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2020 – today
- 2024
[c67]Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher, Mikhail Belkin:
On the Nyström Approximation for Preconditioning in Kernel Machines. AISTATS 2024: 3718-3726- [c66]James B. Simon, Dhruva Karkada, Nikhil Ghosh, Mikhail Belkin:
More is Better: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. ICLR 2024 - [c65]Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin:
Quadratic models for understanding catapult dynamics of neural networks. ICLR 2024 - [c64]Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin:
Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning. ICML 2024 - [i66]Adityanarayanan Radhakrishnan, Mikhail Belkin, Dmitriy Drusvyatskiy:
Linear Recursive Feature Machines provably recover low-rank matrices. CoRR abs/2401.04553 (2024) - [i65]Yijiang River Dong, Hongzhou Lin, Mikhail Belkin, Ramón Huerta, Ivan Vulic:
Unmemorization in Large Language Models via Self-Distillation and Deliberate Imagination. CoRR abs/2402.10052 (2024) - [i64]Daniel Beaglehole, Peter Súkeník, Marco Mondelli, Mikhail Belkin:
Average gradient outer product as a mechanism for deep neural collapse. CoRR abs/2402.13728 (2024) - [i63]Neil Mallinar, Daniel Beaglehole, Libin Zhu, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin:
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product. CoRR abs/2407.20199 (2024) - 2023
- [j16]Daniel Beaglehole, Mikhail Belkin, Parthe Pandit:
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions. SIAM J. Math. Data Sci. 5(4): 854-872 (2023) - [j15]Nikhil Ghosh, Mikhail Belkin:
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors. SIAM J. Math. Data Sci. 5(4): 977-1004 (2023) - [c63]Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit:
Toward Large Kernel Models. ICML 2023: 61-78 - [c62]Like Hui, Mikhail Belkin, Stephen Wright:
Cut your Losses with Squentropy. ICML 2023: 14114-14131 - [c61]Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin:
Neural tangent kernel at initialization: linear width suffices. UAI 2023: 110-118 - [i62]Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit:
Toward Large Kernel Models. CoRR abs/2302.02605 (2023) - [i61]Like Hui, Mikhail Belkin, Stephen Wright:
Cut your Losses with Squentropy. CoRR abs/2302.03952 (2023) - [i60]Chaoyue Liu, Amirhesam Abedsoltan, Mikhail Belkin:
On Emergence of Clean-Priority Learning in Early Stopped Neural Networks. CoRR abs/2306.02533 (2023) - [i59]Chaoyue Liu, Dmitriy Drusvyatskiy, Mikhail Belkin, Damek Davis, Yi-An Ma:
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. CoRR abs/2306.02601 (2023) - [i58]Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin:
Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning. CoRR abs/2306.04815 (2023) - [i57]Daniel Beaglehole, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin:
Mechanism of feature learning in convolutional neural networks. CoRR abs/2309.00570 (2023) - [i56]James B. Simon, Dhruva Karkada, Nikhil Ghosh, Mikhail Belkin:
More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. CoRR abs/2311.14646 (2023) - [i55]Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit, Luis Rademacher:
On the Nystrom Approximation for Preconditioning in Kernel Machines. CoRR abs/2312.03311 (2023) - 2022
- [i54]Yuan Cao, Zixiang Chen, Mikhail Belkin, Quanquan Gu:
Benign Overfitting in Two-layer Convolutional Neural Networks. CoRR abs/2202.06526 (2022) - [i53]Like Hui, Mikhail Belkin, Preetum Nakkiran:
Limitations of Neural Collapse for Understanding Generalization in Deep Learning. CoRR abs/2202.08384 (2022) - [i52]Chaoyue Liu, Libin Zhu, Mikhail Belkin:
