Philip M. Long
Affiliations:- Google, Mountain View, CA, USA
According to our database1,
Philip M. Long
authored at least 124 papers
between 1991 and 2024.
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Bibliography
2024
Corrigendum to "Prediction, learning, uniform convergence, and scale-sensitive dimensions" [J. Comput. Syst. Sci. 56 (2) (1998) 174-190].
J. Comput. Syst. Sci., March, 2024
2023
J. Mach. Learn. Res., 2023
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima.
J. Mach. Learn. Res., 2023
2022
The Perils of Being Unhinged: On the Accuracy of Classifiers Minimizing a Noise-Robust Convex Loss.
Neural Comput., 2022
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks.
J. Mach. Learn. Res., 2022
2021
SIAM J. Discret. Math., 2021
J. Mach. Learn. Res., 2021
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime.
J. Mach. Learn. Res., 2021
J. Mach. Learn. Res., 2021
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations?
Proceedings of the Conference on Learning Theory, 2021
2020
New bounds on the price of bandit feedback for mistake-bounded online multiclass learning.
Theor. Comput. Sci., 2020
Proceedings of the 8th International Conference on Learning Representations, 2020
Proceedings of the 8th International Conference on Learning Representations, 2020
Proceedings of the Algorithmic Learning Theory, 2020
2019
On the Effect of the Activation Function on the Distribution of Hidden Nodes in a Deep Network.
Neural Comput., 2019
Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks.
Neural Comput., 2019
Proceedings of the 10th Innovations in Theoretical Computer Science Conference, 2019
Proceedings of the 7th International Conference on Learning Representations, 2019
2018
Representing smooth functions as compositions of near-identity functions with implications for deep network optimization.
CoRR, 2018
Gradient descent with identity initialization efficiently learns positive definite linear transformations.
Proceedings of the 35th International Conference on Machine Learning, 2018
Proceedings of the 59th IEEE Annual Symposium on Foundations of Computer Science, 2018
2017
J. ACM, 2017
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017
2016
2015
2014
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2014
2013
J. Mach. Learn. Res., 2013
The Power of Localization for Efficiently Learning Linear Separators with Malicious Noise.
CoRR, 2013
Proceedings of the Innovations in Theoretical Computer Science, 2013
Proceedings of the 30th International Conference on Machine Learning, 2013
Proceedings of the COLT 2013, 2013
2012
Mach. Learn., 2012
Proceedings of the COLT 2012, 2012
2011
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011
2010
Mach. Learn., 2010
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010
2009
J. Comput. Syst. Sci., 2009
Proceedings of the COLT 2009, 2009
Baum's Algorithm Learns Intersections of Halfspaces with Respect to Log-Concave Distributions.
Proceedings of the Approximation, 2009
2008
J. Comput. Syst. Sci., 2008
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
2007
Inf. Process. Lett., 2007
Proceedings of the Advances in Neural Information Processing Systems 20, 2007
Proceedings of the Advances in Neural Information Processing Systems 20, 2007
2006
Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006
Proceedings of the Advances in Neural Information Processing Systems 19, 2006
Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006
Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006
Proceedings of the Algorithmic Learning Theory, 17th International Conference, 2006
Predicting Electricity Distribution Feeder Failures Using Machine Learning Susceptibility Analysis.
Proceedings of the Proceedings, 2006
2005
Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005
2004
Inf. Comput., 2004
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004
2003
Mach. Learn., 2003
J. Comput. Syst. Sci., 2003
An upper bound on the sample complexity of PAC-learning halfspaces with respect to the uniform distribution.
Inf. Process. Lett., 2003
Algorithmica, 2003
Proceedings of the Computational Learning Theory and Kernel Machines, 2003
2002
Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, July 28, 2002
2001
The one-inclusion graph algorithm is near-optimal for the prediction model of learning.
IEEE Trans. Inf. Theory, 2001
Using the Pseudo-Dimension to Analyze Approximation Algorithms for Integer Programming.
Proceedings of the Algorithms and Data Structures, 7th International Workshop, 2001
Proceedings of the Computational Learning Theory, 2001
Proceedings of the Computational Learning Theory, 2001
2000
Theor. Comput. Sci., 2000
Electron. Colloquium Comput. Complex., 2000
Electron. Colloquium Comput. Complex., 2000
1999
Mach. Learn., 1999
Mach. Learn., 1999
Algorithmica, 1999
Associative Reinforcement Learning using Linear Probabilistic Concepts.
Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27, 1999
1998
IEEE Trans. Circuits Syst. Video Technol., 1998
PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples.
Mach. Learn., 1998
J. Comput. Syst. Sci., 1998
J. Comput. Syst. Sci., 1998
Proceedings of the Eleventh Annual Conference on Computational Learning Theory, 1998
1997
Proceedings of the Tenth Annual Conference on Computational Learning Theory, 1997
1996
Worst-case quadratic loss bounds for prediction using linear functions and gradient descent.
IEEE Trans. Neural Networks, 1996
J. Comput. Syst. Sci., 1996
Efficient Cost Measures for Motion Compensation at Low Bit Rates (Extended Abstract).
Proceedings of the 6th Data Compression Conference (DCC '96), Snowbird, Utah, USA, March 31, 1996
1995
On the sample complexity of PAC learning half-spaces against the uniform distribution.
IEEE Trans. Neural Networks, 1995
A Generalization of Sauer's Lemma.
J. Comb. Theory A, 1995
J. Comput. Syst. Sci., 1995
Proceedings of the Machine Learning, 1995
Proceedings of the IEEE Data Compression Conference, 1995
Proceedings of the Eigth Annual Conference on Computational Learning Theory, 1995
1994
Inf. Comput., September, 1994
Inf. Process. Lett., 1994
Proceedings of the IEEE Data Compression Conference, 1994
1993
Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, 1993
Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, 1993
1992
Proceedings of the 33rd Annual Symposium on Foundations of Computer Science, 1992
Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, 1992
Characterizations of Learnability for Classes of {<i>O, ..., n</i>}-Valued Functions.
Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, 1992
1991
Proceedings of the Fourth Annual Workshop on Computational Learning Theory, 1991