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

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, 1992)

Apple Tasting and Nearly One-Sided Learning - David P. Helmbold; Nick Littlestone; Philip M. Long (FOCS, 1992)

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

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

Explicit Bit Minimization for Motion-Compensated Video Coding. - Dzung T. Hoang; Philip M. Long; Jeffrey Scott Vitter (Data Compression Conference, 1994)

Halfspace Learning, Linear Programming, and Nonmalicious Distributions. - Philip M. Long (Inf. Process. Lett., 1994)

Tracking Drifting Concepts By Minimizing Disagreements. - David P. Helmbold; Philip M. Long (Mach. Learn., 1994)

Composite Geometric Concepts and Polynomial Predictability - Philip M. Long; Manfred K. Warmuth (Inf. Comput., 1994)

More Theorems about Scale-sensitive Dimensions and Learning. - Peter L. Bartlett; Philip M. Long (COLT, 1995)

Multiple-Dictionary Coding Using Partial Matching. - Dzung T. Hoang; Philip M. Long; Jeffrey Scott Vitter (Data Compression Conference, 1995)

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

On-line Learning of Linear Functions. - Nick Littlestone; Philip M. Long; Manfred K. Warmuth (Comput. Complex., 1995)

Characterizations of Learnability for Classes of {0, ..., n}-Valued Functions. - Shai Ben-David; Nicolò Cesa-Bianchi; David Haussler; Philip M. Long (J. Comput. Syst. Sci., 1995)

A Generalization of Sauer's Lemma. - David Haussler; Philip M. Long (J. Comb. Theory A, 1995)

On the Complexity of Function Learning. - Peter Auer; Philip M. Long; Wolfgang Maass 001; Gerhard J. Woeginger (Mach. Learn., 1995)

On-Line Learning of Smooth Functions of a Single Variable. - Don Kimber; Philip M. Long (Theor. Comput. Sci., 1995)

On the sample complexity of PAC learning half-spaces against the uniform distribution. - Philip M. Long (IEEE Trans. Neural Networks, 1995)

Efficient Cost Measures for Motion Compensation at Low Bit Rates (Extended Abstract). - Dzung T. Hoang; Philip M. Long; Jeffrey Scott Vitter (Data Compression Conference, 1996)

Fat-Shattering and the Learnability of Real-Valued Functions. - Peter L. Bartlett; Philip M. Long; Robert C. Williamson (J. Comput. Syst. Sci., 1996)

Worst-case quadratic loss bounds for prediction using linear functions and gradient descent. - Nicolò Cesa-Bianchi; Philip M. Long; Manfred K. Warmuth (IEEE Trans. Neural Networks, 1996)

On-line Evaluation and Prediction using Linear Functions. - Philip M. Long (COLT, 1997)

On the Complexity of Learning from Drifting Distributions. - Rakesh D. Barve; Philip M. Long (Inf. Comput., 1997)

Guest Editor's Introduction. - Philip M. Long (Mach. Learn., 1997)

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

Approximating Hyper-Rectangles: Learning and Pseudorandom Sets. - Peter Auer; Philip M. Long; Aravind Srinivasan (J. Comput. Syst. Sci., 1998)

Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions. - Peter L. Bartlett; Philip M. Long (J. Comput. Syst. Sci., 1998)

PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples. - Philip M. Long; Lei Tan (Mach. Learn., 1998)

Efficient cost measures for motion estimation at low bit rates. - Dzung T. Hoang; Philip M. Long; Jeffrey Scott Vitter (IEEE Trans. Circuits Syst. Video Technol., 1998)

Associative Reinforcement Learning using Linear Probabilistic Concepts. - Naoki Abe; Philip M. Long (ICML, 1999)

Adaptive Disk Spindown via Optimal Rent-to-Buy in Probabilistic Environments. - P. Krishnan; Philip M. Long; Jeffrey Scott Vitter (Algorithmica, 1999)

Dictionary Selection Using Partial Matching. - Dzung T. Hoang; Philip M. Long; Jeffrey Scott Vitter (Inf. Sci., 1999)

