BibSLEIGH — Thorsten

Travelled to:

1 × Brazil
1 × Finland
1 × France
1 × Germany
1 × Ireland
1 × Slovenia
1 × Switzerland
1 × The Netherlands
16 × USA
2 × China
3 × Canada
3 × United Kingdom

Collaborated with:

∅ F.Radlinski Y.Yue K.Raman P.Shivaswamy T.Finley A.Swaminathan C.J.Yu B.Shaparenko S.Chen L.A.Granka G.Gay J.E.Hopcroft R.Klinkenberg T.Scheffer N.Ailon Z.S.Karnin J.Xu M.Kurup R.Kleinberg N.Cristianini J.Shawe-Taylor K.Morik P.Brockhausen S.Pohl T.Schnabel J.Gehrke R.Sipos J.L.Moore D.Turnbull I.Tsochantaridis T.Hofmann Y.Altun Y.Gao O.Chapelle Y.Zhang B.Pan H.Hembrooke

Talks about:

learn (14) structur (7) support (7) vector (7) bandit (6) feedback (5) machin (5) svms (5) data (5) use (5)

Person: Thorsten Joachims

DBLP: Joachims:Thorsten

Facilitated 2 volumes:

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1997

2007

Wrote 44 papers:

ICML-2015-SwaminathanJ #feedback #learning: Counterfactual Risk Minimization: Learning from Logged Bandit Feedback (AS, TJ), pp. 814–823.
ICML-c2-2014-AilonKJ: Reducing Dueling Bandits to Cardinal Bandits (NA, ZSK, TJ), pp. 856–864.
KDD-2014-RamanJ: Methods for ordinal peer grading (KR, TJ), pp. 1037–1046.
ICML-c3-2013-RamanJSS #learning: Stable Coactive Learning via Perturbation (KR, TJ, PS, TS), pp. 837–845.
KDD-2013-ChenXJ #modelling #multi #probability #sequence: Multi-space probabilistic sequence modeling (SC, JX, TJ), pp. 865–873.
KDD-2013-RamanSGJ #big data #pipes and filters: Beyond myopic inference in big data pipelines (KR, AS, JG, TJ), pp. 86–94.
CIKM-2012-SiposSSJ #corpus #summary #using #word: Temporal corpus summarization using submodular word coverage (RS, AS, PS, TJ), pp. 754–763.
ICML-2012-ShivaswamyJ #learning #online #predict: Online Structured Prediction via Coactive Learning (PS, TJ), p. 12.
KDD-2012-ChenMTJ #metric #predict: Playlist prediction via metric embedding (SC, JLM, DT, TJ), pp. 714–722.
KDD-2012-RamanSJ #feedback #learning #online: Online learning to diversify from implicit feedback (KR, PS, TJ), pp. 705–713.
CIKM-2011-RamanJS #learning #ranking: Structured learning of two-level dynamic rankings (KR, TJ, PS), pp. 291–296.
ECIR-2011-Joachims #feedback: The Value of User Feedback (TJ), p. 6.
ICML-2011-YueJ: Beat the Mean Bandit (YY, TJ), pp. 241–248.
SIGIR-2010-YueGCZJ #evaluation #learning #retrieval #statistics: Learning more powerful test statistics for click-based retrieval evaluation (YY, YG, OC, YZ, TJ), pp. 507–514.
ICML-2009-YuJ #learning: Learning structural SVMs with latent variables (CNJY, TJ), pp. 1169–1176.
ICML-2009-YueJ #information retrieval #optimisation #problem: Interactively optimizing information retrieval systems as a dueling bandits problem (YY, TJ), pp. 1201–1208.
SIGIR-2009-ShaparenkoJ #documentation #identification #modelling: Identifying the original contribution of a document via language modeling (BS, TJ), pp. 696–697.
CIKM-2008-RadlinskiKJ #how #quality #question #retrieval: How does clickthrough data reflect retrieval quality? (FR, MK, TJ), pp. 43–52.
ICML-2008-FinleyJ: Training structural SVMs when exact inference is intractable (TF, TJ), pp. 304–311.
ICML-2008-RadlinskiKJ #learning #multi #ranking: Learning diverse rankings with multi-armed bandits (FR, RK, TJ), pp. 784–791.
