David P. Helmbold

Publication Activity (10 Years)

Years Active: 1982-2020
Publications (10 Years): 13

Top Topics

Multi Armed Bandit

Positive Definite

Linear Transformations

Top Venues

J. Mach. Learn. Res.

Publications

Yang Liu, David P. Helmbold
Online Learning Using Only Peer Prediction. AISTATS (2020)
Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth
Mistake bounds on the noise-free multi-armed bandit game. Inf. Comput. 269 (2019)
Rakshit Agrawal, Luca de Alfaro, David P. Helmbold
A New Family of Neural Networks Provably Resistant to Adversarial Attacks. CoRR (2019)
Yang Liu, David P. Helmbold
Online Learning Using Only Peer Assessment. CoRR (2019)
Peter L. Bartlett, David P. Helmbold, Philip M. Long
Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks. Neural Comput. 31 (3) (2019)
Holakou Rahmanian, David P. Helmbold, S. V. N. Vishwanathan
Online Learning of Combinatorial Objects via Extended Formulation. ALT (2018)
Peter L. Bartlett, David P. Helmbold, Philip M. Long
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks. CoRR (2018)
Peter L. Bartlett, David P. Helmbold, Philip M. Long
Gradient descent with identity initialization efficiently learns positive definite linear transformations. ICML (2018)
David P. Helmbold, Philip M. Long
Surprising properties of dropout in deep networks. COLT (2017)
David P. Helmbold, Philip M. Long
Surprising properties of dropout in deep networks. J. Mach. Learn. Res. 18 (2017)
Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth
Noise Free Multi-armed Bandit Game. LATA (2016)
Holakou Rahmanian, S. V. N. Vishwanathan, David P. Helmbold
Extended Formulation for Online Learning of Combinatorial Objects. CoRR (2016)
David P. Helmbold, Philip M. Long
Dropout Versus Weight Decay for Deep Networks. CoRR (2016)
David P. Helmbold, Philip M. Long
On the inductive bias of dropout. J. Mach. Learn. Res. 16 (2015)
David P. Helmbold, Philip M. Long
On the Inductive Bias of Dropout. CoRR (2014)
Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold
Combining initial segments of lists. Theor. Comput. Sci. 519 (2014)
Damian Eads, David P. Helmbold, Edward Rosten
Boosting in Location Space. CoRR (2013)
David P. Helmbold, Philip M. Long
New Bounds for Learning Intervals with Implications for Semi-Supervised Learning. COLT (2012)
David P. Helmbold, Philip M. Long
On the Necessity of Irrelevant Variables CoRR (2012)
James Pettit, David P. Helmbold
Evolutionary learning of policies for MCTS simulations. FDG (2012)
David P. Helmbold, Philip M. Long
On the necessity of irrelevant variables. J. Mach. Learn. Res. 13 (2012)
Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold
Combining Initial Segments of Lists. ALT (2011)
David P. Helmbold, Philip M. Long
On the Necessity of Irrelevant Variables. ICML (2011)
David P. Helmbold, Manfred K. Warmuth
Learning Permutations with Exponential Weights. J. Mach. Learn. Res. 10 (2009)
Damian Eads, Edward Rosten, David P. Helmbold
Learning Object Location Predictors with Boosting and Grammar-Guided Feature Extraction CoRR (2009)
Damian Eads, Edward Rosten, David P. Helmbold
Learning Object Location Predictors with Boosting and Grammar-Guided Feature Extraction. BMVC (2009)
David P. Helmbold, Aleatha Parker-Wood
All-Moves-As-First Heuristics in Monte-Carlo Go. IC-AI (2009)
David P. Helmbold, Manfred K. Warmuth
Learning Permutations with Exponential Weights. COLT (2007)
