My profiles on Google Scholar, Semantic Scholar, and DBLP.
Selected publications:
[PDF][CODE][TALK] Convolutional Networks with Dense Connectivity
Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger
IEEE transactions on pattern analysis and machine intelligence
[PDF][CODE][BIBTEX] Marginalized Stacked Denoising Autoencoders for Domain Adaptation.
Minmin Chen, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, Fei Sha.
Proceedings of 29th International Conference on Machine Learning (ICML), Edingburgh Scotland, Omnipress, pages 767-774, 2012.
[PDF][CODE][BIBTEX] Co-training for domain adaptation.
Minmin Chen, Kilian Q. Weinberger, and John C. Blitzer.
In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, editors, Advances in Neural Information Processing Systems 24 (NeurIPS-24), pages 2456–2464. 2011.
[PDF][CODE][BIBTEX] Distance Metric Learning for Large Margin Nearest Neighbor Classification.
K. Q. Weinberger, L. K. Saul.
Journal of Machine Learning Research (JMLR) 2009, 10:207-244
[PDF][CODE][BIBTEX] Feature Hashing for Large Scale Multitask Learning.
K. Q. Weinberger, A. Dasgupta, J. Langford, A. Smola, J. Attenberg.
In Proceedings of the Twenty Sixth International Confernence on Machine Learning (ICML-09), Canada.
[PDF][CODE][BIBTEX] Unsupervised learning of image manifolds by semidefinite programming.
K. Q. Weinberger and L. K. Saul (2004).
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-04), vol. 2, pages 988-995. Washington D.C. Outstanding student paper award.
(An outdated/incomplete list of) publications by year:
2022
[PDF] J. Venderley, K. Mallayya, M. Matty, M. Krogstad, J. Ruff, G. Pleiss, V. Kishore, D. Mandrus, D. Phelan, L. Poudel, A. Gordon Wilson, K. Weinberger, P. Upreti, M. Norman, S. Rosenkranz, R. Osborn, E.-Ah Kim
Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction
Proceedings of the National Academy of Sciences
Y. You, K. Luo, X. Chen, J. Chen, W. Chao, W. Sun, B. Hariharan, M. Campbell, K. Q Weinberger
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
International Conference on Learning Representations (ICLR) 2022, in press …
R. Ramakrishnan, H. Buddhika Narangodage, M. Schilman, K. Q. Weinberger, R. McDonald
Long-term Control for Dialogue Generation: Methods and Evaluation
Proceedings of Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) 2022, in press…
F. Wu, K. Kim, J. Pan, K. J. Han, K. Q. Weinberger, Y. Artzi
Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2022, in press …
B Li, KQ Weinberger, S Belongie, V Koltun, R Ranftl
Language-driven Semantic Segmentation
International Conference on Learning Representations (ICLR) 2022, in press …
J Bjorck, CP Gomes, KQ Weinberger
Is High Variance Unavoidable in RL? A Case Study in Continuous Control
International Conference on Learning Representations (ICLR) 2022, in press …
V Kishore, X Chen, Y Wang, B Li, KQ Weinberger
Fixed Neural Network Steganography: Train the images, not the network
International Conference on Learning Representations (ICLR) 2022, in press …
2021
[SciSpace] J. Fong, J. R Gardner, J. M Andrews, A. F Cashen, J. E Payton, K. Q Weinberger, J. R Edwards
Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM
Nucleic Acids Research, Volume 49, Issue 16, 20 September 2021, Page e93
N Bjorck, CP Gomes, KQ Weinberger
Towards Deeper Deep Reinforcement Learning with Spectral Normalization
Advances in Neural Information Processing Systems (NeurIPS) 34, 2021, in press …
R Wu, C Guo, Y Su, KQ Weinberger
Online adaptation to label distribution shift
Advances in Neural Information Processing Systems (NeurIPS) 34, 2021, in press …
J Björck, X Chen, C De Sa, CP Gomes, K Weinberger
Low-precision reinforcement learning: Running soft actor-critic in half precision
International Conference on Machine Learning (ICML) 2021, 980-992
[Video] R Wu, C Guo, F Wu, R Kidambi, L Van Der Maaten, K Weinberger
Making paper reviewing robust to bid manipulation attacks
International Conference on Machine Learning (ICML) 2021, 11240-11250
L Yang, Y Wang, M Gao, A Shrivastava, KQ Weinberger, WL Chao, SN Lim
Deep co-training with task decomposition for semi-supervised domain adaptation
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021, 8906-8916
J Bjorck, K Weinberger, C Gomes
Understanding Decoupled and Early Weight Decay
Association for the Advancement of Artificial Intelligence (AAAI), 2021, 6777-6785
