Discriminative Learning of Mixture of Bayesian Network Classifiers for Sequence Classification. - Minyoung Kim 001; Vladimir Pavlovic 001 (CVPR (1), 2006)

Discriminative Learning of Dynamical Systems for Motion Tracking. - Minyoung Kim 001; Vladimir Pavlovic 001 (CVPR, 2007)

Conditional State Space Models for Discriminative Motion Estimation. - Minyoung Kim 001; Vladimir Pavlovic 001 (ICCV, 2007)

A recursive method for discriminative mixture learning. - Minyoung Kim 001; Vladimir Pavlovic 001 (ICML, 2007)

Face tracking and recognition with visual constraints in real-world videos. - Minyoung Kim 001; Sanjiv Kumar; Vladimir Pavlovic 001; Henry A. Rowley (CVPR, 2008)

Dimensionality reduction using covariance operator inverse regression. - Minyoung Kim 001; Vladimir Pavlovic 001 (CVPR, 2008)

Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings. - Minyoung Kim 001; Vladimir Pavlovic 001 (AISTATS, 2009)

Discriminative Learning for Dynamic State Prediction. - Minyoung Kim 001; Vladimir Pavlovic 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Structured Output Ordinal Regression for Dynamic Facial Emotion Intensity Prediction. - Minyoung Kim 001; Vladimir Pavlovic 001 (ECCV (3), 2010)

A Real-Time System for Head Tracking and Pose Estimation. - Zengyin Zhang; Minyoung Kim 001; Fernando De la Torre; Wende Zhang (ECCV Workshops (1), 2010)

Local Minima Embedding. - Minyoung Kim 001; Fernando De la Torre (ICML, 2010)

Gaussian Processes Multiple Instance Learning. - Minyoung Kim 001; Fernando De la Torre (ICML, 2010)

Hidden Conditional Ordinal Random Fields for Sequence Classification. - Minyoung Kim 001; Vladimir Pavlovic 001 (ECML/PKDD (2), 2010)

Large margin cost-sensitive learning of conditional random fields. - Minyoung Kim 001 (Pattern Recognit., 2010)

Sequence classification via large margin hidden Markov models. - Minyoung Kim 001; Vladimir Pavlovic 001 (Data Min. Knowl. Discov., 2011)

Central Subspace Dimensionality Reduction Using Covariance Operators. - Minyoung Kim 001; Vladimir Pavlovic 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2011)

Discriminative semi-supervised learning of dynamical systems for motion estimation. - Minyoung Kim 001 (Pattern Recognit., 2011)

Sequence Alignment by Regression Coding. - Minyoung Kim 001 (IEEE Signal Process. Lett., 2011)

Correlation-based incremental visual tracking. - Minyoung Kim 001 (Pattern Recognit., 2012)

Time-Series Dimensionality Reduction via Granger Causality. - Minyoung Kim 001 (IEEE Signal Process. Lett., 2012)

Accelerated max-margin multiple kernel learning. - Minyoung Kim 001 (Appl. Intell., 2013)

Semi-Supervised Recursive Learning of Discriminative Mixture Models for Time-Series Classification. - Minyoung Kim 001 (Int. J. Fuzzy Log. Intell. Syst., 2013)

Semi-supervised learning of hidden conditional random fields for time-series classification. - Minyoung Kim 001 (Neurocomputing, 2013)

Conditional Alignment Random Fields for Multiple Motion Sequence Alignment. - Minyoung Kim 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Conditional ordinal random fields for structured ordinal-valued label prediction. - Minyoung Kim 001 (Data Min. Knowl. Discov., 2014)

Multiple instance learning via Gaussian processes. - Minyoung Kim 001; Fernando De la Torre (Data Min. Knowl. Discov., 2014)

Robust Video-Based Barcode Recognition via Online Sequential Filtering. - Minyoung Kim 001 (Int. J. Fuzzy Log. Intell. Syst., 2014)

Greedy Learning of Sparse Eigenfaces for Face Recognition and Tracking. - Minyoung Kim 001 (Int. J. Fuzzy Log. Intell. Syst., 2014)

Discriminative Training of Sequence Taggers via Local Feature Matching. - Minyoung Kim 001 (Int. J. Fuzzy Log. Intell. Syst., 2014)