Suresh K. Lodha, Darren N. Fitzpatrick, David P. Helmbold
Aerial Lidar Data Classification using AdaBoost. 3DIM (2007)
Graham Grindlay, David P. Helmbold
Modeling, analyzing, and synthesizing expressive piano performance with graphical models. Mach. Learn. 65 (2-3) (2006)
Suresh K. Lodha, Edward J. Kreps, David P. Helmbold, Darren N. Fitzpatrick
Aerial LiDAR Data Classification Using Support Vector Machines (SVM). 3DPVT (2006)
Oliver Wang, Suresh K. Lodha, David P. Helmbold
A Bayesian Approach to Building Footprint Extraction from Aerial LIDAR Data. 3DPVT (2006)
Nigel Duffy, David P. Helmbold
Boosting Methods for Regression. Mach. Learn. 47 (2-3) (2002)
Nigel Duffy, David P. Helmbold
A geometric approach to leveraging weak learners. Theor. Comput. Sci. 284 (1) (2002)
David P. Helmbold, Sandra Panizza, Manfred K. Warmuth
Direct and indirect algorithms for on-line learning of disjunctions. Theor. Comput. Sci. 284 (1) (2002)
Claudio Gentile, David P. Helmbold
Improved Lower Bounds for Learning from Noisy Examples: An Information-Theoretic Approach. Inf. Comput. 166 (2) (2001)
Nigel Duffy, David P. Helmbold
Leveraging for Regression. COLT (2000)
David P. Helmbold, Nick Littlestone, Philip M. Long
Apple Tasting. Inf. Comput. 161 (2) (2000)
David P. Helmbold, Darrell D. E. Long, Tracey L. Sconyers, Bruce Sherrod
Adaptive disk spin-down for mobile computers. Mob. Networks Appl. 5 (4) (2000)
David P. Helmbold, Nick Littlestone, Philip M. Long
On-Line Learning with Linear Loss Constraints. Inf. Comput. 161 (2) (2000)
Nigel Duffy, David P. Helmbold
Potential Boosters? NIPS (1999)
David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth
Relative loss bounds for single neurons. IEEE Trans. Neural Networks 10 (6) (1999)
Nigel Duffy, David P. Helmbold
A Geometric Approach to Leveraging Weak Learners. EuroCOLT (1999)
David P. Helmbold, Sandra Panizza, Manfred K. Warmuth
Direct and Indirect Algorithms for On-line Learning of Disjunctions. EuroCOLT (1999)
Nicolò Cesa-Bianchi, David P. Helmbold, Sandra Panizza
On Bayes Methods for On-Line Boolean Prediction. Algorithmica 22 (1/2) (1998)
Claudio Gentile, David P. Helmbold
Improved Lower Bounds for Learning from Noisy Examples: An Information-Theoretic Approach. COLT (1998)
Nicolò Cesa-Bianchi, Yoav Freund, David Haussler, David P. Helmbold, Robert E. Schapire, Manfred K. Warmuth
How to use expert advice. J. ACM 44 (3) (1997)
David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth
A Comparison of New and Old Algorithms for a Mixture Estimation Problem. Mach. Learn. 27 (1) (1997)
David P. Helmbold, Stephen Kwek, Leonard Pitt
Learning When to Trust Which Experts. EuroCOLT (1997)
David P. Helmbold, Sandra Panizza
Some Label Efficient Learning Results. COLT (1997)
David P. Helmbold, Robert E. Schapire
Predicting Nearly As Well As the Best Pruning of a Decision Tree. Mach. Learn. 27 (1) (1997)
Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth
On-line Prediction and Conversion Strategies. Mach. Learn. 25 (1) (1996)
David P. Helmbold, Charles E. McDowell
A Class of Synchronization Operations that Permit Efficient Race Detection. PDPTA (1996)
Nicolò Cesa-Bianchi, David P. Helmbold, Sandra Panizza
On Bayes Methods for On-Line Boolean Prediction. COLT (1996)
David P. Helmbold, Charles E. McDowell
A Taxonomy of Race Conditions. J. Parallel Distributed Comput. 33 (2) (1996)
David P. Helmbold, Darrell D. E. Long, Bruce Sherrod