J Bjorck, C Gomes, K Weinberger
Characterizing the Loss Landscape in Non-Negative Matrix Factorization
Association for the Advancement of Artificial Intelligence (AAAI), 2021, 6768-6776
Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, Kilian Weinberger
On feature normalization and data augmentation
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), 12383-12392
T Zhang, F Wu, A Katiyar, KQ Weinberger, Y Artzi
Revisiting few-sample BERT fine-tuning
International Conference on Learning Representations (ICLR) 2021,
2020
D Garg, Y Wang, B Hariharan, M Campbell, KQ Weinberger, WL Chao
Wasserstein Distances for Stereo Disparity Estimation
Advances in Neural Information Processing Systems (NeurIPS) 33, 2020: 22517-22529
G Pleiss, T Zhang, ER Elenberg, KQ Weinberger
Identifying Mislabeled Data using the Area Under the Margin Ranking
Advances in Neural Information Processing Systems (NeurIPS) 33, 2020: 17044-17056
[PDF] Yan Wang, Xiangyu Chen, Yurong You, Li Erran Li, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020, pp. 11713--11723
[PDF] Rui Qian, Divyansh Garg, Yan Wang, Yurong You, Serge Belongie, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao,
End-to-end Pseudo-LiDAR for Image-Based 3D Object Detection
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020, pp. 5881–5890.
[PDF] Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
International Conference on Learning Representations (ICLR) 2020, Addis Ababa, Ethiopia, April 26-30
[Project] [PDF] Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, Yoav Artzi
BERTScore: Evaluating Text Generation with BERT
International Conference on Learning Representations (ICLR) 2020, Addis Ababa, Ethiopia, April 26-30
2019
[PDF] Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge Belongie
Positional Normalization
Neural Information Processing Systems (NeurIPS), 2019
[PDF] Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Neural Information Processing Systems (NeurIPS), 2019
[PDF] Ke Wang, Geoff Pleiss, Jacob Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
Exact Gaussian Processes on a Million Data Points
Neural Information Processing Systems (NeurIPS), 2019
[PDF] Carlos Diaz-Ruiz, Yan Wang, Wei-Lun Chao, Kilian Weinberger, Mark Campbell
Vision-only 3D Tracking for Self-Driving Cars
IEEE 15th International Conference on Automation Science and Engineering (CASE)
[PDF] Y Wang, WL Chao, KQ Weinberger, L van der Maaten
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Technical Report
[PDF] BH Wang, WL Chao, Y Wang, B Hariharan, KQ Weinberger, M Campbell,
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images
IEEE Robotics and Automation Letters 4 (3), 2902-2909
Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Q. Weinberger
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
Conference on Computer Vision and Pattern Recognition (CVPR), 2019, In press …
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger
Simplifying Graph Convolutional Networks
International Conference on Machine Learning (ICML), 2019, In press …
Chuan Guo, Jacob R. Gardner, Yurong You, Andrew G. Wilson, Kilian Q. Weinberger
Simple Black-box Adversarial Attacks
International Conference on Machine Learning (ICML), 2019, In press …
Yan Wang, Zihang Lai, Gao Huang, Brian Wang, Laurens van der Maaten, Mark Campbell, Kilian Q. Weinberger
Anytime Stereo Image Depth Estimation on Mobile Devices
International Conference on Robotics and Automation (ICRA), 2019, In press …
Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie and Kilian Q. Weinberger
Adversarial deep averaging networks for cross-lingual sentiment classification
Transactions of the Association for Computational Linguistics (TACL) 6, 557-570 (Talk at EMNLP 2018)
2018
[PDF] Jacob Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew Wilson
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Neural Information Processing Systems (NeurIPS), 2018. In press…
[PDF] Nils Bjorck, Carla P Gomes, Bart Selman, Kilian Q. Weinberger
Understanding Batch Normalization
Neural Information Processing Systems (NeurIPS), 2018. In press…
[arXiv Pre-print][CODE] Geoff Pleiss, Jacob Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
Constant-Time Predictive Distributions for Gaussian Processes
International Conference on Machine Learning (ICML) 2018, In press ….