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models. CoRR abs/2203.05104 (2022) - [i51]Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler:
Wide and Deep Neural Networks Achieve Optimality for Classification. CoRR abs/2204.14126 (2022) - [i50]Libin Zhu, Chaoyue Liu, Mikhail Belkin:
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture. CoRR abs/2205.11786 (2022) - [i49]Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin:
Quadratic models for understanding neural network dynamics. CoRR abs/2205.11787 (2022) - [i48]Daniel Beaglehole, Mikhail Belkin, Parthe Pandit:
Kernel Ridgeless Regression is Inconsistent for Low Dimensions. CoRR abs/2205.13525 (2022) - [i47]Libin Zhu, Parthe Pandit, Mikhail Belkin:
A note on Linear Bottleneck networks and their Transition to Multilinearity. CoRR abs/2206.15058 (2022) - [i46]Neil Mallinar, James B. Simon, Amirhesam Abedsoltan, Parthe Pandit, Mikhail Belkin, Preetum Nakkiran:
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting. CoRR abs/2207.06569 (2022) - [i45]Nikhil Ghosh, Mikhail Belkin:
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors. CoRR abs/2207.11621 (2022) - [i44]Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin:
Restricted Strong Convexity of Deep Learning Models with Smooth Activations. CoRR abs/2209.15106 (2022) - [i43]Adityanarayanan Radhakrishnan, Daniel Beaglehole, Parthe Pandit, Mikhail Belkin:
Feature learning in neural networks and kernel machines that recursively learn features. CoRR abs/2212.13881 (2022) - 2021
- [j14]Mikhail Belkin:
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numer. 30: 203-248 (2021) - [j13]Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian
, Mikhail Belkin, Daniel Hsu, Anant Sahai:
Classification vs regression in overparameterized regimes: Does the loss function matter? J. Mach. Learn. Res. 22: 222:1-222:69 (2021) - [c60]Like Hui, Mikhail Belkin:
Evaluation of Neural Architectures trained with square Loss vs Cross-Entropy in Classification Tasks. ICLR 2021 - [c59]Yuan Cao, Quanquan Gu, Mikhail Belkin:
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. NeurIPS 2021: 8407-8418 - [c58]Lin Chen, Yifei Min, Mikhail Belkin, Amin Karbasi:
Multiple Descent: Design Your Own Generalization Curve. NeurIPS 2021: 8898-8912 - [e1]Mikhail Belkin, Samory Kpotufe:
Conference on Learning Theory, COLT 2021, 15-19 August 2021, Boulder, Colorado, USA. Proceedings of Machine Learning Research 134, PMLR 2021 [contents] - [i42]Yuan Cao, Quanquan Gu, Mikhail Belkin:
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. CoRR abs/2104.13628 (2021) - [i41]Mikhail Belkin:
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. CoRR abs/2105.14368 (2021) - [i40]Adityanarayanan Radhakrishnan, George Stefanakis, Mikhail Belkin, Caroline Uhler:
Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks. CoRR abs/2108.00131 (2021) - [i39]Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler:
Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size. CoRR abs/2112.14872 (2021) - 2020
- [j12]Qichao Que, Mikhail Belkin:
Back to the Future: Radial Basis Function Network Revisited. IEEE Trans. Pattern Anal. Mach. Intell. 42(8): 1856-1867 (2020) - [j11]Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler:
Overparameterized neural networks implement associative memory. Proc. Natl. Acad. Sci. USA 117(44): 27162-27170 (2020) - [j10]Mikhail Belkin, Daniel Hsu, Ji Xu:
Two Models of Double Descent for Weak Features. SIAM J. Math. Data Sci. 2(4): 1167-1180 (2020) - [c57]Chaoyue Liu, Mikhail Belkin:
Accelerating SGD with momentum for over-parameterized learning. ICLR 2020 - [c56]Chaoyue Liu, Libin Zhu, Mikhail Belkin:
On the linearity of large non-linear models: when and why the tangent kernel is constant. NeurIPS 2020 - [i38]Chaoyue Liu, Libin Zhu, Mikhail Belkin:
Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning. CoRR abs/2003.00307 (2020) - [i37]Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel J. Hsu, Anant Sahai:
Classification vs regression in overparameterized regimes: Does the loss function matter? CoRR abs/2005.08054 (2020) - [i36]Like Hui, Mikhail Belkin:
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks. CoRR abs/2006.07322 (2020) - [i35]Lin Chen, Yifei Min, Mikhail Belkin, Amin Karbasi:
Multiple Descent: Design Your Own Generalization Curve. CoRR abs/2008.01036 (2020) - [i34]Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler:
Linear Convergence and Implicit Regularization of Generalized Mirror Descent with Time-Dependent Mirrors. CoRR abs/2009.08574 (2020) - [i33]Chaoyue Liu, Libin Zhu, Mikhail Belkin:
On the linearity of large non-linear models: when and why the tangent kernel is constant. CoRR abs/2010.01092 (2020)
2010 – 2019
- 2019
- [c55]Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov:
Does data interpolation contradict statistical optimality? AISTATS 2019: 1611-1619 - [c54]Like Hui, Siyuan Ma, Mikhail Belkin:
Kernel Machines Beat Deep Neural Networks on Mask-Based Single-Channel Speech Enhancement. INTERSPEECH 2019: 2748-2752 - [c53]Siyuan Ma, Mikhail Belkin:
Kernel Machines That Adapt To Gpus For Effective Large Batch Training. SysML 2019 - [i32]Mikhail Belkin, Daniel Hsu, Ji Xu:
Two models of double descent for weak features. CoRR abs/1903.07571 (2019) - [i31]Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler:
Overparameterized Neural Networks Can Implement Associative Memory. CoRR abs/1909.12362 (2019) - 2018
- [j9]Mikhail Belkin, Luis Rademacher, James R. Voss:
Eigenvectors of Orthogonally Decomposable Functions. SIAM J. Comput. 47(2): 547-615 (2018) - [c52]Justin Eldridge, Mikhail Belkin, Yusu Wang:
Unperturbed: spectral analysis beyond Davis-Kahan. ALT 2018: 321-358 - [c51]Mikhail Belkin:
Approximation beats concentration? An approximation view on inference with smooth radial kernels. COLT 2018: 1348-1361 - [c50]Mikhail Belkin, Siyuan Ma, Soumik Mandal:
To Understand Deep Learning We Need to Understand Kernel Learning. ICML 2018: 540-548 - [c49]Siyuan Ma, Raef Bassily, Mikhail Belkin:
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning. ICML 2018: 3331-3340 - [c48]Mikhail Belkin, Daniel J. Hsu, Partha Mitra:
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. NeurIPS 2018: 2306-2317 - [i30]Mikhail Belkin:
Approximation beats concentration? An approximation view on inference with smooth radial kernels. CoRR abs/1801.03437 (2018) - [i29]Mikhail Belkin, Siyuan Ma, Soumik Mandal:
To understand deep learning we need to understand kernel learning. CoRR abs/1802.01396 (2018) - [i28]Akshay Mehra, Jihun Hamm, Mikhail Belkin:
Fast Interactive Image Retrieval using large-scale unlabeled data. CoRR abs/1802.04204 (2018) - [i27]Chaoyue Liu, Mikhail Belkin:
Parametrized Accelerated Methods Free of Condition Number. CoRR abs/1802.10235 (2018) - [i26]Mikhail Belkin, Daniel Hsu, Partha Mitra:
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. CoRR abs/1806.05161 (2018) - [i25]Siyuan Ma, Mikhail Belkin:
Learning kernels that adapt to GPU. CoRR abs/1806.06144 (2018) - [i24]Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov:
Does data interpolation contradict statistical optimality? CoRR abs/1806.09471 (2018) - [i23]Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler:
Downsampling leads to Image Memorization in Convolutional Autoencoders. CoRR abs/1810.10333 (2018) - [i22]Chaoyue Liu, Mikhail Belkin:
MaSS: an Accelerated Stochastic Method for Over-parametrized Learning. CoRR abs/1810.13395 (2018) - [i21]Like Hui, Siyuan Ma, Mikhail Belkin:
Kernel Machines Beat Deep Neural Networks on Mask-based Single-channel Speech Enhancement. CoRR abs/1811.02095 (2018) - [i20]Raef Bassily, Mikhail Belkin, Siyuan Ma:
On exponential convergence of SGD in non-convex over-parametrized learning. CoRR abs/1811.02564 (2018) - [i19]Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal:
Reconciling modern machine learning and the bias-variance trade-off. CoRR abs/1812.11118 (2018) - 2017
- [c47]Siyuan Ma, Mikhail Belkin:
Diving into the shallows: a computational perspective on large-scale shallow learning. NIPS 2017: 3778-3787 - [i18]Siyuan Ma, Mikhail Belkin:
Diving into the shallows: a computational perspective on large-scale shallow learning. CoRR abs/1703.10622 (2017) - [i17]Justin Eldridge, Mikhail Belkin, Yusu Wang:
Unperturbed: spectral analysis beyond Davis-Kahan. CoRR abs/1706.06516 (2017) - [i16]Siyuan Ma, Raef Bassily, Mikhail Belkin:
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning. CoRR abs/1712.06559 (2017) - 2016
- [c46]James R. Voss, Mikhail Belkin, Luis Rademacher:
The Hidden Convexity of Spectral Clustering. AAAI 2016: 2108-2114 - [c45]Qichao Que, Mikhail Belkin:
Back to the Future: Radial Basis Function Networks Revisited. AISTATS 2016: 1375-1383 - [c44]Mikhail Belkin, Luis Rademacher, James R. Voss:
Basis Learning as an Algorithmic Primitive. COLT 2016: 446-487 - [c43]Jihun Hamm, Yingjun Cao, Mikhail Belkin:
Learning privately from multiparty data. ICML 2016: 555-563 - [c42]Justin Eldridge, Mikhail Belkin, Yusu Wang:
Graphons, mergeons, and so on! NIPS 2016: 2307-2315 - [c41]Chaoyue Liu, Mikhail Belkin:
Clustering with Bregman Divergences: an Asymptotic Analysis. NIPS 2016: 2343-2351 - [i15]Jihun Hamm, Paul Cao, Mikhail Belkin:
Learning Privately from Multiparty Data. CoRR abs/1602.03552 (2016) - [i14]Justin Eldridge, Mikhail Belkin, Yusu Wang:
Graphons, mergeons, and so on! CoRR abs/1607.01718 (2016) - 2015
- [j8]Mikhail Belkin, Kaushik Sinha:
Polynomial Learning of Distribution Families. SIAM J. Comput. 44(4): 889-911 (2015) - [c40]Justin Eldridge, Mikhail Belkin, Yusu Wang:
Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering. COLT 2015: 588-606 - [c39]Mikhail Belkin, Vladimir Iakovlev:
Microwave-Band Circuit-Level Semiconductor Laser Modeling. EMS 2015: 443-445 - [c38]Jihun Hamm, Adam C. Champion, Guoxing Chen, Mikhail Belkin, Dong Xuan:
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices. ICDCS 2015: 11-20 - [c37]James R. Voss, Mikhail Belkin, Luis Rademacher:
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA. NIPS 2015: 2872-2880 - [i13]Jihun Hamm, Adam C. Champion, Guoxing Chen, Mikhail Belkin, Dong Xuan:
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices. CoRR abs/1501.02484 (2015) - [i12]James R. Voss, Mikhail Belkin, Luis Rademacher:
Optimal Recovery in Noisy ICA. CoRR abs/1502.04148 (2015) - [i11]Jihun Hamm, Mikhail Belkin:
Probabilistic Zero-shot Classification with Semantic Rankings. CoRR abs/1502.08039 (2015) - 2014
- [c36]Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss:
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. COLT 2014: 1135-1164 - [c35]Qichao Que, Mikhail Belkin, Yusu Wang:
Learning with Fredholm Kernels. NIPS 2014: 2951-2959 - [i10]Mikhail Belkin, Luis Rademacher, James R. Voss:
The Hidden Convexity of Spectral Clustering. CoRR abs/1403.0667 (2014) - [i9]Mikhail Belkin, Luis Rademacher, James R. Voss:
Learning a Hidden Basis Through Imperfect Measurements: An Algorithmic Primitive. CoRR abs/1411.1420 (2014) - 2013
- [j7]Mikhail Belkin, Hariharan Narayanan, Partha Niyogi:
Heat flow and a faster algorithm to compute the surface area of a convex body. Random Struct. Algorithms 43(4): 407-428 (2013) - [c34]Mikhail Belkin, Luis Rademacher, James R. Voss:
Blind Signal Separation in the Presence of Gaussian Noise. COLT 2013: 270-287 - [c33]Qichao Que, Mikhail Belkin:
Inverse Density as an Inverse Problem: the Fredholm Equation Approach. NIPS 2013: 1484-1492 - [c32]James R. Voss, Luis Rademacher, Mikhail Belkin:
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis. NIPS 2013: 2544-2552 - [i8]Qichao Que, Mikhail Belkin:
Inverse Density as an Inverse Problem: The Fredholm Equation Approach. CoRR abs/1304.5575 (2013) - [i7]Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss:
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. CoRR abs/1311.2891 (2013) - 2012
- [c31]Yuwen Zhuang, Mikhail Belkin, Simon Dennis:
Metric Based Automatic Event Segmentation. MobiCASE 2012: 129-148 - [c30]Jihun Hamm, Benjamin Stone, Mikhail Belkin, Simon Dennis:
Automatic Annotation of Daily Activity from Smartphone-Based Multisensory Streams. MobiCASE 2012: 328-342 - [c29]Mikhail Belkin, Qichao Que, Yusu Wang, Xueyuan Zhou:
Toward Understanding Complex Spaces: Graph Laplacians on Manifolds with Singularities and Boundaries. COLT 2012: 36.1-36.26 - [i6]Mikhail Belkin, Luis Rademacher, James R. Voss:
Blind Signal Separation in the Presence of Gaussian Noise. CoRR abs/1211.1716 (2012) - [i5]Mikhail Belkin, Qichao Que, Yusu Wang, Xueyuan Zhou:
Graph Laplacians on Singular Manifolds: Toward understanding complex spaces: graph Laplacians on manifolds with singularities and boundaries. CoRR abs/1211.6727 (2012) - 2011
- [j6]Stefano Melacci, Mikhail Belkin:
Laplacian Support Vector Machines Trained in the Primal. J. Mach. Learn. Res. 12: 1149-1184 (2011) - [c28]Xueyuan Zhou, Mikhail Belkin, Nathan Srebro:
An iterated graph laplacian approach for ranking on manifolds. KDD 2011: 877-885 - [c27]Xiaoyin Ge, Issam Safa, Mikhail Belkin, Yusu Wang:
Data Skeletonization via Reeb Graphs. NIPS 2011: 837-845 - [c26]Xueyuan Zhou, Mikhail Belkin:
Semi-supervised Learning by Higher Order Regularization. AISTATS 2011: 892-900 - [i4]Xueyuan Zhou, Mikhail Belkin:
Behavior of Graph Laplacians on Manifolds with Boundary. CoRR abs/1105.3931 (2011) - 2010
- [j5]Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito:
On Learning with Integral Operators. J. Mach. Learn. Res. 11: 905-934 (2010) - [c25]Mikhail Belkin, Kaushik Sinha:
Toward Learning Gaussian Mixtures with Arbitrary Separation. COLT 2010: 407-419 - [c24]Mikhail Belkin, Kaushik Sinha:
Polynomial Learning of Distribution Families. FOCS 2010: 103-112 - [c23]Andrew R. Plummer, Mary E. Beckman, Mikhail Belkin, Eric Fosler-Lussier, Benjamin Munson:
Learning speaker normalization using semisupervised manifold alignment. INTERSPEECH 2010: 2918-2921 - [i3]Mikhail Belkin, Kaushik Sinha:
Polynomial Learning of Distribution Families. CoRR abs/1004.4864 (2010)
2000 – 2009
- 2009
- [c22]Kaushik Sinha, Mikhail Belkin:
Semi-Supervised Learning Using Sparse Eigenfunction Bases. AAAI Fall Symposium: Manifold Learning and Its Applications 2009 - [c21]Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito:
A Note on Learning with Integral Operators. COLT 2009 - [c20]Kaushik Sinha, Mikhail Belkin:
Semi-supervised Learning using Sparse Eigenfunction Bases. NIPS 2009: 1687-1695 - [c19]Mikhail Belkin, Jian Sun, Yusu Wang:
Constructing Laplace operator from point clouds in Rd. SODA 2009: 1031-1040 - [i2]Mikhail Belkin, Kaushik Sinha:
Learning Gaussian Mixtures with Arbitrary Separation. CoRR abs/0907.1054 (2009) - 2008
- [j4]Mikhail Belkin, Partha Niyogi:
Towards a theoretical foundation for Laplacian-based manifold methods. J. Comput. Syst. Sci. 74(8): 1289-1308 (2008) - [c18]Mikhail Belkin, Jian Sun, Yusu Wang:
Discrete laplace operator on meshed surfaces. SCG 2008: 278-287 - [c17]Tao Shi, Mikhail Belkin, Bin Yu:
Data spectroscopy: learning mixture models using eigenspaces of convolution operators. ICML 2008: 936-943 - [c16]Lei Ding, Mikhail Belkin:
Probabilistic mixtures of differential profiles for shape recognition. ICPR 2008: 1-4 - [c15]Lei Ding, Mikhail Belkin:
Component based shape retrieval using differential profiles. Multimedia Information Retrieval 2008: 216-222 - 2007
- [c14]Kaushik Sinha, Mikhail Belkin:
The Value of Labeled and Unlabeled Examples when the Model is Imperfect. NIPS 2007: 1361-1368 - 2006
- [j3]Mikhail Belkin, Partha Niyogi, Vikas Sindhwani:
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. J. Mach. Learn. Res. 7: 2399-2434 (2006) - [c13]Mikhail Belkin, Hariharan Narayanan, Partha Niyogi:
Heat Flow and a Faster Algorithm to Compute the Surface Area of a Convex Body. FOCS 2006: 47-56 - [c12]Mikhail Belkin, Partha Niyogi:
Convergence of Laplacian Eigenmaps. NIPS 2006: 129-136 - [c11]Hariharan Narayanan, Mikhail Belkin, Partha Niyogi:
On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts. NIPS 2006: 1025-1032 - 2005
- [c10]Mikhail Belkin, Partha Niyogi:
Towards a Theoretical Foundation for Laplacian-Based Manifold Methods. COLT 2005: 486-500 - [c9]Vikas Sindhwani, Partha Niyogi, Mikhail Belkin:
Beyond the point cloud: from transductive to semi-supervised learning. ICML 2005: 824-831 - [c8]Yasemin Altun, David A. McAllester, Mikhail Belkin:
Margin Semi-Supervised Learning for Structured Variables. NIPS 2005: 33-40 - 2004
- [j2]Mikhail Belkin, Partha Niyogi:
Semi-Supervised Learning on Riemannian Manifolds. Mach. Learn. 56(1-3): 209-239 (2004) - [c7]Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin:
On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. COLT 2004: 457-471 - [c6]Mikhail Belkin, Irina Matveeva, Partha Niyogi:
Regularization and Semi-supervised Learning on Large Graphs. COLT 2004: 624-638 - [c5]Mikhail Belkin, Irina Matveeva, Partha Niyogi:
Tikhonov regularization and semi-supervised learning on large graphs. ICASSP (3) 2004: 1000-1003 - [c4]Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin:
Limits of Spectral Clustering. NIPS 2004: 857-864 - 2003
- [j1]Mikhail Belkin, Partha Niyogi:
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Comput. 15(6): 1373-1396 (2003) - 2002
- [c3]Mikhail Belkin, Partha Niyogi:
Using Manifold Stucture for Partially Labeled Classification. NIPS 2002: 929-936 - [c2]Mikhail Belkin, John A. Goldsmith:
Using eigenvectors of the bigram graph to infer morpheme identity. SIGMORPHON 2002: 41-47 - [i1]Mikhail Belkin, John A. Goldsmith:
Using eigenvectors of the bigram graph to infer morpheme identity. CoRR cs.CL/0207002 (2002) - 2001
- [c1]Mikhail Belkin, Partha Niyogi:
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. NIPS 2001: 585-591
Coauthor Index
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Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
.
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last updated on 2024-10-07 22:11 CEST by the dblp team
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