Nicolò Cesa-Bianchi | Academic Influence

Nicolò Cesa-Bianchi's AcademicInfluence.com Rankings

Nicolò Cesa-Bianchi

Computer Science

#2436

World Rank

#2544

Historical Rank

Machine Learning

#469

World Rank

#475

Historical Rank

Artificial Intelligence

#2207

World Rank

#2245

Historical Rank

Database

#3346

World Rank

#3487

Historical Rank

computer-science Degrees

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Computer Science

Nicolò Cesa-Bianchi's Degrees

PhD Computer Science University of California, San Diego
Masters Computer Science University of California, San Diego
Bachelors Computer Science University of Milan

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Why Is Nicolò Cesa-Bianchi Influential?

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According to Wikipedia, Nicolò Cesa-Bianchi is a computer scientist and Professor of Computer Science at the Department of Computer Science of the University of Milan. He is a researcher in the field of machine learning, and co-author of the books "Prediction, Learning, and Games" with Gabor Lugosi and "Regret analysis of stochastic and nonstochastic multi-armed bandit problems"

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Nicolò Cesa-Bianchi'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

Finite-time Analysis of the Multiarmed Bandit Problem (2002) (5760)
Prediction, learning, and games (2006) (3528)
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012) (2394)
The Nonstochastic Multiarmed Bandit Problem (2002) (2154)
Gambling in a rigged casino: The adversarial multi-armed bandit problem (1995) (871)
How to use expert advice (1993) (660)
On the generalization ability of on-line learning algorithms (2001) (547)
Scale-sensitive dimensions, uniform convergence, and learnability (1993) (461)
Combinatorial Bandits (2012) (398)
Advances in Neural Information Processing Systems 31 (2018) (354)
Incremental Algorithms for Hierarchical Classification (2004) (279)
Improved second-order bounds for prediction with expert advice (2005) (247)
Adaptive and Self-Confident On-Line Learning Algorithms (2000) (243)
Bandits With Heavy Tail (2012) (232)
A Second-Order Perceptron Algorithm (2002) (217)
How to use expert advice (1997) (215)
Tracking the best hyperplane with a simple budget Perceptron (2006) (194)
Linear Algorithms for Online Multitask Classification (2010) (193)
Hierarchical classification: combining Bayes with SVM (2006) (178)
Regret Minimization Under Partial Monitoring (2006) (161)
Regret Minimization for Reserve Prices in Second-Price Auctions (2013) (161)
Worst-case quadratic loss bounds for prediction using linear functions and gradient descent (1996) (144)
A Gang of Bandits (2013) (137)
Worst-Case Analysis of Selective Sampling for Linear Classification (2006) (137)
Towards Minimax Policies for Online Linear Optimization with Bandit Feedback (2012) (127)
Characterizations of Learnability for Classes of {0, ..., n}-Valued Functions (1995) (122)
Online Learning with Feedback Graphs: Beyond Bandits (2015) (121)
Minimizing regret with label efficient prediction (2004) (115)
Delay and Cooperation in Nonstochastic Bandits (2016) (114)
Boltzmann Exploration Done Right (2017) (102)
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback (2014) (100)
Online Learning with Switching Costs and Other Adaptive Adversaries (2013) (99)
On Prediction of Individual Sequences (1998) (97)
PAC-Bayesian Inequalities for Martingales (2011) (91)
Finite-Time Regret Bounds for the Multiarmed Bandit Problem (1998) (88)
Analysis of two gradient-based algorithms for on-line regression (1997) (88)
Efficient Learning with Partially Observed Attributes (2010) (86)
Mirror Descent Meets Fixed Share (and feels no regret) (2012) (80)
Efficient Second Order Online Learning by Sketching (2016) (78)
Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference (2012) (78)
From Bandits to Experts: A Tale of Domination and Independence (2013) (75)
