Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. - Mikhail Belkin; Partha Niyogi (NIPS, 2001)

Using Manifold Stucture for Partially Labeled Classification. - Mikhail Belkin; Partha Niyogi (NIPS, 2002)

Using eigenvectors of the bigram graph to infer morpheme identity. - Mikhail Belkin; John A. Goldsmith (SIGMORPHON, 2002)

Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. - Mikhail Belkin; Partha Niyogi (Neural Comput., 2003)

Regularization and Semi-supervised Learning on Large Graphs. - Mikhail Belkin; Irina Matveeva; Partha Niyogi (COLT, 2004)

On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. - Ulrike von Luxburg; Olivier Bousquet; Mikhail Belkin (COLT, 2004)

Tikhonov regularization and semi-supervised learning on large graphs. - Mikhail Belkin; Irina Matveeva; Partha Niyogi (ICASSP (3), 2004)

Limits of Spectral Clustering. - Ulrike von Luxburg; Olivier Bousquet; Mikhail Belkin (NIPS, 2004)

Semi-Supervised Learning on Riemannian Manifolds. - Mikhail Belkin; Partha Niyogi (Mach. Learn., 2004)

Beyond the point cloud: from transductive to semi-supervised learning. - Vikas Sindhwani; Partha Niyogi; Mikhail Belkin (ICML, 2005)

Margin Semi-Supervised Learning for Structured Variables. - Yasemin Altun; David A. McAllester; Mikhail Belkin (NIPS, 2005)

Convergence of Laplacian Eigenmaps. - Mikhail Belkin; Partha Niyogi (NIPS, 2006)

On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts. - Hariharan Narayanan; Mikhail Belkin; Partha Niyogi (NIPS, 2006)

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. - Mikhail Belkin; Partha Niyogi; Vikas Sindhwani (J. Mach. Learn. Res., 2006)

The Value of Labeled and Unlabeled Examples when the Model is Imperfect. - Kaushik Sinha; Mikhail Belkin (NIPS, 2007)

Discrete laplace operator on meshed surfaces. - Mikhail Belkin; Jian Sun 002; Yusu Wang 001 (SCG, 2008)

Data spectroscopy: learning mixture models using eigenspaces of convolution operators. - Tao Shi; Mikhail Belkin; Bin Yu 001 (ICML, 2008)

Probabilistic mixtures of differential profiles for shape recognition. - Lei Ding; Mikhail Belkin (ICPR, 2008)

Component based shape retrieval using differential profiles. - Lei Ding 002; Mikhail Belkin (Multimedia Information Retrieval, 2008)

Towards a theoretical foundation for Laplacian-based manifold methods. - Mikhail Belkin; Partha Niyogi (J. Comput. Syst. Sci., 2008)

A Note on Learning with Integral Operators. - Lorenzo Rosasco; Mikhail Belkin; Ernesto De Vito (COLT, 2009)

Semi-supervised Learning using Sparse Eigenfunction Bases. - Kaushik Sinha; Mikhail Belkin (NIPS, 2009)

Constructing Laplace operator from point clouds in Rd. - Mikhail Belkin; Jian Sun 002; Yusu Wang 001 (SODA, 2009)

Learning Gaussian Mixtures with Arbitrary Separation - Mikhail Belkin; Kaushik Sinha (CoRR, 2009)

Toward Learning Gaussian Mixtures with Arbitrary Separation. - Mikhail Belkin; Kaushik Sinha (COLT, 2010)

Learning speaker normalization using semisupervised manifold alignment. - Andrew R. Plummer; Mary E. Beckman; Mikhail Belkin; Eric Fosler-Lussier; Benjamin Munson (INTERSPEECH, 2010)

On Learning with Integral Operators. - Lorenzo Rosasco; Mikhail Belkin; Ernesto De Vito (J. Mach. Learn. Res., 2010)

An iterated graph laplacian approach for ranking on manifolds. - Xueyuan Zhou; Mikhail Belkin; Nathan Srebro (KDD, 2011)

