Scheduling Precedence Graphs of Bounded Height. - Danny Dolev; Manfred K. Warmuth (J. Algorithms, 1984)

On the Complexity of Iterated Shuffle. - Manfred K. Warmuth; David Haussler (J. Comput. Syst. Sci., 1984)

Scheduling Flat Graphs. - Danny Dolev; Manfred K. Warmuth (SIAM J. Comput., 1985)

Applications of Scheduling Theory to Formal Language Theory. - Jakob Gonczarowski; Manfred K. Warmuth (Theor. Comput. Sci., 1985)

Finding a Shortest Solution for the N × N Extension of the 15-PUZZLE Is Intractable. - Daniel Ratner; Manfred K. Warmuth (AAAI, 1986)

Classifying Learnable Geometric Concepts with the Vapnik-Chervonenkis Dimension (Extended Abstract) - Anselm Blumer; Andrzej Ehrenfeucht; David Haussler; Manfred K. Warmuth (STOC, 1986)

Membership for Growing Context-Sensitive Grammars is Polynomial. - Elias Dahlhaus; Manfred K. Warmuth (J. Comput. Syst. Sci., 1986)

The Parallel Complexity of Scheduling with Precedence Constraints. - Danny Dolev; Eli Upfal; Manfred K. Warmuth (J. Parallel Distributed Comput., 1986)

Manipulating Derivation Forests by Scheduling Techniques. - Jakob Gonczarowski; Manfred K. Warmuth (Theor. Comput. Sci., 1986)

Occam's Razor. - Anselm Blumer; Andrzej Ehrenfeucht; David Haussler; Manfred K. Warmuth (Inf. Process. Lett., 1987)

Reductions among prediction problems: on the difficulty of predicting automata. - Leonard Pitt; Manfred K. Warmuth (SCT, 1988)

Predicting {0, 1}-Functions on Randomly Drawn Points. - David Haussler; Nick Littlestone; Manfred K. Warmuth (COLT, 1988)

Predicting {0,1}-Functions on Randomly Drawn Points (Extended Abstract) - David Haussler; Nick Littlestone; Manfred K. Warmuth (FOCS, 1988)

Computing on an anonymous ring. - Hagit Attiya; Marc Snir; Manfred K. Warmuth (J. ACM, 1988)

Towards Representation Independence in PAC Learning. - Manfred K. Warmuth (AII, 1989)

The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial (abstract). - Leonard Pitt; Manfred K. Warmuth (SCT, 1989)

Scattered Versus Context-Sensitive Rewriting. - Jakob Gonczarowski; Manfred K. Warmuth (Acta Informatica, 1989)

Learnability and the Vapnik-Chervonenkis dimension. - Anselm Blumer; Andrzej Ehrenfeucht; David Haussler; Manfred K. Warmuth (J. ACM, 1989)

A Fast Algorithm for Multiprocessor Scheduling of Unit-Length Jobs. - Barbara B. Simons; Manfred K. Warmuth (SIAM J. Comput., 1989)

Parallel Approximation Algorithms for Bin Packing - Richard J. Anderson 001; Ernst W. Mayr; Manfred K. Warmuth (Inf. Comput., 1989)

Prediction-Preserving Reducibility. - Leonard Pitt; Manfred K. Warmuth (J. Comput. Syst. Sci., 1990)

NxN Puzzle and Related Relocation Problem. - Daniel Ratner; Manfred K. Warmuth (J. Symb. Comput., 1990)

Learning Nested Differences of Intersection-Closed Concept Classes. - David P. Helmbold; Robert Sloan 001; Manfred K. Warmuth (Mach. Learn., 1990)

Polynomial Learnability of Probabilistic Concepts with Respect to the Kullback-Leibler Divergence. - Naoki Abe; Manfred K. Warmuth; Jun'ichi Takeuchi (COLT, 1991)

Equivalence of Models for Polynomial Learnability - David Haussler; Michael J. Kearns; Nick Littlestone; Manfred K. Warmuth (Inf. Comput., 1991)

