Ng, Andrew Y.

113 publications

NeurIPS 2025 STARC-9: A Large-Scale Dataset for Multi-Class Tissue Classification for CRC Histopathology Barathi Subramanian, Rathinaraja Jeyaraj, Mitchell Nevin Peterson, Terry Guo, Nigam Shah, Curtis Langlotz, Andrew Y. Ng, Jeanne Shen

MIDL 2024 Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation Tanvi Deshpande, Eva Prakash, Elsie Gyang Ross, Curtis Langlotz, Andrew Y. Ng, Jeya Maria Jose Valanarasu

ICMLW 2024 Many-Shot In-Context Learning in Multimodal Foundation Models Yixing Jiang, Jeremy Andrew Irvin, Ji Hun Wang, Muhammad Ahmed Chaudhry, Jonathan H Chen, Andrew Y. Ng

NeurIPS 2023 DataPerf: Benchmarks for Data-Centric AI Development Mark Mazumder, Colby Banbury, Xiaozhe Yao, Bojan Karlaš, William Gaviria Rojas, Sudnya Diamos, Greg Diamos, Lynn He, Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Will Cukierski, Juan Ciro, Lora Aroyo, Bilge Acun, Lingjiao Chen, Mehul Raje, Max Bartolo, Evan Sabri Eyuboglu, Amirata Ghorbani, Emmett Goodman, Addison Howard, Oana Inel, Tariq Kane, Christine R. Kirkpatrick, D. Sculley, Tzu-Sheng Kuo, Jonas W Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen Paritosh, Ce Zhang, James Y Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi

MIDL 2023 MedSelect: Selective Labeling for Medical Image Classification Using Meta-Learning Damir Vrabac, Akshay Smit, Yujie He, Andrew Y. Ng, Andrew L. Beam, Pranav Rajpurkar

MLHC 2021 CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-Rays Emma Chen, Andy Kim, Rayan Krishnan, Jin Long, Andrew Y. Ng, Pranav Rajpurkar

ICLR 2021 Evaluating the Disentanglement of Deep Generative Models Through Manifold Topology Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon

MLHC 2021 MedAug: Contrastive Learning Leveraging Patient Metadata Improves Representations for Chest X-Ray Interpretation Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y. Ng, Pranav Rajpurkar

CVPRW 2020 Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Y. Ng

CVPRW 2020 The 1st Agriculture-Vision Challenge: Methods and Results Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer A. Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander G. Schwing, Robert Brunner, Ivan Dozier, Wyatt Dozier, Karen Ghandilyan, David Wilson, Hyunseong Park, Jun Hee Kim, Sungho Kim, Qinghui Liu, Michael C. Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg, Alexandre Barbosa, Rodrigo G. Trevisan, Bingchen Zhao, Shaozuo Yu, Siwei Yang, Yin Wang, Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Y. Ng, Van Thong Huynh, Soo-Hyung Kim, In Seop Na, Ujjwal Baid, Shubham Innani, Prasad Dutande, Bhakti Baheti, Sanjay N. Talbar, Jianyu Tang

AAAI 2019 CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Christopher Chute, Henrik Marklund, Behzad Haghgoo, Robyn L. Ball, Katie S. Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, Andrew Y. Ng

UAI 2019 Countdown Regression: Sharp and Calibrated Survival Predictions Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Y. Ng

ICLR 2017 Data Noising as Smoothing in Neural Network Language Models Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, Andrew Y. Ng

CVPR 2016 End-to-End People Detection in Crowded Scenes Russell Stewart, Mykhaylo Andriluka, Andrew Y. Ng

ICLR 2014 Deep Learning for Class-Generic Object Detection Brody Huval, Adam Coates, Andrew Y. Ng

ICLR 2013 Learning New Facts from Knowledge Bases with Neural Tensor Networks and Semantic Word Vectors Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng

ICLR 2013 Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng

ICML 2012 Building High-Level Features Using Large Scale Unsupervised Learning Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Greg Corrado, Kai Chen, Jeffrey Dean, Andrew Y. Ng

NeurIPS 2012 Convolutional-Recursive Deep Learning for 3D Object Classification Richard Socher, Brody Huval, Bharath Bath, Christopher D. Manning, Andrew Y. Ng

