Misha Belkin

Misha Belkin's AcademicInfluence.com Rankings

Misha Belkin

Computer Science

#6218

World Rank

#6558

Historical Rank

Data Mining

#138

World Rank

#138

Historical Rank

Machine Learning

#1945

World Rank

#1971

Historical Rank

Database

#3322

World Rank

#3462

Historical Rank

computer-science Degrees

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Computer Science

Misha Belkin's Degrees

PhD Computer Science Stanford University
Masters Computer Science Stanford University
Bachelors Computer Science Stanford University

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Misha Belkin'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

Laplacian Eigenmaps for Dimensionality Reduction and Data Representation (2003) (7497)
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering (2001) (4582)
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples (2006) (3863)
Reconciling modern machine-learning practice and the classical bias–variance trade-off (2018) (990)
Semi-Supervised Learning on Riemannian Manifolds (2004) (856)
Towards a theoretical foundation for Laplacian-based manifold methods (2005) (617)
Regularization and Semi-supervised Learning on Large Graphs (2004) (608)
Consistency of spectral clustering (2008) (594)
Beyond the point cloud: from transductive to semi-supervised learning (2005) (496)
A Co-Regularization Approach to Semi-supervised Learning with Multiple Views (2005) (414)
Laplacian Support Vector Machines Trained in the Primal (2009) (367)
Manifold Regularization : A Geometric Framework for Learning from Examples (2004) (326)
To understand deep learning we need to understand kernel learning (2018) (322)
Convergence of Laplacian Eigenmaps (2006) (285)
Two models of double descent for weak features (2019) (285)
Using manifold structure for partially labelled classification (2002) (235)
Discrete laplace operator on meshed surfaces (2008) (224)
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning (2017) (220)
On Learning with Integral Operators (2010) (210)
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate (2018) (210)
Polynomial Learning of Distribution Families (2010) (204)
Constructing Laplace operator from point clouds in Rd (2009) (186)
Does data interpolation contradict statistical optimality? (2018) (166)
Problems of learning on manifolds (2003) (154)
Learning privately from multiparty data (2016) (148)
On Manifold Regularization (2005) (138)
Limits of Spectral Clustering (2004) (124)
Reconciling modern machine learning and the bias-variance trade-off (2018) (123)
Maximum Margin Semi-Supervised Learning for Structured Variables (2005) (116)
Tikhonov regularization and semi-supervised learning on large graphs (2004) (102)
Data Skeletonization via Reeb Graphs (2011) (102)
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks (2020) (99)
Classification vs regression in overparameterized regimes: Does the loss function matter? (2020) (98)
DATA SPECTROSCOPY: EIGENSPACES OF CONVOLUTION OPERATORS AND CLUSTERING (2008) (96)
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures (2013) (87)
Semi-supervised Learning by Higher Order Regularization (2011) (86)
Unperturbed: spectral analysis beyond Davis-Kahan (2017) (83)
Loss landscapes and optimization in over-parameterized non-linear systems and neural networks (2020) (83)
Using Manifold Stucture for Partially Labeled Classification (2002) (75)
Linear Manifold Regularization for Large Scale Semi-supervised Learning (2005) (74)
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation (2021) (72)
On the linearity of large non-linear models: when and why the tangent kernel is constant (2020) (71)
On exponential convergence of SGD in non-convex over-parametrized learning (2018) (71)
Accelerating SGD with momentum for over-parameterized learning (2018) (61)
Back to the Future: Radial Basis Function Network Revisited (2016) (60)
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices (2015) (60)
Diving into the shallows: a computational perspective on large-scale shallow learning (2017) (56)
Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning (2020) (55)
On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts (2006) (55)
On the Convergence of Spectral Clustering on Random Samples: The Normalized Case (2004) (54)
Approximation beats concentration? An approximation view on inference with smooth radial kernels (2018) (50)
Semi-Supervised Learning Using Sparse Eigenfunction Bases (2009) (48)
Multiple Descent: Design Your Own Generalization Curve (2020) (45)
Semi-Supervised Learning (2021) (41)
Robust features for the automatic identification of autism spectrum disorder in children (2014) (40)
Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering (2015) (40)
An iterated graph laplacian approach for ranking on manifolds (2011) (37)
The Geometric Basis of Semi-Supervised Learning (2006) (37)
Automatic Annotation of Daily Activity from Smartphone-Based Multisensory Streams (2012) (34)
Using eigenvectors of the bigram graph to infer morpheme identity (2002) (32)
Toward Understanding Complex Spaces: Graph Laplacians on Manifolds with Singularities and Boundaries (2012) (32)
Overparameterized neural networks implement associative memory (2019) (32)
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures (2021) (32)
Memorization in Overparameterized Autoencoders (2018) (31)
The Value of Labeled and Unlabeled Examples when the Model is Imperfect (2007) (30)
Data spectroscopy: learning mixture models using eigenspaces of convolution operators (2008) (28)
