Timezone: »
Poster
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice
Yishay Mansour · Richard Nock · Robert C. Williamson
A landmark negative result of Long and Servedio has had a considerable impact on research and development in boosting algorithms, around the now famous tagline that "noise defeats all convex boosters". In this paper, we appeal to the half-century+ founding theory of losses for class probability estimation, an extension of Long and Servedio's results and a new general convex booster to demonstrate that the source of their negative result is in fact the model class, linear separators. Losses or algorithms are neither to blame. This leads us to a discussion on an otherwise praised aspect of ML, parameterisation.
Author Information
Yishay Mansour (Google and Tel Aviv University)
Richard Nock (Google Research)
Robert C. Williamson (University of Tuebingen)
Related Events (a corresponding poster, oral, or spotlight)
-
2023 Oral: Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice »
Thu. Jul 27th 03:04 -- 03:12 AM Room Ballroom B
More from the Same Authors
-
2021 : Minimax Regret for Stochastic Shortest Path »
Alon Cohen · Yonathan Efroni · Yishay Mansour · Aviv Rosenberg -
2021 : Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure »
Aviv Rosenberg · Yishay Mansour -
2021 : Learning Adversarial Markov Decision Processes with Delayed Feedback »
Tal Lancewicki · Aviv Rosenberg · Yishay Mansour -
2022 : Optimism in Face of a Context: Regret Guarantees for Stochastic Contextual MDP »
Orin Levy · Yishay Mansour -
2022 : Near-optimal Regret for Adversarial MDP with Delayed Bandit Feedback »
Tiancheng Jin · Tal Lancewicki · Haipeng Luo · Yishay Mansour · Aviv Rosenberg -
2023 Poster: Fair Densities via Boosting the Sufficient Statistics of Exponential Families »
Alexander Soen · Hisham Husain · Richard Nock -
2023 Poster: Reinforcement Learning Can Be More Efficient with Multiple Rewards »
Christoph Dann · Yishay Mansour · Mehryar Mohri -
2023 Poster: Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation »
Uri Sherman · Tomer Koren · Yishay Mansour -
2023 Poster: Regret Minimization and Convergence to Equilibria in General-sum Markov Games »
Liad Erez · Tal Lancewicki · Uri Sherman · Tomer Koren · Yishay Mansour -
2023 Poster: LegendreTron: Uprising Proper Multiclass Loss Learning »
Kevin H. Lam · Christian Walder · Spiridon Penev · Richard Nock -
2023 Poster: Concurrent Shuffle Differential Privacy Under Continual Observation »
Jay Tenenbaum · Haim Kaplan · Yishay Mansour · Uri Stemmer -
2023 Poster: Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation »
Orin Levy · Alon Cohen · Asaf Cassel · Yishay Mansour -
2022 : Near-optimal Regret for Adversarial MDP with Delayed Bandit Feedback »
Tiancheng Jin · Tal Lancewicki · Haipeng Luo · Yishay Mansour · Aviv Rosenberg -
2022 Poster: Neural Network Poisson Models for Behavioural and Neural Spike Train Data »
Moein Khajehnejad · Forough Habibollahi Saatlou · Richard Nock · Ehsan Arabzadeh · Peter Dayan · Amir Dezfouli -
2022 Spotlight: Neural Network Poisson Models for Behavioural and Neural Spike Train Data »
Moein Khajehnejad · Forough Habibollahi Saatlou · Richard Nock · Ehsan Arabzadeh · Peter Dayan · Amir Dezfouli -
2022 Poster: Generative Trees: Adversarial and Copycat »
Richard Nock · Mathieu Guillame-Bert -
2022 Oral: Generative Trees: Adversarial and Copycat »
Richard Nock · Mathieu Guillame-Bert -
2022 Poster: Cooperative Online Learning in Stochastic and Adversarial MDPs »
Tal Lancewicki · Aviv Rosenberg · Yishay Mansour -
2022 Poster: Being Properly Improper »
Tyler Sypherd · Richard Nock · Lalitha Sankar -
2022 Poster: FriendlyCore: Practical Differentially Private Aggregation »
Eliad Tsfadia · Edith Cohen · Haim Kaplan · Yishay Mansour · Uri Stemmer -
2022 Spotlight: Being Properly Improper »
Tyler Sypherd · Richard Nock · Lalitha Sankar -
2022 Oral: Cooperative Online Learning in Stochastic and Adversarial MDPs »
Tal Lancewicki · Aviv Rosenberg · Yishay Mansour -
