wasserstein gan

Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems L4DC 2026 Layer-wise Quantization for Quantized Optimistic Dual Averaging ICML 2025 Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution ICML 2024 A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems NeurIPS 2023 Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions ICLR 2022 Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport? NeurIPS 2022 Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations CoRL 2022 Wasserstein Adversarial Transformer for Cloud Workload Prediction AAAI 2022 Wasserstein GANs with Gradient Penalty Compute Congested Transport COLT 2022 Approximating Lipschitz continuous functions with GroupSort neural networks AISTATS 2021 Learning Deep Generative Models for Queuing Systems AAAI 2021 Neural SDEs as Infinite-Dimensional GANs ICML 2021 R-MNet: A Perceptual Adversarial Network for Image Inpainting WACV 2021 Some Theoretical Insights into Wasserstein GANs JMLR 2021 A Characteristic Function Approach to Deep Implicit Generative Modeling CVPR 2020 Adversarial Feature Hallucination Networks for Few-Shot Learning CVPR 2020 Adversarial Lipschitz Regularization ICLR 2020 Generating Realistic Stock Market Order Streams AAAI 2020 GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators NeurIPS 2020 Meta-Learning for Generalized Zero-Shot Learning AAAI 2020 On Relativistic f-Divergences ICML 2020 Semialgebraic Optimization for Lipschitz Constants of ReLU Networks NeurIPS 2020 SGD Learns One-Layer Networks in WGANs ICML 2020 DISTRIBUTIONAL CONCAVITY REGULARIZATION FOR GANS ICLR 2019 EWGAN: Entropy-Based Wasserstein GAN for Imbalanced Learning AAAI 2019