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Publications Influence

How to use expert advice

- N. Cesa-Bianchi, Y. Freund, D. Helmbold, D. Haussler, R. Schapire, Manfred K. Warmuth
- Mathematics, Computer Science
- STOC '93
- 1 June 1993

We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called `experts''. Our analysis is for worst-case situations, i.e., we make no… Expand

607 44- PDF

On-Line Portfolio Selection Using Multiplicative Updates

- D. Helmbold, R. Schapire, Y. Singer, Manfred K. Warmuth
- Computer Science, Economics
- ICML
- 1 October 1998

We present an on-line investment algorithm that achieves almost the same wealth as the best constant-rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithm… Expand

279 36- PDF

Learning Permutations with Exponential Weights

- D. Helmbold, Manfred K. Warmuth
- Mathematics, Computer Science
- COLT
- 13 June 2007

We give an algorithm for learning a permutation on-line. The algorithm maintains its uncertainty about the target permutation as a doubly stochastic matrix. This matrix is updated by multiplying the… Expand

110 18- PDF

Debugging concurrent programs

- C. McDowell, D. Helmbold
- Computer Science
- CSUR
- 1 December 1989

The main problems associated with debugging concurrent programs are increased complexity, the "probe effect," nonrepeatability, and the lack of a synchronized global clock. The probe effect refers to… Expand

452 17- PDF

How to use expert advice

- N. Cesa-Bianchi, Y. Freund, D. Haussler, D. Helmbold, R. Schapire, Manfred K. Warmuth
- Computer Science
- JACM
- 1 May 1997

We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worst-case situations, i.e., we make no… Expand

209 14

How to use expert advice

- N. Cesa-Bianchi, Y. Freund, D. Haussler, D. Helmbold, R. Schapire, Manfred K. Warmuth
- Mathematics, Computer Science
- JACM
- 1 May 1997

We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worst-case situations, i.e., we make no… Expand

210 14

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

- D. Helmbold, R. Schapire
- Computer Science
- COLT
- 5 July 1995

Many algorithms for inferring a decision tree from data involve a two-phase process: First, a very large decision tree is grown which typically ends up “over-fitting” the data. To reduce… Expand

101 14

Adaptive disk spin‐down for mobile computers

- D. Helmbold, D. Long, Tracey L. Sconyers, Bruce Sherrod
- Computer Science
- Mob. Networks Appl.
- 1 December 2000

We address the problem of deciding when to spin down the disk of a mobile computer in order to extend battery life. One of the most critical resources in mobile computing environments is battery… Expand

137 10- PDF

Aerial Lidar Data Classification using AdaBoost

- S. Lodha, D. Fitzpatrick, D. Helmbold
- Computer Science
- Sixth International Conference on 3-D Digital…
- 21 August 2007

We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation,… Expand

94 10- PDF

Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks

- P. Bartlett, D. Helmbold, Philip M. Long
- Computer Science, Mathematics
- Neural Computation
- 16 February 2018

We analyze algorithms for approximating a function f(x)=Φx mapping ℜd to ℜd using deep linear neural networks, that is, that learn a function h parameterized by matrices Θ1,…,ΘL and defined by… Expand

58 10- PDF