Manfred K. Warmuth

Manfred K. Warmuth's AcademicInfluence.com Rankings

Manfred K. Warmuth

Computer Science

#364

World Rank

#379

Historical Rank

Machine Learning

#136

World Rank

#137

Historical Rank

Artificial Intelligence

#146

World Rank

#149

Historical Rank

Database

#643

World Rank

#674

Historical Rank

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Computer Science

Manfred K. Warmuth's Degrees

PhD Computer Science University of Bonn

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Why Is Manfred K. Warmuth Influential?

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According to Wikipedia, Manfred Klaus Warmuth is a computer scientist known for his pioneering research in computational learning theory. He is a Distinguished Professor emeritus at the University of California, Santa Cruz.

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Manfred K. Warmuth's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

The weighted majority algorithm (1989) (2412)
Learnability and the Vapnik-Chervonenkis dimension (1989) (1995)
Exponentiated Gradient Versus Gradient Descent for Linear Predictors (1997) (951)
How to use expert advice (1993) (660)
Occam's Razor (1987) (599)
Tracking the Best Expert (1995) (591)
On‐Line Portfolio Selection Using Multiplicative Updates (1998) (364)
Active Learning with Support Vector Machines in the Drug Discovery Process (2003) (309)
Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions (1999) (305)
Additive versus exponentiated gradient updates for linear prediction (1995) (292)
Using and combining predictors that specialize (1997) (275)
Finding a Shortest Solution for the N × N Extension of the 15-PUZZLE Is Intractable (1986) (270)
Predicting (0, 1)-functions on randomly drawn points (1988) (266)
Relating Data Compression and Learnability (2003) (258)
Tracking the Best Linear Predictor (2001) (247)
The minimum consistent DFA problem cannot be approximated within any polynomial (1989) (245)
Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension (1995) (244)
Path Kernels and Multiplicative Updates (2002) (235)
Equivalence of models for polynomial learnability (1988) (231)
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection (2004) (221)
How to use expert advice (1997) (215)
Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension (1986) (201)
Tracking a Small Set of Experts by Mixing Past Posteriors (2003) (192)
THE CMU SPHINX-4 SPEECH RECOGNITION SYSTEM (2001) (189)
Sequential Prediction of Individual Sequences Under General Loss Functions (1998) (175)
Prediction-Preserving Reducibility (1990) (163)
Exponentially many local minima for single neurons (1995) (161)
NxN Puzzle and Related Relocation Problem (1990) (159)
Randomized Online PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension (2008) (158)
Worst-case quadratic loss bounds for prediction using linear functions and gradient descent (1996) (144)
Relative Loss Bounds for Multidimensional Regression Problems (1997) (143)
Averaging Expert Predictions (1999) (142)
Computing on an anonymous ring (1985) (142)
Boosting as entropy projection (1999) (139)
On the computational complexity of approximating distributions by probabilistic automata (1990) (128)
Totally corrective boosting algorithms that maximize the margin (2006) (127)
Learning Permutations with Exponential Weights (2007) (126)
Efficient Margin Maximizing with Boosting (2005) (123)
Computing on an anonymous ring (1988) (116)
The perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant (1995) (116)
Linear Hinge Loss and Average Margin (1998) (109)
Hedging Structured Concepts (2010) (109)
Tracking the Best Disjunction (1995) (106)
Learning integer lattices (1990) (106)
Tight worst-case loss bounds for predicting with expert advice (1994) (101)
Learning Nested Differences of Intersection-Closed Concept Classes (1989) (99)
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences (2019) (94)
On Weak Learning (1995) (92)