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

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

NeurIPS 2012 Large Scale Distributed Deep Networks Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc'aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc V. Le, Andrew Y. Ng

NeurIPS 2011 Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection Richard Socher, Eric H. Huang, Jeffrey Pennin, Christopher D. Manning, Andrew Y. Ng

NeurIPS 2011 ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng

ICML 2011 Learning Deep Energy Models Jiquan Ngiam, Zhenghao Chen, Pang Wei Koh, Andrew Y. Ng

CVPR 2011 Learning Hierarchical Invariant Spatio-Temporal Features for Action Recognition with Independent Subspace Analysis Quoc V. Le, Will Y. Zou, Serena Y. Yeung, Andrew Y. Ng

ICML 2011 Multimodal Deep Learning Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, Andrew Y. Ng

ICML 2011 On Optimization Methods for Deep Learning Quoc V. Le, Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow, Andrew Y. Ng

ICML 2011 On Random Weights and Unsupervised Feature Learning Andrew M. Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Y. Ng

ICML 2011 Parsing Natural Scenes and Natural Language with Recursive Neural Networks Richard Socher, Cliff Chiung-Yu Lin, Andrew Y. Ng, Christopher D. Manning

NeurIPS 2011 Selecting Receptive Fields in Deep Networks Adam Coates, Andrew Y. Ng

NeurIPS 2011 Sparse Filtering Jiquan Ngiam, Zhenghao Chen, Sonia A. Bhaskar, Pang W. Koh, Andrew Y. Ng

ICML 2011 The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization Adam Coates, Andrew Y. Ng

NeurIPS 2011 Unsupervised Learning Models of Primary Cortical Receptive Fields and Receptive Field Plasticity Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Saxe, Andrew Y. Ng

CVPR 2010 A Steiner Tree Approach to Efficient Object Detection Olga Russakovsky, Andrew Y. Ng

NeurIPS 2010 Energy Disaggregation via Discriminative Sparse Coding J. Z. Kolter, Siddharth Batra, Andrew Y. Ng

NeurIPS 2010 Tiled Convolutional Neural Networks Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pang W. Koh, Quoc V. Le, Andrew Y. Ng

ICML 2009 A Majorization-Minimization Algorithm for (multiple) Hyperparameter Learning Chuan-Sheng Foo, Chuong B. Do, Andrew Y. Ng

ICML 2009 Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng

IJCAI 2009 Exponential Family Sparse Coding with Application to Self-Taught Learning Honglak Lee, Rajat Raina, Alex Teichman, Andrew Y. Ng

ICML 2009 Large-Scale Deep Unsupervised Learning Using Graphics Processors Rajat Raina, Anand Madhavan, Andrew Y. Ng

NeurIPS 2009 Measuring Invariances in Deep Networks Ian Goodfellow, Honglak Lee, Quoc V. Le, Andrew Saxe, Andrew Y. Ng

ICML 2009 Near-Bayesian Exploration in Polynomial Time J. Zico Kolter, Andrew Y. Ng

ICML 2009 Regularization and Feature Selection in Least-Squares Temporal Difference Learning J. Zico Kolter, Andrew Y. Ng

UAI 2009 UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009 Jeff A. Bilmes, Andrew Y. Ng

NeurIPS 2009 Unsupervised Feature Learning for Audio Classification Using Convolutional Deep Belief Networks Honglak Lee, Peter Pham, Yan Largman, Andrew Y. Ng

AAAI 2008 A Fast Data Collection and Augmentation Procedure for Object Recognition Benjamin Sapp, Ashutosh Saxena, Andrew Y. Ng

AAAI 2008 Learning Grasp Strategies with Partial Shape Information Ashutosh Saxena, Lawson L. S. Wong, Andrew Y. Ng

ICML 2008 Learning for Control from Multiple Demonstrations Adam Coates, Pieter Abbeel, Andrew Y. Ng

AAAI 2008 Make3D: Depth Perception from a Single Still Image Ashutosh Saxena, Min Sun, Andrew Y. Ng

ICML 2008 Space-Indexed Dynamic Programming: Learning to Follow Trajectories J. Zico Kolter, Adam Coates, Andrew Y. Ng, Yi Gu, Charles DuHadway

ICCV 2007 3-D Reconstruction from Sparse Views Using Monocular Vision Ashutosh Saxena, Min Sun, Andrew Y. Ng

