Travelled to:1 × China
1 × France
1 × United Kingdom
2 × Canada
6 × USA
Collaborated with:M.Volkovs B.M.Marlin K.Swersky L.Charlin Y.Li H.Larochelle R.Kiros R.Salakhutdinov C.Boutilier D.A.Ross S.Osindero J.Snoek R.P.Adams D.Tarlow I.Sutskever Y.Wu T.Pitassi C.Dwork K.Xu J.Ba K.Cho A.C.Courville Y.Bengio
Talks about:learn (6) rank (4) supervis (3) collabor (3) problem (2) multipl (2) generat (2) prefer (2) neural (2) filter (2)
Person: Richard S. Zemel
DBLP: Zemel:Richard_S=
Contributed to:
20152014
2014
20132013201220122009
200920062004Wrote 15 papers:
- ICML-2015-LiSZ #generative #network
- Generative Moment Matching Networks (YL, KS, RSZ), pp. 1718–1727.
- ICML-2015-XuBKCCSZB #generative #image #visual notation
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention (KX, JB, RK, KC, ACC, RS, RSZ, YB), pp. 2048–2057.
- ICML-c2-2014-KirosSZ #modelling #multimodal
- Multimodal Neural Language Models (RK, RS, RSZ), pp. 595–603.
- ICML-c2-2014-LiZ #higher-order #learning #problem
- High Order Regularization for Semi-Supervised Learning of Structured Output Problems (YL, RSZ), pp. 1368–1376.
- ICML-c2-2014-SnoekSZA #optimisation
- Input Warping for Bayesian Optimization of Non-Stationary Functions (JS, KS, RSZ, RPA), pp. 1674–1682.
- KDD-2014-CharlinZL #collaboration #library
- Leveraging user libraries to bootstrap collaborative filtering (LC, RSZ, HL), pp. 173–182.
- CIKM-2013-VolkovsZ #framework
- CRF framework for supervised preference aggregation (MV, RSZ), pp. 89–98.
- ICML-c3-2013-TarlowSCSZ #learning #probability
- Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning (DT, KS, LC, IS, RSZ), pp. 199–207.
- ICML-c3-2013-ZemelWSPD #learning
- Learning Fair Representations (RSZ, YW, KS, TP, CD), pp. 325–333.
- CIKM-2012-VolkovsLZ #learning #rank
- Learning to rank by aggregating expert preferences (MV, HL, RSZ), pp. 843–851.
- ICML-2012-CharlinZB #learning #problem
- Active Learning for Matching Problems (LC, RSZ, CB), p. 23.
- ICML-2009-VolkovsZ #learning #named #ranking
- BoltzRank: learning to maximize expected ranking gain (MV, RSZ), pp. 1089–1096.
- RecSys-2009-MarlinZ #collaboration #predict #ranking
- Collaborative prediction and ranking with non-random missing data (BMM, RSZ), pp. 5–12.
- ICML-2006-RossOZ
- Combining discriminative features to infer complex trajectories (DAR, SO, RSZ), pp. 761–768.
- ICML-2004-MarlinZ #collaboration #multi
- The multiple multiplicative factor model for collaborative filtering (BMM, RSZ).