Elad Hazan
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Israeli-American computer scientist
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Computer Science
Why Is Elad Hazan Influential?
(Suggest an Edit or Addition)According to Wikipedia, Elad Hazan is an Israeli-American computer scientist, academic, author and researcher. He is a Professor of Computer Science at Princeton University, and the co-founder and director of Google AI Princeton.
Elad Hazan'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
- Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (2011) (9035)
- Introduction to Online Convex Optimization (2016) (1371)
- Logarithmic regret algorithms for online convex optimization (2006) (1041)
- The Multiplicative Weights Update Method: a Meta-Algorithm and Applications (2012) (932)
- Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization (2010) (397)
- On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization (2018) (364)
- Variance Reduction for Faster Non-Convex Optimization (2016) (347)
- Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization (2008) (313)
- Finding approximate local minima faster than gradient descent (2016) (230)
- Algorithms for portfolio management based on the Newton method (2006) (210)
- Adaptive Online Gradient Descent (2007) (207)
- Projection-free Online Learning (2012) (206)
- On the complexity of approximating k-set packing (2006) (184)
- Sparse Approximate Solutions to Semidefinite Programs (2008) (176)
- Provably Efficient Maximum Entropy Exploration (2018) (173)
- Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets (2014) (170)
- Efficient learning algorithms for changing environments (2009) (167)
- Online Control with Adversarial Disturbances (2019) (165)
- Fast algorithms for approximate semidefinite programming using the multiplicative weights update method (2005) (155)
- Variance-Reduced and Projection-Free Stochastic Optimization (2016) (153)
- Extracting certainty from uncertainty: regret bounded by variation in costs (2008) (152)
- Second-Order Stochastic Optimization for Machine Learning in Linear Time (2016) (148)
- Beyond Convexity: Stochastic Quasi-Convex Optimization (2015) (135)
- The convex optimization approach to regret minimization (2011) (133)
- Online Learning for Time Series Prediction (2013) (130)
- Sublinear Optimization for Machine Learning (2010) (123)
- How hard is it to approximate the best Nash equilibrium? (2009) (119)
- A Fast Random Sampling Algorithm for Sparsifying Matrices (2006) (104)
- Adaptive Algorithms for Online Decision Problems (2007) (96)
- Improper Learning for Non-Stochastic Control (2020) (96)
- Blackwell Approachability and No-Regret Learning are Equivalent (2010) (94)
- Hyperparameter Optimization: A Spectral Approach (2017) (91)
- Better Algorithms for Benign Bandits (2009) (90)
- Bandit Convex Optimization: Towards Tight Bounds (2014) (90)
- Fast and Simple PCA via Convex Optimization (2015) (86)
- Second Order Stochastic Optimization in Linear Time (2016) (86)
- A Linearly Convergent Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization (2013) (86)
- On Graduated Optimization for Stochastic Non-Convex Problems (2015) (84)
- Optimal Black-Box Reductions Between Optimization Objectives (2016) (84)
- Logarithmic Regret for Online Control (2019) (81)
- Finding Approximate Local Minima for Nonconvex Optimization in Linear Time (2016) (81)
- The Nonstochastic Control Problem (2019) (79)
- Spectral Filtering for General Linear Dynamical Systems (2018) (79)
- O(√log n) approximation to SPARSEST CUT in Õ(n 2) time (2004) (78)
- Volumetric Spanners: An Efficient Exploration Basis for Learning (2013) (78)
- Learning Linear Dynamical Systems via Spectral Filtering (2017) (77)
- Online Time Series Prediction with Missing Data (2015) (77)
- Interior-Point Methods for Full-Information and Bandit Online Learning (2012) (74)
- Near-Optimal Algorithms for Online Matrix Prediction (2012) (73)
- Efficient Regret Minimization in Non-Convex Games (2017) (72)
- Online submodular minimization (2009) (72)
- Faster Eigenvector Computation via Shift-and-Invert Preconditioning (2016) (68)
- On non-approximability for quadratic programs (2005) (68)
- A Linearly Convergent Variant of the Conditional Gradient Algorithm under Strong Convexity, with Applications to Online and Stochastic Optimization (2016) (67)
