Mehryar Mohri Mohri

Mehryar Mohri Mohri's AcademicInfluence.com Rankings

Mehryar Mohri Mohri

Computer Science

#6890

World Rank

#7257

Historical Rank

Computational Linguistics

#1285

World Rank

#1299

Historical Rank

Machine Learning

#2381

World Rank

#2411

Historical Rank

Artificial Intelligence

#2664

World Rank

#2705

Historical Rank

computer-science Degrees

Mehryar Mohri Mohri

Mathematics

#6702

World Rank

#9233

Historical Rank

Measure Theory

#1527

World Rank

#1900

Historical Rank

mathematics Degrees

Download Badge

Computer Science
Mathematics

Mehryar Mohri Mohri's Degrees

PhD Computer Science Paris-Saclay University
Masters Computer Science Paris-Saclay University
Bachelors Mathematics Sharif University of Technology

Similar Degrees You Can Earn

Why Is Mehryar Mohri Mohri Influential?

(Suggest an Edit or Addition)

(See a Problem?)

Mehryar Mohri Mohri's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Advances and Open Problems in Federated Learning (2019) (2826)
Foundations of Machine Learning (2012) (2707)
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning (2019) (880)
Domain Adaptation: Learning Bounds and Algorithms (2009) (640)
AUC Optimization vs. Error Rate Minimization (2003) (617)
Agnostic Federated Learning (2019) (528)
Multi-armed Bandit Algorithms and Empirical Evaluation (2005) (520)
Domain Adaptation with Multiple Sources (2008) (465)
Algorithms for Learning Kernels Based on Centered Alignment (2012) (425)
Sampling Methods for the Nyström Method (2012) (334)
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models (2009) (318)
Learning Non-Linear Combinations of Kernels (2009) (313)
Three Approaches for Personalization with Applications to Federated Learning (2020) (312)
L2 Regularization for Learning Kernels (2009) (302)
Sample Selection Bias Correction Theory (2008) (300)
Learning Bounds for Importance Weighting (2010) (289)
AdaNet: Adaptive Structural Learning of Artificial Neural Networks (2016) (243)
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning (2019) (233)
Bandit Problems (2018) (203)
Two-Stage Learning Kernel Algorithms (2010) (201)
Rational Kernels: Theory and Algorithms (2004) (198)
A Field Guide to Federated Optimization (2021) (195)
Confidence Intervals for the Area Under the ROC Curve (2004) (185)
Domain adaptation and sample bias correction theory and algorithm for regression (2014) (173)
Learning with Rejection (2016) (166)
On the Impact of Kernel Approximation on Learning Accuracy (2010) (150)
Sampling Techniques for the Nystrom Method (2009) (148)
Rademacher Complexity Bounds for Non-I.I.D. Processes (2008) (139)
Stability Bounds for Stationary φ-mixing and β-mixing Processes (2010) (138)
Multiple Source Adaptation and the Rényi Divergence (2009) (136)
Learning Theory and Algorithms for revenue optimization in second price auctions with reserve (2013) (134)
Ensemble Nystrom Method (2009) (132)
Algorithms and Theory for Multiple-Source Adaptation (2018) (123)
On Transductive Regression (2006) (121)
Generalization Bounds for Learning Kernels (2010) (120)
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. (2020) (112)
Magnitude-preserving ranking algorithms (2007) (108)
A general regression technique for learning transductions (2005) (101)
Domain Adaptation in Regression (2011) (96)
New Analysis and Algorithm for Learning with Drifting Distributions (2012) (92)
Learning Kernels Using Local Rademacher Complexity (2013) (91)
On sampling-based approximate spectral decomposition (2009) (90)
Margin-Based Ranking Meets Boosting in the Middle (2005) (83)
FedBoost: A Communication-Efficient Algorithm for Federated Learning (2020) (82)
An Efficient Reduction of Ranking to Classification (2007) (79)
Accuracy at the Top (2012) (79)
Boosting with Abstention (2016) (78)
Deep Boosting (2014) (78)
Rational Kernels (2002) (70)
Large-scale SVD and manifold learning (2013) (67)
Learning Theory and Algorithms for Forecasting Non-stationary Time Series (2015) (63)
Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers (2014) (60)
Multi-Class Deep Boosting (2014) (60)
Adaptation Based on Generalized Discrepancy (2019) (59)
Adaptation Algorithm and Theory Based on Generalized Discrepancy (2014) (57)
Adapting to Misspecification in Contextual Bandits (2021) (57)
Tight Lower Bound on the Probability of a Binomial Exceeding its Expectation (2013) (56)
Polynomial Semantic Indexing (2009) (53)
Logistic Regression: The Importance of Being Improper (2018) (52)
Generalization Bounds for Time Series Prediction with Non-stationary Processes (2014) (49)
Generalization bounds for non-stationary mixing processes (2016) (48)
Positive Definite Rational Kernels (2003) (48)
Structured Prediction Theory Based on Factor Graph Complexity (2016) (48)
Stability of transductive regression algorithms (2008) (47)
