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

Neural computation, 2019-03, Vol.31 (3), p.477-502

2019

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

Information and computation, 2019-12, Vol.269, p.104453, Article 104453

2019

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How to use expert advice

Journal of the ACM, 1997-05, Vol.44 (3), p.427-485

1997

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On-Line Portfolio Selection Using Multiplicative Updates

Mathematical finance, 1998-10, Vol.8 (4), p.325-347

1998

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Adaptive disk spin-down for mobile computers

Mobile networks and applications, 2000-12, Vol.5 (4), p.285-297

2000

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Boosting Methods for Regression

Machine learning, 2002-05, Vol.47 (2-3), p.153-200

2002

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Debugging concurrent programs

ACM computing surveys, 1989-12, Vol.21 (4), p.593-622

1989

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Combining initial segments of lists

Theoretical computer science, 2014-01, Vol.519, p.29-45

2014

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Aerial Lidar Data Classification using AdaBoost

Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007), 2007, p.435-442

2007

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

Machine learning, 1997, Vol.27 (1), p.97-119

1997

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Noise Free Multi-armed Bandit Game

Language and Automata Theory and Applications, p.412-423

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On-line prediction and conversion strategies

Machine learning, 1996, Vol.25 (1), p.71-110

1996

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Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06), 2006, p.567-574

2006

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Discrete Prediction Games with Arbitrary Feedback and Loss

Computational Learning Theory, 2001, Vol.2111, p.208-223

2001

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Combining Initial Segments of Lists

Algorithmic Learning Theory, p.219-233

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Tracking drifting concepts by minimizing disagreements

Machine learning, 1994-01, Vol.14 (1), p.27-45

1994

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Learning Permutations with Exponential Weights

Learning Theory, p.469-483

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Determining possible event orders by analyzing sequential traces

IEEE transactions on parallel and distributed systems, 1993-07, Vol.4 (7), p.827-840

1993

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Two processor scheduling is in NC

VLSI Algorithms and Architectures, 2005, p.12-25

2005

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Evolutionary learning of policies for MCTS simulations

Proceedings of the International Conference on the Foundations of Digital Games, 2012, p.212-219

2012

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Relative loss bounds for single neurons

IEEE transactions on neural networks, 1999, Vol.10 (6), p.1291-1304

1999

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Improved lower bounds for learning from noisy examples: an information-theoretic approach

Annual Workshop on Computational Learning Theory: Proceedings of the eleventh annual conference on Computational learning theory; 24-26 July 1998, 1998, p.104-115

1998

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Learning when to trust which experts

Computational Learning Theory, 2005, p.134-149

2005

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Potential-Based Algorithms in Online Prediction and Game Theory

Computational Learning Theory, 2001, Vol.2111, p.48-64

2001

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Modelling speedup (n) greater than n

IEEE transactions on parallel and distributed systems, 1990-04, Vol.1 (2), p.250-256

1990

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

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

How to use expert advice

On-Line Portfolio Selection Using Multiplicative Updates

Adaptive disk spin-down for mobile computers

Boosting Methods for Regression

Debugging concurrent programs

Combining initial segments of lists

Aerial Lidar Data Classification using AdaBoost

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

Noise Free Multi-armed Bandit Game

On-line prediction and conversion strategies

Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

Discrete Prediction Games with Arbitrary Feedback and Loss

Combining Initial Segments of Lists

Tracking drifting concepts by minimizing disagreements

Learning Permutations with Exponential Weights

Determining possible event orders by analyzing sequential traces

Two processor scheduling is in NC

Evolutionary learning of policies for MCTS simulations

Relative loss bounds for single neurons

Improved lower bounds for learning from noisy examples: an information-theoretic approach

Learning when to trust which experts

Potential-Based Algorithms in Online Prediction and Game Theory

Modelling speedup (n) greater than n

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