Travelled to:1 × China
1 × Germany
1 × United Kingdom
4 × USA
Collaborated with:M.F.P.O'Boyle T.V.Nguyen A.Robles-Kelly J.Cavazos F.V.Agakov M.Zuluaga N.P.Topham H.Leather G.Fursin J.Thomson C.K.I.Williams O.Temam B.Franke M.Toussaint
Talks about:optim (3) learn (3) predict (2) machin (2) compil (2) use (2) probabilist (1) processor (1) distribut (1) discrimin (1)
Person: Edwin V. Bonilla
DBLP: Bonilla:Edwin_V=
Contributed to:
2014
20122012
2009200720062006Wrote 7 papers:
- ICML-c1-2014-NguyenB #performance #process
- Fast Allocation of Gaussian Process Experts (TVN, EVB), pp. 145–153.
- DATE-2012-ZuluagaBT #case study #design #predict #trade-off
- Predicting best design trade-offs: A case study in processor customization (MZ, EVB, NPT), pp. 1030–1035.
- ICML-2012-BonillaR #learning #probability #prototype
- Discriminative Probabilistic Prototype Learning (EVB, ARK), p. 155.
- CGO-2009-LeatherBO #automation #compilation #generative #machine learning #optimisation
- Automatic Feature Generation for Machine Learning Based Optimizing Compilation (HL, EVB, MFPO), pp. 81–91.
- CGO-2007-CavazosFABOT #compilation #optimisation #performance #using
- Rapidly Selecting Good Compiler Optimizations using Performance Counters (JC, GF, FVA, EVB, MFPO, OT), pp. 185–197.
- CGO-2006-AgakovBCFFOTTW #machine learning #optimisation #using
- Using Machine Learning to Focus Iterative Optimization (FVA, EVB, JC, BF, GF, MFPO, JT, MT, CKIW), pp. 295–305.
- ICML-2006-BonillaWACTO #predict
- Predictive search distributions (EVB, CKIW, FVA, JC, JT, MFPO), pp. 121–128.