Probabilistic Sequence Translation-Alignment Model for Time-Series Classification. - Minyoung Kim 001 (IEEE Trans. Knowl. Data Eng., 2014)

Efficient Kernel Sparse Coding Via First-Order Smooth Optimization. - Minyoung Kim 001 (IEEE Trans. Neural Networks Learn. Syst., 2014)

Sparse discriminative region selection algorithm for face recognition. - Minyoung Kim 001 (Appl. Intell., 2015)

Multiple-concept feature generative models for multi-label image classification. - Minyoung Kim 001 (Comput. Vis. Image Underst., 2015)

Online Selective-Sample Learning of Hidden Markov Models for Sequence Classification. - Minyoung Kim 001 (Int. J. Fuzzy Log. Intell. Syst., 2015)

Greedy ensemble learning of structured predictors for sequence tagging. - Minyoung Kim 001 (Neurocomputing, 2015)

Greedy approaches to semi-supervised subspace learning. - Minyoung Kim 001 (Pattern Recognit., 2015)

Sparse inverse covariance learning of conditional Gaussian mixtures for multiple-output regression. - Minyoung Kim 001 (Appl. Intell., 2016)

Model-induced term-weighting schemes for text classification. - Hyun Kyung Kim; Minyoung Kim 001 (Appl. Intell., 2016)

Robust Algorithms for Combining Multiple Term Weighting Vectors for Document Classification. - Minyoung Kim 001 (Int. J. Fuzzy Log. Intell. Syst., 2016)

Adaptive Bayesian Object Tracking with Histograms of Dense Local Image Descriptors. - Minyoung Kim 001 (Int. J. Fuzzy Log. Intell. Syst., 2016)

Document Summarization via Convex-Concave Programming. - Minyoung Kim 001 (Int. J. Fuzzy Log. Intell. Syst., 2016)

Sparse conditional copula models for structured output regression. - Minyoung Kim 001 (Pattern Recognit., 2016)

Dual soft assignment clustering algorithm for human action video clustering. - Minyoung Kim 001 (Comput. Vis. Image Underst., 2017)

Efficient histogram dictionary learning for text/image modeling and classification. - Minyoung Kim 001 (Data Min. Knowl. Discov., 2017)

Simultaneous Kernel Learning and Label Imputation for Pattern Classification with Partially Labeled Data. - Minyoung Kim 001 (Int. J. Fuzzy Log. Intell. Syst., 2017)

Simultaneous Learning of Sentence Clustering and Class Prediction for Improved Document Classification. - Minyoung Kim 001 (Int. J. Fuzzy Log. Intell. Syst., 2017)

Mixtures of Conditional Random Fields for Improved Structured Output Prediction. - Minyoung Kim 001 (IEEE Trans. Neural Networks Learn. Syst., 2017)

Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data. - Minyoung Kim 001 (ICML, 2018)

Variational Inference for Gaussian Process Models for Survival Analysis. - Minyoung Kim 001; Vladimir Pavlovic 001 (UAI, 2018)

A maximum-likelihood and moment-matching density estimator for crowd-sourcing label prediction. - Minyoung Kim 001 (Appl. Intell., 2018)

Dynamic sparse coding for sparse time-series modeling via first-order smooth optimization. - Minyoung Kim 001 (Appl. Intell., 2018)

Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach. - Minyoung Kim 001; Pritish Sahu; Behnam Gholami; Vladimir Pavlovic 001 (CVPR, 2019)

Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement. - Minyoung Kim 001; Yuting Wang 004; Pritish Sahu; Vladimir Pavlovic 001 (ICCV, 2019)

Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation. - Behnam Gholami; Pritish Sahu; Minyoung Kim 001; Vladimir Pavlovic 001 (ICCV Workshops, 2019)

Efficient Deep Gaussian Process Models for Variable-Sized Inputs. - Issam H. Laradji; Mark Schmidt 001; Vladimir Pavlovic 001; Minyoung Kim 001 (IJCNN, 2019)

Large-margin learning of Cox proportional hazard models for survival analysis. - Minyoung Kim 001 (Appl. Intell., 2019)

Sparse large-margin nearest neighbor embedding via greedy dyad functional optimization. - Minyoung Kim 001 (Appl. Intell., 2019)

