nhật Hồ

Attack on Prompt: Backdoor Attack in Prompt-Based Continual Learning AAAI 2025 Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective ICCV 2025 Improving Generalization with Flat Hilbert Bayesian Inference ICML 2025 Instability, Computational Efficiency and Statistical Accuracy JMLR 2025 Lightspeed Geometric Dataset Distance via Sliced Optimal Transport ICML 2025 On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation ICML 2025 RepLoRA: Reparameterizing Low-rank Adaptation via the Perspective of Mixture of Experts ICML 2025 Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts ICLR 2025 Statistical Advantages of Perturbing Cosine Router in Mixture of Experts ICLR 2025 Towards Marginal Fairness Sliced Wasserstein Barycenter ICLR 2025 Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Experts AISTATS 2025 X-Drive: Cross-modality Consistent Multi-Sensor Data Synthesis for Driving Scenarios ICLR 2025 A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings NeurIPS 2024 A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts ICML 2024 Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization NeurIPS 2024 Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders ICLR 2024 Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction ICLR 2024 FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion NeurIPS 2024 Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions NeurIPS 2024 Improving Computational Complexity in Statistical Models with Local Curvature Information ICML 2024 Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning CVPR 2024 Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts? ICML 2024 Mixture of Experts Meets Prompt-Based Continual Learning NeurIPS 2024 Neural Collapse for Cross-entropy Class-Imbalanced Learning with Unconstrained ReLU Features Model ICML 2024 On Least Square Estimation in Softmax Gating Mixture of Experts ICML 2024 On Parameter Estimation in Deviated Gaussian Mixture of Experts AISTATS 2024 On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing JMLR 2024 Quasi-Monte Carlo for 3D Sliced Wasserstein ICLR 2024 Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation ICLR 2024 Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts NeurIPS 2024 Sliced Wasserstein Estimation with Control Variates ICLR 2024 Sliced Wasserstein with Random-Path Projecting Directions ICML 2024 Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts ICLR 2024 Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks ICML 2024 Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts AISTATS 2024 A Primal-Dual Framework for Transformers and Neural Networks ICLR 2023 Demystifying Softmax Gating Function in Gaussian Mixture of Experts NeurIPS 2023 Designing Robust Transformers using Robust Kernel Density Estimation NeurIPS 2023 Energy-Based Sliced Wasserstein Distance NeurIPS 2023 Global-Local Regularization Via Distributional Robustness AISTATS 2023 Hierarchical Sliced Wasserstein Distance ICLR 2023 Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering AAAI 2023 LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching NeurIPS 2023 Markovian Sliced Wasserstein Distances: Beyond Independent Projections NeurIPS 2023 Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models NeurIPS 2023 Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data ICML 2023 On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances ICML 2023 Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature ICML 2023 Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction ICML 2023 Amortized Projection Optimization for Sliced Wasserstein Generative Models NeurIPS 2022 Architecture Agnostic Federated Learning for Neural Networks ICML 2022 Beyond black box densities: Parameter learning for the deviated components NeurIPS 2022 Convergence Rates for Gaussian Mixtures of Experts JMLR 2022 Entropic Gromov-Wasserstein between Gaussian Distributions ICML 2022 FourierFormer: Transformer Meets Generalized Fourier Integral Theorem NeurIPS 2022 Improving Mini-batch Optimal Transport via Partial Transportation ICML 2022 Improving Transformers with Probabilistic Attention Keys ICML 2022 Improving Transformer with an Admixture of Attention Heads NeurIPS 2022 On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexity AISTATS 2022 On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms AISTATS 2022 On the Complexity of Approximating Multimarginal Optimal Transport JMLR 2022 On the Efficiency of Entropic Regularized Algorithms for Optimal Transport JMLR 2022 On Transportation of Mini-batches: A Hierarchical Approach ICML 2022 Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models ICML 2022 Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution NeurIPS 2022 Stochastic Multiple Target Sampling Gradient Descent NeurIPS 2022 Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent AISTATS 2022 Weak Separation in Mixture Models and Implications for Principal Stratification AISTATS 2022 Distributional Sliced-Wasserstein and Applications to Generative Modeling ICLR 2021 Flow-based Alignment Approaches for Probability Measures in Different Spaces AISTATS 2021 Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein ICLR 2021 LAMDA: Label Matching Deep Domain Adaptation ICML 2021 On efficient multilevel Clustering via Wasserstein distances JMLR 2021 On Robust Optimal Transport: Computational Complexity and Barycenter Computation NeurIPS 2021 On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression AISTATS 2021 Point-Set Distances for Learning Representations of 3D Point Clouds ICCV 2021 Structured Dropout Variational Inference for Bayesian Neural Networks NeurIPS 2021 Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter AISTATS 2020 Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm NeurIPS 2020 On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm ICML 2020 Projection Robust Wasserstein Distance and Riemannian Optimization NeurIPS 2020 Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models AISTATS 2020 On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms ICML 2019 Probabilistic Multilevel Clustering via Composite Transportation Distance AISTATS 2019 Theoretical guarantees for EM under misspecified Gaussian mixture models NeurIPS 2018 Multilevel Clustering via Wasserstein Means ICML 2017