2022 Spotlight: FriendlyCore: Practical Differentially Private Aggregation »
Eliad Tsfadia · Edith Cohen · Haim Kaplan · Yishay Mansour · Uri Stemmer -
2021 Poster: Differentially-Private Clustering of Easy Instances »
Edith Cohen · Haim Kaplan · Yishay Mansour · Uri Stemmer · Eliad Tsfadia -
2021 Spotlight: Differentially-Private Clustering of Easy Instances »
Edith Cohen · Haim Kaplan · Yishay Mansour · Uri Stemmer · Eliad Tsfadia -
2021 Poster: The Impact of Record Linkage on Learning from Feature Partitioned Data »
Richard Nock · Stephen J Hardy · Wilko Henecka · Hamish Ivey-Law · Jakub Nabaglo · Giorgio Patrini · Guillaume Smith · Brian Thorne -
2021 Poster: Adversarial Dueling Bandits »
Aadirupa Saha · Tomer Koren · Yishay Mansour -
2021 Spotlight: The Impact of Record Linkage on Learning from Feature Partitioned Data »
Richard Nock · Stephen J Hardy · Wilko Henecka · Hamish Ivey-Law · Jakub Nabaglo · Giorgio Patrini · Guillaume Smith · Brian Thorne -
2021 Spotlight: Adversarial Dueling Bandits »
Aadirupa Saha · Tomer Koren · Yishay Mansour -
2021 Poster: Generalised Lipschitz Regularisation Equals Distributional Robustness »
Zac Cranko · Zhan Shi · Xinhua Zhang · Richard Nock · Simon Kornblith -
2021 Poster: Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions »
Tal Lancewicki · Shahar Segal · Tomer Koren · Yishay Mansour -
2021 Spotlight: Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions »
Tal Lancewicki · Shahar Segal · Tomer Koren · Yishay Mansour -
2021 Spotlight: Generalised Lipschitz Regularisation Equals Distributional Robustness »
Zac Cranko · Zhan Shi · Xinhua Zhang · Richard Nock · Simon Kornblith -
2021 Poster: Dueling Convex Optimization »
Aadirupa Saha · Tomer Koren · Yishay Mansour -
2021 Spotlight: Dueling Convex Optimization »
Aadirupa Saha · Tomer Koren · Yishay Mansour -
2020 Poster: Near-optimal Regret Bounds for Stochastic Shortest Path »
Aviv Rosenberg · Alon Cohen · Yishay Mansour · Haim Kaplan -
2020 Poster: Supervised learning: no loss no cry »
Richard Nock · Aditya Menon -
2019 Poster: Adversarial Online Learning with noise »
Alon Resler · Yishay Mansour -
2019 Poster: Online Convex Optimization in Adversarial Markov Decision Processes »
Aviv Rosenberg · Yishay Mansour -
2019 Poster: Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret »
Alon Cohen · Tomer Koren · Yishay Mansour -
2019 Poster: Differentially Private Learning of Geometric Concepts »
Haim Kaplan · Yishay Mansour · Yossi Matias · Uri Stemmer -
2019 Oral: Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret »
Alon Cohen · Tomer Koren · Yishay Mansour -
2019 Oral: Adversarial Online Learning with noise »
Alon Resler · Yishay Mansour -
2019 Oral: Differentially Private Learning of Geometric Concepts »
Haim Kaplan · Yishay Mansour · Yossi Matias · Uri Stemmer -
2019 Oral: Online Convex Optimization in Adversarial Markov Decision Processes »
Aviv Rosenberg · Yishay Mansour -
2019 Poster: Monge blunts Bayes: Hardness Results for Adversarial Training »
Zac Cranko · Aditya Menon · Richard Nock · Cheng Soon Ong · Zhan Shi · Christian Walder -
2019 Poster: Lossless or Quantized Boosting with Integer Arithmetic »
Richard Nock · Robert C Williamson -
2019 Oral: Lossless or Quantized Boosting with Integer Arithmetic »
Richard Nock · Robert C Williamson -
2019 Oral: Monge blunts Bayes: Hardness Results for Adversarial Training »
Zac Cranko · Aditya Menon · Richard Nock · Cheng Soon Ong · Zhan Shi · Christian Walder -
2019 Poster: Boosted Density Estimation Remastered »
Zac Cranko · Richard Nock -
2019 Oral: Boosted Density Estimation Remastered »
Zac Cranko · Richard Nock -
2018 Poster: Variational Network Inference: Strong and Stable with Concrete Support »
Amir Dezfouli · Edwin Bonilla · Richard Nock -
2018 Oral: Variational Network Inference: Strong and Stable with Concrete Support »
Amir Dezfouli · Edwin Bonilla · Richard Nock -
2017 Workshop: Human in the Loop Machine Learning »
Richard Nock · Cheng Soon Ong