default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMinyoung Kim 0001
Person information
- affiliation: Samsung AI Center, Cambridge, UK
- affiliation (2010 - 2019): Seoul National University of Science & Technology, Department of Electronics & IT Media Engineering, Seoul, Korea
- affiliation (2009 - 2010): Carnegie Mellon University, Robotics Institute, Pittsburgh, PA, USA
- affiliation (PhD 2008): Rutgers University, Department of Computer Science, Piscataway, NJ, USA
Other persons with the same name
- Min-Young Kim (aka: Min Young Kim, MinYoung Kim, Minyoung Kim) — disambiguation page
- Minyoung Kim 0002 — SRI International, Menlo Park, CA, USA (and 2 more)
- Min Young Kim 0003
![[0000-0001-7263-3403]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0001-7263-3403")
(aka: Min-Young Kim 0003, Minyoung Kim 0003) — Kyungpook National University, School of Electronics Engineering, Research Center for Neurosurgical Robotic System, Daegu, Republic of Korea (and 3 more)
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
Journal Articles
- 2019
[j39]Minyoung Kim:
Large-margin learning of Cox proportional hazard models for survival analysis. Appl. Intell. 49(5): 1675-1687 (2019)- [j38]Minyoung Kim:
Sparse large-margin nearest neighbor embedding via greedy dyad functional optimization. Appl. Intell. 49(10): 3628-3640 (2019) - 2018
- [j37]Minyoung Kim:
A maximum-likelihood and moment-matching density estimator for crowd-sourcing label prediction. Appl. Intell. 48(2): 381-389 (2018) - [j36]Minyoung Kim:
Dynamic sparse coding for sparse time-series modeling via first-order smooth optimization. Appl. Intell. 48(11): 3889-3901 (2018) - 2017
- [j35]Minyoung Kim:
Dual soft assignment clustering algorithm for human action video clustering. Comput. Vis. Image Underst. 155: 106-112 (2017) - [j34]Minyoung Kim:
Efficient histogram dictionary learning for text/image modeling and classification. Data Min. Knowl. Discov. 31(1): 203-232 (2017) - [j33]Minyoung Kim:
Simultaneous Kernel Learning and Label Imputation for Pattern Classification with Partially Labeled Data. Int. J. Fuzzy Log. Intell. Syst. 17(1): 10-16 (2017) - [j32]Minyoung Kim:
Simultaneous Learning of Sentence Clustering and Class Prediction for Improved Document Classification. Int. J. Fuzzy Log. Intell. Syst. 17(1): 35-42 (2017) - [j31]Minyoung Kim:
Mixtures of Conditional Random Fields for Improved Structured Output Prediction. IEEE Trans. Neural Networks Learn. Syst. 28(5): 1233-1240 (2017) - 2016
- [j30]Minyoung Kim:
Sparse inverse covariance learning of conditional Gaussian mixtures for multiple-output regression. Appl. Intell. 44(1): 17-29 (2016) - [j29]Hyun Kyung Kim, Minyoung Kim:
Model-induced term-weighting schemes for text classification. Appl. Intell. 45(1): 30-43 (2016) - [j28]Minyoung Kim:
Robust Algorithms for Combining Multiple Term Weighting Vectors for Document Classification. Int. J. Fuzzy Log. Intell. Syst. 16(2): 81-86 (2016) - [j27]Minyoung Kim:
Adaptive Bayesian Object Tracking with Histograms of Dense Local Image Descriptors. Int. J. Fuzzy Log. Intell. Syst. 16(2): 104-110 (2016) - [j26]Minyoung Kim:
Document Summarization via Convex-Concave Programming. Int. J. Fuzzy Log. Intell. Syst. 16(4): 293-298 (2016) - [j25]Minyoung Kim:
Sparse conditional copula models for structured output regression. Pattern Recognit. 60: 761-769 (2016) - 2015
- [j24]Minyoung Kim:
Sparse discriminative region selection algorithm for face recognition. Appl. Intell. 42(4): 817-828 (2015) - [j23]Minyoung Kim:
