Travelled to:1 × France
1 × USA
2 × China
Collaborated with:M.A.Gelbart S.W.Linderman O.Rippel A.Miller J.Snoek K.Swersky D.Maclaurin D.K.Duvenaud L.Bornn K.Goldsberry R.H.Affandi E.B.Fox B.Taskar R.S.Zemel Prabhat J.M.Hernández-Lobato M.W.Hoffman Z.Ghahramani A.Waterland E.Angelino J.Appavoo M.I.Seltzer J.Regier J.McAuliffe M.D.Hoffman D.Lang D.Schlegel R.Kiros N.Satish N.Sundaram M.M.A.Patwary
Talks about:optim (4) bayesian (3) process (3) point (3) learn (3) scalabl (2) network (2) hyperparamet (1) determinant (1) stationari (1)
Person: Ryan P. Adams
DBLP: Adams:Ryan_P=
Contributed to:
2015
201420142014Wrote 10 papers:
- ICML-2015-Hernandez-Lobato15a #constraints #optimisation #predict
- Predictive Entropy Search for Bayesian Optimization with Unknown Constraints (JMHL, MAG, MWH, RPA, ZG), pp. 1699–1707.
- ICML-2015-MaclaurinDA #learning #optimisation
- Gradient-based Hyperparameter Optimization through Reversible Learning (DM, DKD, RPA), pp. 2113–2122.
- ICML-2015-RegierMMAHLSP #generative #image #named
- Celeste: Variational inference for a generative model of astronomical images (JR, AM, JM, RPA, MDH, DL, DS, P), pp. 2095–2103.
- ICML-2015-SnoekRSKSSPPA #network #optimisation #scalability #using
- Scalable Bayesian Optimization Using Deep Neural Networks (JS, OR, KS, RK, NS, NS, MMAP, P, RPA), pp. 2171–2180.
- ASPLOS-2014-WaterlandAAAS #automation #named #scalability
- ASC: automatically scalable computation (AW, EA, RPA, JA, MIS), pp. 575–590.
- ICML-c1-2014-MillerBAG #analysis #process
- Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball (AM, LB, RPA, KG), pp. 235–243.
- ICML-c2-2014-AffandiFAT #kernel #learning #parametricity #process
- Learning the Parameters of Determinantal Point Process Kernels (RHA, EBF, RPA, BT), pp. 1224–1232.
- ICML-c2-2014-LindermanA #network #process
- Discovering Latent Network Structure in Point Process Data (SWL, RPA), pp. 1413–1421.
- ICML-c2-2014-RippelGA #learning #order
- Learning Ordered Representations with Nested Dropout (OR, MAG, RPA), pp. 1746–1754.
- ICML-c2-2014-SnoekSZA #optimisation
- Input Warping for Bayesian Optimization of Non-Stationary Functions (JS, KS, RSZ, RPA), pp. 1674–1682.