Some Weak Learning Results. - David P. Helmbold; Manfred K. Warmuth (COLT, 1992)

On the Computational Complexity of Approximating Distributions by Probabilistic Automata. - Naoki Abe; Manfred K. Warmuth (Mach. Learn., 1992)

Learning Integer Lattices. - David P. Helmbold; Robert Sloan 001; Manfred K. Warmuth (SIAM J. Comput., 1992)

Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule. - Nicolò Cesa-Bianchi; Philip M. Long; Manfred K. Warmuth (COLT, 1993)

Using experts for predicting continuous outcomes. - Jyrki Kivinen; Manfred K. Warmuth (EuroCOLT, 1993)

The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial. - Leonard Pitt; Manfred K. Warmuth (J. ACM, 1993)

Gap Theorems for Distributed Computation. - Shlomo Moran; Manfred K. Warmuth (SIAM J. Comput., 1993)

Bounds on approximate steepest descent for likelihood maximization in exponential families. - Nicolò Cesa-Bianchi; Anders Krogh; Manfred K. Warmuth (IEEE Trans. Inf. Theory, 1994)

The Distributed Bit Complexity of the Ring: From the Anonymous to the Non-anonymous Case - Hans L. Bodlaender; Shlomo Moran; Manfred K. Warmuth (Inf. Comput., 1994)

The Weighted Majority Algorithm - Nick Littlestone; Manfred K. Warmuth (Inf. Comput., 1994)

Composite Geometric Concepts and Polynomial Predictability - Philip M. Long; Manfred K. Warmuth (Inf. Comput., 1994)

Predicting 0,1 -Functions on Randomly Drawn Points - David Haussler; Nick Littlestone; Manfred K. Warmuth (Inf. Comput., 1994)

The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds when few Input Variables are Relevant. - Jyrki Kivinen; Manfred K. Warmuth (COLT, 1995)

Tight worst-case loss bounds for predicting with expert advice. - David Haussler; Jyrki Kivinen; Manfred K. Warmuth (EuroCOLT, 1995)

Exponentially many local minima for single neurons. - Peter Auer; Mark Herbster; Manfred K. Warmuth (NIPS, 1995)

Worst-case Loss Bounds for Single Neurons. - David P. Helmbold; Jyrki Kivinen; Manfred K. Warmuth (NIPS, 1995)

Additive versus exponentiated gradient updates for linear prediction. - Jyrki Kivinen; Manfred K. Warmuth (STOC, 1995)

On-line Learning of Linear Functions. - Nick Littlestone; Philip M. Long; Manfred K. Warmuth (Comput. Complex., 1995)

On Weak Learning. - David P. Helmbold; Manfred K. Warmuth (J. Comput. Syst. Sci., 1995)

Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension. - Sally Floyd; Manfred K. Warmuth (Mach. Learn., 1995)

Learning Binary Relations Using Weighted Majority Voting. - Sally A. Goldman; Manfred K. Warmuth (Mach. Learn., 1995)

Learning of Depth Two Neural Networks with Constant Fan-In at the Hidden Nodes (Extended Abstract). - Peter Auer; Stephen Kwek; Wolfgang Maass 001; Manfred K. Warmuth (COLT, 1996)

On-Line Portfolio Selection Using Multiplicative Updates. - David P. Helmbold; Robert E. Schapire; Yoram Singer; Manfred K. Warmuth (ICML, 1996)

Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions. - Yoram Singer; Manfred K. Warmuth (NIPS, 1996)

On-line Prediction and Conversion Strategies. - Nicolò Cesa-Bianchi; Yoav Freund; David P. Helmbold; Manfred K. Warmuth (Mach. Learn., 1996)

On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. - Robert E. Schapire; Manfred K. Warmuth (Mach. Learn., 1996)

Worst-case quadratic loss bounds for prediction using linear functions and gradient descent. - Nicolò Cesa-Bianchi; Philip M. Long; Manfred K. Warmuth (IEEE Trans. Neural Networks, 1996)

Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension. - Manfred K. Warmuth (EuroCOLT, 1997)