Structural Results About On-line Learning Models With and Without Queries. - Peter Auer; Philip M. Long (Mach. Learn., 1999)

The Complexity of Learning According to Two Models of a Drifting Environment. - Philip M. Long (Mach. Learn., 1999)

Text compression via alphabet re-representation. - Philip M. Long; Apostol Natsev; Jeffrey Scott Vitter (Neural Networks, 1999)

Simulating Access to Hidden Information while Learning - Peter Auer; Philip M. Long (Electron. Colloquium Comput. Complex., 2000)

Approximating Hyper-Rectangles: Learning and Pseudo-random Sets - Peter Auer; Philip M. Long; Aravind Srinivasan (Electron. Colloquium Comput. Complex., 2000)

Apple Tasting. - David P. Helmbold; Nick Littlestone; Philip M. Long (Inf. Comput., 2000)

On-Line Learning with Linear Loss Constraints. - David P. Helmbold; Nick Littlestone; Philip M. Long (Inf. Comput., 2000)

Improved bounds about on-line learning of smooth-functions of a single variable. - Philip M. Long (Theor. Comput. Sci., 2000)

Agnostic Boosting. - Shai Ben-David; Philip M. Long; Yishay Mansour (COLT/EuroCOLT, 2001)

A Theoretical Analysis of Query Selection for Collaborative Filtering. - Wee Sun Lee; Philip M. Long (COLT/EuroCOLT, 2001)

On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses. - Philip M. Long (COLT/EuroCOLT, 2001)

Using the Pseudo-Dimension to Analyze Approximation Algorithms for Integer Programming. - Philip M. Long (WADS, 2001)

Improved Bounds on the Sample Complexity of Learning. - Yi Li; Philip M. Long; Aravind Srinivasan (J. Comput. Syst. Sci., 2001)

The one-inclusion graph algorithm is near-optimal for the prediction model of learning. - Yi Li; Philip M. Long; Aravind Srinivasan (IEEE Trans. Inf. Theory, 2001)

Minimum Majority Classification and Boosting. - Philip M. Long (AAAI/IAAI, 2002)

The Relaxed Online Maximum Margin Algorithm. - Yi Li; Philip M. Long (Mach. Learn., 2002)

Boosting with Diverse Base Classifiers. - Sanjoy Dasgupta; Philip M. Long (COLT, 2003)

Reinforcement Learning with Immediate Rewards and Linear Hypotheses. - Naoki Abe; Alan W. Biermann; Philip M. Long (Algorithmica, 2003)

An upper bound on the sample complexity of PAC-learning halfspaces with respect to the uniform distribution. - Philip M. Long (Inf. Process. Lett., 2003)

On the difficulty of approximately maximizing agreements. - Shai Ben-David; Nadav Eiron; Philip M. Long (J. Comput. Syst. Sci., 2003)

A Theoretical Analysis of Query Selection for Collaborative Filtering. - Sanjoy Dasgupta; Wee Sun Lee; Philip M. Long (Mach. Learn., 2003)

Boosting and Microarray Data. - Philip M. Long; Vinsensius Berlian Vega SN (Mach. Learn., 2003)

Mistake Bounds for Maximum Entropy Discrimination. - Philip M. Long; Xinyu Wu (NIPS, 2004)

Efficient algorithms for learning functions with bounded variation. - Philip M. Long (Inf. Comput., 2004)

Martingale Boosting. - Philip M. Long; Rocco A. Servedio (COLT, 2005)

Unsupervised evidence integration. - Philip M. Long; Vinay Varadan; Sarah Gilman; Mark Treshock; Rocco A. Servedio (ICML, 2005)

Performance guarantees for hierarchical clustering. - Sanjoy Dasgupta; Philip M. Long (J. Comput. Syst. Sci., 2005)

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; et al. (AAAI, 2006)

Editors' Introduction. - José L. Balcázar; Philip M. Long; Frank Stephan 001 (ALT, 2006)

Online Multitask Learning. - Ofer Dekel; Philip M. Long; Yoram Singer (COLT, 2006)

