David P. Helmbold

87 papers A* 19A 2B 4C 2Misc 2Journal 39Unranked 17

Year	Rank	Type	Title / Venue / Authors
2020	A	`conf`	Online Learning Using Only Peer Prediction. AISTATS Yang Liu, David P. Helmbold
2019	J	`jnl`	A New Family of Neural Networks Provably Resistant to Adversarial Attacks. CoRR Rakshit Agrawal, Luca de Alfaro, David P. Helmbold
2019	J	`jnl`	Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks. Neural Comput. Peter L. Bartlett, David P. Helmbold, Philip M. Long
2019	J	`jnl`	Mistake bounds on the noise-free multi-armed bandit game. Inf. Comput. Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth
2019	J	`jnl`	Online Learning Using Only Peer Assessment. CoRR Yang Liu, David P. Helmbold
2018	J	`jnl`	Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks. CoRR Peter L. Bartlett, David P. Helmbold, Philip M. Long
2018	A*	`conf`	Gradient descent with identity initialization efficiently learns positive definite linear transformations. ICML Peter L. Bartlett, David P. Helmbold, Philip M. Long
2018	B	`conf`	Online Learning of Combinatorial Objects via Extended Formulation. ALT Holakou Rahmanian, David P. Helmbold, S. V. N. Vishwanathan
2017	A*	`conf`	Surprising properties of dropout in deep networks. COLT David P. Helmbold, Philip M. Long
2017	J	`jnl`	Surprising properties of dropout in deep networks. J. Mach. Learn. Res. David P. Helmbold, Philip M. Long
2016	J	`jnl`	Dropout Versus Weight Decay for Deep Networks. CoRR David P. Helmbold, Philip M. Long
2016	J	`jnl`	Extended Formulation for Online Learning of Combinatorial Objects. CoRR Holakou Rahmanian, S. V. N. Vishwanathan, David P. Helmbold
2016	C	`conf`	Noise Free Multi-armed Bandit Game. LATA Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth
2015	J	`jnl`	On the inductive bias of dropout. J. Mach. Learn. Res. David P. Helmbold, Philip M. Long
2014	J	`jnl`	Combining initial segments of lists. Theor. Comput. Sci. Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold
2014	J	`jnl`	On the Inductive Bias of Dropout. CoRR David P. Helmbold, Philip M. Long
2013	J	`jnl`	Boosting in Location Space. CoRR Damian Eads, David P. Helmbold, Edward Rosten
2012	C	`conf`	Evolutionary learning of policies for MCTS simulations. FDG James Pettit, David P. Helmbold
2012	A*	`conf`	New Bounds for Learning Intervals with Implications for Semi-Supervised Learning. COLT David P. Helmbold, Philip M. Long
2012	J	`jnl`	On the Necessity of Irrelevant Variables CoRR David P. Helmbold, Philip M. Long
2012	J	`jnl`	On the necessity of irrelevant variables. J. Mach. Learn. Res. David P. Helmbold, Philip M. Long
2011	B	`conf`	Combining Initial Segments of Lists. ALT Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold
2011	A*	`conf`	On the Necessity of Irrelevant Variables. ICML David P. Helmbold, Philip M. Long
2009	Misc	`conf`	All-Moves-As-First Heuristics in Monte-Carlo Go. IC-AI David P. Helmbold, Aleatha Parker-Wood
2009	J	`jnl`	Learning Object Location Predictors with Boosting and Grammar-Guided Feature Extraction CoRR Damian Eads, Edward Rosten, David P. Helmbold
2009	A	`conf`	Learning Object Location Predictors with Boosting and Grammar-Guided Feature Extraction. BMVC Damian Eads, Edward Rosten, David P. Helmbold
2009	J	`jnl`	Learning Permutations with Exponential Weights. J. Mach. Learn. Res. David P. Helmbold, Manfred K. Warmuth
2007	—	`conf`	Aerial Lidar Data Classification using AdaBoost. 3DIM Suresh K. Lodha, Darren N. Fitzpatrick, David P. Helmbold
2007	A*	`conf`	Learning Permutations with Exponential Weights. COLT David P. Helmbold, Manfred K. Warmuth
