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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)
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2020 – today
- 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 - [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
- [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 - [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
- [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 - [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
- [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 - [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
- [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 - [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)
2010 – 2019
- 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) - [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 - [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) - 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) - [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 - 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) - [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
2000 – 2009
- 2009
- [j1]Minyoung Kim, Vladimir Pavlovic:
Discriminative Learning for Dynamic State Prediction. IEEE Trans. Pattern Anal. Mach. Intell. 31(10): 1847-1861 (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
Coauthor Index
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