World's Best Scientists 2026 revealed!
Alexander Rakhlin

Alexander Rakhlin

D-Index & Metrics

Engineering and Technology

D-Index
56
Citations
8512
World Ranking
2910
National Ranking
876

Overview

Alexander Rakhlin is affiliated with MIT in the United States and has contributed extensively to the field of Computer Science, with a specialization in Artificial Intelligence, Management Science and Operations Research, Computer Vision and Pattern Recognition, Computational Mechanics, and Statistics and Probability.

Their recent publications include works spanning several venues and topics. Notable papers are: "Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles" (2020, arXiv, Cornell University), published twice with different citation counts; "ColocML: machine learning quantifies co-localization between mass spectrometry images" (2020, Bioinformatics); "DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks" (2020, Molecular Systems Biology); and "Learning nonlinear dynamical systems from a single trajectory" (2020, arXiv, Cornell University).

Their research focuses on topics such as Advanced Bandit Algorithms Research, Machine Learning and Algorithms, Data Stream Mining Techniques, Reinforcement Learning in Robotics, Stochastic Gradient Optimization Techniques, Sparse and Compressive Sensing Techniques, and Adversarial Robustness in Machine Learning.

Frequent co-authors include Dylan J. Foster, Adam Block, Jian Qian, Ayush Sekhari, and Zeyu Jia. Many of these collaborations reflect sustained partnerships over multiple publications.

Alexander Rakhlin has published primarily in the following venues: arXiv (Cornell University) with 46 publications, Bioinformatics, Molecular Systems Biology, and Acta Numerica. These venues highlight a combination of preprint archives and specialized journals in computational biology and numerical analysis.

  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Data Stream Mining Techniques
  • Reinforcement Learning in Robotics
  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Adversarial Robustness in Machine Learning

  • Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles (2020, arXiv, Cornell University)
  • ColocML: machine learning quantifies co-localization between mass spectrometry images (2020, Bioinformatics)
  • DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks (2020, Molecular Systems Biology)
  • Learning nonlinear dynamical systems from a single trajectory (2020, arXiv, Cornell University)

  • Dylan J. Foster
  • Adam Block
  • Jian Qian
  • Ayush Sekhari
  • Zeyu Jia

  • arXiv (Cornell University)
  • Bioinformatics
  • Molecular Systems Biology
  • Acta Numerica

Best Publications

  • Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

    Alexander Rakhlin;Ohad Shamir;Karthik Sridharan

  • Automatic Instrument Segmentation in Robot-Assisted Surgery using Deep Learning

    Alexey A. Shvets;Alexander Rakhlin;Alexandr A. Kalinin;Vladimir I. Iglovikov

  • Size-independent sample complexity of neural networks

    Noah Golowich;Alexander Rakhlin;Ohad Shamir

  • Competing in the dark: An efficient algorithm for bandit linear optimization

    Jacob D Abernethy;Elad Hazan;Alexander Rakhlin

  • Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

    Alexander Rakhlin;Alexey Shvets;Vladimir Iglovikov;Alexandr A. Kalinin

  • Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis

    Maxim Raginsky;Alexander Rakhlin;Matus Telgarsky

  • Adaptive Online Gradient Descent

    Elad Hazan;Alexander Rakhlin;Peter L. Bartlett

  • Just interpolate: Kernel “Ridgeless” regression can generalize

    Tengyuan Liang;Alexander Rakhlin

  • Online Learning With Predictable Sequences

    Alexander Rakhlin;Karthik Sridharan

  • Online Optimization : Competing with Dynamic Comparators

    Ali Jadbabaie;Alexander Rakhlin;Shahin Shahrampour;Karthik Sridharan

  • Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks

    Vladimir I. Iglovikov;Alexander Rakhlin;Alexandr A. Kalinin;Alexey A. Shvets

  • Stochastic Convex Optimization with Bandit Feedback

    Alekh Agarwal;Dean P. Foster;Daniel J. Hsu;Sham M. Kakade

  • Optimal Strategies and Minimax Lower Bounds for Online Convex Games

    Jacob Duncan Abernethy;Peter Bartlett;Alexander Rakhlin;Ambuj Tewari

  • Deep learning: a statistical viewpoint

    Peter L. Bartlett;Andrea Montanari;Alexander Rakhlin

  • Fisher-Rao Metric, Geometry, and Complexity of Neural Networks

    Tengyuan Liang;Tomaso A. Poggio;Alexander Rakhlin;James Stokes

  • Does data interpolation contradict statistical optimality

    Mikhail Belkin;Alexander Rakhlin;Alexandre B. Tsybakov

  • Stability of K-Means Clustering

    Alexander Rakhlin;Andrea Caponnetto

  • Partial Monitoring—Classification, Regret Bounds, and Algorithms

    Gábor Bartók;Dean P. Foster;Dávid Pál;Alexander Rakhlin

  • Online Learning: Random Averages, Combinatorial Parameters, and Learnability

    Alexander Rakhlin;Karthik Sridharan;Ambuj Tewari

  • Distributed Detection: Finite-Time Analysis and Impact of Network Topology

    Shahin Shahrampour;Alexander Rakhlin;Ali Jadbabaie

  • Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles

    Dylan Foster;Alexander Rakhlin

Frequent Co-Authors

Karthik Sridharan
Karthik Sridharan Cornell University
Ambuj Tewari
Ambuj Tewari University of Michigan–Ann Arbor
Peter L. Bartlett
Peter L. Bartlett University of California, Berkeley
Tommaso Cai
Tommaso Cai University of Pennsylvania
Dean P. Foster
Dean P. Foster Amazon (United States)
Ohad Shamir
Ohad Shamir Weizmann Institute of Science
Alekh Agarwal
Alekh Agarwal Google (United States)

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