Long, Philip M.

76 publications

JMLR 2024 Sharpness-Aware Minimization and the Edge of Stability Philip M. Long, Peter L. Bartlett

JMLR 2023 Deep Linear Networks Can Benignly Overfit When Shallow Ones Do Niladri S. Chatterji, Philip M. Long

JMLR 2023 The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima Peter L. Bartlett, Philip M. Long, Olivier Bousquet

JMLR 2022 Foolish Crowds Support Benign Overfitting Niladri S. Chatterji, Philip M. Long

JMLR 2022 The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett

JMLR 2021 Failures of Model-Dependent Generalization Bounds for Least-Norm Interpolation Peter L. Bartlett, Philip M. Long

JMLR 2021 Finite-Sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime Niladri S. Chatterji, Philip M. Long

JMLR 2021 When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks? Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett

COLT 2021 When Does Gradient Descent with Logistic Loss Interpolate Using Deep Networks with Smoothed ReLU Activations? Niladri S. Chatterji, Philip M. Long, Peter Bartlett

ICLR 2020 Generalization Bounds for Deep Convolutional Neural Networks Philip M. Long, Hanie Sedghi

JMLR 2020 Learning Sums of Independent Random Variables with Sparse Collective Support Anindya De, Philip M. Long, Rocco A. Servedio

ALT 2020 On the Complexity of Proper Distribution-Free Learning of Linear Classifiers Philip M. Long, Raphael J. Long

ICLR 2020 On the Global Convergence of Training Deep Linear ResNets Difan Zou, Philip M. Long, Quanquan Gu

ICLR 2020 Size-Free Generalization Bounds for Convolutional Neural Networks Philip M. Long, Hanie Sedghi

ICLR 2019 The Singular Values of Convolutional Layers Hanie Sedghi, Vineet Gupta, Philip M. Long

ALT 2017 New Bounds on the Price of Bandit Feedback for Mistake-Bounded Online Multiclass Learning Philip M. Long

COLT 2017 Surprising Properties of Dropout in Deep Networks David P. Helmbold, Philip M. Long

JMLR 2015 On the Inductive Bias of Dropout David P. Helmbold, Philip M. Long

WACV 2014 Benchmarking Large-Scale Fine-Grained Categorization Anelia Angelova, Philip M. Long

COLT 2013 Active and Passive Learning of Linear Separators Under Log-Concave Distributions Maria-Florina Balcan, Philip M. Long

JMLR 2013 Algorithms and Hardness Results for Parallel Large Margin Learning Philip M. Long, Rocco A. Servedio

MLJ 2012 Linear Classifiers Are Nearly Optimal When Hidden Variables Have Diverse Effects Nader H. Bshouty, Philip M. Long

COLT 2012 New Bounds for Learning Intervals with Implications for Semi-Supervised Learning David P. Helmbold, Philip M. Long

JMLR 2012 On the Necessity of Irrelevant Variables David P. Helmbold, Philip M. Long

ICML 2011 On the Necessity of Irrelevant Variables David P. Helmbold, Philip M. Long

ICML 2010 Finding Planted Partitions in Nearly Linear Time Using Arrested Spectral Clustering Nader H. Bshouty, Philip M. Long

MLJ 2010 Random Classification Noise Defeats All Convex Potential Boosters Philip M. Long, Rocco A. Servedio

ICML 2010 Restricted Boltzmann Machines Are Hard to Approximately Evaluate or Simulate Philip M. Long, Rocco A. Servedio

JMLR 2009 Learning Halfspaces with Malicious Noise Adam R. Klivans, Philip M. Long, Rocco A. Servedio

COLT 2009 Linear Classifiers Are Nearly Optimal When Hidden Variables Have Diverse Effect Nader H. Bshouty, Philip M. Long

ICML 2008 Random Classification Noise Defeats All Convex Potential Boosters Philip M. Long, Rocco A. Servedio

JMLR 2007 Online Learning of Multiple Tasks with a Shared Loss Ofer Dekel, Philip M. Long, Yoram Singer

ALT 2006 Algorithmic Learning Theory, 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings José L. Balcázar, Philip M. Long, Frank Stephan

NeurIPS 2006 Attribute-Efficient Learning of Decision Lists and Linear Threshold Functions Under Unconcentrated Distributions Philip M. Long, Rocco Servedio

COLT 2006 Discriminative Learning Can Succeed Where Generative Learning Fails Philip M. Long, Rocco A. Servedio

