| 2008 |
|---|
| 91 | | Benjamin Sapp,
Ashutosh Saxena,
Andrew Y. Ng:
A Fast Data Collection and Augmentation Procedure for Object Recognition.
AAAI 2008: 1402-1408 |
| 90 | | Ashutosh Saxena,
Lawson L. S. Wong,
Andrew Y. Ng:
Learning Grasp Strategies with Partial Shape Information.
AAAI 2008: 1491-1494 |
| 89 | | Ashutosh Saxena,
Min Sun,
Andrew Y. Ng:
Make3D: Depth Perception from a Single Still Image.
AAAI 2008: 1571-1576 |
| 88 | EE | 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 |
| 87 | EE | Adam Coates,
Pieter Abbeel,
Andrew Y. Ng:
Learning for control from multiple demonstrations.
ICML 2008: 144-151 |
| 86 | EE | J. Zico Kolter,
Adam Coates,
Andrew Y. Ng,
Yi Gu,
Charles DuHadway:
Space-indexed dynamic programming: learning to follow trajectories.
ICML 2008: 488-495 |
| 85 | EE | J. Zico Kolter,
Mike P. Rodgers,
Andrew Y. Ng:
A control architecture for quadruped locomotion over rough terrain.
ICRA 2008: 811-818 |
| 84 | EE | Pieter Abbeel,
Dmitri Dolgov,
Andrew Y. Ng,
Sebastian Thrun:
Apprenticeship learning for motion planning with application to parking lot navigation.
IROS 2008: 1083-1090 |
| 83 | EE | Pieter Abbeel,
Adam Coates,
Timothy Hunter,
Andrew Y. Ng:
Autonomous Autorotation of an RC Helicopter.
ISER 2008: 385-394 |
| 82 | EE | 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) |
| 2007 |
|---|
| 81 | EE | Ashutosh Saxena,
Min Sun,
Andrew Y. Ng:
3-D Reconstruction from Sparse Views using Monocular Vision.
ICCV 2007: 1-8 |
| 80 | EE | Ashutosh Saxena,
Min Sun,
Andrew Y. Ng:
Learning 3-D Scene Structure from a Single Still Image.
ICCV 2007: 1-8 |
| 79 | EE | Rajat Raina,
Alexis Battle,
Honglak Lee,
Benjamin Packer,
Andrew Y. Ng:
Self-taught learning: transfer learning from unlabeled data.
ICML 2007: 759-766 |
| 78 | EE | 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 |
| 77 | EE | Anna Petrovskaya,
Andrew Y. Ng:
Probabilistic Mobile Manipulation in Dynamic Environments, with Application to Opening Doors.
IJCAI 2007: 2178-2184 |
| 76 | EE | Ashutosh Saxena,
Jamie Schulte,
Andrew Y. Ng:
Depth Estimation Using Monocular and Stereo Cues.
IJCAI 2007: 2197-2203 |
| 75 | EE | Ted Kremenek,
Andrew Y. Ng,
Dawson R. Engler:
A Factor Graph Model for Software Bug Finding.
IJCAI 2007: 2510-2516 |
| 74 | EE | Chuong B. Do,
Chuan-Sheng Foo,
Andrew Y. Ng:
Efficient multiple hyperparameter learning for log-linear models.
NIPS 2007 |
| 73 | EE | J. Zico Kolter,
Pieter Abbeel,
Andrew Y. Ng:
Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion.
NIPS 2007 |
| 72 | EE | Honglak Lee,
Chaitanya Ekanadham,
Andrew Y. Ng:
Sparse deep belief net model for visual area V2.
NIPS 2007 |
| 71 | EE | J. Zico Kolter,
Andrew Y. Ng:
Learning omnidirectional path following using dimensionality reduction.
Robotics: Science and Systems 2007 |
| 2006 |
|---|
| 70 | | Su-In Lee,
Honglak Lee,
Pieter Abbeel,
Andrew Y. Ng:
Efficient L1 Regularized Logistic Regression.