The Perceptron Algorithm Versus Winnow: Linear Versus Logarithmic Mistake Bounds when Few Input Variables are Relevant (Technical Note) (1997) (90)
Membership for Growing Context-Sensitive Grammars is Polynomial (1986) (87)
Active Learning in the Drug Discovery Process (2001) (85)
On-line learning of linear functions (1991) (85)
Engineering proteinase K using machine learning and synthetic genes (2007) (84)
Boosting Algorithms for Maximizing the Soft Margin (2007) (82)
The p-norm generalization of the LMS algorithm for adaptive filtering (2006) (82)
A Comparison of New and Old Algorithms for a Mixture Estimation Problem (1995) (81)
Online variance minimization (2006) (74)
A Fast Algorithm for Multiprocessor Scheduling of Unit-Length Jobs (1989) (73)
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension (2006) (73)
On the Complexity of Iterated Shuffle (1984) (72)
Adaptive Caching by Refetching (2002) (72)
Sample compression, learnability, and the Vapnik-Chervonenkis dimension (2004) (69)
Relative loss bounds for single neurons (1999) (67)
Entropy Regularized LPBoost (2008) (65)
TriMap: Large-scale Dimensionality Reduction Using Triplets (2019) (65)
Reductions among prediction problems: on the difficulty of predicting automata (1988) (65)
Unlabeled Compression Schemes for Maximum Classes, (2007) (62)
Efficient Learning With Virtual Threshold Gates (1995) (60)
WORST-CASE QUADRATIC LOSS BOUNDS FOR ON-LINE PREDICTION OF LINEAR FUNCTIONS BY GRADIENT DESCENT (1993) (60)
Parallel Approximation Algorithms for Bin Packing (1988) (58)
Profile Scheduling of Opposing Forests and Level Orders (1985) (57)
Relative Expected Instantaneous Loss Bounds (2000) (55)
Maximizing the Margin with Boosting (2002) (54)
Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions (1996) (51)
Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy (1998) (48)
Scheduling Precedence Graphs of Bounded Height (1984) (47)
On the Convergence of Leveraging (2001) (46)
On-line Prediction and Conversion Strategies (1994) (45)
The minimum consistent DFA problem cannot be approximated within and polynomial (1989) (45)
Leveraged volume sampling for linear regression (2018) (44)
Unbiased estimates for linear regression via volume sampling (2017) (43)
Barrier Boosting (2000) (42)
Composite geometric concepts and polynomial predictability (1990) (42)
Reverse iterative volume sampling for linear regression (2018) (41)
Putting Bayes to sleep (2012) (40)
The Last-Step Minimax Algorithm (2000) (38)
Scheduling Flat Graphs (1985) (37)
Learning Binary Relations Using Weighted Majority Voting (1993) (36)
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms (2005) (35)
Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence (1991) (35)
Online PCA with Optimal Regrets (2013) (35)
Predicting nearly as well as the best pruning of a planar decision graph (2002) (35)
Compressing to VC Dimension Many Points (2003) (34)
Gap Theorems for Distributed Computation (1993) (33)
Optimal strategies from random walks (2008) (31)
Bayesian generalized probability calculus for density matrices (2009) (30)
The Probably Approximately Correct (PAC) and Other Learning Models (1993) (30)
Tracking the best regressor (1998) (30)
Leaving the Span (2005) (30)
Support Vector Machines for Active Learning in the Drug Discovery Process (2002) (29)
Towards Representation Independence in PAC Learning (1989) (28)
Gap theorems for distributed computing (1986) (28)
Winnowing subspaces (2007) (28)
The Distributed Bit Complexity of the Ring: From the Anonymous to the Non-anonymous Case (1989) (27)
Minimax Fixed-Design Linear Regression (2015) (26)
Using experts for predicting continuous outcomes (1994) (26)
The Minimax Strategy for Gaussian Density Estimation. pp (2000) (25)
Online kernel PCA with entropic matrix updates (2007) (24)
Worst-case quadratic loss bounds for a generalization of the Widrow-Hoff rule (1993) (23)
Optimum Follow the Leader Algorithm (2005) (22)
Labeled Compression Schemes for Extremal Classes (2015) (22)