Multiple-concept feature generative models for multi-label image classification. Comput. Vis. Image Underst. 136: 69-78 (2015) - [j22]Minyoung Kim:
Online Selective-Sample Learning of Hidden Markov Models for Sequence Classification. Int. J. Fuzzy Log. Intell. Syst. 15(3): 145-152 (2015) - [j21]Minyoung Kim:
Greedy ensemble learning of structured predictors for sequence tagging. Neurocomputing 150: 449-457 (2015) - [j20]Minyoung Kim:
Greedy approaches to semi-supervised subspace learning. Pattern Recognit. 48(4): 1563-1570 (2015) - 2014
- [j19]Minyoung Kim:
Conditional ordinal random fields for structured ordinal-valued label prediction. Data Min. Knowl. Discov. 28(2): 378-401 (2014) - [j18]Minyoung Kim, Fernando De la Torre:
Multiple instance learning via Gaussian processes. Data Min. Knowl. Discov. 28(4): 1078-1106 (2014) - [j17]Minyoung Kim:
Robust Video-Based Barcode Recognition via Online Sequential Filtering. Int. J. Fuzzy Log. Intell. Syst. 14(1): 8-16 (2014) - [j16]Minyoung Kim:
Greedy Learning of Sparse Eigenfaces for Face Recognition and Tracking. Int. J. Fuzzy Log. Intell. Syst. 14(3): 162-170 (2014) - [j15]Minyoung Kim:
Discriminative Training of Sequence Taggers via Local Feature Matching. Int. J. Fuzzy Log. Intell. Syst. 14(3): 209-215 (2014) - [j14]Minyoung Kim:
Probabilistic Sequence Translation-Alignment Model for Time-Series Classification. IEEE Trans. Knowl. Data Eng. 26(2): 426-437 (2014) - [j13]Minyoung Kim:
Efficient Kernel Sparse Coding Via First-Order Smooth Optimization. IEEE Trans. Neural Networks Learn. Syst. 25(8): 1447-1459 (2014) - 2013
- [j12]Minyoung Kim:
Accelerated max-margin multiple kernel learning. Appl. Intell. 38(1): 45-57 (2013) - [j11]Minyoung Kim:
Semi-Supervised Recursive Learning of Discriminative Mixture Models for Time-Series Classification. Int. J. Fuzzy Log. Intell. Syst. 13(3): 186-199 (2013) - [j10]Minyoung Kim:
Semi-supervised learning of hidden conditional random fields for time-series classification. Neurocomputing 119: 339-349 (2013) - [j9]Minyoung Kim:
Conditional Alignment Random Fields for Multiple Motion Sequence Alignment. IEEE Trans. Pattern Anal. Mach. Intell. 35(11): 2803-2809 (2013) - 2012
- [j8]Minyoung Kim:
Correlation-based incremental visual tracking. Pattern Recognit. 45(3): 1050-1060 (2012) - [j7]Minyoung Kim:
Time-Series Dimensionality Reduction via Granger Causality. IEEE Signal Process. Lett. 19(10): 611-614 (2012) - 2011
- [j6]Minyoung Kim, Vladimir Pavlovic:
Sequence classification via large margin hidden Markov models. Data Min. Knowl. Discov. 23(2): 322-344 (2011) - [j5]Minyoung Kim, Vladimir Pavlovic:
Central Subspace Dimensionality Reduction Using Covariance Operators. IEEE Trans. Pattern Anal. Mach. Intell. 33(4): 657-670 (2011) - [j4]Minyoung Kim:
Discriminative semi-supervised learning of dynamical systems for motion estimation. Pattern Recognit. 44(10-11): 2325-2333 (2011) - [j3]Minyoung Kim:
Sequence Alignment by Regression Coding. IEEE Signal Process. Lett. 18(12): 721-724 (2011) - 2010
- [j2]Minyoung Kim:
Large margin cost-sensitive learning of conditional random fields. Pattern Recognit. 43(10): 3683-3692 (2010) - 2009
- [j1]Minyoung Kim, Vladimir Pavlovic:
Discriminative Learning for Dynamic State Prediction. IEEE Trans. Pattern Anal. Mach. Intell. 31(10): 1847-1861 (2009)
Conference and Workshop Papers
- 2024
- [c32]Minyoung Kim, Timothy M. Hospedales:
A Hierarchical Bayesian Model for Few-Shot Meta Learning. ICLR 2024 - [c31]Xinnuo Xu, Minyoung Kim, Royson Lee, Brais Martínez, Timothy M. Hospedales:
A Bayesian Approach to Data Point Selection. NeurIPS 2024 - 2023
- [c30]Minyoung Kim:
SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval. AISTATS 2023: 9167-9190 - [c29]Minyoung Kim, Da Li, Timothy M. Hospedales:
Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach. ICLR 2023 - [c28]Minyoung Kim, Timothy M. Hospedales:
BayesTune: Bayesian Sparse Deep Model Fine-tuning. NeurIPS 2023 - [c27]Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy M. Hospedales, Ferenc Huszar, Nicholas D. Lane:
FedL2P: Federated Learning to Personalize. NeurIPS 2023 - 2022
- [c26]Minyoung Kim, Ricardo Guerrero, Hai Xuan Pham, Vladimir Pavlovic:
Variational Continual Proxy-Anchor for Deep Metric Learning. AISTATS 2022: 4552-4573 - [c25]Minyoung Kim:
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders. CVPR 2022: 244-253 - [c24]Shell Xu Hu, Da Li, Jan Stühmer, Minyoung Kim, Timothy M. Hospedales:
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference. CVPR 2022: 9058-9067 - [c23]Minyoung Kim:
Differentiable Expectation-Maximization for Set Representation Learning. ICLR 2022 - [c22]Minyoung Kim, Da Li, Shell Xu Hu, Timothy M. Hospedales:
Fisher SAM: Information Geometry and Sharpness Aware Minimisation. ICML 2022: 11148-11161 - 2021
- [c21]Minyoung Kim, Ricardo Guerrero, Vladimir Pavlovic:
Learning Disentangled Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach. ACM Multimedia 2021: 2862-2870 - 2020
- [c20]Minyoung Kim, Vladimir Pavlovic:
Ordinal-Content VAE: Isolating Ordinal-Valued Content Factors in Deep Latent Variable Models. ICDM 2020: 252-261 - [c19]Minyoung Kim, Vladimir Pavlovic:
Recursive Inference for Variational Autoencoders. NeurIPS 2020 - 2019
- [c18]Minyoung Kim, Pritish Sahu, Behnam Gholami, Vladimir Pavlovic:
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach. CVPR 2019: 4380-4390 - [c17]Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic:
Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement. ICCV 2019: 2979-2987 - [c16]Behnam Gholami, Pritish Sahu, Minyoung Kim, Vladimir Pavlovic:
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation. ICCV Workshops 2019: 1327-1336 - [c15]Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim:
Efficient Deep Gaussian Process Models for Variable-Sized Inputs. IJCNN 2019: 1-7 - 2018
- [c14]Minyoung Kim:
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data. ICML 2018: 2645-2653 - [c13]Minyoung Kim, Vladimir Pavlovic:
Variational Inference for Gaussian Process Models for Survival Analysis. UAI 2018: 435-445 - 2010
- [c12]Zengyin Zhang, Minyoung Kim, Fernando De la Torre, Wende Zhang:
A Real-Time System for Head Tracking and Pose Estimation. ECCV Workshops (1) 2010: 329-341 - [c11]Minyoung Kim, Vladimir Pavlovic:
Structured Output Ordinal Regression for Dynamic Facial Emotion Intensity Prediction. ECCV (3) 2010: 649-662 - [c10]Minyoung Kim, Fernando De la Torre:
Local Minima Embedding. ICML 2010: 527-534 - [c9]Minyoung Kim, Fernando De la Torre:
Gaussian Processes Multiple Instance Learning. ICML 2010: 535-542 - [c8]Minyoung Kim, Vladimir Pavlovic:
Hidden Conditional Ordinal Random Fields for Sequence Classification. ECML/PKDD (2) 2010: 51-65 - 2009
- [c7]Minyoung Kim, Vladimir Pavlovic:
Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings. AISTATS 2009: 280-287 - 2008
- [c6]Minyoung Kim, Sanjiv Kumar, Vladimir Pavlovic, Henry A. Rowley:
Face tracking and recognition with visual constraints in real-world videos. CVPR 2008 - [c5]Minyoung Kim, Vladimir Pavlovic:
Dimensionality reduction using covariance operator inverse regression. CVPR 2008 - 2007
- [c4]Minyoung Kim, Vladimir Pavlovic:
Discriminative Learning of Dynamical Systems for Motion Tracking. CVPR 2007 - [c3]Minyoung Kim, Vladimir Pavlovic:
