David P. Helmbold

According to our database, David P. Helmbold authored at least 67 papers between 1982 and 2020.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2020

Online Learning Using Only Peer Prediction.

Yang Liu, David P. Helmbold

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

Mistake bounds on the noise-free multi-armed bandit game.

Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth

Inf. Comput., 2019

Online Learning Using Only Peer Assessment.

Yang Liu, David P. Helmbold

CoRR, 2019

A New Family of Neural Networks Provably Resistant to Adversarial Attacks.

Rakshit Agrawal, Luca de Alfaro, David P. Helmbold

CoRR, 2019

2018

Gradient descent with identity initialization efficiently learns positive definite linear transformations.

Peter L. Bartlett, David P. Helmbold, Philip M. Long

Proceedings of the 35th International Conference on Machine Learning, 2018

Online Learning of Combinatorial Objects via Extended Formulation.

Holakou Rahmanian, David P. Helmbold, S. V. N. Vishwanathan

Proceedings of the Algorithmic Learning Theory, 2018

2017

Surprising properties of dropout in deep networks.

David P. Helmbold, Philip M. Long

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Extended Formulation for Online Learning of Combinatorial Objects.

Holakou Rahmanian, S. V. N. Vishwanathan, David P. Helmbold

CoRR, 2016

Dropout Versus Weight Decay for Deep Networks.

David P. Helmbold, Philip M. Long

CoRR, 2016

Noise Free Multi-armed Bandit Game.

Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth

Proceedings of the Language and Automata Theory and Applications, 2016

2015

On the inductive bias of dropout.

David P. Helmbold, Philip M. Long

J. Mach. Learn. Res., 2015

2013

Boosting in Location Space.

Damian Eads, David P. Helmbold, Edward Rosten

CoRR, 2013

2012

New Bounds for Learning Intervals with Implications for Semi-Supervised Learning.

David P. Helmbold, Philip M. Long

Proceedings of the COLT 2012, 2012

Evolutionary learning of policies for MCTS simulations.

James Pettit, David P. Helmbold

Proceedings of the International Conference on the Foundations of Digital Games, 2012

2011

On the Necessity of Irrelevant Variables.

David P. Helmbold, Philip M. Long

Proceedings of the 28th International Conference on Machine Learning, 2011

Combining Initial Segments of Lists.

Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold

Proceedings of the Algorithmic Learning Theory - 22nd International Conference, 2011

2009

All-Moves-As-First Heuristics in Monte-Carlo Go.

David P. Helmbold, Aleatha Parker-Wood

Proceedings of the 2009 International Conference on Artificial Intelligence, 2009

Learning Object Location Predictors with Boosting and Grammar-Guided Feature Extraction.

Damian Eads, Edward Rosten, David P. Helmbold

Proceedings of the British Machine Vision Conference, 2009

2007

Learning Permutations with Exponential Weights.

David P. Helmbold, Manfred K. Warmuth

Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

Aerial Lidar Data Classification using AdaBoost.

Suresh K. Lodha, Darren N. Fitzpatrick, David P. Helmbold

Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling, 2007

2006

Modeling, analyzing, and synthesizing expressive piano performance with graphical models.

Graham Grindlay, David P. Helmbold

Mach. Learn., 2006

A Bayesian Approach to Building Footprint Extraction from Aerial LIDAR Data.

Oliver Wang, Suresh K. Lodha, David P. Helmbold

Proceedings of the 3rd International Symposium on 3D Data Processing, 2006

Aerial LiDAR Data Classification Using Support Vector Machines (SVM).

Suresh K. Lodha, Edward J. Kreps, David P. Helmbold, Darren N. Fitzpatrick

Proceedings of the 3rd International Symposium on 3D Data Processing, 2006

2002

Boosting Methods for Regression.

Nigel Duffy, David P. Helmbold

Mach. Learn., 2002

2000

Adaptive disk spin-down for mobile computers.

David P. Helmbold, Darrell D. E. Long, Tracey L. Sconyers, Bruce Sherrod

Mob. Networks Appl., 2000

On-Line Learning with Linear Loss Constraints.

David P. Helmbold, Nick Littlestone, Philip M. Long

Inf. Comput., 2000

Apple Tasting.

David P. Helmbold, Nick Littlestone, Philip M. Long

Inf. Comput., 2000

Leveraging for Regression.

Nigel Duffy, David P. Helmbold

Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT 2000), June 28, 2000

1999

Relative loss bounds for single neurons.

David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth

IEEE Trans. Neural Networks, 1999

Potential Boosters?

Nigel Duffy, David P. Helmbold

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Direct and Indirect Algorithms for On-line Learning of Disjunctions.

David P. Helmbold, Sandra Panizza, Manfred K. Warmuth

Proceedings of the Computational Learning Theory, 4th European Conference, 1999

A Geometric Approach to Leveraging Weak Learners.

Nigel Duffy, David P. Helmbold

Proceedings of the Computational Learning Theory, 4th European Conference, 1999

1998

Improved Lower Bounds for Learning from Noisy Examples: An Information-Theoretic Approach.

Claudio Gentile, David P. Helmbold

Proceedings of the Eleventh Annual Conference on Computational Learning Theory, 1998

1997

Learning When to Trust Which Experts.

David P. Helmbold, Stephen Kwek, Leonard Pitt

Proceedings of the Computational Learning Theory, Third European Conference, 1997

Some Label Efficient Learning Results.