Relevance Factor VAE: Learning and Identifying Disentangled Factors. - Minyoung Kim 001; Yuting Wang 004; Pritish Sahu; Vladimir Pavlovic 001 (CoRR, 2019)

Efficient Deep Gaussian Process Models for Variable-Sized Input. - Issam H. Laradji; Mark Schmidt 001; Vladimir Pavlovic 001; Minyoung Kim 001 (CoRR, 2019)

Ordinal-Content VAE: Isolating Ordinal-Valued Content Factors in Deep Latent Variable Models. - Minyoung Kim 001; Vladimir Pavlovic 001 (ICDM, 2020)

Recursive Inference for Variational Autoencoders. - Minyoung Kim 001; Vladimir Pavlovic 001 (NeurIPS, 2020)

Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach. - Minyoung Kim 001; Ricardo Guerrero; Vladimir Pavlovic 001 (CoRR, 2020)

Learning Disentangled Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach. - Minyoung Kim 001; Ricardo Guerrero; Vladimir Pavlovic 001 (ACM Multimedia, 2021)

Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach. - Minyoung Kim 001; Vladimir Pavlovic 001 (CoRR, 2021)

On PyTorch Implementation of Density Estimators for von Mises-Fisher and Its Mixture. - Minyoung Kim 001 (CoRR, 2021)

Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation. - Minyoung Kim 001; Timothy M. Hospedales (CoRR, 2021)

Variational Continual Proxy-Anchor for Deep Metric Learning. - Minyoung Kim 001; Ricardo Guerrero; Hai Xuan Pham; Vladimir Pavlovic 001 (AISTATS, 2022)

Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference. - Shell Xu Hu; Da Li 001; Jan Stühmer; Minyoung Kim 001; Timothy M. Hospedales (CVPR, 2022)

Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders. - Minyoung Kim 001 (CVPR, 2022)

Differentiable Expectation-Maximization for Set Representation Learning. - Minyoung Kim 001 (ICLR, 2022)

Fisher SAM: Information Geometry and Sharpness Aware Minimisation. - Minyoung Kim 001; Da Li 001; Shell Xu Hu; Timothy M. Hospedales (ICML, 2022)

SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval. - Minyoung Kim 001 (AISTATS, 2023)

Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach. - Minyoung Kim 001; Da Li 001; Timothy M. Hospedales (ICLR, 2023)

BayesTune: Bayesian Sparse Deep Model Fine-tuning. - Minyoung Kim 001; Timothy M. Hospedales (NeurIPS, 2023)

FedL2P: Federated Learning to Personalize. - Royson Lee; Minyoung Kim 001; Da Li 001; Xinchi Qiu; Timothy M. Hospedales; Ferenc Huszar; Nicholas D. Lane (NeurIPS, 2023)

FedHB: Hierarchical Bayesian Federated Learning. - Minyoung Kim 001; Timothy M. Hospedales (CoRR, 2023)

A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning. - Minyoung Kim 001; Timothy M. Hospedales (CoRR, 2023)

BayesDLL: Bayesian Deep Learning Library. - Minyoung Kim 001; Timothy M. Hospedales (CoRR, 2023)

A Hierarchical Bayesian Model for Few-Shot Meta Learning. - Minyoung Kim 001; Timothy M. Hospedales (ICLR, 2024)

A Bayesian Approach to Data Point Selection. - Xinnuo Xu; Minyoung Kim 001; Royson Lee; Brais Martínez; Timothy M. Hospedales (NeurIPS, 2024)

A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning. - Minyoung Kim 001; Timothy M. Hospedales (AAAI, 2025)

LiFT: Learning to Fine-Tune via Bayesian Parameter Efficient Meta Fine-Tuning. - Minyoung Kim 001; Timothy M. Hospedales (ICLR, 2025)

FedP2EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs. - Royson Lee; Minyoung Kim 001; Fady Rezk; Rui Li 052; Stylianos I. Venieris; Timothy M. Hospedales (CoRR, 2025)

Model Diffusion for Certifiable Few-shot Transfer Learning. - Fady Rezk; Royson Lee; Henry Gouk; Timothy M. Hospedales; Minyoung Kim 001 (CoRR, 2025)

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning. - Taehoon Kim; Henry Gouk; Minyoung Kim 001; Timothy M. Hospedales (CoRR, 2025)