`Travelled to:`

1 × Canada

1 × Finland

1 × Germany

1 × Greece

1 × Israel

1 × Slovenia

6 × USA

`Collaborated with:`

R.A.Servedio P.Auer D.P.Helmbold N.H.Bshouty N.Abe P.Awasthi M.Balcan A.R.Klivans A.Srinivasan P.Krishnan J.S.Vitter N.Littlestone M.K.Warmuth V.Varadan S.Gilman M.Treshock

`Talks about:`

learn (7) linear (4) nois (3) approxim (2) consist (2) classif (2) random (2) simul (2) use (2) probabilist (1)

## Person: Philip M. Long

### DBLP: Long:Philip_M=

### Contributed to:

### Wrote 13 papers:

- STOC-2014-AwasthiBL #learning #linear #locality #power of
- The power of localization for efficiently learning linear separators with noise (PA, MFB, PML), pp. 449–458.
- ICML-c3-2013-LongS #classification #consistency #multi
- Consistency versus Realizable H-Consistency for Multiclass Classification (PML, RAS), pp. 801–809.
- ICML-2011-HelmboldL #on the
- On the Necessity of Irrelevant Variables (DPH, PML), pp. 281–288.
- ICML-2010-BshoutyL #clustering #linear #using
- Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering (NHB, PML), pp. 135–142.
- ICML-2010-LongS #approximate #simulation #strict
- Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate (PML, RAS), pp. 703–710.
- ICALP-v1-2009-KlivansLS #learning
- Learning Halfspaces with Malicious Noise (ARK, PML, RAS), pp. 609–621.
- ICML-2008-LongS #classification #random
- Random classification noise defeats all convex potential boosters (PML, RAS), pp. 608–615.
- ICML-2005-LongVGTS #integration
- Unsupervised evidence integration (PML, VV, SG, MT, RAS), pp. 521–528.
- ICML-1999-AbeL #concept #learning #linear #probability #using
- Associative Reinforcement Learning using Linear Probabilistic Concepts (NA, PML), pp. 3–11.
- STOC-1997-AuerLS #approximate #learning #pseudo #set
- Approximating Hyper-Rectangles: Learning and Pseudo-Random Sets (PA, PML, AS), pp. 314–323.
- ICML-1995-KrishnanLV #learning
- Learning to Make Rent-to-Buy Decisions with Systems Applications (PK, PML, JSV), pp. 233–330.
- STOC-1994-AuerL #learning #simulation
- Simulating access to hidden information while learning (PA, PML), pp. 263–272.
- STOC-1991-LittlestoneLW #learning #linear #online
- On-Line Learning of Linear Functions (NL, PML, MKW), pp. 465–475.