Travelled to:1 × Finland
1 × France
1 × United Kingdom
2 × Canada
2 × China
6 × USA
Collaborated with:M.Chen Z.E.Xu M.J.Kusner Y.Chen F.Sha W.Chen L.K.Saul S.Tyree O.Chapelle A.X.Zheng G.Huang J.R.Gardner R.Garnett Y.Sun N.I.Kolkin Y.Bengio K.Agrawal L.v.d.Maaten J.T.Wilson J.Cunningham A.Dasgupta J.Langford A.J.Smola J.Attenberg Carlos Diaz-Ruiz Yan Wang 0051 W.Chao Mark E. Campbell P.K.Shivaswamy S.Vadrevu Y.Zhang B.L.Tseng B.Bai J.Weston D.Grangier R.Collobert K.Sadamasa Y.Qi
Talks about:learn (7) margin (4) featur (4) represent (3) nonlinear (3) fast (3) compress (2) bayesian (2) distanc (2) search (2)
Person: Kilian Q. Weinberger
DBLP: Weinberger:Kilian_Q=
Contributed to:
20152014
201420132013
20122012201120102009200920082004
2019Wrote 23 papers:
- ICML-2015-ChenWTWC #network
- Compressing Neural Networks with the Hashing Trick (WC, JTW, ST, KQW, YC), pp. 2285–2294.
- ICML-2015-KusnerGGW #optimisation
- Differentially Private Bayesian Optimization (MJK, JRG, RG, KQW), pp. 918–927.
- ICML-2015-KusnerSKW #documentation #word
- From Word Embeddings To Document Distances (MJK, YS, NIK, KQW), pp. 957–966.
- ICML-c2-2014-ChenWSB
- Marginalized Denoising Auto-encoders for Nonlinear Representations (MC, KQW, FS, YB), pp. 1476–1484.
- ICML-c2-2014-GardnerKZWC #constraints #difference #optimisation
- Bayesian Optimization with Inequality Constraints (JRG, MJK, ZEX, KQW, JC), pp. 937–945.
- ICML-c2-2014-KusnerTWA #probability
- Stochastic Neighbor Compression (MJK, ST, KQW, KA), pp. 622–630.
- KDD-2014-ChenCW #classification #performance #scalability
- Fast flux discriminant for large-scale sparse nonlinear classification (WC, YC, KQW), pp. 621–630.
- KDD-2014-XuHWZ #feature model
- Gradient boosted feature selection (ZEX, GH, KQW, AXZ), pp. 522–531.
- ICML-c1-2013-ChenWC
- Maximum Variance Correction with Application to A* Search (WC, KQW, YC), pp. 302–310.
- ICML-c1-2013-MaatenCTW #learning
- Learning with Marginalized Corrupted Features (LvdM, MC, ST, KQW), pp. 410–418.
- ICML-c1-2013-XuKWC #classification
- Cost-Sensitive Tree of Classifiers (ZEX, MJK, KQW, MC), pp. 133–141.
- ICML-c3-2013-ChenZW #image #performance
- Fast Image Tagging (MC, AXZ, KQW), pp. 1274–1282.
- ICML-c3-2013-XuKHW #learning #representation
- Anytime Representation Learning (ZEX, MJK, GH, KQW), pp. 1076–1084.
- CIKM-2012-XuCWS #representation
- From sBoW to dCoT marginalized encoders for text representation (ZEX, MC, KQW, FS), pp. 1879–1884.
- ICML-2012-ChenXWS #adaptation
- Marginalized Denoising Autoencoders for Domain Adaptation (MC, ZEX, KQW, FS), p. 212.
- ICML-2012-XuWC #learning
- The Greedy Miser: Learning under Test-time Budgets (ZEX, KQW, OC), p. 169.
- ICML-2011-ChenWC #automation #composition
- Automatic Feature Decomposition for Single View Co-training (MC, KQW, YC), pp. 953–960.
- KDD-2010-ChapelleSVWZT #learning #multi #ranking #web
- Multi-task learning for boosting with application to web search ranking (OC, PKS, SV, KQW, YZ, BLT), pp. 1189–1198.
- CIKM-2009-BaiWGCSQCW #semantics
- Supervised semantic indexing (BB, JW, DG, RC, KS, YQ, OC, KQW), pp. 187–196.
- ICML-2009-WeinbergerDLSA #learning #multi #scalability
- Feature hashing for large scale multitask learning (KQW, AD, JL, AJS, JA), pp. 1113–1120.
- ICML-2008-WeinbergerS #distance #implementation #learning #metric #performance
- Fast solvers and efficient implementations for distance metric learning (KQW, LKS), pp. 1160–1167.
- ICML-2004-WeinbergerSS #kernel #learning #matrix #reduction
- Learning a kernel matrix for nonlinear dimensionality reduction (KQW, FS, LKS).
- CASE-2019-Diaz-RuizWCWC #3d #self
- Vision-only 3D Tracking for Self-Driving Cars (CDR, YW0, WLC, KQW, MEC), pp. 1029–1034.