Conditional State Space Models for Discriminative Motion Estimation. ICCV 2007: 1-8 - [c2]Minyoung Kim, Vladimir Pavlovic:
A recursive method for discriminative mixture learning. ICML 2007: 409-416 - 2006
- [c1]Minyoung Kim, Vladimir Pavlovic:
Discriminative Learning of Mixture of Bayesian Network Classifiers for Sequence Classification. CVPR (1) 2006: 268-275
Informal and Other Publications
- 2024
- [i21]Minyoung Kim, Timothy M. Hospedales:
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning. CoRR abs/2410.10417 (2024) - [i20]Xinnuo Xu, Minyoung Kim, Royson Lee, Brais Martínez, Timothy M. Hospedales:
A Bayesian Approach to Data Point Selection. CoRR abs/2411.03768 (2024) - 2023
- [i19]Minyoung Kim, Da Li, Timothy M. Hospedales:
Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach. CoRR abs/2302.12047 (2023) - [i18]Minyoung Kim, Timothy M. Hospedales:
FedHB: Hierarchical Bayesian Federated Learning. CoRR abs/2305.04979 (2023) - [i17]Minyoung Kim, Timothy M. Hospedales:
A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning. CoRR abs/2306.09702 (2023) - [i16]Minyoung Kim, Timothy M. Hospedales:
BayesDLL: Bayesian Deep Learning Library. CoRR abs/2309.12928 (2023) - [i15]Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy M. Hospedales, Ferenc Huszár, Nicholas D. Lane:
FedL2P: Federated Learning to Personalize. CoRR abs/2310.02420 (2023) - 2022
- [i14]Shell Xu Hu, Da Li, Jan Stühmer, Minyoung Kim, Timothy M. Hospedales:
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference. CoRR abs/2204.07305 (2022) - [i13]Minyoung Kim, Da Li, Shell Xu Hu, Timothy M. Hospedales:
Fisher SAM: Information Geometry and Sharpness Aware Minimisation. CoRR abs/2206.04920 (2022) - 2021
- [i12]Minyoung Kim, Vladimir Pavlovic:
Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach. CoRR abs/2102.03151 (2021) - [i11]Minyoung Kim:
On PyTorch Implementation of Density Estimators for von Mises-Fisher and Its Mixture. CoRR abs/2102.05340 (2021) - [i10]Minyoung Kim, Timothy M. Hospedales:
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation. CoRR abs/2111.05392 (2021) - [i9]Minyoung Kim:
SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval. CoRR abs/2111.05814 (2021) - 2020
- [i8]Minyoung Kim, Vladimir Pavlovic:
Ordinal-Content VAE: Isolating Ordinal-Valued Content Factors in Deep Latent Variable Models. CoRR abs/2009.03034 (2020) - [i7]Minyoung Kim, Vladimir Pavlovic:
Recursive Inference for Variational Autoencoders. CoRR abs/2011.08544 (2020) - [i6]Minyoung Kim, Ricardo Guerrero, Vladimir Pavlovic:
Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach. CoRR abs/2012.00682 (2020) - 2019
- [i5]Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic:
Relevance Factor VAE: Learning and Identifying Disentangled Factors. CoRR abs/1902.01568 (2019) - [i4]Minyoung Kim, Pritish Sahu, Behnam Gholami, Vladimir Pavlovic:
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach. CoRR abs/1902.08727 (2019) - [i3]Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim:
Efficient Deep Gaussian Process Models for Variable-Sized Input. CoRR abs/1905.06982 (2019) - [i2]Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic:
Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement. CoRR abs/1909.02820 (2019) - [i1]Behnam Gholami, Pritish Sahu, Minyoung Kim, Vladimir Pavlovic:
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation. CoRR abs/1909.12366 (2019)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-02-20 20:42 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