- Online Learning with Prior Knowledge (2007) (66)
- Online learning of quantum states (2018) (65)
- Oracle-Based Robust Optimization via Online Learning (2014) (63)
- A linear-time algorithm for trust region problems (2014) (62)
- Beating SGD: Learning SVMs in Sublinear Time (2011) (60)
- O(sqrt(log(n)) Approximation to SPARSEST CUT in Õ(n2) Time (2004) (57)
- Logistic Regression: Tight Bounds for Stochastic and Online Optimization (2014) (52)
- Black-Box Control for Linear Dynamical Systems (2020) (51)
- Online Learning for Adversaries with Memory: Price of Past Mistakes (2015) (50)
- Online Gradient Boosting (2015) (49)
- Efficient algorithms for online convex optimization and their applications (2006) (49)
- Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls (2017) (48)
- On the Complexity of Approximating k-Dimensional Matching (2003) (48)
- The computational power of optimization in online learning (2015) (47)
- Analysis and algorithms for content-based event matching (2005) (45)
- An optimal algorithm for bandit convex optimization (2016) (44)
- Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction (2011) (42)
- Learning in Non-convex Games with an Optimization Oracle (2018) (39)
- Online Learning of Eigenvectors (2015) (39)
- On Stochastic and Worst-case Models for Investing (2009) (37)
- Towards Provable Control for Unknown Linear Dynamical Systems (2018) (37)
- Linear Regression with Limited Observation (2012) (36)
- AN ONLINE PORTFOLIO SELECTION ALGORITHM WITH REGRET LOGARITHMIC IN PRICE VARIATION (2015) (36)
- Approximating Semidefinite Programs in Sublinear Time (2011) (36)
- Classification with Low Rank and Missing Data (2015) (35)
- Faster Projection-free Online Learning (2020) (35)
- Lower Bounds for Higher-Order Convex Optimization (2017) (35)
- Playing Non-linear Games with Linear Oracles (2013) (33)
- O(/spl radic/log n) approximation to SPARSEST CUT in O/spl tilde/(n/sup 2/) time (2004) (32)
- Adaptive Regret for Control of Time-Varying Dynamics (2020) (31)
- Exponentiated Gradient Meets Gradient Descent (2019) (31)
- Efficient Full-Matrix Adaptive Regularization (2020) (29)
- On the Hardness of Approximating k-Dimensional Matching (2003) (28)
- Revisiting the Polyak step size (2019) (27)
- O( p logn) Approximation to Sparsest Cut in O(n2) Time (2004) (25)
- Disentangling Adaptive Gradient Methods from Learning Rates (2020) (24)
- Sublinear time algorithms for approximate semidefinite programming (2016) (24)
- Better Rates for Any Adversarial Deterministic MDP (2013) (24)
- Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier (2015) (23)
- A simple multi-armed bandit algorithm with optimal variation-bounded regret (2011) (21)
- The Case for Full-Matrix Adaptive Regularization (2018) (20)
- Online Control of Unknown Time-Varying Dynamical Systems (2022) (18)
- Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria (2007) (18)
- A Non-generative Framework and Convex Relaxations for Unsupervised Learning (2016) (18)
- The gradient complexity of linear regression (2019) (18)
- HAPLOFREQ-Estimating Haplotype Frequencies Efficiently (2006) (17)
- Efficient Algorithms for Online Game Playing and Universal Portfolio Management (2006) (17)
- Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning (2019) (17)
- (weak) Calibration is Computationally Hard (2012) (16)
- Online Improper Learning with an Approximation Oracle (2018) (15)
- Finding Local Minima for Nonconvex Optimization in Linear Time (2016) (12)
- Online Learning with Low Rank Experts (2016) (12)
- Approximate Convex Optimization by Online Game Playing (2006) (11)
- Non-Stochastic Control with Bandit Feedback (2020) (11)
- Boosting for Control of Dynamical Systems (2019) (11)
- On-line Variance Minimization in O(n2) per Trial? (2010) (11)
- Private Learning Implies Online Learning: An Efficient Reduction (2019) (11)
- A Regret Minimization Approach to Iterative Learning Control (2021) (10)
- Hard-Margin Active Linear Regression (2014) (10)
- Learning rotations with little regret (2010) (9)
- Optimal Algorithms for Ridge and Lasso Regression with Partially Observed Attributes (2011) (9)
- Beyond Convexity: Online Submodular Minimization (2009) (9)
