comparison of Model Selection Methods. COLT 1995: 21-30

Coauthor Index

Colors in the list of coauthors

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