Data Skeletonization via Reeb Graphs. - Xiaoyin Ge; Issam Safa; Mikhail Belkin; Yusu Wang 001 (NIPS, 2011)

Behavior of Graph Laplacians on Manifolds with Boundary - Xueyuan Zhou; Mikhail Belkin (CoRR, 2011)

Laplacian Support Vector Machines Trained in the Primal. - Stefano Melacci; Mikhail Belkin (J. Mach. Learn. Res., 2011)

Semi-supervised Learning by Higher Order Regularization. - Xueyuan Zhou; Mikhail Belkin (AISTATS, 2011)

Automatic Annotation of Daily Activity from Smartphone-Based Multisensory Streams. - Jihun Hamm; Benjamin Stone; Mikhail Belkin; Simon Dennis (MobiCASE, 2012)

Metric Based Automatic Event Segmentation. - Yuwen Zhuang; Mikhail Belkin; Simon Dennis (MobiCASE, 2012)

Graph Laplacians on Singular Manifolds: Toward understanding complex spaces: graph Laplacians on manifolds with singularities and boundaries - Mikhail Belkin; Qichao Que; Yusu Wang 001; Xueyuan Zhou (CoRR, 2012)

Toward Understanding Complex Spaces: Graph Laplacians on Manifolds with Singularities and Boundaries. - Mikhail Belkin; Qichao Que; Yusu Wang 001; Xueyuan Zhou (COLT, 2012)

Blind Signal Separation in the Presence of Gaussian Noise. - Mikhail Belkin; Luis Rademacher; James R. Voss (COLT, 2013)

Inverse Density as an Inverse Problem: the Fredholm Equation Approach. - Qichao Que; Mikhail Belkin (NIPS, 2013)

Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis. - James R. Voss; Luis Rademacher; Mikhail Belkin (NIPS, 2013)

Heat flow and a faster algorithm to compute the surface area of a convex body. - Mikhail Belkin; Hariharan Narayanan; Partha Niyogi (Random Struct. Algorithms, 2013)

The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. - Joseph Anderson; Mikhail Belkin; Navin Goyal; Luis Rademacher; James R. Voss (COLT, 2014)

Learning with Fredholm Kernels. - Qichao Que; Mikhail Belkin; Yusu Wang 001 (NIPS, 2014)

Learning a Hidden Basis Through Imperfect Measurements: An Algorithmic Primitive. - Mikhail Belkin; Luis Rademacher; James R. Voss (CoRR, 2014)

Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering. - Justin Eldridge; Mikhail Belkin; Yusu Wang 001 (COLT, 2015)

Microwave-Band Circuit-Level Semiconductor Laser Modeling. - Mikhail Belkin; Vladimir Iakovlev (EMS, 2015)

Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices. - Jihun Hamm; Adam C. Champion; Guoxing Chen; Mikhail Belkin; Dong Xuan (ICDCS, 2015)

A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA. - James R. Voss; Mikhail Belkin; Luis Rademacher (NIPS, 2015)

Probabilistic Zero-shot Classification with Semantic Rankings. - Jihun Hamm; Mikhail Belkin (CoRR, 2015)

Optimal Recovery in Noisy ICA. - James R. Voss; Mikhail Belkin; Luis Rademacher (CoRR, 2015)

Polynomial Learning of Distribution Families. - Mikhail Belkin; Kaushik Sinha (SIAM J. Comput., 2015)

The Hidden Convexity of Spectral Clustering. - James R. Voss; Mikhail Belkin; Luis Rademacher (AAAI, 2016)

Back to the Future: Radial Basis Function Networks Revisited. - Qichao Que; Mikhail Belkin (AISTATS, 2016)

Basis Learning as an Algorithmic Primitive. - Mikhail Belkin; Luis Rademacher; James R. Voss (COLT, 2016)

Learning privately from multiparty data. - Jihun Hamm; Yingjun Cao; Mikhail Belkin (ICML, 2016)

