Kilian Q. Weinberger

Associate Professor of Computer Science


Pasted Graphic Impact Statistics

Selected publications:

[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 (NIPS-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.


All publications by year:


Preprints:

[PDF] Deep Manifold Traversal: Changing Labels with Convolutional Features
Jacob R. Gardner, Paul Upchurch, Matt J. Kusner, Yixuan Li, Kilian Q. Weinberger, Kavita Bala, John E. Hopcroft

[PDF][CODE] Multi-Scale Dense Convolutional Networks for Efficient Prediction
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger

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] Densely Connected Convolutional Networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
Computer Vision and Pattern Recognition- (CVPR) 2017, In press …

[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] Supervised Word Mover's Distance
Gao Huang, Chuan Guo, Matt Kusner, Yu Sun, Fei Sha and Kilian Q. Weinberger.
Neural Information Processing Systems (NIPS), 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 (NIPS), 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 (NIPS), 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][BIBTEX][SLIDES][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 (NIPS-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 (NIPS-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 (NIPS-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 (NIPS-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 (NIPS-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 (NIPS-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 (NIPS-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 (NIPS-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.]