Relative Loss Bounds, the Minimum Relative Entropy Principle, and EM. - Manfred K. Warmuth (NIPS, 1997)

Using and Combining Predictors That Specialize. - Yoav Freund; Robert E. Schapire; Yoram Singer; Manfred K. Warmuth (STOC, 1997)

The Perceptron Algorithm Versus Winnow: Linear Versus Logarithmic Mistake Bounds when Few Input Variables are Relevant (Technical Note). - Jyrki Kivinen; Manfred K. Warmuth; Peter Auer (Artif. Intell., 1997)

Exponentiated Gradient Versus Gradient Descent for Linear Predictors. - Jyrki Kivinen; Manfred K. Warmuth (Inf. Comput., 1997)

How to use expert advice. - Nicolò Cesa-Bianchi; Yoav Freund; David Haussler; David P. Helmbold; Robert E. Schapire; Manfred K. Warmuth (J. ACM, 1997)

A Comparison of New and Old Algorithms for a Mixture Estimation Problem. - David P. Helmbold; Robert E. Schapire; Yoram Singer; Manfred K. Warmuth (Mach. Learn., 1997)

Tracking the Best Regressor. - Mark Herbster; Manfred K. Warmuth (COLT, 1998)

Linear Hinge Loss and Average Margin. - Claudio Gentile; Manfred K. Warmuth (NIPS, 1998)

Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy. - Yoram Singer; Manfred K. Warmuth (NIPS, 1998)

Efficient Learning With Virtual Threshold Gates. - Wolfgang Maass 001; Manfred K. Warmuth (Inf. Comput., 1998)

Tracking the Best Disjunction. - Peter Auer; Manfred K. Warmuth (Mach. Learn., 1998)

Tracking the Best Expert. - Mark Herbster; Manfred K. Warmuth (Mach. Learn., 1998)

Sequential Prediction of Individual Sequences Under General Loss Functions. - David Haussler; Jyrki Kivinen; Manfred K. Warmuth (IEEE Trans. Inf. Theory, 1998)

Boosting as Entropy Projection. - Jyrki Kivinen; Manfred K. Warmuth (COLT, 1999)

Averaging Expert Predictions - Jyrki Kivinen; Manfred K. Warmuth (EuroCOLT, 1999)

Relative Loss Bounds for On-line Density Estirnation with the Exponential Family of Distributions. - Katy S. Azoury; Manfred K. Warmuth (UAI, 1999)

Relative loss bounds for single neurons. - David P. Helmbold; Jyrki Kivinen; Manfred K. Warmuth (IEEE Trans. Neural Networks, 1999)

The Last-Step Minimax Algorithm. - Eiji Takimoto; Manfred K. Warmuth (ALT, 2000)

Barrier Boosting. - Gunnar Rätsch; Manfred K. Warmuth; Sebastian Mika; Takashi Onoda; Steven Lemm; Klaus-Robert Müller (COLT, 2000)

The Minimax Strategy for Gaussian Density Estimation. pp. - Eiji Takimoto; Manfred K. Warmuth (COLT, 2000)

Learning of Depth Two Neural Networks with Constant Fan-in at the Hidden Nodes - Peter Auer; Stephen Kwek; Wolfgang Maass 001; Manfred K. Warmuth (Electron. Colloquium Comput. Complex., 2000)

On the Convergence of Leveraging. - Gunnar Rätsch; Sebastian Mika; Manfred K. Warmuth (NIPS, 2001)

Active Learning in the Drug Discovery Process. - Manfred K. Warmuth; Gunnar Rätsch; Michael Mathieson; Jun Liao; Christian Lemmen (NIPS, 2001)

Tracking the Best Linear Predictor. - Mark Herbster; Manfred K. Warmuth (J. Mach. Learn. Res., 2001)

Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions. - Katy S. Azoury; Manfred K. Warmuth (Mach. Learn., 2001)

Relative Loss Bounds for Multidimensional Regression Problems. - Jyrki Kivinen; Manfred K. Warmuth (Mach. Learn., 2001)