IJCAI 2007 A Factor Graph Model for Software Bug Finding Ted Kremenek, Andrew Y. Ng, Dawson R. Engler

IJCAI 2007 Depth Estimation Using Monocular and Stereo Cues Ashutosh Saxena, Jamie Schulte, Andrew Y. Ng

NeurIPS 2007 Efficient Multiple Hyperparameter Learning for Log-Linear Models Chuan-sheng Foo, Chuong B. Do, Andrew Y. Ng

NeurIPS 2007 Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion J. Z. Kolter, Pieter Abbeel, Andrew Y. Ng

ICCV 2007 Learning 3-D Scene Structure from a Single Still Image Ashutosh Saxena, Min Sun, Andrew Y. Ng

IJCAI 2007 Peripheral-Foveal Vision for Real-Time Object Recognition and Tracking in Video Stephen Gould, Joakim Arfvidsson, Adrian Kaehler, Benjamin Sapp, Marius Messner, Gary R. Bradski, Paul Baumstarck, Sukwon Chung, Andrew Y. Ng

IJCAI 2007 Probabilistic Mobile Manipulation in Dynamic Environments, with Application to Opening Doors Anna Petrovskaya, Andrew Y. Ng

ICML 2007 Self-Taught Learning: Transfer Learning from Unlabeled Data Rajat Raina, Alexis J. Battle, Honglak Lee, Benjamin Packer, Andrew Y. Ng

UAI 2007 Shift-Invariance Sparse Coding for Audio Classification Roger B. Grosse, Rajat Raina, Helen Kwong, Andrew Y. Ng

NeurIPS 2007 Sparse Deep Belief Net Model for Visual Area V2 Honglak Lee, Chaitanya Ekanadham, Andrew Y. Ng

CVPR 2006 A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image Erick Delage, Honglak Lee, Andrew Y. Ng

NeurIPS 2006 An Application of Reinforcement Learning to Aerobatic Helicopter Flight Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng

ICML 2006 Constructing Informative Priors Using Transfer Learning Rajat Raina, Andrew Y. Ng, Daphne Koller

AAAI 2006 Efficient L1 Regularized Logistic Regression Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. Ng

NeurIPS 2006 Efficient Sparse Coding Algorithms Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y. Ng

JMLR 2006 Learning Factor Graphs in Polynomial Time and Sample Complexity Pieter Abbeel, Daphne Koller, Andrew Y. Ng

ALT 2006 Reinforcement Learning and Apprenticeship Learning for Robotic Control Andrew Y. Ng

NeurIPS 2006 Robotic Grasping of Novel Objects Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y. Ng

ICML 2006 Using Inaccurate Models in Reinforcement Learning Pieter Abbeel, Morgan Quigley, Andrew Y. Ng

NeurIPS 2006 mAP-Reduce for Machine Learning on Multicore Cheng-tao Chu, Sang K. Kim, Yi-an Lin, Yuanyuan Yu, Gary Bradski, Kunle Olukotun, Andrew Y. Ng

CVPR 2005 Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data Dragomir Anguelov, Benjamin Taskar, Vassil Chatalbashev, Daphne Koller, Dinkar Gupta, Geremy Heitz, Andrew Y. Ng

ICML 2005 Exploration and Apprenticeship Learning in Reinforcement Learning Pieter Abbeel, Andrew Y. Ng

NeurIPS 2005 Fast Gaussian Process Regression Using KD-Trees Yirong Shen, Matthias Seeger, Andrew Y. Ng

ICML 2005 High Speed Obstacle Avoidance Using Monocular Vision and Reinforcement Learning Jeff Michels, Ashutosh Saxena, Andrew Y. Ng

NeurIPS 2005 Learning Depth from Single Monocular Images Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng

UAI 2005 Learning Factor Graphs in Polynomial Time & Sample Complexity Pieter Abbeel, Daphne Koller, Andrew Y. Ng

NeurIPS 2005 Learning Vehicular Dynamics, with Application to Modeling Helicopters Pieter Abbeel, Varun Ganapathi, Andrew Y. Ng

NeurIPS 2005 On Local Rewards and Scaling Distributed Reinforcement Learning Drew Bagnell, Andrew Y. Ng

