Person: Philip M. Long

DBLP: Long:Philip_M=

Contributed to:

2014

2013

2011

2010

2009

2008

2005

1999

1997

1995

1994

1991

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.