Graphons, mergeons, and so on! - Justin Eldridge; Mikhail Belkin; Yusu Wang 001 (NIPS, 2016)

Clustering with Bregman Divergences: an Asymptotic Analysis. - Chaoyue Liu 001; Mikhail Belkin (NIPS, 2016)

Learning Privately from Multiparty Data. - Jihun Hamm; Paul Cao; Mikhail Belkin (CoRR, 2016)

Diving into the shallows: a computational perspective on large-scale shallow learning. - Siyuan Ma; Mikhail Belkin (NIPS, 2017)

Unperturbed: spectral analysis beyond Davis-Kahan. - Justin Eldridge; Mikhail Belkin; Yusu Wang 001 (ALT, 2018)

Approximation beats concentration? An approximation view on inference with smooth radial kernels. - Mikhail Belkin (COLT, 2018)

To Understand Deep Learning We Need to Understand Kernel Learning. - Mikhail Belkin; Siyuan Ma; Soumik Mandal (ICML, 2018)

The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning. - Siyuan Ma; Raef Bassily; Mikhail Belkin (ICML, 2018)

Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. - Mikhail Belkin; Daniel J. Hsu; Partha Mitra (NeurIPS, 2018)

Fast Interactive Image Retrieval using large-scale unlabeled data. - Akshay Mehra; Jihun Hamm; Mikhail Belkin (CoRR, 2018)

Parametrized Accelerated Methods Free of Condition Number. - Chaoyue Liu 001; Mikhail Belkin (CoRR, 2018)

Learning kernels that adapt to GPU. - Siyuan Ma; Mikhail Belkin (CoRR, 2018)

Downsampling leads to Image Memorization in Convolutional Autoencoders. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (CoRR, 2018)

MaSS: an Accelerated Stochastic Method for Over-parametrized Learning. - Chaoyue Liu 001; Mikhail Belkin (CoRR, 2018)

On exponential convergence of SGD in non-convex over-parametrized learning. - Raef Bassily; Mikhail Belkin; Siyuan Ma (CoRR, 2018)

Reconciling modern machine learning and the bias-variance trade-off. - Mikhail Belkin; Daniel Hsu 001; Siyuan Ma; Soumik Mandal (CoRR, 2018)

Eigenvectors of Orthogonally Decomposable Functions. - Mikhail Belkin; Luis Rademacher; James R. Voss (SIAM J. Comput., 2018)

Does data interpolation contradict statistical optimality? - Mikhail Belkin; Alexander Rakhlin; Alexandre B. Tsybakov (AISTATS, 2019)

Kernel Machines Beat Deep Neural Networks on Mask-Based Single-Channel Speech Enhancement. - Like Hui; Siyuan Ma; Mikhail Belkin (INTERSPEECH, 2019)

Kernel Machines That Adapt To Gpus For Effective Large Batch Training. - Siyuan Ma; Mikhail Belkin (SysML, 2019)

Overparameterized Neural Networks Can Implement Associative Memory. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (CoRR, 2019)

Accelerating SGD with momentum for over-parameterized learning. - Chaoyue Liu 001; Mikhail Belkin (ICLR, 2020)

On the linearity of large non-linear models: when and why the tangent kernel is constant. - Chaoyue Liu 001; Libin Zhu; Mikhail Belkin (NeurIPS, 2020)

Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning. - Chaoyue Liu 001; Libin Zhu; Mikhail Belkin (CoRR, 2020)

Linear Convergence and Implicit Regularization of Generalized Mirror Descent with Time-Dependent Mirrors. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (CoRR, 2020)

Back to the Future: Radial Basis Function Network Revisited. - Qichao Que; Mikhail Belkin (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Overparameterized neural networks implement associative memory. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (Proc. Natl. Acad. Sci. USA, 2020)

Two Models of Double Descent for Weak Features. - Mikhail Belkin; Daniel Hsu 001; Ji Xu 003 (SIAM J. Math. Data Sci., 2020)