[PDF][CODE] Jacob Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew G. Wilson
Product Kernel Interpolation for Scalable Gaussian Processes
Artificial Intelligence and Statistics (AISTATS), 2018, in press …
[PDF][CODE] Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger
Multi-Scale Dense Networks for Resource Efficient Image Classification
International Conference on Learning Representations (ICLR), 2018, in press …
[PDF][CODE] Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger
CondenseNet: An Efficient DenseNet using Learned Group Convolutions
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, in press…
[PDF][CODE] Yann Wang, Lequnn Wang, Yurongn You, Xun Zou, Vincentn Chen, Serenan Li, Bharathn Hariharan, Gao Huang, Kilian Q. Weinberger
Resource Aware Person Re-identification across Multiple Resolutions
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, in press…
2017
[PDF] On Calibration of Modern Neural Networks
Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
International Conference on Machine Learning (ICML) 2017, In press ….
[PDF][CODE][TALK] Densely Connected Convolutional Networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
Computer Vision and Pattern Recognition- (CVPR) 2017
[Winner of best paper award.]
[PDF] Deep Feature Interpolation for Image Content Changes
Paul Upchurch, Jake Gardner, Kavita Bala, Rrobert Pless, Noah Snavely, Kilian Weinberger
Computer Vision and Pattern Recognition (CVPR) 2017, In press …
[PDF][CODE] Snapshot ensembles: Train 1, get m for free
G Huang, Y Li, G Pleiss, Z Liu, JE Hopcroft, KQ Weinberger
International Conference on Learning Representations (ICLR) 2017
[PDF] Discovering and Exploiting Additive Structure for Bayesian Optimization
J Gardner, C Guo, K Weinberger, R Garnett, R Grosse
Artificial Intelligence and Statistics (AISTATS) 2017, pages 1311-1319
2016
[PDF] [BibTeX][TALK] Supervised Word Mover's Distance
Gao Huang, Chuan Guo, Matt Kusner, Yu Sun, Fei Sha and Kilian Q. Weinberger.
Neural Information Processing Systems (NeurIPS), 2015, Curran Associates, Barcelona, Dec., 2016, in press…
[PDF] Deep Networks with Stochastic Depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Q. Weinberger
The 14th European Conference on Computer Vision (ECCV) – Amsterdam, The Netherlands, in press…
[PDF] Inferring the Causal Direction Privately
Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger
Artificial Intelligence and Statistics (AISTATS), 2016
[PDF] Compressing Convolutional Neural Networks in Frequency Domain.
W. Chen, J. Wilson, S. Tyree, Kilian Q. Weinberger, and Y. Chen,
Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016.
[PDF] Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke
JS Siegel, LE Ramsey, AZ Snyder, NV Metcalf, RV Chacko, Kilian Q. Weinberger, Antonello Baldassarrea, Carl D. Hackerc, Gordon L. Shulmana, and Maurizio Corbetta
Proceedings of the National Academy of Sciences, 201521083
2015
[PDF][BIBTEX] Fast Distributed k-Center Clustering with Outliers on Massive Data
Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley
Neural Information Processing Systems (NeurIPS), 2015, Curran Associates, pages 1063--1071.
[PDF][BIBTEX] Bayesian Active Model Selection with an Application to Automated Audiometry
Jacob Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham
Neural Information Processing Systems (NeurIPS), 2015, Curran Associates, pages 2386--2394.