Multi-Class Classification with Maximum Margin Multiple Kernel (2013) (44)
Parameter-Free Online Learning via Model Selection (2017) (40)
LP Distance and Equivalence of Probabilistic Automata (2007) (39)
Accelerating Online Convex Optimization via Adaptive Prediction (2016) (38)
Revenue Optimization against Strategic Buyers (2015) (37)
Preference-based learning to rank (2010) (36)
On the Computation of the Relative Entropy of Probabilistic Automata (2008) (35)
Ensemble Methods for Structured Prediction (2014) (35)
Learning with User-Level Privacy (2021) (35)
An Alternative Ranking Problem for Search Engines (2007) (34)
Time series prediction and online learning (2016) (33)
Online Learning with Abstention (2017) (33)
Learning Algorithms for Second-Price Auctions with Reserve (2016) (33)
Stability Bounds for Non-i.i.d. Processes (2007) (33)
Relative deviation learning bounds and generalization with unbounded loss functions (2013) (32)
A General Regression Framework for Learning String-to-String Mappings (2006) (30)
Corralling Stochastic Bandit Algorithms (2020) (29)
Kernel Learning: Automatic Selection of Optimal Kernels (2008) (29)
Kernel methods for learning languages (2008) (29)
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks (2020) (28)
Learning sequence kernels (2008) (28)
Can matrix coherence be efficiently and accurately estimated? (2011) (28)
Gaussian Margin Machines (2009) (26)
Lattice kernels for spoken-dialog classification (2003) (26)
Hypothesis Set Stability and Generalization (2019) (25)
Context-Free Recognition with Weighted Automata (2000) (24)
Rademacher Complexity Margin Bounds for Learning with a Large Number of Classes (2015) (23)
Perceptron Mistake Bounds (2013) (21)
On the Computation of Some Standard Distances Between Probabilistic Automata (2006) (21)
Learning Linearly Separable Languages (2006) (20)
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data (2020) (19)
Discrepancy-Based Algorithms for Non-Stationary Rested Bandits (2017) (19)
Efficient Computation of the Relative Entropy of Probabilistic Automata (2006) (19)
Learning N-Gram Language Models from Uncertain Data (2016) (18)
Active Learning with Disagreement Graphs (2019) (18)
Learning Languages with Rational Kernels (2007) (17)
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles (2019) (17)
Region-Based Active Learning (2019) (16)
Optimistic Bandit Convex Optimization (2016) (16)
Bandits with Feedback Graphs and Switching Costs (2019) (15)
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning (2021) (15)
Stability Bounds for Stationary phi-mixing and beta-mixing Processes (2008) (15)
Online Learning with Sleeping Experts and Feedback Graphs (2019) (15)
Discrepancy-Based Theory and Algorithms for Forecasting Non-Stationary Time Series (2020) (14)
Ensembles of Kernel Predictors (2011) (14)
Beyond Individual and Group Fairness (2020) (13)
Learning with Deep Cascades (2015) (13)
Policy Regret in Repeated Games (2018) (13)
Foundations of Coupled Nonlinear Dimensionality Reduction (2015) (13)
Adaptive Region-Based Active Learning (2020) (13)
A Disambiguation Algorithm for Finite Automata and Functional Transducers (2012) (13)
Non-parametric Revenue Optimization for Generalized Second Price auctions (2015) (12)
Stability Analysis and Learning Bounds for Transductive Regression Algorithms (2009) (12)
A Machine Learning Framework for Spoken-Dialog Classification (2008) (11)
Automata and graph compression (2015) (11)
On the Existence of the Adversarial Bayes Classifier (Extended Version) (2021) (11)
Calibration and Consistency of Adversarial Surrogate Losses (2021) (11)
New Generalization Bounds for Learning Kernels (2009) (10)
Theory and Algorithms for Forecasting Time Series (2018) (10)
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning (2022) (10)
On the Rademacher Complexity of Linear Hypothesis Sets (2020) (10)
Weighted automata kernels - general framework and algorithms (2003) (10)
Multiple-source adaptation theory and algorithms (2020) (9)
Learning with Weighted Transducers (2009) (8)
Relative Deviation Margin Bounds (2020) (8)
Communication-Efficient Agnostic Federated Averaging (2021) (7)
Large-Scale Training of SVMs with Automata Kernels (2010) (7)
Conditional Swap Regret and Conditional Correlated Equilibrium (2014) (7)
Distribution kernels based on moments of counts (2004) (7)
Competing with Automata-based Expert Sequences (2018) (7)
Regularized Gradient Boosting (2019) (7)
On-Line Learning Algorithms for Path Experts with Non-Additive Losses (2015) (6)
Learning GANs and Ensembles Using Discrepancy (2019) (6)
Learning Ensembles of Structured Prediction Rules (2014) (6)
Revenue Optimization in Posted-Price Auctions with Strategic Buyers (2014) (6)
Online Learning with Transductive Regret (2017) (6)