Evaluation of Neural Architectures trained with square Loss vs Cross-Entropy in Classification Tasks. - Like Hui; Mikhail Belkin (ICLR, 2021)

Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. - Yuan Cao 006; Quanquan Gu; Mikhail Belkin (NeurIPS, 2021)

Multiple Descent: Design Your Own Generalization Curve. - Lin Chen 003; Yifei Min; Mikhail Belkin; Amin Karbasi (NeurIPS, 2021)

Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks. - Adityanarayanan Radhakrishnan; George Stefanakis; Mikhail Belkin; Caroline Uhler (CoRR, 2021)

Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (CoRR, 2021)

Classification vs regression in overparameterized regimes: Does the loss function matter? - Vidya Muthukumar; Adhyyan Narang; Vignesh Subramanian; Mikhail Belkin; Daniel Hsu 001; Anant Sahai (J. Mach. Learn. Res., 2021)

Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. - Mikhail Belkin (Acta Numer., 2021)

Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models. - Chaoyue Liu 001; Libin Zhu; Mikhail Belkin (ICLR, 2022)

Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting. - Neil Mallinar; James B. Simon; Amirhesam Abedsoltan; Parthe Pandit; Mikhail Belkin; Preetum Nakkiran (NeurIPS, 2022)

Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture. - Libin Zhu; Chaoyue Liu 001; Mikhail Belkin (NeurIPS, 2022)

Benign Overfitting in Two-layer Convolutional Neural Networks. - Yuan Cao 006; Zixiang Chen; Mikhail Belkin; Quanquan Gu (CoRR, 2022)

Limitations of Neural Collapse for Understanding Generalization in Deep Learning. - Like Hui; Mikhail Belkin; Preetum Nakkiran (CoRR, 2022)

Wide and Deep Neural Networks Achieve Optimality for Classification. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (CoRR, 2022)

Quadratic models for understanding neural network dynamics. - Libin Zhu; Chaoyue Liu 001; Adityanarayanan Radhakrishnan; Mikhail Belkin (CoRR, 2022)

Kernel Ridgeless Regression is Inconsistent for Low Dimensions. - Daniel Beaglehole; Mikhail Belkin; Parthe Pandit (CoRR, 2022)

A note on Linear Bottleneck networks and their Transition to Multilinearity. - Libin Zhu; Parthe Pandit; Mikhail Belkin (CoRR, 2022)

Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting. - Neil Mallinar; James B. Simon; Amirhesam Abedsoltan; Parthe Pandit; Mikhail Belkin; Preetum Nakkiran (CoRR, 2022)

Feature learning in neural networks and kernel machines that recursively learn features. - Adityanarayanan Radhakrishnan; Daniel Beaglehole; Parthe Pandit; Mikhail Belkin (CoRR, 2022)

Restricted Strong Convexity of Deep Learning Models with Smooth Activations. - Arindam Banerjee 001; Pedro Cisneros-Velarde; Libin Zhu; Mikhail Belkin (ICLR, 2023)

Toward Large Kernel Models. - Amirhesam Abedsoltan; Mikhail Belkin; Parthe Pandit (ICML, 2023)

Cut your Losses with Squentropy. - Like Hui; Mikhail Belkin; Stephen Wright (ICML, 2023)

Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. - Chaoyue Liu 001; Dmitriy Drusvyatskiy; Mikhail Belkin; Damek Davis; Yi-An Ma (NeurIPS, 2023)

Neural tangent kernel at initialization: linear width suffices. - Arindam Banerjee 001; Pedro Cisneros-Velarde; Libin Zhu; Mikhail Belkin (UAI, 2023)

On Emergence of Clean-Priority Learning in Early Stopped Neural Networks. - Chaoyue Liu 001; Amirhesam Abedsoltan; Mikhail Belkin (CoRR, 2023)

Mechanism of feature learning in convolutional neural networks. - Daniel Beaglehole; Adityanarayanan Radhakrishnan; Parthe Pandit; Mikhail Belkin (CoRR, 2023)