AAAI 2006 |
| 69 | EE | Rion Snow,
Daniel Jurafsky,
Andrew Y. Ng:
Semantic Taxonomy Induction from Heterogenous Evidence.
ACL 2006 |
| 68 | EE | Andrew Y. Ng:
Reinforcement Learning and Apprenticeship Learning for Robotic Control.
ALT 2006: 29-31 |
| 67 | EE | Mike Brzozowski,
Kendra Carattini,
Scott R. Klemmer,
Patrick Mihelich,
Jiang Hu,
Andrew Y. Ng:
groupTime: preference based group scheduling.
CHI 2006: 1047-1056 |
| 66 | EE | 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 |
| 65 | EE | Andrew Y. Ng:
Reinforcement Learning and Apprenticeship Learning for Robotic Control.
Discovery Science 2006: 14 |
| 64 | EE | Pieter Abbeel,
Morgan Quigley,
Andrew Y. Ng:
Using inaccurate models in reinforcement learning.
ICML 2006: 1-8 |
| 63 | EE | Rajat Raina,
Andrew Y. Ng,
Daphne Koller:
Constructing informative priors using transfer learning.
ICML 2006: 713-720 |
| 62 | | Honglak Lee,
Yirong Shen,
Chih-Han Yu,
Gurjeet Singh,
Andrew Y. Ng:
Quadruped Robot Obstacle Negotiation via Reinforcement Learning.
ICRA 2006: 3003-3010 |
| 61 | | Anna Petrovskaya,
Oussama Khatib,
Sebastian Thrun,
Andrew Y. Ng:
Bayesian Estimation for Autonomous Object Manipulation based on Tactile Sensors.
ICRA 2006: 707-714 |
| 60 | EE | Ashutosh Saxena,
Justin Driemeyer,
Justin Kearns,
Chioma Osondu,
Andrew Y. Ng:
Learning to Grasp Novel Objects Using Vision.
ISER 2006: 33-42 |
| 59 | EE | Pieter Abbeel,
Adam Coates,
Morgan Quigley,
Andrew Y. Ng:
An Application of Reinforcement Learning to Aerobatic Helicopter Flight.
NIPS 2006: 1-8 |
| 58 | EE | Ashutosh Saxena,
Justin Driemeyer,
Justin Kearns,
Andrew Y. Ng:
Robotic Grasping of Novel Objects.
NIPS 2006: 1209-1216 |
| 57 | EE | 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 |
| 56 | EE | Honglak Lee,
Alexis Battle,
Rajat Raina,
Andrew Y. Ng:
Efficient sparse coding algorithms.
NIPS 2006: 801-808 |
| 55 | EE | Ted Kremenek,
Paul Twohey,
Godmar Back,
Andrew Y. Ng,
Dawson R. Engler:
From Uncertainty to Belief: Inferring the Specification Within.
OSDI 2006: 161-176 |
| 54 | EE | Einat Minkov,
William W. Cohen,
Andrew Y. Ng:
Contextual search and name disambiguation in email using graphs.
SIGIR 2006: 27-34 |
| 53 | EE | 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) |
| 2005 |
|---|
| 52 | | Rajat Raina,
Andrew Y. Ng,
Christopher D. Manning:
Robust Textual Inference Via Learning and Abductive Reasoning.
AAAI 2005: 1099-1105 |
| 51 | EE | Honglak Lee,
Andrew Y. Ng:
Spam Deobfuscation using a Hidden Markov Model.
CEAS 2005 |
| 50 | EE | 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 |
| 49 | EE | 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 |
| 48 | EE | Aria Haghighi,
Andrew Y. Ng,
Christopher D. Manning:
Robust Textual Inference via Graph Matching.
HLT/EMNLP 2005 |
| 47 | EE | Pieter Abbeel,
Andrew Y. Ng:
Exploration and apprenticeship learning in reinforcement learning.