2006	—	`conf`	A Bayesian Approach to Building Footprint Extraction from Aerial LIDAR Data. 3DPVT Oliver Wang, Suresh K. Lodha, David P. Helmbold
2006	—	`conf`	Aerial LiDAR Data Classification Using Support Vector Machines (SVM). 3DPVT Suresh K. Lodha, Edward J. Kreps, David P. Helmbold, Darren N. Fitzpatrick
2006	J	`jnl`	Modeling, analyzing, and synthesizing expressive piano performance with graphical models. Mach. Learn. Graham Grindlay, David P. Helmbold
2002	J	`jnl`	A geometric approach to leveraging weak learners. Theor. Comput. Sci. Nigel Duffy, David P. Helmbold
2002	J	`jnl`	Boosting Methods for Regression. Mach. Learn. Nigel Duffy, David P. Helmbold
2002	J	`jnl`	Direct and indirect algorithms for on-line learning of disjunctions. Theor. Comput. Sci. David P. Helmbold, Sandra Panizza, Manfred K. Warmuth
2001	—	`ed.`	Computational Learning Theory, 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, The Netherlands, July 16-19, 2001, Proceedings COLT/EuroCOLT David P. Helmbold, Robert C. Williamson
2001	J	`jnl`	Improved Lower Bounds for Learning from Noisy Examples: An Information-Theoretic Approach. Inf. Comput. Claudio Gentile, David P. Helmbold
2000	J	`jnl`	Adaptive disk spin-down for mobile computers. Mob. Networks Appl. David P. Helmbold, Darrell D. E. Long, Tracey L. Sconyers, Bruce Sherrod
2000	J	`jnl`	Apple Tasting. Inf. Comput. David P. Helmbold, Nick Littlestone, Philip M. Long
2000	A*	`conf`	Leveraging for Regression. COLT Nigel Duffy, David P. Helmbold
2000	J	`jnl`	On-Line Learning with Linear Loss Constraints. Inf. Comput. David P. Helmbold, Nick Littlestone, Philip M. Long
1999	—	`conf`	A Geometric Approach to Leveraging Weak Learners. EuroCOLT Nigel Duffy, David P. Helmbold
1999	—	`conf`	Direct and Indirect Algorithms for On-line Learning of Disjunctions. EuroCOLT David P. Helmbold, Sandra Panizza, Manfred K. Warmuth
1999	—	`conf`	Potential Boosters? NIPS Nigel Duffy, David P. Helmbold
1999	J	`jnl`	Relative loss bounds for single neurons. IEEE Trans. Neural Networks David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth
1998	A*	`conf`	Improved Lower Bounds for Learning from Noisy Examples: An Information-Theoretic Approach. COLT Claudio Gentile, David P. Helmbold
1998	J	`jnl`	On Bayes Methods for On-Line Boolean Prediction. Algorithmica Nicolò Cesa-Bianchi, David P. Helmbold, Sandra Panizza
1997	J	`jnl`	A Comparison of New and Old Algorithms for a Mixture Estimation Problem. Mach. Learn. David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth
1997	J	`jnl`	How to use expert advice. J. ACM Nicolò Cesa-Bianchi, Yoav Freund, David Haussler, David P. Helmbold, Robert E. Schapire, Manfred K. Warmuth
1997	—	`conf`	Learning When to Trust Which Experts. EuroCOLT David P. Helmbold, Stephen Kwek, Leonard Pitt
1997	J	`jnl`	Predicting Nearly As Well As the Best Pruning of a Decision Tree. Mach. Learn. David P. Helmbold, Robert E. Schapire
1997	A*	`conf`	Some Label Efficient Learning Results. COLT David P. Helmbold, Sandra Panizza
1996	Misc	`conf`	A Class of Synchronization Operations that Permit Efficient Race Detection. PDPTA David P. Helmbold, Charles E. McDowell
1996	A*	`conf`	A Dynamic Disk Spin-Down Technique for Mobile Computing. MobiCom David P. Helmbold, Darrell D. E. Long, Bruce Sherrod
1996	J	`jnl`	A Taxonomy of Race Conditions. J. Parallel Distributed Comput. David P. Helmbold, Charles E. McDowell
1996	A*	`conf`	On Bayes Methods for On-Line Boolean Prediction. COLT Nicolò Cesa-Bianchi, David P. Helmbold, Sandra Panizza
1996	A*	`conf`	On-Line Portfolio Selection Using Multiplicative Updates. ICML David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth
