Wojciech Kotlowski

Orcid: 0000-0002-5905-8069

Affiliations:
  • Poznań University of Technology, Institute of Computing Science


According to our database1, Wojciech Kotlowski authored at least 63 papers between 2006 and 2024.

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Bibliography

2024
Noise misleads rotation invariant algorithms on sparse targets.
CoRR, 2024

A General Online Algorithm for Optimizing Complex Performance Metrics.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Consistent algorithms for multi-label classification with macro-at-k metrics.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data.
CoRR, 2023

Generalized test utilities for long-tail performance in extreme multi-label classification.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Learning from Randomly Initialized Neural Network Features.
CoRR, 2022

2021
Robust Online Convex Optimization in the Presence of Outliers.
Proceedings of the Conference on Learning Theory, 2021

A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer.
Proceedings of the Algorithmic Learning Theory, 2021

2020
Scale-invariant unconstrained online learning.
Theor. Comput. Sci., 2020

A case where a spindly two-layer linear network whips any neural network with a fully connected input layer.
CoRR, 2020

Open Problem: Fast and Optimal Online Portfolio Selection.
Proceedings of the Conference on Learning Theory, 2020

Learning to Crawl.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models.
Proceedings of the 36th International Conference on Machine Learning, 2019

Bandit Principal Component Analysis.
Proceedings of the Conference on Learning Theory, 2019

2018
On minimaxity of Follow the Leader strategy in the stochastic setting.
Theor. Comput. Sci., 2018

Online Principal Component Analysis for Evolving Data Streams.
Proceedings of the Computer and Information Sciences - 32nd International Symposium, 2018

The Many Faces of Exponential Weights in Online Learning.
Proceedings of the Conference On Learning Theory, 2018

2017
Surrogate regret bounds for generalized classification performance metrics.
Mach. Learn., 2017

Random Permutation Online Isotonic Regression.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Consistency Analysis for Binary Classification Revisited.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Online PCA with Optimal Regret.
J. Mach. Learn. Res., 2016

Online Isotonic Regression.
CoRR, 2016

Consistency of Probabilistic Classifier Trees.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Online Isotonic Regression.
Proceedings of the 29th Conference on Learning Theory, 2016

2015
PCA with Gaussian perturbations.
CoRR, 2015

2014
Kernelization of matrix updates, when and how?
Theor. Comput. Sci., 2014

Consistent optimization of AMS by logistic loss minimization.
Proceedings of the Workshop on High-energy Physics and Machine Learning, 2014

Follow the Leader with Dropout Perturbations.
Proceedings of The 27th Conference on Learning Theory, 2014

2013
On Nonparametric Ordinal Classification with Monotonicity Constraints.
IEEE Trans. Knowl. Data Eng., 2013

On-line PCA with Optimal Regrets.
CoRR, 2013

Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization.
Proceedings of the 30th International Conference on Machine Learning, 2013

Matrix Factorization for Travel Time Estimation in Large Traffic Networks.
Proceedings of the Artificial Intelligence and Soft Computing, 2013

Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families.
Proceedings of the COLT 2013, 2013

Online PCA with Optimal Regrets.
Proceedings of the Algorithmic Learning Theory - 24th International Conference, 2013

2012
Sequential normalized maximum likelihood in log-loss prediction.
Proceedings of the 2012 IEEE Information Theory Workshop, 2012

Consistent Multilabel Ranking through Univariate Losses.
Proceedings of the 29th International Conference on Machine Learning, 2012

Community Traffic: A Technology for the Next Generation Car Navigation.
Proceedings of the New Trends in Databases and Information Systems, 2012

2011
Minimax Algorithm for Learning Rotations.
Proceedings of the COLT 2011, 2011

Maximum Likelihood vs. Sequential Normalized Maximum Likelihood in On-line Density Estimation.
Proceedings of the COLT 2011, 2011

Bounds on Individual Risk for Log-loss Predictors.
Proceedings of the COLT 2011, 2011

Learning Eigenvectors for Free.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Bipartite Ranking through Minimization of Univariate Loss.
Proceedings of the 28th International Conference on Machine Learning, 2011

2010
Beyond Sequential Covering - Boosted Decision Rules.
Proceedings of the Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S. Michalski, 2010

ENDER: a statistical framework for boosting decision rules.
Data Min. Knowl. Discov., 2010

Prequential plug-in codes that achieve optimal redundancy rates even if the model is wrong.
Proceedings of the IEEE International Symposium on Information Theory, 2010

Following the Flattened Leader.
Proceedings of the COLT 2010, 2010

Learning of Rule Ensembles for Multiple Attribute Ranking Problems.
Proceedings of the Preference Learning., 2010

2009
Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints.
Fundam. Informaticae, 2009

Rule learning with monotonicity constraints.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Stochastic dominance-based rough set model for ordinal classification.
Inf. Sci., 2008

Effective Prediction of Web User Behaviour with User-Level Models.
Fundam. Informaticae, 2008

Ensemble of Decision Rules for Ordinal Classification with Monotonicity Constraints.
Proceedings of the Rough Sets and Knowledge Technology, Third International Conference, 2008

Maximum likelihood rule ensembles.
Proceedings of the Machine Learning, 2008

Solving Regression by Learning an Ensemble of Decision Rules.
Proceedings of the Artificial Intelligence and Soft Computing, 2008

On selecting the best individual in noisy environments.
Proceedings of the Genetic and Evolutionary Computation Conference, 2008

2007
Optimized Generalized Decision in Dominance-Based Rough Set Approach.
Proceedings of the Rough Sets and Knowledge Technology, Second International Conference, 2007

Relationship Between Loss Functions and Confirmation Measures.
Proceedings of the Rough Sets, 2007

Statistical Model for Rough Set Approach to Multicriteria Classification.
Proceedings of the Knowledge Discovery in Databases: PKDD 2007, 2007

Ordinal Classification with Decision Rules.
Proceedings of the Mining Complex Data, ECML/PKDD 2007 Third International Workshop, 2007

2006
Quality of Rough Approximation in Multi-criteria Classification Problems.
Proceedings of the Rough Sets and Current Trends in Computing, 2006

Ensembles of Decision Rules for Solving Binary Classification Problems in the Presence of Missing Values.
Proceedings of the Rough Sets and Current Trends in Computing, 2006

Additive Preference Model with Piecewise Linear Components Resulting from Dominance-Based Rough Set Approximations.
Proceedings of the Artificial Intelligence and Soft Computing, 2006

Mining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods.
Proceedings of the Data Warehousing and Knowledge Discovery, 8th International Conference, 2006


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