Adaptive scale-invariant online algorithms for learning linear models (2019) (22)
Online PCA with Optimal Regret (2016) (21)
Repeated Games against Budgeted Adversaries (2010) (21)
A Bayes Rule for Density Matrices (2005) (20)
Subsampling for Ridge Regression via Regularized Volume Sampling (2017) (20)
When Random Play is Optimal Against an Adversary (2008) (20)
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression (2019) (19)
Two-temperature logistic regression based on the Tsallis divergence (2017) (18)
Correcting the bias in least squares regression with volume-rescaled sampling (2018) (18)
A Bayesian Probability Calculus for Density Matrices (2006) (18)
Unlabeled sample compression schemes and corner peelings for ample and maximum classes (2018) (18)
Continuous Experts and the Binning Algorithm (2006) (18)
Speech Recognition: Keyword Spotting Through Image Recognition (2018) (17)
A more globally accurate dimensionality reduction method using triplets (2018) (16)
Scattered versus context-sensitive rewriting (1989) (16)
On the worst-case analysis of temporal-difference learning algorithms (2004) (16)
Proceedings of the seventh annual conference on Computational learning theory (1994) (16)
Worst-case Loss Bounds for Single Neurons (1995) (15)
Direct and indirect algorithms for on-line learning of disjunctions (1999) (15)
The Optimal PAC Algorithm (2004) (15)
On the Computational Complexity of Approximating Distributions by Probabilistic Automata (1990) (14)
Classification with free energy at raised temperatures (2003) (14)
Reparameterizing Mirror Descent as Gradient Descent (2020) (14)
The Parallel Complexity of Scheduling with Precedence Constraints (1986) (13)
Winnowing with Gradient Descent (2020) (12)
Learning of depth two neural networks with constant fan-in at the hidden nodes (extended abstract) (1996) (11)
Applications of Scheduling Theory to Formal Language Theory (1985) (11)
Bounds on approximate steepest descent for likelihood maximization in exponential families (1994) (11)
On-line Variance Minimization in O(n2) per Trial? (2010) (11)
Unbiased estimators for random design regression (2019) (11)
PCA with Gaussian perturbations (2015) (10)
Some weak learning results (1992) (10)
THE WEIGHTED MAJORITY ALGORITHM (Supersedes 89-16) (1992) (10)
Online Dynamic Programming (2017) (10)
A New Parameter Estimation Method for Gaussian Mixtures (1998) (9)
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond (2021) (9)
Learning nested differences of intersection-closed concept classes (2004) (9)
Learning rotations with little regret (2010) (9)
Combining initial segments of lists (2011) (8)
The limits of squared Euclidean distance regularization (2014) (8)
Open Problem: Shifting Experts on Easy Data (2014) (8)
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint (2019) (8)
Relative Loss Bounds for Temporal-Difference Learning (2000) (8)
Manipulating Derivation Forests by Scheduling Techniques (1986) (7)
LocoProp: Enhancing BackProp via Local Loss Optimization (2021) (7)
Inline updates for HMMs (2003) (6)
Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings (2003) (6)
Boosting versus Covering (2003) (6)
On-Line Learning Algorithms for Path Experts with Non-Additive Losses (2015) (6)
Learning from Randomly Initialized Neural Network Features (2022) (6)
When Is There a Free Matrix Lunch? (2007) (6)
Learning binary relations using weighted majority voting (2004) (6)
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models (2019) (5)
Kernelization of matrix updates, when and how? (2012) (5)
Scheduling on profiles of constant breadth (1981) (5)
Low-dimensional Data Embedding via Robust Ranking (2016) (5)
Step-size Adaptation Using Exponentiated Gradient Updates (2022) (5)
Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment (2020) (4)
Learning Eigenvectors for Free (2011) (4)
Active Learning with Support Vector Machines in the Drug Discovery Process. (2003) (4)
On-line prediction and conversion strategies (2004) (4)
Tail bounds for volume sampled linear regression (2018) (4)
