conftrace_

distribution learning

75 papers

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Co-occurring keywords

sample complexity (1169) generative adversarial network (1944) differential privacy (1016) discrete distribution (49) probability distribution (265) learning theory (519) wasserstein distance (417) generative model (2892) loss function (462) total variation distance (52)

Papers

No Scale Sensitive Dimension for Distribution Learning ALT 2026
Convergence and Divergence of Language Models under Different Random Seeds EMNLP 2025
ProPose: Probabilistic 3D Human Pose Estimation with Instance-Level Distribution and Normalizing Flow AAAI 2025
ZeroDL: Zero-shot Distribution Learning for Text Clustering via Large Language Models ACL 2025
Robust Distribution Learning with Local and Global Adversarial Corruptions (extended abstract) COLT 2024
An Improved Algorithm for Learning Drifting Discrete Distributions AISTATS 2024
Inherent limitations of dimensions for characterizing learnability of distribution classes COLT 2024
Learning to Embed Distributions via Maximum Kernel Entropy NIPS 2024
Cryptographic Hardness of Score Estimation NIPS 2024
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples ALT 2024
Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective JMLR 2024
Distribution Learning with Valid Outputs Beyond the Worst-Case NIPS 2024
Understanding Entropic Regularization in GANs JMLR 2024
Not All Learnable Distribution Classes are Privately Learnable ALT 2024
Sample-Efficient Private Learning of Mixtures of Gaussians NIPS 2024
The Whole Is Bigger Than the Sum of Its Parts: Modeling Individual Annotators to Capture Emotional Variability INTERSPEECH 2024
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures COLT 2023
HumanDiffusion: diffusion model using perceptual gradients INTERSPEECH 2023
Neural networks trained with SGD learn distributions of increasing complexity ICML 2023
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition NIPS 2023
Private Distribution Learning with Public Data: The View from Sample Compression NIPS 2023
Learning Mixtures of Gaussians Using the DDPM Objective NIPS 2023
Aligning Language Models with Preferences through $f$-divergence Minimization ICML 2023
Distribution Learnability and Robustness NIPS 2023
OPT-GAN: A Broad-Spectrum Global Optimizer for Black-Box Problems by Learning Distribution AAAI 2023