1996	J	`jnl`	On-line Prediction and Conversion Strategies. Mach. Learn. Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth
1995	A*	`conf`	A Comparison of New and Old Algorithms for a Mixture Estimation Problem. COLT David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth
1995	J	`jnl`	On Weak Learning. J. Comput. Syst. Sci. David P. Helmbold, Manfred K. Warmuth
1995	A*	`conf`	Predicting Nearly as Well as the Best Pruning of a Decision Tree. COLT David P. Helmbold, Robert E. Schapire
1995	—	`conf`	Worst-case Loss Bounds for Single Neurons. NIPS David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth
1994	J	`jnl`	Tracking Drifting Concepts By Minimizing Disagreements. Mach. Learn. David P. Helmbold, Philip M. Long
1993	J	`jnl`	Determining Possible Event Orders by Analyzing Sequential Traces. IEEE Trans. Parallel Distributed Syst. David P. Helmbold, Charles E. McDowell, Jian-Zhong Wang
1993	A*	`conf`	How to use expert advice. STOC Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert E. Schapire, Manfred K. Warmuth
1993	—	`conf`	On-line prediction and conversion strategies. EuroCOLT Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth
1992	A*	`conf`	Apple Tasting and Nearly One-Sided Learning FOCS David P. Helmbold, Nick Littlestone, Philip M. Long
1992	J	`jnl`	Learning Integer Lattices. SIAM J. Comput. David P. Helmbold, Robert Sloan, Manfred K. Warmuth
1992	A*	`conf`	Some Weak Learning Results. COLT David P. Helmbold, Manfred K. Warmuth
1991	—	`conf`	Computing Reachable States of Parallel Programs. Workshop on Parallel and Distributed Debugging David P. Helmbold, Charles E. McDowell
1991	A*	`conf`	Tracking Drifting Concepts Using Random Examples. COLT David P. Helmbold, Philip M. Long
1990	—	`conf`	Analyzing Traces with Anonymous Synchronization. ICPP (2) David P. Helmbold, Charles E. McDowell, Jian-Zhong Wang
1990	A*	`conf`	Learning Integer Lattices. COLT David P. Helmbold, Robert Sloan, Manfred K. Warmuth
1990	J	`jnl`	Learning Nested Differences of Intersection-Closed Concept Classes. Mach. Learn. David P. Helmbold, Robert Sloan, Manfred K. Warmuth
1990	J	`jnl`	Modeling Speedup ( IEEE Trans. Parallel Distributed Syst. David P. Helmbold, Charles E. McDowell
1989	J	`jnl`	Debugging Concurrent Programs. ACM Comput. Surv. Charles E. McDowell, David P. Helmbold
1989	A*	`conf`	Learning Nested Differences of Intersection-Closed Concept Classes. COLT David P. Helmbold, Robert Sloan, Manfred K. Warmuth
1989	—	`conf`	Modeling Speedup greater than n. ICPP (3) David P. Helmbold, Charles E. McDowell
1987	—	`—`	Parallel algorithms for scheduling and related problems. David P. Helmbold
1987	—	`conf`	Task Sequencing Language for Specifying Distributed Ada Systems. PARLE (2) David C. Luckham, David P. Helmbold, D. L. Bryan, M. A. Haberler
1987	J	`jnl`	Two Processor Scheduling is in NC. SIAM J. Comput. David P. Helmbold, Ernst W. Mayr
1986	B	`conf`	Applications of Parallel Scheduling to Perfect Graphs. WG David P. Helmbold, Ernst W. Mayr
1986	B	`conf`	Perfect Graphs and Parallel Algorithms. ICPP David P. Helmbold, Ernst W. Mayr
1986	—	`conf`	Task Sequencing Languages for Specifying Distributed Ada Systems. System Development and Ada David C. Luckham, David P. Helmbold, Sigurd Meldal, D. L. Bryan, M. A. Haberler
1986	—	`conf`	Two Processor Scheduling is in NC. Aegean Workshop on Computing David P. Helmbold, Ernst W. Mayr
1985	—	`conf`	TSL: task sequencing language. SIGAda David P. Helmbold, David C. Luckham
1982	—	`conf`	Monitoring for deadlocks in Ada tasking. AdaTEC Conference on Ada Steven M. German, David P. Helmbold, David C. Luckham