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

Learnability and the doubling dimension. - Yi Li; Philip M. Long (NIPS, 2006)

Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions. - Philip M. Long; Rocco A. Servedio (NIPS, 2006)

One-Pass Boosting. - Zafer Barutçuoglu; Philip M. Long; Rocco A. Servedio (NIPS, 2007)

Boosting the Area under the ROC Curve. - Philip M. Long; Rocco A. Servedio (NIPS, 2007)

Discriminative learning can succeed where generative learning fails. - Philip M. Long; Rocco A. Servedio; Hans Ulrich Simon (Inf. Process. Lett., 2007)

Online Learning of Multiple Tasks with a Shared Loss. - Ofer Dekel; Philip M. Long; Yoram Singer (J. Mach. Learn. Res., 2007)

Adaptive Martingale Boosting. - Philip M. Long; Rocco A. Servedio (NIPS, 2008)

Guest editors' introduction: Special issue on learning theory. - Peter Auer; Philip M. Long (J. Comput. Syst. Sci., 2008)

Preface. - Philip M. Long; Frank Stephan 001 (Theor. Comput. Sci., 2008)

Baum's Algorithm Learns Intersections of Halfspaces with Respect to Log-Concave Distributions. - Adam R. Klivans; Philip M. Long; Alex K. Tang (APPROX-RANDOM, 2009)

Linear Classifiers are Nearly Optimal When Hidden Variables Have Diverse Effect. - Nader H. Bshouty; Philip M. Long (COLT, 2009)

Using the doubling dimension to analyze the generalization of learning algorithms. - Nader H. Bshouty; Yi Li; Philip M. Long (J. Comput. Syst. Sci., 2009)

Learning Halfspaces with Malicious Noise. - Adam R. Klivans; Philip M. Long; Rocco A. Servedio (J. Mach. Learn. Res., 2009)

Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering. - Nader H. Bshouty; Philip M. Long (ICML, 2010)

Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate. - Philip M. Long; Rocco A. Servedio (ICML, 2010)

Random classification noise defeats all convex potential boosters. - Philip M. Long; Rocco A. Servedio (Mach. Learn., 2010)

Learning large-margin halfspaces with more malicious noise. - Philip M. Long; Rocco A. Servedio (NIPS, 2011)

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

On the necessity of irrelevant variables. - David P. Helmbold; Philip M. Long (J. Mach. Learn. Res., 2012)

Linear classifiers are nearly optimal when hidden variables have diverse effects. - Nader H. Bshouty; Philip M. Long (Mach. Learn., 2012)

Active and passive learning of linear separators under log-concave distributions. - Maria-Florina Balcan; Philip M. Long (COLT, 2013)

Consistency versus Realizable H-Consistency for Multiclass Classification. - Philip M. Long; Rocco A. Servedio (ICML (3), 2013)

Low-weight halfspaces for sparse boolean vectors. - Philip M. Long; Rocco A. Servedio (ITCS, 2013)

The Power of Localization for Efficiently Learning Linear Separators with Malicious Noise. - Pranjal Awasthi; Maria-Florina Balcan; Philip M. Long (CoRR, 2013)

Algorithms and hardness results for parallel large margin learning. - Philip M. Long; Rocco A. Servedio (J. Mach. Learn. Res., 2013)

Benchmarking large-scale Fine-Grained Categorization. - Anelia Angelova; Philip M. Long (WACV, 2014)

On the Weight of Halfspaces over Hamming Balls. - Philip M. Long; Rocco A. Servedio (SIAM J. Discret. Math., 2014)

Special Issue on New Theoretical Challenges in Machine Learning. - Avrim Blum; Philip M. Long (Algorithmica, 2015)

On the inductive bias of dropout. - David P. Helmbold; Philip M. Long (J. Mach. Learn. Res., 2015)

Dropout Versus Weight Decay for Deep Networks. - David P. Helmbold; Philip M. Long (CoRR, 2016)

How to select a winner in evolutionary optimization? - Risto Miikkulainen; Hormoz Shahrzad; Nigel Duffy; Philip M. Long (SSCI, 2017)