ICML-2008-YueJ #predict #set #using: Predicting diverse subsets using structural SVMs (YY, TJ), pp. 1224–1231.
KDD-2008-YuJ #kernel #using: Training structural svms with kernels using sampled cuts (CNJY, TJ), pp. 794–802.
KDD-2007-RadlinskiJ #learning #ranking: Active exploration for learning rankings from clickthrough data (FR, TJ), pp. 570–579.
KDD-2007-ShaparenkoJ #database #documentation: Information genealogy: uncovering the flow of ideas in non-hyperlinked document databases (BS, TJ), pp. 619–628.
SIGIR-2007-YueFRJ #optimisation #precise: A support vector method for optimizing average precision (YY, TF, FR, TJ), pp. 271–278.
KDD-2006-Joachims #linear: Training linear SVMs in linear time (TJ), pp. 217–226.
ICML-2005-FinleyJ #clustering: Supervised clustering with support vector machines (TF, TJ), pp. 217–224.
ICML-2005-Joachims #metric #multi #performance: A support vector method for multivariate performance measures (TJ), pp. 377–384.
ICML-2005-JoachimsH #bound #clustering #correlation #fault: Error bounds for correlation clustering (TJ, JEH), pp. 385–392.
KDD-2005-RadlinskiJ #feedback #learning #query #rank: Query chains: learning to rank from implicit feedback (FR, TJ), pp. 239–248.
SIGIR-2005-JoachimsGPHG #feedback: Accurately interpreting clickthrough data as implicit feedback (TJ, LAG, BP, HH, GG), pp. 154–161.
ICML-2004-TsochantaridisHJA #machine learning: Support vector machine learning for interdependent and structured output spaces (IT, TH, TJ, YA).
SIGIR-2004-GrankaJG #analysis #behaviour: Eye-tracking analysis of user behavior in WWW search (LAG, TJ, GG), pp. 478–479.
ICML-2003-Joachims #clustering #graph #learning: Transductive Learning via Spectral Graph Partitioning (TJ), pp. 290–297.
KDD-2002-Joachims #optimisation #using: Optimizing search engines using clickthrough data (TJ), pp. 133–142.
ICML-2001-JoachimsCS #categorisation #hypermedia #kernel: Composite Kernels for Hypertext Categorisation (TJ, NC, JST), pp. 250–257.
SIGIR-2001-Joachims #classification #learning #statistics: A Statistical Learning Model of Text Classification for Support Vector Machines (TJ), pp. 128–136.
ICML-2000-Joachims #performance: Estimating the Generalization Performance of an SVM Efficiently (TJ), pp. 431–438.
ICML-2000-KlinkenbergJ #concept #detection: Detecting Concept Drift with Support Vector Machines (RK, TJ), pp. 487–494.
ICML-1999-Joachims #classification #using: Transductive Inference for Text Classification using Support Vector Machines (TJ), pp. 200–209.
ICML-1999-MorikBJ #approach #case study #knowledge-based #learning #monitoring #statistics: Combining Statistical Learning with a Knowledge-Based Approach — A Case Study in Intensive Care Monitoring (KM, PB, TJ), pp. 268–277.
ICML-1999-SchefferJ #analysis #fault: Expected Error Analysis for Model Selection (TS, TJ), pp. 361–370.
ICML-1997-Joachims #algorithm #analysis #categorisation #probability: A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization (TJ), pp. 143–151.
JCDL-2007-PohlRJ #library #recommendation: Recommending related papers based on digital library access records (SP, FR, TJ), pp. 417–418.