A generalized online mirror descent with applications to classification and regression (2013) (67)
Robust bounds for classification via selective sampling (2009) (63)
Improved Risk Tail Bounds for On-Line Algorithms (2005) (62)
WORST-CASE QUADRATIC LOSS BOUNDS FOR ON-LINE PREDICTION OF LINEAR FUNCTIONS BY GRADIENT DESCENT (1993) (60)
Active Learning on Trees and Graphs (2013) (53)
On-line learning with malicious noise and the closure algorithm (1994) (51)
Kernel Methods for Document Filtering (2002) (50)
Potential-Based Algorithms in On-Line Prediction and Game Theory (2003) (50)
Better Algorithms for Selective Sampling (2011) (48)
Learning Probabilistic Linear-Threshold Classifiers via Selective Sampling (2003) (48)
On-line Prediction and Conversion Strategies (1994) (45)
Online Learning of Noisy Data (2011) (44)
Hierarchical Cost-Sensitive Algorithms for Genome-Wide Gene Function Prediction (2009) (44)
Towards Highly Adaptive Services for Mobile Computing (2004) (42)
Fast and Optimal Prediction on a Labeled Tree (2009) (41)
Sample-efficient strategies for learning in the presence of noise (1999) (39)
Random Spanning Trees and the Prediction of Weighted Graphs (2010) (39)
Nonstochastic Multiarmed Bandits with Unrestricted Delays (2019) (36)
Learning noisy linear classifiers via adaptive and selective sampling (2011) (34)
Nonstochastic Bandits with Composite Anonymous Feedback (2018) (32)
Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms (2004) (32)
Beyond Logarithmic Bounds in Online Learning (2012) (32)
A Correlation Clustering Approach to Link Classification in Signed Networks (2012) (32)
Stochastic Bandits with Delay-Dependent Payoffs (2019) (32)
Worst-Case Bounds for the Logarithmic Loss of Predictors (1999) (32)
Minimax regret under log loss for general classes of experts (1999) (31)
OM-2: An online multi-class Multi-Kernel Learning algorithm Luo Jie (2010) (31)
On the Complexity of Learning with Kernels (2014) (31)
A new look at shifting regret (2012) (29)
Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning (2017) (29)
Linear Classification and Selective Sampling Under Low Noise Conditions (2008) (28)
Characterizations of learnability for classes of {O, …, n}-valued functions (1992) (27)
Cooperative Online Learning: Keeping your Neighbors Updated (2019) (24)
Worst-case quadratic loss bounds for a generalization of the Widrow-Hoff rule (1993) (23)
HCGene: a software tool to support the hierarchical classification of genes (2008) (23)
See the Tree Through the Lines: The Shazoo Algorithm (2011) (23)
Efficient Online Learning via Randomized Rounding (2011) (21)
PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits (2011) (21)
Regret Minimization for Branching Experts (2013) (20)
Regret Bounds for Hierarchical Classification with Linear-Threshold Functions (2004) (19)
On Bayes Methods for On-Line Boolean Prediction (1998) (19)
Online Action Recognition via Nonparametric Incremental Learning (2014) (18)
Online Learning of Noisy Data with Kernels (2010) (18)
Splitting with confidence in decision trees with application to stream mining (2015) (18)
Efficient Linear Bandits through Matrix Sketching (2018) (17)
Dynamic Pricing with Finitely Many Unknown Valuations (2018) (17)
The ABACOC Algorithm: A Novel Approach for Nonparametric Classification of Data Streams (2015) (17)
Confidence Decision Trees via Online and Active Learning for Streaming Data (2016) (15)
Distribution-Dependent Analysis of Gibbs-ERM Principle (2019) (13)
Guest Editorial: Learning from multiple sources (2010) (13)
Bounds on approximate steepest descent for likelihood maximization in exponential families (1994) (11)
An Algorithm for Stochastic and Adversarial Bandits with Switching Costs (2021) (10)
Correlation Clustering with Adaptive Similarity Queries (2019) (10)
A Graph-theoretic Generalization of the Sauer-Shelah Lemma (1998) (10)
Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries (2021) (10)