ALT 2006 Editors' Introduction José L. Balcázar, Philip M. Long, Frank Stephan

NeurIPS 2006 Learnability and the Doubling Dimension Yi Li, Philip M. Long

COLT 2006 Online Multitask Learning Ofer Dekel, Philip M. Long, Yoram Singer

AAAI 2006 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

COLT 2005 Martingale Boosting Philip M. Long, Rocco A. Servedio

ICML 2005 Unsupervised Evidence Integration Philip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio

NeurIPS 2004 Mistake Bounds for Maximum Entropy Discrimination Philip M. Long, Xinyu Wu

MLJ 2003 A Theoretical Analysis of Query Selection for Collaborative Filtering Sanjoy Dasgupta, Wee Sun Lee, Philip M. Long

MLJ 2003 Boosting and Microarray Data Philip M. Long, Vinsensius Berlian Vega Sn

COLT 2003 Boosting with Diverse Base Classifiers Sanjoy Dasgupta, Philip M. Long

AAAI 2002 Minimum Majority Classification and Boosting Philip M. Long

MLJ 2002 The Relaxed Online Maximum Margin Algorithm Yi Li, Philip M. Long

COLT 2001 A Theoretical Analysis of Query Selection for Collaborative Filtering Wee Sun Lee, Philip M. Long

COLT 2001 Agnostic Boosting Shai Ben-David, Philip M. Long, Yishay Mansour

COLT 2001 On Agnostic Learning with 0, *, 1-Valued and Real-Valued Hypotheses Philip M. Long

COLT 2000 On the Difficulty of Approximately Maximizing Agreements Shai Ben-David, Nadav Eiron, Philip M. Long

ICML 1999 Associative Reinforcement Learning Using Linear Probabilistic Concepts Naoki Abe, Philip M. Long

COLT 1999 Proceedings of the Twelfth Annual Conference on Computational Learning Theory, COLT 1999, Santa Cruz, CA, USA, July 7-9, 1999 Shai Ben-David, Philip M. Long

MLJ 1999 Structural Results About On-Line Learning Models with and Without Queries Peter Auer, Philip M. Long

MLJ 1999 The Complexity of Learning According to Two Models of a Drifting Environment Philip M. Long

NeurIPS 1999 The Relaxed Online Maximum Margin Algorithm Yi Li, Philip M. Long

COLT 1998 On the Sample Complexity of Learning Functions with Bounded Variation Philip M. Long

MLJ 1998 PAC Learning Axis-Aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples Philip M. Long, Lei Tan

COLT 1998 The Complexity of Learning According to Two Models of a Drifting Environment Philip M. Long

MLJ 1997 Guest Editor's Introduction Philip M. Long

COLT 1997 On-Line Evaluation and Prediction Using Linear Functions Philip M. Long

ALT 1996 Improved Bounds About On-Line Learning of Smooth Functions of a Single Variable Philip M. Long

COLT 1996 On the Complexity of Learning from Drifting Distributions Rakesh D. Barve, Philip M. Long

COLT 1996 PAC Learning Axis-Aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples Philip M. Long, Lei Tan

ICML 1995 Learning to Make Rent-to-Buy Decisions with Systems Applications P. Krishnan, Philip M. Long, Jeffrey Scott Vitter

COLT 1995 More Theorems About Scale-Sensitive Dimensions and Learning Peter L. Bartlett, Philip M. Long

MLJ 1995 On the Complexity of Function Learning Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger

COLT 1994 Fat-Shattering and the Learnability of Real-Valued Functions Peter L. Bartlett, Philip M. Long, Robert C. Williamson

MLJ 1994 Tracking Drifting Concepts by Minimizing Disagreements David P. Helmbold, Philip M. Long

COLT 1993 On the Complexity of Function Learning Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger

COLT 1993 On-Line Learning with Linear Loss Constraints Nick Littlestone, Philip M. Long

COLT 1993 Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule Nicolò Cesa-Bianchi, Philip M. Long, Manfred K. Warmuth

COLT 1992 Characterizations of Learnability for Classes of O, ..., N-Valued Functions Shai Ben-David, Nicolò Cesa-Bianchi, Philip M. Long

COLT 1992 The Learning Complexity of Smooth Functions of a Single Variable Don Kimber, Philip M. Long

COLT 1991 Tracking Drifting Concepts Using Random Examples David P. Helmbold, Philip M. Long

COLT 1990 Composite Geometric Concepts and Polynomial Predictability Philip M. Long, Manfred K. Warmuth