AAAI 2005 Robust Textual Inference via Learning and Abductive Reasoning Rajat Raina, Andrew Y. Ng, Christopher D. Manning

NeurIPS 2005 Transfer Learning for Text Classification Chuong B. Do, Andrew Y. Ng

ICML 2004 Apprenticeship Learning via Inverse Reinforcement Learning Pieter Abbeel, Andrew Y. Ng

NeurIPS 2004 Learning First-Order Markov Models for Control Pieter Abbeel, Andrew Y. Ng

ICML 2004 Learning Random Walk Models for Inducing Word Dependency Distributions Kristina Toutanova, Christopher D. Manning, Andrew Y. Ng

NeurIPS 2004 Learning Syntactic Patterns for Automatic Hypernym Discovery Rion Snow, Daniel Jurafsky, Andrew Y. Ng

NeurIPS 2004 Online Bounds for Bayesian Algorithms Sham M. Kakade, Andrew Y. Ng

ICML 2004 Online and Batch Learning of Pseudo-Metrics Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng

NeurIPS 2004 Stable Adaptive Control with Online Learning H. J. Kim, Andrew Y. Ng

NeurIPS 2003 Autonomous Helicopter Flight via Reinforcement Learning H. J. Kim, Michael I. Jordan, Shankar Sastry, Andrew Y. Ng

NeurIPS 2003 Classification with Hybrid Generative/Discriminative Models Rajat Raina, Yirong Shen, Andrew McCallum, Andrew Y. Ng

NeurIPS 2003 Policy Search by Dynamic Programming J. A. Bagnell, Sham M. Kakade, Jeff G. Schneider, Andrew Y. Ng

MLJ 2002 A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes Michael J. Kearns, Yishay Mansour, Andrew Y. Ng

NeurIPS 2002 Distance Metric Learning with Application to Clustering with Side-Information Eric P. Xing, Michael I. Jordan, Stuart Russell, Andrew Y. Ng

ICML 2001 Convergence Rates of the Voting Gibbs Classifier, with Application to Bayesian Feature Selection Andrew Y. Ng, Michael I. Jordan

NeurIPS 2001 Latent Dirichlet Allocation David M. Blei, Andrew Y. Ng, Michael I. Jordan

IJCAI 2001 Link Analysis, Eigenvectors and Stability Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan

NeurIPS 2001 On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes Andrew Y. Ng, Michael I. Jordan

NeurIPS 2001 On Spectral Clustering: Analysis and an Algorithm Andrew Y. Ng, Michael I. Jordan, Yair Weiss

ICML 2000 Algorithms for Inverse Reinforcement Learning Andrew Y. Ng, Stuart Russell

UAI 2000 PEGASUS: A Policy Search Method for Large MDPs and POMDPs Andrew Y. Ng, Michael I. Jordan

IJCAI 1999 A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes Michael J. Kearns, Yishay Mansour, Andrew Y. Ng

NeurIPS 1999 Approximate Inference a Lgorithms for Two-Layer Bayesian Networks Andrew Y. Ng, Michael I. Jordan

NeurIPS 1999 Approximate Planning in Large POMDPs via Reusable Trajectories Michael J. Kearns, Yishay Mansour, Andrew Y. Ng

ICML 1999 Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping Andrew Y. Ng, Daishi Harada, Stuart Russell

NeurIPS 1999 Policy Search via Density Estimation Andrew Y. Ng, Ronald Parr, Daphne Koller

AAAI 1998 Applying Online Search Techniques to Continuous-State Reinforcement Learning Scott Davies, Andrew Y. Ng, Andrew W. Moore

ICML 1998 Improving Text Classification by Shrinkage in a Hierarchy of Classes Andrew McCallum, Ronald Rosenfeld, Tom M. Mitchell, Andrew Y. Ng

ICML 1998 On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples Andrew Y. Ng

MLJ 1997 An Experimental and Theoretical Comparison of Model Selection Methods Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron

UAI 1997 An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering Michael J. Kearns, Yishay Mansour, Andrew Y. Ng

ICML 1997 Preventing "Overfitting" of Cross-Validation Data Andrew Y. Ng

COLT 1995 An Experimental and Theoretical Comparison of Model Selection Methods Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron