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Mikhail Belkin

49 papers · 2006–2025 · 10 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (26) 🐝 Cross-Pollinator (13) πŸƒ Academic Marathon (19) 🌍 Conference Polyglot (10) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (5) 🌱 Topic Pioneer πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (12) πŸ† Keyword Champion πŸš€ Conference Pioneer ⚑ Prolific Year (6) πŸ”₯ Unstoppable (13) ❓ The Questioner (5) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (73) πŸ’Ž Century Club (49)

Conferences

NIPS (16) COLT (8) ICML (8) AISTATS (4) ICLR (4) JMLR (4) UAI (2) ALT (1) INTERSPEECH (1) NAACL (1)

Top co-authors

Yusu Wang (6) Luis Rademacher (6) Amirhesam Abedsoltan (4) Chaoyue Liu (4) Libin Zhu (4) Qichao Que (4) SIYUAN MA (4) Parthe Pandit (3) Adityanarayanan Radhakrishnan (3) Justin Eldridge (3)

Research topics

Probability (1)

Keywords

kernel methods (10) kernel machine (5) independent component analysis (5) semi-supervised learning (5) spectral clustering (4) gradient descent (3) manifold learning (3) graph laplacian (3) gaussian noise (3) spectral analysis (2) reproducing kernel hilbert space (2) tensor decomposition (2) eigenvalue decomposition (2) cluster assumption (2) feature learning (2) point cloud (2) signal separation (2) blind source separation (2) stochastic gradient descent (2) dimensionality reduction (2)

Papers

Task Generalization with Autoregressive Compositional Structure: Can Learning from $D$ Tasks Generalize to $D^T$ Tasks? ICML 2025
UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models NAACL 2025
A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis COLT 2025
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product ICML 2025
Uncertainty Estimation with Recursive Feature Machines UAI 2024
More is Better: when Infinite Overparameterization is Optimal and Overfitting is Obligatory ICLR 2024
On the NystrΓΆm Approximation for Preconditioning in Kernel Machines AISTATS 2024
Quadratic models for understanding catapult dynamics of neural networks ICLR 2024
Average gradient outer product as a mechanism for deep neural collapse NIPS 2024
Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning ICML 2024
Toward Large Kernel Models ICML 2023
Neural tangent kernel at initialization: linear width suffices UAI 2023
Cut your Losses with Squentropy ICML 2023
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures NIPS 2021
Multiple Descent: Design Your Own Generalization Curve NIPS 2021
EVALUATION OF NEURAL ARCHITECTURES TRAINED WITH SQUARE LOSS VS CROSS-ENTROPY IN CLASSIFICATION TASKS ICLR 2021
Classification vs regression in overparameterized regimes: Does the loss function matter? JMLR 2021
Conference on Learning Theory 2021: Post-conference Preface COLT 2021
Accelerating SGD with momentum for over-parameterized learning ICLR 2020
Does data interpolation contradict statistical optimality? AISTATS 2019
Kernel Machines Beat Deep Neural Networks on Mask-Based Single-Channel Speech Enhancement INTERSPEECH 2019
Unperturbed: spectral analysis beyond Davis-Kahan ALT 2018
Approximation beats concentration? An approximation view on inference with smooth radial kernels COLT 2018
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate NIPS 2018
To Understand Deep Learning We Need to Understand Kernel Learning ICML 2018
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning ICML 2018
Diving into the shallows: a computational perspective on large-scale shallow learning NIPS 2017
Clustering with Bregman Divergences: an Asymptotic Analysis NIPS 2016
Basis Learning as an Algorithmic Primitive COLT 2016
Back to the Future: Radial Basis Function Networks Revisited AISTATS 2016
Graphons, mergeons, and so on! NIPS 2016
Learning privately from multiparty data ICML 2016
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA NIPS 2015
Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering COLT 2015
Learning with Fredholm Kernels NIPS 2014
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures COLT 2014
Blind Signal Separation in the Presence of Gaussian Noise COLT 2013
Inverse Density as an Inverse Problem: the Fredholm Equation Approach NIPS 2013
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis NIPS 2013
Toward Understanding Complex Spaces: Graph Laplacians on Manifolds with Singularities and Boundaries COLT 2012
Laplacian Support Vector Machines Trained in the Primal JMLR 2011
Data Skeletonization via Reeb Graphs NIPS 2011
Semi-supervised Learning by Higher Order Regularization AISTATS 2011
On Learning with Integral Operators JMLR 2010
Semi-supervised Learning using Sparse Eigenfunction Bases NIPS 2009
The Value of Labeled and Unlabeled Examples when the Model is Imperfect NIPS 2007
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples JMLR 2006
Convergence of Laplacian Eigenmaps NIPS 2006
On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts NIPS 2006