David P. Helmbold, Sandra Panizza

Proceedings of the Tenth Annual Conference on Computational Learning Theory, 1997

1996

A Taxonomy of Race Conditions.

David P. Helmbold, Charles E. McDowell

J. Parallel Distributed Comput., 1996

A Class of Synchronization Operations that Permit Efficient Race Detection.

David P. Helmbold, Charles E. McDowell

Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, 1996

A Dynamic Disk Spin-Down Technique for Mobile Computing.

David P. Helmbold, Darrell D. E. Long, Bruce Sherrod

Proceedings of the MOBICOM '96, 1996

On-Line Portfolio Selection Using Multiplicative Updates.

David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth

Proceedings of the Machine Learning, 1996

On Bayes Methods for On-Line Boolean Prediction.

Nicolò Cesa-Bianchi, David P. Helmbold, Sandra Panizza

Proceedings of the Ninth Annual Conference on Computational Learning Theory, 1996

1995

On Weak Learning.

David P. Helmbold, Manfred K. Warmuth

J. Comput. Syst. Sci., 1995

Worst-case Loss Bounds for Single Neurons.

David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

A Comparison of New and Old Algorithms for a Mixture Estimation Problem.

David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth

Proceedings of the Eigth Annual Conference on Computational Learning Theory, 1995

Predicting Nearly as Well as the Best Pruning of a Decision Tree.

David P. Helmbold, Robert E. Schapire

Proceedings of the Eigth Annual Conference on Computational Learning Theory, 1995

1994

Tracking Drifting Concepts By Minimizing Disagreements.

David P. Helmbold, Philip M. Long

Mach. Learn., 1994

1993

Determining Possible Event Orders by Analyzing Sequential Traces.

David P. Helmbold, Charles E. McDowell, Jian-Zhong Wang

IEEE Trans. Parallel Distributed Syst., 1993

How to use expert advice.

Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert E. Schapire, Manfred K. Warmuth

Proceedings of the Twenty-Fifth Annual ACM Symposium on Theory of Computing, 1993

On-line prediction and conversion strategies.

Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth

Proceedings of the First European Conference on Computational Learning Theory, 1993

1992

Apple Tasting and Nearly One-Sided Learning

David P. Helmbold, Nick Littlestone, Philip M. Long

Proceedings of the 33rd Annual Symposium on Foundations of Computer Science, 1992

Some Weak Learning Results.

David P. Helmbold, Manfred K. Warmuth

Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, 1992

1991

Computing Reachable States of Parallel Programs.

David P. Helmbold, Charles E. McDowell

Proceedings of the ACM/ONR Workshop on Parallel and Distributed Debugging, 1991

Tracking Drifting Concepts Using Random Examples.

David P. Helmbold, Philip M. Long

Proceedings of the Fourth Annual Workshop on Computational Learning Theory, 1991

1990

Modeling Speedup (<i>n</i>) Greater than <i>n</i>.

David P. Helmbold, Charles E. McDowell

IEEE Trans. Parallel Distributed Syst., 1990

Analyzing Traces with Anonymous Synchronization.

David P. Helmbold, Charles E. McDowell, Jian-Zhong Wang

Proceedings of the 1990 International Conference on Parallel Processing, 1990

Learning Integer Lattices.

David P. Helmbold, Robert Sloan, Manfred K. Warmuth

Proceedings of the Third Annual Workshop on Computational Learning Theory, 1990

1989

Debugging Concurrent Programs.

Charles E. McDowell, David P. Helmbold

ACM Comput. Surv., 1989

Modeling Speedup greater than n.

David P. Helmbold, Charles E. McDowell

Proceedings of the International Conference on Parallel Processing, 1989

Learning Nested Differences of Intersection-Closed Concept Classes.

David P. Helmbold, Robert Sloan, Manfred K. Warmuth

Proceedings of the Second Annual Workshop on Computational Learning Theory, 1989

1987

Parallel algorithms for scheduling and related problems.

David P. Helmbold

PhD thesis, 1987

Task Sequencing Language for Specifying Distributed Ada Systems.

David C. Luckham, David P. Helmbold, D. L. Bryan, M. A. Haberler

Proceedings of the PARLE, 1987

1986

Applications of Parallel Scheduling to Perfect Graphs.

David P. Helmbold, Ernst W. Mayr

Proceedings of the Graphtheoretic Concepts in Computer Science, International Workshop, 1986

Perfect Graphs and Parallel Algorithms.

David P. Helmbold, Ernst W. Mayr

Proceedings of the International Conference on Parallel Processing, 1986

Two Processor Scheduling is in NC.

David P. Helmbold, Ernst W. Mayr

Proceedings of the VLSI Algorithms and Architectures, 1986

Task Sequencing Languages for Specifying Distributed Ada Systems.

David C. Luckham, David P. Helmbold, Sigurd Meldal, D. L. Bryan, M. A. Haberler

Proceedings of the Software Development and Ada, 1986

1985

TSL: task sequencing language.

David P. Helmbold, David C. Luckham

Proceedings of the 1985 Annual ACM SIGAda International Conference on Ada, 1985

1982

Monitoring for deadlocks in Ada tasking.

Steven M. German, David P. Helmbold, David C. Luckham

Proceedings of the AdaTEC Conference on Ada, 1982