A Dynamic Disk Spin-Down Technique for Mobile Computing. MobiCom (1996)
David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth
On-Line Portfolio Selection Using Multiplicative Updates. ICML (1996)
David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth
A Comparison of New and Old Algorithms for a Mixture Estimation Problem. COLT (1995)
David P. Helmbold, Manfred K. Warmuth
On Weak Learning. J. Comput. Syst. Sci. 50 (3) (1995)
David P. Helmbold, Robert E. Schapire
Predicting Nearly as Well as the Best Pruning of a Decision Tree. COLT (1995)
David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth
Worst-case Loss Bounds for Single Neurons. NIPS (1995)
David P. Helmbold, Philip M. Long
Tracking Drifting Concepts By Minimizing Disagreements. Mach. Learn. 14 (1) (1994)
Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth
On-line prediction and conversion strategies. EuroCOLT (1993)
Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert E. Schapire, Manfred K. Warmuth
How to use expert advice. STOC (1993)
David P. Helmbold, Charles E. McDowell, Jian-Zhong Wang
Determining Possible Event Orders by Analyzing Sequential Traces. IEEE Trans. Parallel Distributed Syst. 4 (7) (1993)
David P. Helmbold, Manfred K. Warmuth
Some Weak Learning Results. COLT (1992)
David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth
Learning Integer Lattices. SIAM J. Comput. 21 (2) (1992)
David P. Helmbold, Nick Littlestone, Philip M. Long
Apple Tasting and Nearly One-Sided Learning FOCS (1992)
David P. Helmbold, Philip M. Long
Tracking Drifting Concepts Using Random Examples. COLT (1991)
David P. Helmbold, Charles E. McDowell
Computing Reachable States of Parallel Programs. Workshop on Parallel and Distributed Debugging (1991)
David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth
Learning Integer Lattices. COLT (1990)
David P. Helmbold, Charles E. McDowell, Jian-Zhong Wang
Analyzing Traces with Anonymous Synchronization. ICPP (2) (1990)
David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth
Learning Nested Differences of Intersection-Closed Concept Classes. Mach. Learn. 5 (1990)
David P. Helmbold, Charles E. McDowell
. IEEE Trans. Parallel Distributed Syst. 1 (2) (1990)
David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth
Learning Nested Differences of Intersection-Closed Concept Classes. COLT (1989)
Charles E. McDowell, David P. Helmbold
Debugging Concurrent Programs. ACM Comput. Surv. 21 (4) (1989)
David P. Helmbold, Charles E. McDowell
Modeling Speedup greater than n. ICPP (3) (1989)
David P. Helmbold, Ernst W. Mayr
Two Processor Scheduling is in NC. SIAM J. Comput. 16 (4) (1987)
David C. Luckham, David P. Helmbold, D. L. Bryan, M. A. Haberler
Task Sequencing Language for Specifying Distributed Ada Systems. PARLE (2) (1987)
David C. Luckham, David P. Helmbold, Sigurd Meldal, D. L. Bryan, M. A. Haberler
Task Sequencing Languages for Specifying Distributed Ada Systems. System Development and Ada (1986)
David P. Helmbold, Ernst W. Mayr
Two Processor Scheduling is in NC. Aegean Workshop on Computing (1986)
David P. Helmbold, Ernst W. Mayr
Applications of Parallel Scheduling to Perfect Graphs. WG (1986)
David P. Helmbold, Ernst W. Mayr
Perfect Graphs and Parallel Algorithms. ICPP (1986)
David P. Helmbold, David C. Luckham
TSL: task sequencing language. SIGAda (1985)
Steven M. German, David P. Helmbold, David C. Luckham
Monitoring for deadlocks in Ada tasking. AdaTEC Conference on Ada (1982)