[PDF] Psychophysical Detection Testing with Bayesian Active Learning
Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis Barbour, John Cunningham
Conference on Uncertainty in Artificial Intelligence (UAI), Amsterdam, Netherlands, (in press…)
[DOI] Song XD, Wallace BM, Gardner JR, Ledbetter NM, Weinberger KQ, Barbour DL.
Fast, Continuous Audiogram Estimation Using Machine Learning.
Ear and hearing, 2015 Nov-Dec; 36(6):e326-35.
[PDF][From Word Embeddings to Document Distances
Matt J. Kusner, Yu Sun, Nicholas I. Kolkin , Kilian Q. Weinberger
International Conference on Machine Learning (ICML), Lille, France, pp. 957–966, 2015.
[PDF][Supplementary] Differentially Private Bayesian Optimization
Matt J. Kusner, Jacob R. Gardner, Roman Garnett , Kilian Q. Weinberger
International Conference on Machine Learning (ICML), Lille, France, pp. 918–927, 2015.
[PDF][Video] Compressing Neural Networks with the Hashing Trick
Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen
International Conference on Machine Learning (ICML), Lille, France, pp. 2285–2294, 2015.
[PDF] Filtered Search for Submodular Maximization with Controllable Approximation Bounds
Wenlin Chen, Yixin Chen, and Kilian Q. Weinberger
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS), (In press …)
[PDF] Marginalized Denoising for Link Prediction and Multi-label Learning
Zheng Chen, Minmin Chen, Kilian Q. Weinberger, Weixiong Zhang
Association for the Advancement of Artificial Intelligence (AAAI), 2015. (In press...)
Volume 38: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics
[PDF][PDF-Preprint][CODE] A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing
Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen
Association for the Advancement of Artificial Intelligence (AAAI), 2015. (In press...)
[PDF][CODE] Marginalizing Stacked Linear Denoising Autoencoders
Zhixiang (Eddie) Xu, Minmin Chen, Kilian Q. Weinberger, F. Sha.
Journal of Machine Learning Research (JMLR), (in press … )
[DOI] Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications
Mrinal Pahwa , Matthew Kusner , Carl D. Hacker , David T. Bundy , Kilian Q. Weinberger , Eric C. Leuthardt.
10.1371/journal.pone.0142947
2014
[PDF][CODE][BIBTEX] Stochastic Neighbor Compression
Matt Kusner, Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal.
International Conference on Machine Learning (ICML), Beijing China. JMLR W&CP 32 (1) :622-630, 2014
[PDF][CODE][BIBTEX] Bayesian Optimization with Inequality Constraints.
Jacob Gardner, Matt Kusner, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, John Cunningham
International Conference on Machine Learning (ICML), Beijing China. JMLR W&CP 32 (1) :937-945, 2014.
[PDF][CODE][BIBTEX] Marginalized Denoising Auto-encoders for Nonlinear Representations
Minmin Chen, Kilian Q. Weinberger, Fei Sha, Yoshua Bengio.
International Conference on Machine Learning (ICML), Beijing China. JMLR W&CP 32 (1) :1476-1484, 2014.
[PDF][CODE][BIBTEX] Feature-Cost Sensitive Learning with Submodular Trees of Classifiers
Matt Kusner, Wenlin Chen, Quan Zhou, Eddie Xu and Kilian Weinberger
Proc. AAAI Conference on Artificial Intelligence (AAAI-14), 2014. (in press ...)
[PDF][CODE][BIBTEX] Budgeted Learning with Trees and Cascades
Zhixiang Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle
Journal of Machine Learning Research (JMLR), 15(Jun):2113−2144, 2014.
[PDF][CODE][BIBTEX] Gradient Boosted Feature Selection.
Zhixiang (Eddie) Xu, Gao Huang, Kilian Q.Weinberger, Alice Zheng
To appear in 20th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD), 2014, (in press ...).
