← Authors

Mehryar Mohri

118 papers · 2003–2025 · 9 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (29) 🐝 Cross-Pollinator (9) 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (22) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (9) 🏠 Conference Loyalist (52) 🌟 Keyword Trendsetter Combo (4) 🌱 Topic Pioneer πŸ”¬ Deep Specialist (15) πŸ† Keyword Champion (2) 🀝 Dynamic Duo (37) πŸ—ƒοΈ Keyword Collector (148) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (20) ⚑ Prolific Year (13) ❓ The Questioner πŸ’Ž Century Club (118)

Conferences

NIPS (52) ICML (28) AISTATS (12) COLT (7) JMLR (7) ALT (5) ACL (3) INTERSPEECH (2) NAACL (2)

Top co-authors

Corinna Cortes (37) Anqi Mao (22) Yutao Zhong (21) Ananda Theertha Suresh (13) Vitaly Kuznetsov (13) Yishay Mansour (10) Scott Yang (10) Satyen Kale (7) Karthik Sridharan (7) Giulia DeSalvo (7)

Keywords

online learning (20) regret bound (19) surrogate loss (14) rademacher complexity (12) learning theory (11) multi-class classification (10) kernel methods (9) generalization bound (8) multi-armed bandit (7) ensemble learning (7) feedback graph (7) consistency bound (6) h-consistency bound (6) federated learning (6) domain adaptation (6) ensemble method (6) adversarial robustness (5) convex optimization (5) stochastic optimization (5) low-rank approximation (5)

