Travelled to:1 × Australia
1 × Canada
1 × China
1 × Finland
1 × Germany
1 × Italy
1 × United Kingdom
5 × USA
Collaborated with:F.Peng F.Southey S.Wang R.Greiner R.Patrascu L.Xu C.Szepesvári J.Neufeld Y.Yu L.Cheng M.White C.Guestrin F.Lu L.H.Ungar D.P.Foster A.György H.Cheng D.F.Wilkinson F.Jiao T.Wang D.J.Lizotte M.H.Bowling Y.Zhao X.Zhang R.Kiros S.Wang X.Huang N.Cercone S.E.Robertson
Talks about:model (6) learn (6) supervis (4) regular (3) unsupervis (2) character (2) bayesian (2) maximum (2) languag (2) factor (2)
Person: Dale Schuurmans
DBLP: Schuurmans:Dale
Contributed to:
201420132012200920062005
200320032002
200220001997
1992Wrote 16 papers:
- ICML-c2-2014-NeufeldGSS #adaptation #monte carlo
- Adaptive Monte Carlo via Bandit Allocation (JN, AG, CS, DS), pp. 1944–1952.
- ICML-c1-2013-YuCSS #theorem
- Characterizing the Representer Theorem (YY, HC, DS, CS), pp. 570–578.
- ICML-2012-NeufeldYZKS #reduction
- Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations (JN, YY, XZ, RK, DS), p. 191.
- ICML-2009-XuWS #learning #predict
- Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning (LX, MW, DS), pp. 1137–1144.
- ICML-2006-XuWSS #learning #predict
- Discriminative unsupervised learning of structured predictors (LX, DFW, FS, DS), pp. 1057–1064.
- ICML-2005-ChengJSW #image #modelling
- Variational Bayesian image modelling (LC, FJ, DS, SW), pp. 129–136.
- ICML-2005-WangLBS #online #optimisation
- Bayesian sparse sampling for on-line reward optimization (TW, DJL, MHB, DS), pp. 956–963.
- ICML-2005-WangWGSC #markov #modelling #random #semantics
- Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields (SW, SW, RG, DS, LC), pp. 948–955.
- ECIR-2003-PengS #classification #modelling #n-gram #naive bayes
- Combining Naive Bayes and n-Gram Language Models for Text Classification (FP, DS), pp. 335–350.
- ICML-2003-WangSPZ #learning #modelling #principle
- Learning Mixture Models with the Latent Maximum Entropy Principle (SW, DS, FP, YZ), pp. 784–791.
- ICML-2002-GuestrinPS #learning #modelling
- Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs (CG, RP, DS), pp. 235–242.
- ICML-2002-LuPS
- Investigating the Maximum Likelihood Alternative to TD(λ) (FL, RP, DS), pp. 403–410.
- SIGIR-2002-PengHSCR #information retrieval #segmentation #self #using #word
- Using self-supervised word segmentation in Chinese information retrieval (FP, XH, DS, NC, SER), pp. 349–350.
- ICML-2000-SchuurmansS #adaptation #learning
- An Adaptive Regularization Criterion for Supervised Learning (DS, FS), pp. 847–854.
- ICML-1997-SchuurmansUF #performance
- Characterizing the generalization performance of model selection strategies (DS, LHU, DPF), pp. 340–348.
- KR-1992-GreinerS #approximate #learning
- Learning Useful Horn Approximations (RG, DS), pp. 383–392.