Maximizing the Margin with Boosting. - Gunnar Rätsch; Manfred K. Warmuth (COLT, 2002)

Adaptive Caching by Refetching. - Robert B. Gramacy; Manfred K. Warmuth; Scott A. Brandt; Ismail Ari (NIPS, 2002)

Relative Expected Instantaneous Loss Bounds. - Jürgen Forster; Manfred K. Warmuth (J. Comput. Syst. Sci., 2002)

Tracking a Small Set of Experts by Mixing Past Posteriors. - Olivier Bousquet; Manfred K. Warmuth (J. Mach. Learn. Res., 2002)

Direct and indirect algorithms for on-line learning of disjunctions. - David P. Helmbold; Sandra Panizza; Manfred K. Warmuth (Theor. Comput. Sci., 2002)

Predicting nearly as well as the best pruning of a planar decision graph. - Eiji Takimoto; Manfred K. Warmuth (Theor. Comput. Sci., 2002)

Compressing to VC Dimension Many Points. - Manfred K. Warmuth (COLT, 2003)

Inline updates for HMMs. - Ashutosh Garg; Manfred K. Warmuth (INTERSPEECH, 2003)

Classification with free energy at raised temperatures. - Rita Singh; Manfred K. Warmuth; Bhiksha Raj; Paul Lamere (INTERSPEECH, 2003)

Boosting versus Covering. - Kohei Hatano; Manfred K. Warmuth (NIPS, 2003)

Active Learning with Support Vector Machines in the Drug Discovery Process. - Manfred K. Warmuth; Jun Liao; Gunnar Rätsch; Michael Mathieson; Santosh Putta; Christian Lemmen (J. Chem. Inf. Comput. Sci., 2003)

Path Kernels and Multiplicative Updates. - Eiji Takimoto; Manfred K. Warmuth (J. Mach. Learn. Res., 2003)

Relative Loss Bounds for Temporal-Difference Learning. - Jürgen Forster; Manfred K. Warmuth (Mach. Learn., 2003)

The Optimal PAC Algorithm. - Manfred K. Warmuth (COLT, 2004)

Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection. - Koji Tsuda; Gunnar Rätsch; Manfred K. Warmuth (NIPS, 2004)

Optimum Follow the Leader Algorithm. - Dima Kuzmin; Manfred K. Warmuth (COLT, 2005)

Leaving the Span. - Manfred K. Warmuth; S. V. N. Vishwanathan (COLT, 2005)

A Bayes Rule for Density Matrices. - Manfred K. Warmuth (NIPS, 2005)

Efficient Margin Maximizing with Boosting. - Gunnar Rätsch; Manfred K. Warmuth (J. Mach. Learn. Res., 2005)

Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection. - Koji Tsuda; Gunnar Rätsch; Manfred K. Warmuth (J. Mach. Learn. Res., 2005)

Continuous Experts and the Binning Algorithm. - Jacob D. Abernethy; John Langford 001; Manfred K. Warmuth (COLT, 2006)

Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints. - Manfred K. Warmuth (COLT, 2006)

Totally corrective boosting algorithms that maximize the margin. - Manfred K. Warmuth; Jun Liao; Gunnar Rätsch (ICML, 2006)

Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension. - Manfred K. Warmuth; Dima Kuzmin (NIPS, 2006)

A Bayesian Probability Calculus for Density Matrices. - Manfred K. Warmuth; Dima Kuzmin (UAI, 2006)

The p-norm generalization of the LMS algorithm for adaptive filtering. - Jyrki Kivinen; Manfred K. Warmuth; Babak Hassibi (IEEE Trans. Signal Process., 2006)

When Is There a Free Matrix Lunch? - Manfred K. Warmuth (COLT, 2007)

Online kernel PCA with entropic matrix updates. - Dima Kuzmin; Manfred K. Warmuth (ICML, 2007)

Winnowing subspaces. - Manfred K. Warmuth (ICML, 2007)

Boosting Algorithms for Maximizing the Soft Margin. - Manfred K. Warmuth; Karen A. Glocer; Gunnar Rätsch (NIPS, 2007)