- The Limits of Learning with Missing Data (2016) (9)
- Extreme Tensoring for Low-Memory Preconditioning (2019) (9)
- New Algorithms for Repeated Play and Universal Portfolio Management (2006) (8)
- Online Agnostic Boosting via Regret Minimization (2020) (8)
- Geometric Exploration for Online Control (2020) (7)
- Lecture Notes: Optimization for Machine Learning (2019) (7)
- An Efficient Algorithm for Bandit Linear Optimization (2008) (7)
- Boosting simple learners (2020) (7)
- Online Boosting with Bandit Feedback (2020) (6)
- Boosting for Dynamical Systems (2019) (6)
- A Polynomial Time Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization (2013) (6)
- Online Learning of Linear Dynamical Systems (2017) (6)
- Revisiting the Generalization of Adaptive Gradient Methods (2019) (6)
- Introduction to Online Nonstochastic Control (2022) (5)
- Deluca - A Differentiable Control Library: Environments, Methods, and Benchmarking (2021) (5)
- Machine Learning for Mechanical Ventilation Control (2021) (5)
- Generating Adversarial Disturbances for Controller Verification (2020) (5)
- Adaptive Universal Linear Filtering (2013) (4)
- Adaptive Online Learning of Quantum States (2022) (4)
- A Boosting Approach to Reinforcement Learning (2021) (4)
- Non-convex online learning via algorithmic equivalence (2022) (4)
- Boosting for Online Convex Optimization (2021) (4)
- Provable Regret Bounds for Deep Online Learning and Control (2021) (4)
- Volumetric Spanners and their Applications to Machine Learning (2013) (4)
- Adaptive Gradient Methods with Local Guarantees (2022) (4)
- The Blinded Bandit: Learning with Adaptive Feedback (2014) (4)
- Corrigendum to "Learning rotations with little regret" September 7, 2010 (2010) (3)
- Online Convex Optimization Against Adversaries with Memory and Application to Statistical Arbitrage (2013) (3)
- Multiclass Boosting and the Cost of Weak Learning (2021) (3)
- Adaptive Algorithms for Online Optimization (2007) (2)
- Almost Optimal Sublinear Time Algorithm for Semidefinite Programming (2012) (2)
- learning algorithms for changing environments (2009) (1)
- Learning rotations with little regret (2016) (1)
- Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret (2022) (1)
- Projection-free Adaptive Regret with Membership Oracles (2022) (1)
- O ( √ log n ) approximation to SPARSEST CUT can be found in Õ ( n 2 ) time (2004) (1)
- HAPLOFREQ - Estimating Haplotype Frequencies E.ciently (2005) (1)
- A Regret Minimization Approach to Multi-Agent Contro (2022) (1)
- Efficient Adaptive Regret Minimization (2022) (1)
- Conference on Learning Theory 2015: Preface (2015) (0)
- A Polylog Pivot Steps Simplex Algorithm for Classification (2012) (0)
- A linear-time algorithm for trust region problems (2015) (0)
- O( p logn) approximation to SPARSEST CUT can be found in (2004) (0)
- O C ] 1 M ay 2 01 9 Revisiting the Polyak step size (2019) (0)
- Robust Online Control with Model Misspecification (2021) (0)
- 20 F eb 2 01 2 ( weak ) Calibration is Computationally Hard (2021) (0)
- Online Learning for Loss Functions with Memory and Applications to Statistical Arbitrage (2013) (0)
- Sublinear time algorithms for approximate semidefinite programming (2015) (0)
- On Non-Approximability for Quadratic Programs Preliminary Version (0)
- Online Improper Learning in Games with Approximation Oracle (2018) (0)
- Partial Matrix Completion (2022) (0)
- Quantum Error Correcting Codes Lecture Notes for the course : Introduction to Quantum Computation (2001) (0)
- A Nonstochastic Control Approach to Optimization (2023) (0)
- Open problem: Improper learning of mixtures of Gaussians (2018) (0)
- Machine Learning for Mechanical Ventilation Control (Extended Abstract) (2021) (0)
- Universal MMSE Filtering With Logarithmic Adaptive Regret (2011) (0)
- On the Computational Efficiency of Adaptive and Dynamic Regret Minimization (2022) (0)
- A provably efficient simplex algorithm for classification (2012) (0)
- Computational hardness of fast rates for online sparse PCA : improperness does not help (2018) (0)
- Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions (2023) (0)
- Weak Learnability and Boosting 1 . 1 Strong learnability vs Weak learnability (2016) (0)
- Online Learning with an Approximation Oracle ⇤ (2018) (0)
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