Agnostic Learning with Multiple Objectives (2020) (6)
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations (2021) (6)
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation (2022) (5)
Accelerating Optimization via Adaptive Prediction (2015) (5)
A. Pac Learning (2010) (5)
Generalization Bounds for Supervised Dimensionality Reduction (2015) (5)
Online Learning with Dependent Stochastic Feedback Graphs (2020) (5)
Moment Kernels for Regular Distributions (2005) (5)
A Finer Calibration Analysis for Adversarial Robustness (2021) (5)
Multiple-Source Adaptation for Regression Problems (2017) (5)
Expected Sequence Similarity Maximization (2010) (5)
A Discriminative Technique for Multiple-Source Adaptation (2021) (4)
Multi-Armed Bandits with Non-Stationary Rewards (2017) (4)
H-Consistency Bounds for Surrogate Loss Minimizers (2022) (4)
Reinforcement Learning with Feedback Graphs (2020) (4)
On-line Learning with Abstention (2017) (4)
Random Composite Forests (2016) (4)
Corporate learning at scale: lessons from a large online course at google (2014) (4)
PAC-Bayes Learning Bounds for Sample-Dependent Priors (2020) (4)
Kernel Extraction via Voted Risk Minimization (2015) (4)
Large-scale Distributed Optimization for Improving Accuracy at the Top (2012) (3)
Multi-Class $H$-Consistency Bounds (2022) (3)
Discriminative State Space Models (2017) (3)
A Dual Coordinate Descent Algorithm for SVMs Combined with Rational Kernels (2011) (3)
Strategizing against Learners in Bayesian Games (2022) (3)
Generalization bounds for non-stationary mixing processes (2016) (2)
Stability Bounds for Noni . i . d . Processes (2007) (2)
Structural Maxent Models (2015) (2)
Half Transductive Ranking (2010) (2)
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses (2018) (2)
Theory and algorithms for modern problems in machine learning and an analysis of markets (2008) (2)
Differentially Private Learning with Margin Guarantees (2022) (2)
Finite-State Transducers in Computational Biology (2005) (1)
Adaptive Algorithms and Data-Dependent Guarantees for Bandit Convex Optimization (2016) (1)
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality (2022) (1)
Multiple-Source Adaptation with Domain Classifiers (2020) (1)
Foundations Of Machine Learning Mehryar Mohri (2022) (1)
Private Domain Adaptation from a Public Source (2022) (1)
Online Learning with Automata-based Expert Sequences (2017) (1)
Ensemble Nystr̈om (2012) (1)
Multiple-source adaptation theory and algorithms – addendum (2022) (1)
Algorithmic Learning Theory ALT 2018: Preface (2018) (1)
SVM Optimization for Lattice Kernels (2010) (1)
Online Learning with Expert Automata (2017) (1)
On the Estimation of Coherence (2010) (1)
Online Non-Additive Path Learning under Full and Partial Information (2018) (0)
Much of the Solution Written by Afshin Rostami and Umar Syed (2012) (0)
H-Consistency Estimation Error of Surrogate Loss Minimizers (2022) (0)
Tutorial HandOuts – Weighted Finite-State Transducers in Computational Biology (2005) (0)
Online Learning against Expert Automata (2017) (0)
Cross-Entropy Loss Functions: Theoretical Analysis and Applications (2023) (0)
Pseudonorm Approachability and Applications to Regret Minimization (2023) (0)
Reduction of Ranking to Classification (2007) (0)
Hedging Structured Concepts (2010) (0)
Online Convex Optimization (2010) (0)
Structured Prediction Theory and Voted Risk Minimization (2016) (0)
Relative deviation learning bounds and generalization with unbounded loss functions (2019) (0)
cs . L G ] 2 5 M ar 2 01 8 Logistic Regression : The Importance of Being Improper (2018) (0)
Structural Online Learning (2016) (0)
On-line Learning Approach to Ensemble Methods for Structured Prediction (2014) (0)
Discrepancy-Based Theory and Algorithms for Forecasting Non-Stationary Time Series (2020) (0)
Voted Kernel Regularization (2015) (0)
Theoretical Foundations for Learning Kernels in Supervised Kernel PCA (2014) (0)
09 34 9 v 2 [ cs . L G ] 1 4 D ec 2 01 8 Logistic Regression : The Importance of Being Improper (2018) (0)
Data-Dependent Algorithms for Bandit Convex Optimization (2015) (0)
Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees (2022) (0)
Mehryar Mohri Foundations of Machine Learning (2007) (0)
Consider the Learning Kernel Optimization Based on Svm (2015) (0)
The second inequality (2012) (0)
COLT 2010 - The 23rd Conference on Learning Theory, Haifa, Israel, June 27-29, 2010 (2010) (0)
UvA-DARE ( Digital Academic Repository ) Hedging structured concepts (2010) (0)

This paper list is powered by the following services:

Mehryar Mohri Mohri's Academic­Influence.com Rankings

Mehryar Mohri Mohri's Degrees

Similar Degrees You Can Earn

Why Is Mehryar Mohri Mohri Influential?

Mehryar Mohri Mohri's Published Works

Published Works

Mehryar Mohri Mohri's AcademicInfluence.com Rankings