Manfred K. Warmuth
42 papers · 2001–2025 · 8 conferences · across top CS/AI conferences
Achievements
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🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (19) 🌍 Conference Polyglot (8)
🗺️
Taxonomy Completionist
(19)
🌈
Renaissance Researcher
(6)
🧭
Keyword Pioneer
🌟
Keyword Trendsetter Combo
(3)
🔬
Deep Specialist
(16)
🏆
Keyword Champion
(2)
🌱
Topic Pioneer
🤝
Dynamic Duo
(12)
🔥
Unstoppable
(21)
📈
Trend Setter
🚀
Conference Pioneer
⚡
Prolific Year
(5)
💎
Century Club
(42)
🗃️
Keyword Collector
(60)
Conferences
NIPS (12)
JMLR (11)
COLT (9)
AISTATS (3)
ALT (3)
AAAI (2)
ICML (1)
UAI (1)
Top co-authors
Keywords
online learning
(17)
regret bound
(9)
linear regression
(7)
least square
(5)
gradient descent
(5)
unbiased estimator
(5)
volume sampling
(4)
online algorithm
(3)
kernel methods
(3)
principal component analysis
(3)
dimensionality reduction
(3)
multiplicative update
(3)
determinantal point process
(2)
ensemble learning
(2)
expert advice
(2)
sparse linear regression
(2)
posterior mixing
(2)
bregman divergence
(2)
relative entropy
(2)
matrix factorization
(2)
Papers
How rotation invariant algorithms are fooled by noise on sparse targets
ALT 2025
Optimal Transport with Tempered Exponential Measures
AAAI 2024
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
ALT 2024
Hyperbolic Embeddings of Supervised Models
NIPS 2024
Clustering above Exponential Families with Tempered Exponential Measures
AISTATS 2023
Open Problem: Learning sparse linear concepts by priming the features
COLT 2023
Unbiased estimators for random design regression
JMLR 2022
A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer
ALT 2021
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint
AAAI 2020
Reparameterizing Mirror Descent as Gradient Descent
NIPS 2020
Winnowing with Gradient Descent
COLT 2020
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models
UAI 2020
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
NIPS 2019
Correcting the bias in least squares regression with volume-rescaled sampling
AISTATS 2019
Two-temperature logistic regression based on the Tsallis divergence
AISTATS 2019
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
ICML 2019
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
COLT 2019
Reverse Iterative Volume Sampling for Linear Regression
JMLR 2018
Leveraged volume sampling for linear regression
NIPS 2018
Online Dynamic Programming
NIPS 2017
Unbiased estimates for linear regression via volume sampling
NIPS 2017
Online PCA with Optimal Regret
JMLR 2016
Minimax Fixed-Design Linear Regression
COLT 2015
Open Problem: Online Sabotaged Shortest Path
COLT 2015
The limits of squared Euclidean distance regularization
NIPS 2014
Open Problem: Shifting Experts on Easy Data
COLT 2014
Follow the Leader with Dropout Perturbations
COLT 2014
Open Problem: Lower bounds for Boosting with Hadamard Matrices
COLT 2013
Putting Bayes to sleep
NIPS 2012
Learning Eigenvectors for Free
NIPS 2011
Minimax Algorithm for Learning Rotations
COLT 2011
Repeated Games against Budgeted Adversaries
NIPS 2010
Learning Permutations with Exponential Weights
JMLR 2009
Randomized Online PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension
JMLR 2008
Boosting Algorithms for Maximizing the Soft Margin
NIPS 2007
Unlabeled Compression Schemes for Maximum Classes
JMLR 2007
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension
NIPS 2006
Efficient Margin Maximizing with Boosting
JMLR 2005
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
JMLR 2005
Path Kernels and Multiplicative Updates
JMLR 2003
Tracking a Small Set of Experts by Mixing Past Posteriors
JMLR 2002
Tracking the Best Linear Predictor
JMLR 2001