[PDF][CODE][BIBTEX] Fast Flux Discriminant for Large-Scale Sparse Nonlinear Classification
W. Chen, Y. Chen, K. Weinberger
Proc. ACM SIGKDD Conference (KDD), pages 621--630, 2014.
[Winner of best student paper runner up award.]
[PDF][CODE][BIBTEX] Transductive Minimax Probability Machine
Gao Huang, Shiji Song, Zhixiang (Eddie) Xu, Kilian Q. Weinberger
European Conference on Machine Learning (ECML) 2014 (In press ...)
[PDF] Physicochemical signatures of nanoparticle-dependent complement activation.
Dennis G Thomas, Satish Chikkagoudar, Alejandro Heredia-Langner, Mark F Tardiff, Zhixiang Xu,
Dennis E Hourcade, Christine T N Pham, Gregory M Lanza, Kilian Q Weinberger and Nathan A Baker
Computational Science & Discovery 7 (1), 015003
2013
[PDF][CODE][BIBTEX] Fast Image Tagging
Minmin Chen, Alice Zheng, Kilian Q. Weinberger.
Proceedings of 30th International Conference on Machine Learning (ICML), Atlanta, GA, pages 1274-1282, 2013.
[PDF][CODE][BIBTEX] Anytime Representation Learning
Zhixiang (Eddie) Xu, Matt J. Kusner, Gao Huang, Kilian Q. Weinberger
Proceedings of 30th International Conference on Machine Learning (ICML), Atlanta, GA, pages 1076-1084, 2013.
[PDF][CODE][BIBTEX] Learning with Marginalized Corrupted Features
Laurens van der Maaten, Minmin Chen, Stephen Tyree, and Kilian Q. Weinberger
Proceedings of 30th International Conference on Machine Learning (ICML), Atlanta, GA, pages 410-418, 2013.
[PDF][BIBTEX] Maximum Variance Correction with Application to A* Search
Wenlin Chen, Kilian Q. Weinberger, and Yixin Chen
Proceedings of 30th International Conference on Machine Learning (ICML), Atlanta, GA, pages 302-310, 2013.
[PDF][BIBTEX] Cost-Sensitive Tree of Classifiers
Zhixiang (Eddie) Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen
Proceedings of 30th International Conference on Machine Learning (ICML), Atlanta, GA, pages 133-141, 2013.
[PDF] Goal-Oriented Euclidean Heuristics with Manifold Learning
Wenlin Chen, Yixin Chen, Kilian Q. Weinberger, Qiang Lu, and Xiaoping Chen
Proc. AAAI Conference on Artificial Intelligence (AAAI-13), 2013.
[PDF] Utilizing Landmarks in Euclidean Heuristics for Optimal Planning
Q. Lu, W. Chen, Y. Chen, K. Weinberger, and X. Chen
Late-Breaking Track, Proc. AAAI Conference on Artificial Intelligence (AAAI-13), 2013.
2012
[PDF][CODE by Iago Suárez][BIBTEX][SUPP] Nonlinear metric learning.
Dor Kedem, Stephen Tyree, Kilian Q. Weinberger, Fei Sha, Gert Lanckriet.
In Proceedings of Advances in Neural Information Processing Systems 25 (NeurIPS-25), pages 2582-2590, 2012.
[PDF][CODE][BIBTEX] Marginalized Stacked Denoising Autoencoders for Domain Adaptation.
Minmin Chen, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, Fei Sha.
Proceedings of 29th International Conference on Machine Learning (ICML), Edingburgh Scotland, Omnipress, pages 767-774, 2012.
[PDF][CODE][BIBTEX] The Greedy Miser: Learning under Test-time Budgets.
Zhixiang (Eddie) Xu, Kilian Q. Weinberger, Olivier Chapelle.
Proceedings of 29th International Conference on Machine Learning (ICML), Edinburgh Scotland, Omnipress, pages 1175--1182, 2012.
[PDF][CODE][BIBTEX] Classifier Cascade: Tradeoff between Accuracy and Feature Evaluation Cost.