Toward Learning Gaussian Mixtures with Arbitrary Separation (2010) (28)
The Geometry and Dynamics of Lifelogs: Discovering the Organizational Principles of Human Experience (2014) (24)
Blind Signal Separation in the Presence of Gaussian Noise (2012) (22)
Kernel Machines That Adapt To Gpus For Effective Large Batch Training (2018) (19)
Benign Overfitting in Two-layer Convolutional Neural Networks (2022) (19)
Graphons, mergeons, and so on! (2016) (19)
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis (2013) (17)
MaSS: an Accelerated Stochastic Method for Over-parametrized Learning (2018) (17)
Limitations of Neural Collapse for Understanding Generalization in Deep Learning (2022) (17)
Clustering with Bregman Divergences: an Asymptotic Analysis (2016) (16)
Learning speaker normalization using semisupervised manifold alignment (2010) (15)
The Hidden Convexity of Spectral Clustering (2014) (14)
Learning Gaussian Mixtures with Arbitrary Separation (2009) (14)
Inverse Density as an Inverse Problem: the Fredholm Equation Approach (2013) (13)
Learning with Fredholm Kernels (2014) (11)
Heat Flow and a Faster Algorithm to Compute the Surface Area of a Convex Body (2006) (11)
Margin Semi-Supervised Learning for Structured Variables (2005) (11)
Simple, fast, and flexible framework for matrix completion with infinite width neural networks (2021) (11)
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting (2022) (11)
Basis Learning as an Algorithmic Primitive (2014) (8)
Eigenvectors of Orthogonally Decomposable Functions (2014) (8)
Topological Data Analysis and Machine Learning Theory (2012) (8)
Downsampling leads to Image Memorization in Convolutional Autoencoders (2018) (6)
Using eigenvectors of the bigram graph to infer grammatical features and categories (2002) (6)
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA (2015) (6)
Component based shape retrieval using differential profiles (2008) (5)
A Note on Perturbation Results for Learning Empirical Operators (2008) (4)
Overparameterized Neural Networks Can Implement Associative Memory (2019) (4)
Quadratic models for understanding neural network dynamics (2022) (4)
Feature learning in neural networks and kernel machines that recursively learn features (2022) (4)
Kernel Ridgeless Regression is Inconsistent for Low Dimensions (2022) (4)
Linear Convergence and Implicit Regularization of Generalized Mirror Descent with Time-Dependent Mirrors (2020) (4)
Probabilistic Zero-shot Classification with Semantic Rankings (2015) (4)
Reply to Loog et al.: Looking beyond the peaking phenomenon (2020) (4)
Accelerating Stochastic Training for Over-parametrized Learning (2018) (3)
CONSISTENCY OF SPECTRAL CLUSTERING BY ULRIKE (2004) (3)
A Note on Learning with Integral Operators (2009) (3)
Parametrized Accelerated Methods Free of Condition Number (2018) (3)
The dimensionality of episodic images (2010) (3)
Kernel Machines Beat Deep Neural Networks on Mask-based Single-channel Speech Enhancement (2018) (3)
Metric Based Automatic Event Segmentation (2012) (3)
Linear Convergence of Generalized Mirror Descent with Time-Dependent Mirrors (2020) (2)
Denali : A tool for visualizing scalar functions as landscape metaphors (2014) (2)
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture (2022) (2)
Probabilistic mixtures of differential profiles for shape recognition (2008) (2)
Restricted Strong Convexity of Deep Learning Models with Smooth Activations (2022) (2)
The network properties of episodic graphs (2010) (2)
2 3 Ju l 2 00 8 Data Spectroscopy : Eigenspace of Convolution Operators and Clustering (2008) (2)
Learning kernels that adapt to GPU (2018) (2)
Fast Interactive Image Retrieval using large-scale unlabeled data (2018) (1)
Learning a Hidden Basis Through Imperfect Measurements: An Algorithmic Primitive (2014) (1)
Robust features for the automatic identification of autism spectrum disorder in children (2014) (1)
Wide and Deep Neural Networks Achieve Optimality for Classification (2022) (1)
Behavior of Graph Laplacians on Manifolds with Boundary (2011) (1)
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models (2022) (1)
Algebraic Geometry for Learning Mixtures of Gaussians and Other Distributions (2012) (1)
Networks of Memories (2013) (0)
No . TR-134 Consistency of Spectral Clustering (2004) (0)
A note on Linear Bottleneck networks and their Transition to Multilinearity (2022) (0)
Cut your Losses with Squentropy (2023) (0)
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors (2022) (0)
Appreciation to IJCV Reviewers (2013) (0)
Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size (2021) (0)
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions (2022) (0)
From pixels to people: graph based methods for grouping problems in computer vision (2010) (0)
Cluster Assumption and Sparsity in the Eigenfunction Basis [ M-3 A ] (2010) (0)
Learning functions on unknown manifolds (2011) (0)
Optimal Recovery in Noisy ICA (2015) (0)
New directions in gaussian mixture learning and semi-supervised learning (2010) (0)
Image Data Collection , Representation and Distance Measures Microsoft (2010) (0)
Acknowledgment of Reviewers, 2019 (2019) (0)
A Sentiment Analysis Model Integrating Multiple Algorithms and Diverse Features Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By (2010) (0)
Toward Large Kernel Models (2023) (0)

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Misha Belkin's Published Works

Published Works

Misha Belkin's AcademicInfluence.com Rankings