ICML 2005: 1-8 |
| 46 | EE | Jeff Michels,
Ashutosh Saxena,
Andrew Y. Ng:
High speed obstacle avoidance using monocular vision and reinforcement learning.
ICML 2005: 593-600 |
| 45 | EE | Erick Delage,
Honglak Lee,
Andrew Y. Ng:
Automatic Single-Image 3d Reconstructions of Indoor Manhattan World Scenes.
ISRR 2005: 305-321 |
| 44 | EE | Yirong Shen,
Andrew Y. Ng,
Matthias Seeger:
Fast Gaussian Process Regression using KD-Trees.
NIPS 2005 |
| 43 | EE | Ashutosh Saxena,
Sung H. Chung,
Andrew Y. Ng:
Learning Depth from Single Monocular Images.
NIPS 2005 |
| 42 | EE | Pieter Abbeel,
Varun Ganapathi,
Andrew Y. Ng:
Learning vehicular dynamics, with application to modeling helicopters.
NIPS 2005 |
| 41 | EE | J. Andrew Bagnell,
Andrew Y. Ng:
On Local Rewards and Scaling Distributed Reinforcement Learning.
NIPS 2005 |
| 40 | EE | Chuong B. Do,
Andrew Y. Ng:
Transfer learning for text classification.
NIPS 2005 |
| 39 | EE | Pieter Abbeel,
Adam Coates,
Michael Montemerlo,
Andrew Y. Ng,
Sebastian Thrun:
Discriminative Training of Kalman Filters.
Robotics: Science and Systems 2005: 289-296 |
| 38 | EE | Pieter Abbeel,
Daphne Koller,
Andrew Y. Ng:
Learning Factor Graphs in Polynomial Time & Sample Complexity.
UAI 2005: 1-9 |
| 2004 |
|---|
| 37 | EE | Pieter Abbeel,
Andrew Y. Ng:
Apprenticeship learning via inverse reinforcement learning.
ICML 2004 |
| 36 | EE | Kristina Toutanova,
Christopher D. Manning,
Andrew Y. Ng:
Learning random walk models for inducing word dependency distributions.
ICML 2004 |
| 35 | EE | Shai Shalev-Shwartz,
Yoram Singer,
Andrew Y. Ng:
Online and batch learning of pseudo-metrics.
ICML 2004 |
| 34 | EE | 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 |
| 33 | EE | Rion Snow,
Daniel Jurafsky,
Andrew Y. Ng:
Learning Syntactic Patterns for Automatic Hypernym Discovery.
NIPS 2004 |
| 32 | EE | Pieter Abbeel,
Andrew Y. Ng:
Learning first-order Markov models for control.
NIPS 2004 |
| 31 | EE | Sham M. Kakade,
Andrew Y. Ng:
Online Bounds for Bayesian Algorithms.
NIPS 2004 |
| 30 | EE | Andrew Y. Ng,
H. Jin Kim:
Stable adaptive control with online learning.
NIPS 2004 |
| 29 | EE | 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) |
| 2003 |
|---|
| 28 | EE | Andrew Y. Ng,
H. Jin Kim,
Michael I. Jordan,
Shankar Sastry:
Autonomous Helicopter Flight via Reinforcement Learning.
NIPS 2003 |
| 27 | EE | Rajat Raina,
Yirong Shen,
Andrew Y. Ng,
Andrew McCallum:
Classification with Hybrid Generative/Discriminative Models.
NIPS 2003 |
| 26 | EE | J. Andrew Bagnell,
Sham Kakade,
Andrew Y. Ng,
Jeff G. Schneider:
Policy Search by Dynamic Programming.
NIPS 2003 |
| 25 | EE | David M. Blei,
Andrew Y. Ng,
Michael I. Jordan:
Latent Dirichlet Allocation.