Minmin Chen, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, Olivier Chapelle, Dor Kedem.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR W&C Proceedings 22: AISTATS 2012, pages 218-226, MIT Press.
[PDF][CODE] Stochastic Triplet Embedding.
Laurens J.P. van der Maaten and Kilian Q. Weinberger.
In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing,.
[PDF][CODE][BIBTEX] From sBoW to dCoT: Marginalized Encoders for Text Representation.
Zhixiang (Eddie) Xu, Minmin Chen, Kilian Q. Weinberger, Fei Sha.
Proc. of 21st ACM Conf. of Information and Knowledge Management (CIKM), Hawaii, 2012.
2011
[PDF][CODE][BIBTEX] Co-training for domain adaptation.
Minmin Chen, Kilian Q. Weinberger, and John C. Blitzer.
In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, editors, Advances in Neural Information Processing Systems 24 (NeurIPS-24), pages 2456–2464. 2011.
[PDF][CODE][BIBTEX][TALK] Automatic Feature Decomposition for Single View Co-training
Minmin Chen, Kilian Q. Weinberger, Yixin Chen
Proceedings of the 28th International Conference on Machine Learning (ICML-11), pages 953--960, ACM, Bellevue, USA, 2011.
[PDF][BIBTEX] Spam or Ham? Characterizing and Detecting Fraudulent “Not Spam” Reports in Web Mail Systems.
Anirudh Ramachandran, Anirban Dasgupta, Nick Feamster and Kilian Q. Weinberger
Eighth Conference on Email and Anti-Spam (CEAS-11)
[PDF][CODE][BIBTEX] Parallel Boosted Regression Trees for Web Search Ranking
Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin
Proceedings of the 20th international conference on World Wide Web (WWW-11), pages 387-396, ACM, New York, USA, 2011.
[PDF][CODE][BIBTEX] Web-Search Ranking with Initialized Gradient Boosted Regression Trees
Ananth Mohan, Zheng Chen, Kilian Q. Weinberger
Journal of Machine Learning Research, W&C Proceedings 14, Yahoo! Learning to Rank Challenge, pages 77-89, MIT Press, 2011.
[PDF][BIBTEX] Multi-Task Learning for Boosting with Application to Web Search Ranking
Olivier Chapelle, Pannagadatta Shivaswamy, Srinivas Vadrevu, Kilian Q. Weinberger, Ya Zhang, Belle Tseng
Machine Learning Journal, ISSN 0885-6125, pages 1-25, Springer Verlag, 2011.
[PDF][BIBTEX] Rapid Feature Learning with Stacked Linear Denoisers
Zhixiang Eddie Xu, Kilian Q. Weinberger, Fei Sha
arXiv:1105.0972, 2011 (Technical report, not peer-reviewed.) Presented at ICML 2011 Workshop on Unsupervised and Transfer Learning.
[PDF][CODE][BIBTEX] Distance Metric Learning for Kernel Machines
Zhixiang (Eddie) Xu, Kilian Q. Weinberger, Olivier Chapelle.
arXiv:1208.3422, (Technical report, not peer-reviewed.)
2010
[PDF][CODE][BIBTEX] Large Margin Multi-Task Metric Learning
Shibin Parameswaran and Kilian Q. Weinberger
In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R.S. Zemel, and A. Culotta (eds.), Advances in Neural Information Processing Systems 23 (NeurIPS-23), pages 1867-1875, 2010.
[PDF][TALK][BIBTEX] Multi-Task Learning for Boosting with Application to Web Search Ranking
O. Chapelle, S. Vadrevu, K. Q. Weinberger, P. Shivaswamy, Y. Zhang, B. Tseng
KDD 2010. Proceedings of the 16th international conference on Knowledge discovery and data mining (SIGKDD): 1189-1198 ACM.
[PDF][CODE][BIBTEX] Convex optimizations for distance metric learning and pattern classification.
K. Q. Weinberger, F. Sha, and L. K. Saul
IEEE Signal Processing Magazine 27(3): 146-158, 2010.