Papers

Enhanced $H$-Consistency Bounds ALT 2025
Principled Algorithms for Optimizing Generalized Metrics in Binary Classification ICML 2025
Mastering Multiple-Expert Routing: Realizable $H$-Consistency and Strong Guarantees for Learning to Defer ICML 2025
Rate-Preserving Reductions for Blackwell Approachability COLT 2025
Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data ICML 2025
Regression with Multi-Expert Deferral ICML 2024
Multi-Label Learning with Stronger Consistency Guarantees NIPS 2024
Realizable $H$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer NIPS 2024
A Universal Growth Rate for Learning with Smooth Surrogate Losses NIPS 2024
Differentially Private Domain Adaptation with Theoretical Guarantees ICML 2024
$H$-Consistency Guarantees for Regression ICML 2024
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms ALT 2024
Cardinality-Aware Set Prediction and Top-$k$ Classification NIPS 2024
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention AISTATS 2024
Principled Approaches for Private Adaptation from a Public Source AISTATS 2023
Structured Prediction with Stronger Consistency Guarantees NIPS 2023
$H$-Consistency Bounds: Characterization and Extensions NIPS 2023
Two-Stage Learning to Defer with Multiple Experts NIPS 2023
Cross-Entropy Loss Functions: Theoretical Analysis and Applications ICML 2023
$H$-Consistency Bounds for Pairwise Misranking Loss Surrogates ICML 2023
Reinforcement Learning Can Be More Efficient with Multiple Rewards ICML 2023
Pseudonorm Approachability and Applications to Regret Minimization ALT 2023
Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness AISTATS 2023
Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees COLT 2022
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality NIPS 2022
Multi-Class $H$-Consistency Bounds NIPS 2022
Open Problem: Finite-Time Instance Dependent Optimality for Stochastic Online Learning with Feedback Graphs COLT 2022
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation ICML 2022
H-Consistency Bounds for Surrogate Loss Minimizers ICML 2022
Strategizing against Learners in Bayesian Games COLT 2022
Differentially Private Learning with Margin Guarantees NIPS 2022
Communication-Efficient Agnostic Federated Averaging INTERSPEECH 2021
A Discriminative Technique for Multiple-Source Adaptation ICML 2021
Relative Deviation Margin Bounds ICML 2021
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning NIPS 2021
On the Existence of The Adversarial Bayes Classifier NIPS 2021
Calibration and Consistency of Adversarial Surrogate Losses NIPS 2021
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning NIPS 2021
Learning with User-Level Privacy NIPS 2021
Boosting with Multiple Sources NIPS 2021
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations NIPS 2021
Breaking the centralized barrier for cross-device federated learning NIPS 2021
Corralling Stochastic Bandit Algorithms AISTATS 2021
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data AISTATS 2021
Adapting to Misspecification in Contextual Bandits NIPS 2020
PAC-Bayes Learning Bounds for Sample-Dependent Priors NIPS 2020
FedBoost: A Communication-Efficient Algorithm for Federated Learning ICML 2020
Online Learning with Dependent Stochastic Feedback Graphs ICML 2020
Adaptive Region-Based Active Learning ICML 2020
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks ICML 2020
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning ICML 2020
Agnostic Learning with Multiple Objectives NIPS 2020
Reinforcement Learning with Feedback Graphs NIPS 2020
Online Non-Additive Path Learning under Full and Partial Information ALT 2019
Hypothesis Set Stability and Generalization NIPS 2019
Regularized Gradient Boosting NIPS 2019
Learning GANs and Ensembles Using Discrepancy NIPS 2019
Bandits with Feedback Graphs and Switching Costs NIPS 2019
Online Learning with Sleeping Experts and Feedback Graphs ICML 2019
Adaptation Based on Generalized Discrepancy JMLR 2019
Agnostic Federated Learning ICML 2019
Region-Based Active Learning AISTATS 2019
Active Learning with Disagreement Graphs ICML 2019
Algorithmic Learning Theory ALT 2018: Preface ALT 2018
Logistic Regression: The Importance of Being Improper COLT 2018
Algorithms and Theory for Multiple-Source Adaptation NIPS 2018
Competing with Automata-based Expert Sequences AISTATS 2018
Online Learning with Abstention ICML 2018
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses NIPS 2018
Policy Regret in Repeated Games NIPS 2018
Parameter-Free Online Learning via Model Selection NIPS 2017
AdaNet: Adaptive Structural Learning of Artificial Neural Networks ICML 2017
Discriminative State Space Models NIPS 2017
Online Learning with Transductive Regret NIPS 2017
Time series prediction and online learning COLT 2016
Structured Prediction Theory Based on Factor Graph Complexity NIPS 2016
Boosting with Abstention NIPS 2016
Optimistic Bandit Convex Optimization NIPS 2016
Accelerating Online Convex Optimization via Adaptive Prediction AISTATS 2016
Learning N-Gram Language Models from Uncertain Data INTERSPEECH 2016
Learning Algorithms for Second-Price Auctions with Reserve JMLR 2016
On-Line Learning Algorithms for Path Experts with Non-Additive Losses COLT 2015
Revenue Optimization against Strategic Buyers NIPS 2015
Structural Maxent Models ICML 2015
Learning Theory and Algorithms for Forecasting Non-stationary Time Series NIPS 2015
Ensemble Methods for Structured Prediction ICML 2014
Deep Boosting ICML 2014
Learning Ensembles of Structured Prediction Rules ACL 2014
Multi-Class Deep Boosting NIPS 2014
Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers NIPS 2014
Conditional Swap Regret and Conditional Correlated Equilibrium NIPS 2014
Learning Theory and Algorithms for revenue optimization in second price auctions with reserve ICML 2014
Large-scale SVD and Manifold Learning JMLR 2013
Learning Kernels Using Local Rademacher Complexity NIPS 2013
Multi-Class Classification with Maximum Margin Multiple Kernel ICML 2013
Accuracy at the Top NIPS 2012
Sampling Methods for the NystrΓΆm Method JMLR 2012
Algorithms for Learning Kernels Based on Centered Alignment JMLR 2012
Spectral Learning of General Weighted Automata via Constrained Matrix Completion NIPS 2012
Can matrix coherence be efficiently and accurately estimated? AISTATS 2011
Half Transductive Ranking AISTATS 2010
Expected Sequence Similarity Maximization NAACL 2010
Learning Bounds for Importance Weighting NIPS 2010
Stability Bounds for Stationary Ο†-mixing and Ξ²-mixing Processes JMLR 2010
Discriminative Topic Segmentation of Text and Speech AISTATS 2010
On the Impact of Kernel Approximation on Learning Accuracy AISTATS 2010
Ensemble Nystrom Method NIPS 2009
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models NIPS 2009
Polynomial Semantic Indexing NIPS 2009
Learning Non-Linear Combinations of Kernels NIPS 2009
Rademacher Complexity Bounds for Non-I.I.D. Processes NIPS 2008
Domain Adaptation with Multiple Sources NIPS 2008
Stability Bounds for Non-i.i.d. Processes NIPS 2007
On Transductive Regression NIPS 2006
Probabilistic Context-Free Grammar Induction Based on Structural Zeros NAACL 2006
Statistical Modeling for Unit Selection in Speech Synthesis ACL 2004
Rational Kernels: Theory and Algorithms JMLR 2004
Generalized Algorithms for Constructing Statistical Language Models ACL 2003