Exact Recovery of Mangled Clusters with Same-Cluster Queries (2020) (9)
Potential-Based Algorithms in Online Prediction and Game Theory (2001) (9)
A Linear Time Active Learning Algorithm for Link Classification (2012) (9)
Active Incremental Recognition of Human Activities in a Streaming Context (2017) (9)
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (2018) (9)
Margin-Based Algorithms for Information Filtering (2002) (8)
On sequential prediction of individual sequences relative to a set of experts (1998) (8)
Bandit Regret Scaling with the Effective Loss Range (2017) (8)
A Regret Analysis of Bilateral Trade (2021) (7)
Active Learning on Graphs via Spanning Trees (2010) (7)
PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off (2011) (7)
Integrated Profile and Policy Management for Mobile-oriented Internet Services (2003) (6)
Nonparametric Online Regression while Learning the Metric (2017) (6)
Online Learning Algorithms (2021) (6)
Learning Unknown Graphs (2009) (6)
A distributed architecture for management and retrieval of extended points of interest (2005) (6)
Applications of regularized least squares to pattern classification (2007) (5)
Beyond Bandit Feedback in Online Multiclass Classification (2021) (5)
Efficient Learning with Equivalence Queries of Conjunctions of Modulo Functions (1995) (5)
Efficient Transductive Online Learning via Randomized Rounding (2011) (5)
Integrated Profile Management for Mobile Computing (2003) (5)
An Optimal Algorithm for Linear Bandits (2011) (4)
Bilateral Trade: A Regret Minimization Perspective (2021) (4)
Nonstochastic Bandits and Experts with Arm-Dependent Delays (2021) (4)
On-line prediction and conversion strategies (2004) (4)
Some Impossibility Results for Budgeted Learning (2010) (4)
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs (2022) (4)
Noise-tolerant learning near the information-theoretic bound (1996) (4)
On the Troll-Trust Model for Edge Sign Prediction in Social Networks (2016) (3)
On Margin-Based Cluster Recovery with Oracle Queries (2021) (3)
Randomized Hypotheses and Minimum Disagreement Hypotheses for Learning with Noise (1997) (3)
On prediction of individual sequences relative to a set of experts (1998) (3)
Tight bounds on the cumulative profit of distributed voters (1996) (3)
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits (2021) (3)
Multi-armed Bandit Problem (2016) (3)
A Regret-Variance Trade-Off in Online Learning (2022) (3)
Learning on the Edge: Online Learning with Stochastic Feedback Graphs (2022) (3)
Learning the Distribution in the Extended PAC Model (1990) (2)
Algorithmic learning theory : 13th international conference, ALT 2002, Lübeck, Germany, November 24-26, 2002 : proceedings (2002) (2)
Cooperative Online Learning with Feedback Graphs (2021) (2)
ROI Maximization in Stochastic Online Decision-Making (2019) (2)
Break your Bandit Routine with LSD Rewards: a Last Switch Dependent Analysis of Satiation and Seasonality (2021) (2)
Microcanonical annealing on neural networks (1988) (2)
Multitask Online Mirror Descent (2021) (2)
Combining Cost-Sensitive Classification with Negative Selection for Protein Function Prediction (2018) (2)
Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT 2000), June 28 - July 1, 2000, Palo Alto, California, USA (2000) (2)
Implementing Neural Networks (1989) (2)
Predicting the labels of an unknown graph via adaptive exploration (2011) (2)
Active Learning for Online Recognition of Human Activities from Streaming Videos (2016) (1)
Memory Constraint Online Multitask Classification (2012) (1)
Proceedings of the 13th International Conference on Algorithmic Learning Theory (2002) (1)
AdaTask: Adaptive Multitask Online Learning (2022) (1)
Linear Bandits with Memory: from Rotting to Rising (2023) (1)
Worst-case bounds for the redundancy of sequential lossless codes and for the logarithmic loss of predictors (2000) (1)
Algorithmic Learning Theory (2002) (1)
Finite-time Upper Bounds for the Multi-armed Bandit Problem with Bounded Rewards (1998) (1)
