Travelled to:
1 × Chile
1 × Ireland
1 × Israel
1 × Switzerland
1 × United Kingdom
4 × USA
Collaborated with:
S.Guo T.Nguyen C.Downey S.A.McIlraith L.Xie K.W.Lim R.Mehrotra W.L.Buntine J.R.Anderson C.Lebiere M.C.Lovett M.G.Far M.R.Bouadjenek G.Ferraro D.Hawking S.Sedhain D.Braziunas J.Christensen T.Graepel S.Kharazmi S.Karimi
Talks about:
relev (3) model (3) bayesian (2) latent (2) expect (2) divers (2) order (2) optim (2) call (2) relationship (1)
Person: Scott Sanner
DBLP: Sanner:Scott
Contributed to:
Wrote 10 papers:
- SIGIR-2015-FarSBFH #on the
- On Term Selection Techniques for Patent Prior Art Search (MGF, SS, MRB, GF, DH), pp. 803–806.
- RecSys-2014-SedhainSBXC #collaboration #recommendation #social
- Social collaborative filtering for cold-start recommendations (SS, SS, DB, LX, JC), pp. 345–348.
- ICML-c3-2013-NguyenS #algorithm #classification #optimisation
- Algorithms for Direct 0-1 Loss Optimization in Binary Classification (TN, SS), pp. 1085–1093.
- SIGIR-2013-MehrotraSBX #automation #microblog #modelling #topic #twitter
- Improving LDA topic models for microblogs via tweet pooling and automatic labeling (RM, SS, WLB, LX), pp. 889–892.
- SIGIR-2012-LimSG #on the #trade-off
- On the mathematical relationship between expected n-call@k and the relevance vs. diversity trade-off (KWL, SS, SG), pp. 1117–1118.
- CIKM-2011-SannerGGKK #optimisation #retrieval #topic
- Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model (SS, SG, TG, SK, SK), pp. 1977–1980.
- ICML-2010-DowneyS #adaptation #difference
- Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting λ (CD, SS), pp. 311–318.
- SIGIR-2010-GuoS #probability
- Probabilistic latent maximal marginal relevance (SG, SS), pp. 833–834.
- KR-2006-SannerM #calculus #first-order #hybrid #logic #reasoning
- An Ordered Theory Resolution Calculus for Hybrid Reasoning in First-Order Extensions of Description Logic (SS, SAM), pp. 100–111.
- ICML-2000-SannerALL #learning #performance
- Achieving Efficient and Cognitively Plausible Learning in Backgammon (SS, JRA, CL, MCL), pp. 823–830.