More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. - James B. Simon; Dhruva Karkada; Nikhil Ghosh; Mikhail Belkin (CoRR, 2023)

On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions. - Daniel Beaglehole; Mikhail Belkin; Parthe Pandit (SIAM J. Math. Data Sci., 2023)

A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors. - Nikhil Ghosh; Mikhail Belkin (SIAM J. Math. Data Sci., 2023)

On the Nyström Approximation for Preconditioning in Kernel Machines. - Amirhesam Abedsoltan; Parthe Pandit; Luis Rademacher; Mikhail Belkin (AISTATS, 2024)

More is Better: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. - James B. Simon; Dhruva Karkada; Nikhil Ghosh; Mikhail Belkin (ICLR, 2024)

Quadratic models for understanding catapult dynamics of neural networks. - Libin Zhu; Chaoyue Liu 001; Adityanarayanan Radhakrishnan; Mikhail Belkin (ICLR, 2024)

Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning. - Libin Zhu; Chaoyue Liu 001; Adityanarayanan Radhakrishnan; Mikhail Belkin (ICML, 2024)

Average gradient outer product as a mechanism for deep neural collapse. - Daniel Beaglehole; Peter Súkeník; Marco Mondelli; Mikhail Belkin (NeurIPS, 2024)

Uncertainty Estimation with Recursive Feature Machines. - Daniel Gedon; Amirhesam Abedsoltan; Thomas B. Schön; Mikhail Belkin (UAI, 2024)

Linear Recursive Feature Machines provably recover low-rank matrices. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Dmitriy Drusvyatskiy (CoRR, 2024)

Unmemorization in Large Language Models via Self-Distillation and Deliberate Imagination. - Yijiang River Dong; Hongzhou Lin; Mikhail Belkin; Ramón Huerta; Ivan Vulic (CoRR, 2024)

Emergence in non-neural models: grokking modular arithmetic via average gradient outer product. - Neil Mallinar; Daniel Beaglehole; Libin Zhu; Adityanarayanan Radhakrishnan; Parthe Pandit; Mikhail Belkin (CoRR, 2024)

Context-Scaling versus Task-Scaling in In-Context Learning. - Amirhesam Abedsoltan; Adityanarayanan Radhakrishnan; Jingfeng Wu; Mikhail Belkin (CoRR, 2024)

Mirror Descent on Reproducing Kernel Banach Spaces. - Akash Kumar 010; Mikhail Belkin; Parthe Pandit (CoRR, 2024)

Fast training of large kernel models with delayed projections. - Amirhesam Abedsoltan; Siyuan Ma; Parthe Pandit; Mikhail Belkin (CoRR, 2024)

A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis. - Akash Kumar 010; Rahul Parhi; Mikhail Belkin (COLT, 2025)

UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models. - Yijiang River Dong; Hongzhou Lin; Mikhail Belkin; Ramón Huerta; Ivan Vulic (NAACL (Long Papers), 2025)

Aggregate and conquer: detecting and steering LLM concepts by combining nonlinear predictors over multiple layers. - Daniel Beaglehole; Adityanarayanan Radhakrishnan; Enric Boix-Adserà; Mikhail Belkin (CoRR, 2025)

Task Generalization With AutoRegressive Compositional Structure: Can Learning From D Tasks Generalize to DT Tasks? - Amirhesam Abedsoltan; Huaqing Zhang 005; Kaiyue Wen; Hongzhou Lin; Jingzhao Zhang; Mikhail Belkin (CoRR, 2025)

FACT: the Features At Convergence Theorem for neural networks. - Enric Boix-Adserà; Neil Mallinar; James B. Simon; Mikhail Belkin (CoRR, 2025)

xRFM: Accurate, scalable, and interpretable feature learning models for tabular data. - Daniel Beaglehole; David Holzmüller; Adityanarayanan Radhakrishnan; Mikhail Belkin (CoRR, 2025)