[PDF][BIBTEX] Learning to Rank with (a Lot of) Word Features.
B. Bai, J. Weston, D. Grangier, R. Collobert, K. Sadamasa, Y. Qi, O. Chapelle, K. Q. Weinberger
Journal of Information Retrieval. Special Issue on Learning to Rank for Information Retrieval 13(3): 291-314. Springer Verlag, 2010.
[PDF][BIBTEX] Decoding Ipsilateral Finger Movements from ECoG Signals in Humans.
Yuzong Liu, Mohit Sharma, Charles M. Gaona, Jonathan D. Breshears, Jarod Roland, Zachary V. Freudenburg, Kilian Q. Weinberger, and Eric C. Leuthardt
In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R.S. Zemel, and A. Culotta (eds.), Advances in Neural Information Processing Systems 23 (NeurIPS-23), pages 1468-1476, 2010.
[Attention! Results in Figure 3 could not be reproduced and should be considered invalid. We apologize for this mishap. All other results are not affected. ]
2009
[PDF][BIBTEX] Supervised Semantic Indexing.
B. Bai, J. Weston, D. Grangier, R. Collobert, O. Chapelle, K. Q. Weinberger
The 18th ACM Conference on Information and Knowledge Management (CIKM), 2009.
[PDF][BIBTEX] Collaborative Spam Filtering with the Hashing Trick.
J. Attenberg, K. Q. Weinberger, A. Smola, A. Dasgupta, M. Zinkevich
Virus Bulletin (VB) 2009.
[PDF][CODE][BIBTEX] Distance Metric Learning for Large Margin Nearest Neighbor Classification.
K. Q. Weinberger, L. K. Saul
Journal of Machine Learning Research (JMLR) 2009, 10:207-244
[PDF][BIBTEX] Collaborative Email-Spam Filtering with the Hashing-Trick.
J. Attenberg, K. Q. Weinberger, A. Smola, A. Dasgupta, M. Zinkevich
Sixth Conference on Email and Anti-Spam (CEAS) 2009
[PDF][BIBTEX] Unsupervised Image Ranking.
E. Hörster, M. Slaney, M. Ranzato, K. Q. Weinberger (2009).
ACM Workshop on Web-Scale Multimedia Corpus (WSMC 2009), Beijing, China, October 2009
[PDF][BIBTEX] Feature Hashing for Large Scale Multitask Learning.
K. Q. Weinberger, A. Dasgupta, J. Langford, A. Smola, J. Attenberg.
In Proceedings of the Twenty Sixth International Confernence on Machine Learning (ICML-09), Canada.
[PDF][BIBTEX] Reliable Tags Using Image Similarity: Mining Specificity and Expertise from Large-Scale Multimedia Databases.
L. Kennedy, M. Slaney, K. Q. Weinberger (2009).
ACM Workshop on Web-Scale Multimedia Corpus (WSMC 2009), Beijing, China, October 2009
2008
[PDF][BIBTEX] Large Margin Taxonomy Embedding with an Application to Document Categorization.
K. Q. Weinberger and O. Chapelle (2008).
Advances in Neural Information Processing Systems 21 (NeurIPS-21), 2009, 1737-1744.
[PDF][BIBTEX] Resolving Tag Ambiguity.
K. Q. Weinberger, M. Slaney, and R. v. Zwol (2008),
Proceeding of the 16th ACM international conference on Multimedia, ACM, 2008, 111-120
[PDF][BIBTEX] Mapping Uncharted Waters: Exploratory Analysis, Visualization, and Clustering of Oceanographic Data.
J. M. Lewis, P. M. Hull, K. Q. Weinberger, and L. K. Saul (2008).
In Proceedings of the Seventh International Conference on Machine Learning and Applications (ICMLA-08). San Diego, CA.
[PDF][BIBTEX] Learning a Metric for Music Similarity.
Malcolm Slaney, K. Q. Weinberger, William White
International Symposium on Music Information Retrieval (ISMIR), September 2008.
[PDF][TALK][BIBTEX] Fast solvers and efficient implementations for distance metric learning.
