Travelled to:1 × France
2 × USA
Collaborated with:L.Getoor B.London S.H.Bach J.L.Boyd-Graber B.Taskar N.Ramakrishnan P.Butler S.Muthiah N.Self R.P.Khandpur P.Saraf W.Wang J.Cadena A.Vullikanti G.Korkmaz C.J.Kuhlman A.Marathe L.Zhao T.Hua F.Chen C.Lu A.Srinivasan K.Trinh G.Katz A.Doyle C.Ackermann I.Zavorin J.Ford K.M.Summers Y.Fayed J.Arredondo D.Gupta D.Mares
Talks about:learn (2) structur (1) forecast (1) approxim (1) variabl (1) predict (1) general (1) collect (1) benefit (1) unrest (1)
Person: Bert Huang
DBLP: Huang:Bert
Contributed to:
2015
20142013Wrote 4 papers:
- ICML-2015-BachHBG #learning #performance
- Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs (SHB, BH, JLBG, LG), pp. 381–390.
- ICML-2015-LondonHG #approximate #learning
- The Benefits of Learning with Strongly Convex Approximate Inference (BL, BH, LG), pp. 410–418.
- KDD-2014-RamakrishnanBMSKSWCVKKMZHCLHSTGKDAZFSFAGM #open source #using
- “Beating the news” with EMBERS: forecasting civil unrest using open source indicators (NR, PB, SM, NS, RPK, PS, WW, JC, AV, GK, CJK, AM, LZ, TH, FC, CTL, BH, AS, KT, LG, GK, AD, CA, IZ, JF, KMS, YF, JA, DG, DM), pp. 1799–1808.
- ICML-c3-2013-LondonHTG #predict
- Collective Stability in Structured Prediction: Generalization from One Example (BL, BH, BT, LG), pp. 828–836.