Locally-Adaptive Nonparametric Online Learning (2020) (1)
A generalized online mirror descent with applications to classification and regression (2014) (1)
Applications of Regularized Least Squares to Classification Problems (2004) (1)
Prediction, Learning, and Games: Sequential Investment (2006) (1)
Prediction and Playing Games (2006) (1)
Sequential methods for robust decision making (2018) (0)
Quantity Makes Quality: Learning with Partial Views (2011) (0)
Fast and Optimal Algorithms for Weighted Graph Prediction (2009) (0)
Multi-Sided Matching Markets with Consistent Preferences and Cooperative Partners (2021) (0)
The Game-Theoretic Approach to Machine Learning and Adaptation (2011) (0)
A distributed voting scheme to maximize preferences (2006) (0)
Information-Theoretic Regret Bounds for Bandits with Fixed Expert Advice (2023) (0)
Fe b 20 15 Online Learning with Feedback Graphs : Beyond (2018) (0)
Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification (2018) (0)
Learning from Noisy Data under Distributional Assumptions (2010) (0)
Prediction with Expert Advice (2006) (0)
Ensembles and Multiple Classifiers: A Game-Theoretic View (2011) (0)
MULTIARMED BANDITS IN THE WORST CASE (2002) (0)
A New Theoretical Framework for Fast and Accurate Online Decision-Making (2021) (0)
Prediction, Learning, and Games: Appendix (2006) (0)
Prediction, Learning, and Games: Linear Pattern Recognition (2006) (0)
Repeated A/B Testing (2019) (0)
Material for “ Efficient Linear Bandits through Matrix Sketching ” A Proofs (2019) (0)
Foreword (2001) (0)
Application to Stream Mining (2015) (0)
Prediction, Learning, and Games: Linear Classification (2006) (0)
Prediction, Learning, and Games: Absolute Loss (2006) (0)
20 D ec 2 01 1 An Optimal Algorithm for Linear Bandits (0)
Prediction with Limited Feedback (2006) (0)
Tight Bounds for Specific Losses (2006) (0)
Learning Ordinary Differential Equations with the Line Integral Loss Function (2022) (0)
Guest Editors' Introduction (2004) (0)
Nonstochastic bandits with anonymous feedback (2018) (0)
Editors' Introduction (2002) (0)
Bayesian Alignments of Warped Multi-Output Gaussian Processes (2018) (0)
Fast and Accurate Online Decision-Making (2021) (0)
Prediction, Learning, and Games: Introduction (2006) (0)
Prediction, Learning, and Games: Efficient Forecasters for Large Classes of Experts (2006) (0)
Online discriminative learning: theory and applications (2009) (0)
Managing a Large network of excellence: Case Study of the PASCAL Network (2007) (0)
Prediction, Learning, and Games: Randomized Prediction (2006) (0)
Graph Prediction with Random Trees (2008) (0)
Online Graph Prediction with Random Trees (2008) (0)
Online learning for CAT applications (2009) (0)
Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference (2011) (0)
CORDI-S-CRI Paris ] Sequential methods for robust decision making (2018) (0)
UvA-DARE (Digital Academic Repository) 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data (2021) (0)
Efficient Link Classification in Social Networks (2015) (0)
Adaptive Sampling Under Low Noise Conditions 1 (2009) (0)
Online Nonparametric Learning, Chaining, and the Role of Partial Feedback (2017) (0)
Adaptive Sampling Under Low Noise Conditions (2020) (0)
Prediction, Learning, and Games: References (2006) (0)
Genome-wide hierarchical classification of gene function (2009) (0)
Repeated Bilateral Trade Against a Smoothed Adversary (2023) (0)
Recent Results in On-line Prediction and Boosting (1997) (0)
Fast and Accurate Repeated Decision Making (2020) (0)
Finding Stable Matchings in PhD Markets with Consistent Preferences and Cooperative Partners (2021) (0)
Online Learning in Supply-Chain Games (2022) (0)
Active Learning of Classifiers with Label and Seed Queries (2022) (0)

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What Schools Are Affiliated With Nicolò Cesa-Bianchi?

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