K. Q. Weinberger and L. K. Saul (2008).
In Proceedings of the 25th International Conference on Machine Learning (ICML-08), Helsinki, Finland, 2008.
2007
[PDF][BIBTEX] Metric Learning with Convex Optimization.
K. Q. Weinberger PhD Thesis dissertation,
University of Pennsylvania. PhD committee: Lawrence K. Saul (chair), Fernando C. N. Pereira, Daniel D. Lee, Gert Lanckriet, Ben Taskar
[PDF][BIBTEX][CODE] Metric learning for kernel regression.
K. Q. Weinberger, G. Tesauro (2007).
In Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics (AISTATS-07), Puerto Rico.
2006
[PDF][CODE][BIBTEX] Graph Laplacian Regularization for Large-Scale Semidefinite Programming.
K. Q. Weinberger, F. Sha, Q. Zhu and L. K. Saul (2007).
In B. Schoelkopf, J. Platt, and T. Hofmann (eds.). Advances in Neural Information Processing Systems 19 (NeurIPS-19). MIT Press: Cambridge, MA.
[PDF][CODE][BIBTEX] An Introduction to Nonlinear Dimensionality Reduction by Maximum Variance Unfolding.
K. Q. Weinberger and L. K. Saul (2006).
National Conference on Artificial Intelligence (AAAI), Nectar paper, Boston MA
[PDF][CODE][BIBTEX] Unsupervised Learning of Image Manifolds by Semidefinite Programming.
K. Q. Weinberger and L. K. Saul
International Journal of Computer Vision 2006. Please download from www.springerlink.com.
In Special Issue: Computer Vision and Pattern Recognition-CVPR 2004 Guest Editor(s): Aaron Bobick, Rama Chellappa, Larry Davis, Pages 77-90, Volume 70, Number 1, Springer Netherlands
[PDF][BIBTEX] Spectral methods for dimensionality reduction.
L. K. Saul, K. Q. Weinberger, J. H. Ham, F. Sha, and D. D. Lee (2006).
In O. Chapelle, B. Schoelkopf, and A. Zien (eds.), Semisupervised Learning, pages 293-308. MIT Press: Cambridge, MA.
2005
[PDF][CODE][BIBTEX] Nonlinear dimensionality reduction by semidefinite programming and kernel matrix factorization.
K. Q. Weinberger, B. D. Packer, and L. K. Saul (2005).
In Z. Ghahramani and R. Cowell (eds.), Proceedings of the Tenth International Conference on Artificial Intelligence and Statistics (AISTATS), pages 381-388. Barbados, West Indies.
[Winner of outstanding student paper award.]
[PDF][CODE][BIBTEX][SLIDES] Distance Metric Learning for Large Margin Nearest Neighbor Classification,
K. Q. Weinberger, J. Blitzer, and L. K. Saul (2006). In Y. Weiss, B. Schoelkopf, and J. Platt (eds.),
Advances in Neural Information Processing Systems 18 (NeurIPS-18). MIT Press: Cambridge, MA.
[PDF][BIBTEX] Hierarchical distributed representations for statistical language models.
J. Blitzer, K. Q. Weinberger, L. K. Saul, and F. C. N. Pereira (2005).
In L. K. Saul, Y. Weiss, and L. Bottou (eds.), Advances in Neural Information Processing Systems 17 (NeurIPS-17), pages 185-192. MIT Press: Cambridge, MA.
2004
[PDF][CODE][BIBTEX] Learning a kernel matrix for nonlinear dimensionality reduction,
K. Q. Weinberger, F. Sha, and L. K. Saul (2004),
In Proceedings of the Twenty First International Confernence on Machine Learning (ICML-04), Banff, Canada.
[Winner of outstanding student paper award.]
[PDF][CODE][BIBTEX] Unsupervised learning of image manifolds by semidefinite programming.
K. Q. Weinberger and L. K. Saul
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2004, vol. 2, pages 988-995. Washington D.C. Outstanding student paper award.
[Winner of outstanding student paper award.]