Unlabeled Compression Schemes for Maximum Classes. - Dima Kuzmin; Manfred K. Warmuth (J. Mach. Learn. Res., 2007)

Entropy Regularized LPBoost. - Manfred K. Warmuth; Karen A. Glocer; S. V. N. Vishwanathan (ALT, 2008)

When Random Play is Optimal Against an Adversary. - Jacob D. Abernethy; Manfred K. Warmuth; Joel Yellin (COLT, 2008)

Learning Rotations. - Adam M. Smith 001; Manfred K. Warmuth (COLT, 2008)

Minimax Games with Bandits. - Jacob D. Abernethy; Manfred K. Warmuth (COLT, 2009)

Tutorial summary: Survey of boosting from an optimization perspective. - Manfred K. Warmuth; S. V. N. Vishwanathan (ICML, 2009)

Learning Permutations with Exponential Weights. - David P. Helmbold; Manfred K. Warmuth (J. Mach. Learn. Res., 2009)

The Blessing and the Curse of the Multiplicative Updates. - Manfred K. Warmuth (ALT, 2010)

On-line Variance Minimization in O(n2) per Trial? - Elad Hazan; Satyen Kale; Manfred K. Warmuth (COLT, 2010)

Hedging Structured Concepts. - Wouter M. Koolen; Manfred K. Warmuth; Jyrki Kivinen (COLT, 2010)

New combination coefficients for AdaBoost algorithms. - Shuisheng Zhou; Manfred K. Warmuth; Yinli Dong; Feng Ye (ICNC, 2010)

Repeated Games against Budgeted Adversaries. - Jacob D. Abernethy; Manfred K. Warmuth (NIPS, 2010)

Bayesian generalized probability calculus for density matrices. - Manfred K. Warmuth; Dima Kuzmin (Mach. Learn., 2010)

Learning Eigenvectors for Free. - Wouter M. Koolen; Wojciech Kotlowski; Manfred K. Warmuth (NIPS, 2011)

Minimax Algorithm for Learning Rotations. - Wojciech Kotlowski; Manfred K. Warmuth (COLT, 2011)

Putting Bayes to sleep. - Wouter M. Koolen; Dmitry Adamskiy; Manfred K. Warmuth (NIPS, 2012)

Online variance minimization. - Manfred K. Warmuth; Dima Kuzmin (Mach. Learn., 2012)

Online PCA with Optimal Regrets. - Jiazhong Nie; Wojciech Kotlowski; Manfred K. Warmuth (ALT, 2013)

Learning a set of directions. - Wouter M. Koolen; Jiazhong Nie; Manfred K. Warmuth (COLT, 2013)

Open Problem: Lower bounds for Boosting with Hadamard Matrices. - Jiazhong Nie; Manfred K. Warmuth; S. V. N. Vishwanathan; Xinhua Zhang (COLT, 2013)

On-line PCA with Optimal Regrets. - Jiazhong Nie; Wojciech Kotlowski; Manfred K. Warmuth (CoRR, 2013)

Open Problem: Shifting Experts on Easy Data. - Manfred K. Warmuth; Wouter M. Koolen (COLT, 2014)

The limits of squared Euclidean distance regularization. - Michal Derezinski; Manfred K. Warmuth (NIPS, 2014)

Combining initial segments of lists. - Manfred K. Warmuth; Wouter M. Koolen; David P. Helmbold (Theor. Comput. Sci., 2014)

Kernelization of matrix updates, when and how? - Manfred K. Warmuth; Wojciech Kotlowski; Shuisheng Zhou (Theor. Comput. Sci., 2014)

Minimax Fixed-Design Linear Regression. - Peter L. Bartlett; Wouter M. Koolen; Alan Malek; Eiji Takimoto; Manfred K. Warmuth (COLT, 2015)

On-Line Learning Algorithms for Path Experts with Non-Additive Losses. - Corinna Cortes; Vitaly Kuznetsov; Mehryar Mohri; Manfred K. Warmuth (COLT, 2015)

