Philip M. Long

Publication record assembled from the DBLP archive of ranked conferences.

Papers indexed

Venues

Active years

1990–2021

Best venue rank

Where they publish

Papers

58 indexed papers, newest first.

Year	Venue	Title	Authors
2021	COLT	When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations?	Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
2020	ALT	On the Complexity of Proper Distribution-Free Learning of Linear Classifiers.	Philip M. Long, Raphael J. Long
2020	ICLR	Generalization bounds for deep convolutional neural networks.	Philip M. Long, Hanie Sedghi
2020	ICLR	On the Global Convergence of Training Deep Linear ResNets.	Difan Zou, Philip M. Long, Quanquan Gu
2019	ICLR	The Singular Values of Convolutional Layers.	Hanie Sedghi, Vineet Gupta, Philip M. Long
2018	FOCS	Learning Sums of Independent Random Variables with Sparse Collective Support.	Anindya De, Philip M. Long, Rocco A. Servedio
2018	ICML	Gradient descent with identity initialization efficiently learns positive definite linear transformations.	Peter L. Bartlett, David P. Helmbold, Philip M. Long
2017	ALT	New bounds on the price of bandit feedback for mistake-bounded online multiclass learning.	Philip M. Long
2017	COLT	Surprising properties of dropout in deep networks.	David P. Helmbold, Philip M. Long
2014	WACV	Benchmarking large-scale Fine-Grained Categorization.	Anelia Angelova, Philip M. Long
2014	STOC	The power of localization for efficiently learning linear separators with noise.	Pranjal Awasthi, Maria-Florina Balcan, Philip M. Long
2013	COLT	Active and passive learning of linear separators under log-concave distributions.	Maria-Florina Balcan, Philip M. Long
2013	ICML	Consistency versus Realizable H-Consistency for Multiclass Classification.	Philip M. Long, Rocco A. Servedio
2011	ICML	On the Necessity of Irrelevant Variables.	David P. Helmbold, Philip M. Long
2010	ICML	Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering.	Nader H. Bshouty, Philip M. Long
2010	ICML	Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate.	Philip M. Long, Rocco A. Servedio
2009	COLT	Linear Classifiers are Nearly Optimal When Hidden Variables Have Diverse Effect.	Nader H. Bshouty, Philip M. Long
2009	ICALP	Learning Halfspaces with Malicious Noise.	Adam R. Klivans, Philip M. Long, Rocco A. Servedio
2008	ICML	Random classification noise defeats all convex potential boosters.	Philip M. Long, Rocco A. Servedio
2006	AAAI	Predicting Electricity Distribution Feeder Failures Using Machine Learning Susceptibility Analysis.	Philip Gross, Albert Boulanger, Marta Arias, David L. Waltz, Philip M. Long, Charles Lawson, Roger Anderson, Matthew Koenig, Mark Mastrocinque, William Fairechio, John A. Johnson, Serena Lee, Frank Doherty, Arthur Kressner
2006	ALT	Editors' Introduction.	Jos L. Balczar, Philip M. Long, Frank Stephan
2006	COLT	Online Multitask Learning.	Ofer Dekel, Philip M. Long, Yoram Singer
2006	COLT	Discriminative Learning Can Succeed Where Generative Learning Fails.	Philip M. Long, Rocco A. Servedio
2005	COLT	Martingale Boosting.	Philip M. Long, Rocco A. Servedio
2005	ICML	Unsupervised evidence integration.	Philip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio
2003	COLT	Boosting with Diverse Base Classifiers.	Sanjoy Dasgupta, Philip M. Long
2002	AAAI	Minimum Majority Classification and Boosting.	Philip M. Long
2001	COLT	Agnostic Boosting.	Shai Ben-David, Philip M. Long, Yishay Mansour
2001	COLT	A Theoretical Analysis of Query Selection for Collaborative Filtering.	Wee Sun Lee, Philip M. Long
2001	COLT	On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses.	Philip M. Long
2001	WADS	Using the Pseudo-Dimension to Analyze Approximation Algorithms for Integer Programming.	Philip M. Long
2000	COLT	On the Difficulty of Approximately Maximizing Agreements.	Shai Ben-David, Nadav Eiron, Philip M. Long
2000	SODA	Improved bounds on the sample complexity of learning.	Yi Li, Philip M. Long, Aravind Srinivasan
1999	ICML	Associative Reinforcement Learning using Linear Probabilistic Concepts.	Naoki Abe, Philip M. Long
1998	COLT	The complexity of learning according to two models of a drifting environment.	Philip M. Long
1998	COLT	On the Sample Complexity of Learning Functions with Bounded Variation.	Philip M. Long
1997	COLT	On-line Evaluation and Prediction using Linear Functions.	Philip M. Long
1997	DCC	Text Compression Via Alphabet Re-Representation.	Philip M. Long, Apostol Natsev, Jeffrey Scott Vitter
1997	STOC	Approximating Hyper-Rectangles: Learning and Pseudo-Random Sets.	Peter Auer, Philip M. Long, Aravind Srinivasan
1996	ALT	Improved Bounds about On-line Learning of Smooth Functions of a Single Variable.	Philip M. Long
1996	COLT	On the Complexity of Learning from Drifting Distributions.	Rakesh D. Barve, Philip M. Long
1996	COLT	PAC Learning Axis-Aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples.	Philip M. Long, Lei Tan
1996	DCC	Efficient Cost Measures for Motion Compensation at Low Bit Rates (Extended Abstract).	Dzung T. Hoang, Philip M. Long, Jeffrey Scott Vitter
1995	COLT	More Theorems about Scale-sensitive Dimensions and Learning.	Peter L. Bartlett, Philip M. Long
1995	DCC	Multiple-Dictionary Coding Using Partial Matching.	Dzung T. Hoang, Philip M. Long, Jeffrey Scott Vitter
1995	ICML	Learning to Make Rent-to-Buy Decisions with Systems Applications.	P. Krishnan, Philip M. Long, Jeffrey Scott Vitter
1994	COLT	Fat-Shattering and the Learnability of Real-Valued Functions.	Peter L. Bartlett, Philip M. Long, Robert C. Williamson
1994	DCC	Explicit Bit Minimization for Motion-Compensated Video Coding.	Dzung T. Hoang, Philip M. Long, Jeffrey Scott Vitter
1994	STOC	Simulating access to hidden information while learning.	Peter Auer, Philip M. Long
1993	COLT	On the Complexity of Function Learning.	Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger
1993	COLT	Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule.	Nicol Cesa-Bianchi, Philip M. Long, Manfred K. Warmuth
1993	COLT	On-Line Learning with Linear Loss Constraints.	Nick Littlestone, Philip M. Long
1992	COLT	Characterizations of Learnability for Classes of {	Shai Ben-David, Nicol Cesa-Bianchi, Philip M. Long
1992	COLT	The Learning Complexity of Smooth Functions of a Single Variable.	Don Kimber, Philip M. Long
1992	FOCS	Apple Tasting and Nearly One-Sided Learning	David P. Helmbold, Nick Littlestone, Philip M. Long
1991	COLT	Tracking Drifting Concepts Using Random Examples.	David P. Helmbold, Philip M. Long
1991	STOC	On-Line Learning of Linear Functions	Nick Littlestone, Philip M. Long, Manfred K. Warmuth
1990	COLT	Composite Geometric Concepts and Polynomial Predictability.	Philip M. Long, Manfred K. Warmuth