Journal of Machine Learning Research 3: 993-1022 (2003) |
| 2002 |
|---|
| 24 | EE | 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 |
| 23 | EE | Susan T. Dumais,
Michele Banko,
Eric Brill,
Jimmy J. Lin,
Andrew Y. Ng:
Web question answering: is more always better?.
SIGIR 2002: 291-298 |
| 22 | | 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) |
| 2001 |
|---|
| 21 | | Andrew Y. Ng,
Michael I. Jordan:
Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection.
ICML 2001: 377-384 |
| 20 | | Andrew Y. Ng,
Alice X. Zheng,
Michael I. Jordan:
Link Analysis, Eigenvectors and Stability.
IJCAI 2001: 903-910 |
| 19 | EE | David M. Blei,
Andrew Y. Ng,
Michael I. Jordan:
Latent Dirichlet Allocation.
NIPS 2001: 601-608 |
| 18 | EE | Andrew Y. Ng,
Michael I. Jordan:
On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes.
NIPS 2001: 841-848 |
| 17 | EE | Andrew Y. Ng,
Michael I. Jordan,
Yair Weiss:
On Spectral Clustering: Analysis and an algorithm.
NIPS 2001: 849-856 |
| 16 | | Alice X. Zheng,
Andrew Y. Ng,
Michael I. Jordan:
Stable Algorithms for Link Analysis.
SIGIR 2001: 258-266 |
| 15 | EE | Eric Brill,
Jimmy J. Lin,
Michele Banko,
Susan T. Dumais,
Andrew Y. Ng:
Data-Intensive Question Answering.
TREC 2001 |
| 2000 |
|---|
| 14 | | Andrew Y. Ng,
Stuart J. Russell:
Algorithms for Inverse Reinforcement Learning.
ICML 2000: 663-670 |
| 13 | EE | Andrew Y. Ng,
Michael I. Jordan:
PEGASUS: A policy search method for large MDPs and POMDPs.
UAI 2000: 406-415 |
| 1999 |
|---|
| 12 | | Andrew Y. Ng,
Daishi Harada,
Stuart J. Russell:
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping.
ICML 1999: 278-287 |
| 11 | | 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 |
| 10 | EE | Michael J. Kearns,
Yishay Mansour,
Andrew Y. Ng:
Approximate Planning in Large POMDPs via Reusable Trajectories.
NIPS 1999: 1001-1007 |
| 9 | EE | Andrew Y. Ng,
Ronald Parr,
Daphne Koller:
Policy Search via Density Estimation.
NIPS 1999: 1022-1028 |
| 8 | EE | Andrew Y. Ng,
Michael I. Jordan:
Approximate Inference A lgorithms for Two-Layer Bayesian Networks.
NIPS 1999: 533-539 |
| 1998 |
|---|
| 7 | | Scott Davies,
Andrew Y. Ng,
Andrew W. Moore:
Applying Online Search Techniques to Continuous-State Reinforcement Learning.
AAAI/IAAI 1998: 753-760 |
| 6 | | Andrew McCallum,
Ronald Rosenfeld,
Tom M. Mitchell,
Andrew Y. Ng:
Improving Text Classification by Shrinkage in a Hierarchy of Classes.
ICML 1998: 359-367 |
| 5 | | Andrew Y. Ng:
On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples.
ICML 1998: 404-412 |
| 1997 |
|---|
| 4 | | Andrew Y. Ng:
Preventing "Overfitting" of Cross-Validation Data.
ICML 1997: 245-253 |
| 3 | EE | Michael J. Kearns,
Yishay Mansour,
Andrew Y. Ng:
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering.
UAI 1997: 282-293 |
| 2 | | 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) |
| 1995 |
|---|
| 1 | EE | Michael J. Kearns,
Yishay Mansour,
Andrew Y. Ng,
Dana Ron:
An Experimental and Theoretical Comparison of Model Selection Methods.
COLT 1995: 21-30 |