The Blessing and the Curse of the Multiplicative Updates (2010) (3)
Corrigendum to "Learning rotations with little regret" September 7, 2010 (2010) (3)
Open Problem: Online Sabotaged Shortest Path (2015) (3)
Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints (2006) (3)
Shifting Experts on Easy Data (2014) (3)
The P-Norn Generalization of the LMS Algorithm for Adaptive Filtering (2003) (3)
Minimax Algorithm for Learning Rotations (2011) (3)
Learning Rotations (2008) (3)
Learning Theory and Kernel Machines : 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003. Proceedings (2003) (3)
Learning Rotations Online (2010) (2)
Proceedings of the Fourth Annual Workshop on Computational Learning Theory, University of California, Santa Cruz, August 5-7, 1991 (1991) (2)
Efficient Learning Algorithms. (1990) (2)
Noise Free Multi-armed Bandit Game (2015) (2)
Predicting Round Trip Time for the TCP Protocol (2013) (2)
Learning a set of directions (2013) (2)
Proceedings of the Second Annual Workshop on Computational Learning Theory : University of California, Santa Cruz, July 31-August 2, 1989 (1989) (2)
Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent (2020) (2)
Tutorial summary: Survey of boosting from an optimization perspective (2009) (1)
Open Problem: Lower bounds for Boosting with Hadamard Matrices (2013) (1)
Theory and Praxis of Machine Learning (2008) (1)
Mistake bounds on the noise-free multi-armed bandit game (2019) (1)
Learning rotations with little regret (2016) (1)
Learning algorithms for tracking changing concepts and an investigation into the error surfaces of single artificial neurons (1998) (1)
A case where a spindly two-layer linear network whips any neural network with a fully connected input layer (2020) (1)
Online variance minimization (2011) (1)
Minimax Games with Bandits (2009) (1)
Entropy regularised LPboost (2008) (1)
The Binning Algorithm (1)
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation (2022) (1)
Membership for Growing Context Sensitive Grammars is Polynomial (1986) (1)
Matrix Exponential Updates for On-line Learning and Bregman Projection (0)
Deriving and Analyzing Learning Algorithms (0)
Mar ginal Boosting 1 (2001) (0)
Relative Loss Bounds, the Minimum Relative Entropy Principle, and EM (1997) (0)
Online Learning on Spheres (2005) (0)
Unlabeled Compression Schemes for Maximum Classes (2007) (0)
RI : Small : Collaborative Research : Probabilistic Models using Generalized Exponential Families (2014) (0)
the Liar Game. References (0)
The Robustness of Quadratic Voting (2016) (0)
New combination coefficients for AdaBoost algorithms (2010) (0)
On the Complexity of Iterated Shuffle ; CU-CS-201-81 (1981) (0)
Online Non-Additive Path Learning under Full and Partial Information (2018) (0)
Clustering above Exponential Families with Tempered Exponential Measures (2022) (0)
CLASSIFICATION WITH FREE ENERGY A (2003) (0)
Analyzing the Performance of Learning Algorithms. (1993) (0)
Theory and Praxis of Machine Learning (Dagstuhl Seminar 9426) (2021) (0)
rst-Case ratic Loss Bounds for Pre sing Linear Functions and Gra (1996) (0)
Active Learning and Feature Selection in the Drug Discovery Process (2002) (0)
Online learning with matrix parameters (2009) (0)
Measuring the “on-lineness” of data streams (2015) (0)
The { dollar } p { dollar }-Norm Generalization of the LMS Algorithm for Adaptive Filtering (0)
Computational Power of low-resource distributed systems: • Jukka Suomela: "Survey of Local Algorithms", Journal ACM Computing Surveys, 2013. (2013) (0)
A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer (2021) (0)
Lower bounds for Boosting with Hadamard Matrices (2013) (0)
E 1 Analysing Iterative Machine Learning Algorithms with In formation Geometric Methods (0)

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Manfred K. Warmuth's Published Works

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What Schools Are Affiliated With Manfred K. Warmuth?

Manfred K. Warmuth's AcademicInfluence.com Rankings