Open Problem: Online Sabotaged Shortest Path. - Wouter M. Koolen; Manfred K. Warmuth; Dmitry Adamskiy (COLT, 2015)

PCA with Gaussian perturbations. - Wojciech Kotlowski; Manfred K. Warmuth (CoRR, 2015)

Labeled Compression Schemes for Extremal Classes. - Shay Moran; Manfred K. Warmuth (ALT, 2016)

Noise Free Multi-armed Bandit Game. - Atsuyoshi Nakamura; David P. Helmbold; Manfred K. Warmuth (LATA, 2016)

t-Exponential Triplet Embedding. - Ehsan Amid; Nikos Vlassis; Manfred K. Warmuth (CoRR, 2016)

Online PCA with Optimal Regret. - Jiazhong Nie; Wojciech Kotlowski; Manfred K. Warmuth (J. Mach. Learn. Res., 2016)

Learning rotations with little regret. - Elad Hazan; Satyen Kale; Manfred K. Warmuth (Mach. Learn., 2016)

Unbiased estimates for linear regression via volume sampling. - Michal Derezinski; Manfred K. Warmuth (NIPS, 2017)

Online Dynamic Programming. - Holakou Rahmanian; Manfred K. Warmuth (NIPS, 2017)

Two-temperature logistic regression based on the Tsallis divergence. - Ehsan Amid; Manfred K. Warmuth (CoRR, 2017)

Online Dynamic Programming. - Holakou Rahmanian; S. V. N. Vishwanathan; Manfred K. Warmuth (CoRR, 2017)

Subsampling for Ridge Regression via Regularized Volume Sampling. - Michal Derezinski; Manfred K. Warmuth (AISTATS, 2018)

Leveraged volume sampling for linear regression. - Michal Derezinski; Manfred K. Warmuth; Daniel J. Hsu (NeurIPS, 2018)

Tail bounds for volume sampled linear regression. - Michal Derezinski; Manfred K. Warmuth; Daniel Hsu 001 (CoRR, 2018)

A more globally accurate dimensionality reduction method using triplets. - Ehsan Amid; Manfred K. Warmuth (CoRR, 2018)

Speech Recognition: Keyword Spotting Through Image Recognition. - Sanjay Krishna Gouda; Salil Kanetkar; David Harrison; Manfred K. Warmuth (CoRR, 2018)

Reverse Iterative Volume Sampling for Linear Regression. - Michal Derezinski; Manfred K. Warmuth (J. Mach. Learn. Res., 2018)

Two-temperature logistic regression based on the Tsallis divergence. - Ehsan Amid; Manfred K. Warmuth; Sriram Srinivasan 004 (AISTATS, 2019)

Correcting the bias in least squares regression with volume-rescaled sampling. - Michal Derezinski; Manfred K. Warmuth; Daniel Hsu 001 (AISTATS, 2019)

Online Non-Additive Path Learning under Full and Partial Information. - Corinna Cortes; Vitaly Kuznetsov; Mehryar Mohri; Holakou Rahmanian; Manfred K. Warmuth (ALT, 2019)

Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. - Michal Derezinski; Kenneth L. Clarkson; Michael W. Mahoney; Manfred K. Warmuth (COLT, 2019)

Adaptive Scale-Invariant Online Algorithms for Learning Linear Models. - Michal Kempka; Wojciech Kotlowski; Manfred K. Warmuth (ICML, 2019)

Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. - Ehsan Amid; Manfred K. Warmuth; Rohan Anil; Tomer Koren (NeurIPS, 2019)

TriMap: Large-scale Dimensionality Reduction Using Triplets. - Ehsan Amid; Manfred K. Warmuth (CoRR, 2019)

Mistake bounds on the noise-free multi-armed bandit game. - Atsuyoshi Nakamura; David P. Helmbold; Manfred K. Warmuth (Inf. Comput., 2019)

An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. - Ehsan Amid; Manfred K. Warmuth (AAAI, 2020)

Winnowing with Gradient Descent. - Ehsan Amid; Manfred K. Warmuth (COLT, 2020)

Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment. - Hossein Talebi; Ehsan Amid; Peyman Milanfar; Manfred K. Warmuth (ICIP, 2020)

Reparameterizing Mirror Descent as Gradient Descent. - Ehsan Amid; Manfred K. Warmuth (NeurIPS, 2020)

Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. - Ehsan Amid; Manfred K. Warmuth (UAI, 2020)

Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent. - Ehsan Amid; Manfred K. Warmuth (CoRR, 2020)

A case where a spindly two-layer linear network whips any neural network with a fully connected input layer. - Manfred K. Warmuth; Wojciech Kotlowski; Ehsan Amid (CoRR, 2020)

A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer. - Manfred K. Warmuth; Wojciech Kotlowski; Ehsan Amid (ALT, 2021)

Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond. - Negin Majidi; Ehsan Amid; Hossein Talebi; Manfred K. Warmuth (CoRR, 2021)

LocoProp: Enhancing BackProp via Local Loss Optimization. - Ehsan Amid; Rohan Anil; Manfred K. Warmuth (AISTATS, 2022)

Step-size Adaptation Using Exponentiated Gradient Updates. - Ehsan Amid; Rohan Anil; Christopher Fifty; Manfred K. Warmuth (CoRR, 2022)

Learning from Randomly Initialized Neural Network Features. - Ehsan Amid; Rohan Anil; Wojciech Kotlowski; Manfred K. Warmuth (CoRR, 2022)

Layerwise Bregman Representation Learning with Applications to Knowledge Distillation. - Ehsan Amid; Rohan Anil; Christopher Fifty; Manfred K. Warmuth (CoRR, 2022)

Unlabeled sample compression schemes and corner peelings for ample and maximum classes. - Jérémie Chalopin; Victor Chepoi; Shay Moran; Manfred K. Warmuth (J. Comput. Syst. Sci., 2022)

Unbiased estimators for random design regression. - Michal Derezinski; Manfred K. Warmuth; Daniel Hsu 001 (J. Mach. Learn. Res., 2022)

Clustering above Exponential Families with Tempered Exponential Measures. - Ehsan Amid; Richard Nock; Manfred K. Warmuth (AISTATS, 2023)

Open Problem: Learning sparse linear concepts by priming the features. - Manfred K. Warmuth; Ehsan Amid (COLT, 2023)

Boosting with Tempered Exponential Measures. - Richard Nock; Ehsan Amid; Manfred K. Warmuth (NeurIPS, 2023)

The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs. - Ehsan Amid; Frank Nielsen; Richard Nock; Manfred K. Warmuth (CoRR, 2023)

Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation. - Ehsan Amid; Rohan Anil; Christopher Fifty; Manfred K. Warmuth (Trans. Mach. Learn. Res., 2023)

Optimal Transport with Tempered Exponential Measures. - Ehsan Amid; Frank Nielsen; Richard Nock; Manfred K. Warmuth (AAAI, 2024)

A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. - Jacob D. Abernethy; Alekh Agarwal; Teodor Vanislavov Marinov; Manfred K. Warmuth (ALT, 2024)

Hyperbolic Embeddings of Supervised Models. - Richard Nock; Ehsan Amid; Frank Nielsen; Alexander Soen; Manfred K. Warmuth (NeurIPS, 2024)

Tempered Calculus for ML: Application to Hyperbolic Model Embedding. - Richard Nock; Ehsan Amid; Frank Nielsen; Alexander Soen; Manfred K. Warmuth (CoRR, 2024)

Noise misleads rotation invariant algorithms on sparse targets. - Manfred K. Warmuth; Wojciech Kotlowski; Matt Jones 002; Ehsan Amid (CoRR, 2024)

How rotation invariant algorithms are fooled by noise on sparse targets. - Manfred K. Warmuth; Wojciech Kotlowski; Matt Jones 002; Ehsan Amid (ALT, 2025)

Selective Matching Losses - Not All Scores Are Created Equal. - Gil I. Shamir; Manfred K. Warmuth (CoRR, 2025)