The Power of Localization for Efficiently Learning Linear Separators with Noise. - Pranjal Awasthi; Maria-Florina Balcan; Philip M. Long (J. ACM, 2017)

Surprising properties of dropout in deep networks. - David P. Helmbold; Philip M. Long (J. Mach. Learn. Res., 2017)

Learning Sums of Independent Random Variables with Sparse Collective Support. - Anindya De; Philip M. Long; Rocco A. Servedio (FOCS, 2018)

Gradient descent with identity initialization efficiently learns positive definite linear transformations. - Peter L. Bartlett; David P. Helmbold; Philip M. Long (ICML, 2018)

Representing smooth functions as compositions of near-identity functions with implications for deep network optimization. - Peter L. Bartlett; Steven N. Evans; Philip M. Long (CoRR, 2018)

The Singular Values of Convolutional Layers. - Hanie Sedghi; Vineet Gupta 001; Philip M. Long (ICLR (Poster), 2019)

Density Estimation for Shift-Invariant Multidimensional Distributions. - Anindya De; Philip M. Long; Rocco A. Servedio (ITCS, 2019)

Size-free generalization bounds for convolutional neural networks. - Philip M. Long; Hanie Sedghi (CoRR, 2019)

Benign Overfitting in Linear Regression. - Peter L. Bartlett; Philip M. Long; Gábor Lugosi; Alexander Tsigler (CoRR, 2019)

Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks. - Peter L. Bartlett; David P. Helmbold; Philip M. Long (Neural Comput., 2019)

On the Effect of the Activation Function on the Distribution of Hidden Nodes in a Deep Network. - Philip M. Long; Hanie Sedghi (Neural Comput., 2019)

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

Generalization bounds for deep convolutional neural networks. - Philip M. Long; Hanie Sedghi (ICLR, 2020)

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

Oracle lower bounds for stochastic gradient sampling algorithms. - Niladri S. Chatterji; Peter L. Bartlett; Philip M. Long (CoRR, 2020)

New bounds on the price of bandit feedback for mistake-bounded online multiclass learning. - Philip M. Long (Theor. Comput. Sci., 2020)

When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? - Niladri S. Chatterji; Philip M. Long; Peter L. Bartlett (COLT, 2021)

Properties of the After Kernel. - Philip M. Long (CoRR, 2021)

Failures of Model-dependent Generalization Bounds for Least-norm Interpolation. - Peter L. Bartlett; Philip M. Long (J. Mach. Learn. Res., 2021)

Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime. - Niladri S. Chatterji; Philip M. Long (J. Mach. Learn. Res., 2021)

When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks? - Niladri S. Chatterji; Philip M. Long; Peter L. Bartlett (J. Mach. Learn. Res., 2021)

Superlinear Integrality Gaps for the Minimum Majority Problem. - Philip M. Long (SIAM J. Discret. Math., 2021)

Foolish Crowds Support Benign Overfitting. - Niladri S. Chatterji; Philip M. Long (J. Mach. Learn. Res., 2022)

The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks. - Niladri S. Chatterji; Philip M. Long; Peter L. Bartlett (J. Mach. Learn. Res., 2022)

The Perils of Being Unhinged: On the Accuracy of Classifiers Minimizing a Noise-Robust Convex Loss. - Philip M. Long; Rocco A. Servedio (Neural Comput., 2022)

The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima. - Peter L. Bartlett; Philip M. Long; Olivier Bousquet (J. Mach. Learn. Res., 2023)

Deep linear networks can benignly overfit when shallow ones do. - Niladri S. Chatterji; Philip M. Long (J. Mach. Learn. Res., 2023)

Sharpness-Aware Minimization and the Edge of Stability. - Philip M. Long; Peter L. Bartlett (J. Mach. Learn. Res., 2024)

Corrigendum to "Prediction, learning, uniform convergence, and scale-sensitive dimensions" [J. Comput. Syst. Sci. 56 (2) (1998) 174-190]. - Peter L. Bartlett; Philip M. Long (J. Comput. Syst. Sci., 2024)