Saved in:
| Main Authors: | Daudel, Kamélia, Tran, Minh-Ngoc, Zhang, Cheng |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.01412 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning with Importance Weighted Variational Inference
by: Daudel, Kamélia, et al.
Published: (2024)
by: Daudel, Kamélia, et al.
Published: (2024)
Bures-Wasserstein Importance-Weighted Evidence Lower Bound: Exposition and Applications
by: Jiang, Peiwen, et al.
Published: (2026)
by: Jiang, Peiwen, et al.
Published: (2026)
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
by: Lin, Wu, et al.
Published: (2019)
by: Lin, Wu, et al.
Published: (2019)
End-to-End Training for Back-Translation with Categorical Reparameterization Trick
by: Heo, DongNyeong, et al.
Published: (2022)
by: Heo, DongNyeong, et al.
Published: (2022)
DiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick
by: Vali, Mohammad Hassan, et al.
Published: (2025)
by: Vali, Mohammad Hassan, et al.
Published: (2025)
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood
by: Nguyen, Nhat-Minh, et al.
Published: (2023)
by: Nguyen, Nhat-Minh, et al.
Published: (2023)
Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts
by: Le, Minh, et al.
Published: (2024)
by: Le, Minh, et al.
Published: (2024)
Reparameterization through Coverings and Topological Weight Priors
by: Beketov, Maxim, et al.
Published: (2026)
by: Beketov, Maxim, et al.
Published: (2026)
Signals, Concepts, and Laws: Toward Universal, Explainable Time-Series Forecasting
by: Ma, Hongwei, et al.
Published: (2025)
by: Ma, Hongwei, et al.
Published: (2025)
PREIG: Physics-informed and Reinforcement-driven Interpretable GRU for Commodity Demand Forecasting
by: Ma, Hongwei, et al.
Published: (2025)
by: Ma, Hongwei, et al.
Published: (2025)
Inversion-Free Natural Gradient Descent on Riemannian Manifolds
by: Draca, Dario, et al.
Published: (2026)
by: Draca, Dario, et al.
Published: (2026)
RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts
by: Truong, Tuan, et al.
Published: (2025)
by: Truong, Tuan, et al.
Published: (2025)
Kernel Semi-Implicit Variational Inference
by: Cheng, Ziheng, et al.
Published: (2024)
by: Cheng, Ziheng, et al.
Published: (2024)
Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate
by: Xu, Huangyu, et al.
Published: (2026)
by: Xu, Huangyu, et al.
Published: (2026)
Adaptive multi-gradient methods for quasiconvex vector optimization and applications to multi-task learning
by: Minh, Nguyen Anh, et al.
Published: (2024)
by: Minh, Nguyen Anh, et al.
Published: (2024)
Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model
by: Peiris, Rangika, et al.
Published: (2024)
by: Peiris, Rangika, et al.
Published: (2024)
LOCO Feature Importance Inference without Data Splitting via Minipatch Ensembles
by: Gan, Luqin, et al.
Published: (2022)
by: Gan, Luqin, et al.
Published: (2022)
A Kernel Approach for Semi-implicit Variational Inference
by: Yu, Longlin, et al.
Published: (2026)
by: Yu, Longlin, et al.
Published: (2026)
PRE: Vision-Language Prompt Learning with Reparameterization Encoder
by: Pham, Thi Minh Anh, et al.
Published: (2023)
by: Pham, Thi Minh Anh, et al.
Published: (2023)
A Variational Approach to Bayesian Phylogenetic Inference
by: Zhang, Cheng, et al.
Published: (2022)
by: Zhang, Cheng, et al.
Published: (2022)
Frankenstein Optimizer: Harnessing the Potential by Revisiting Optimization Tricks
by: Hsu, Chia-Wei, et al.
Published: (2025)
by: Hsu, Chia-Wei, et al.
Published: (2025)
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
by: Di, Hao, et al.
Published: (2024)
by: Di, Hao, et al.
Published: (2024)
Categorical Reparameterization with Denoising Diffusion models
by: Gourevitch, Samson, et al.
Published: (2026)
by: Gourevitch, Samson, et al.
Published: (2026)
Reparameterization invariance in approximate Bayesian inference
by: Roy, Hrittik, et al.
Published: (2024)
by: Roy, Hrittik, et al.
Published: (2024)
Mitigating Reward Over-optimization in Direct Alignment Algorithms with Importance Sampling
by: Nguyen, Phuc Minh, et al.
Published: (2025)
by: Nguyen, Phuc Minh, et al.
Published: (2025)
Inference-Time Scaling of Discrete Diffusion Models via Importance Weighting and Optimal Proposal Design
by: Ou, Zijing, et al.
Published: (2025)
by: Ou, Zijing, et al.
Published: (2025)
Reparameterization Flow Policy Optimization
by: Zhong, Hai, et al.
Published: (2026)
by: Zhong, Hai, et al.
Published: (2026)
Reparameterization Proximal Policy Optimization
by: Zhong, Hai, et al.
Published: (2025)
by: Zhong, Hai, et al.
Published: (2025)
Improving Generalization with Flat Hilbert Bayesian Inference
by: Truong, Tuan, et al.
Published: (2024)
by: Truong, Tuan, et al.
Published: (2024)
ReMAP: Neural Reparameterization for Scalable MAP Inference in Arbitrary-Order Markov Random Fields
by: Wang, Yaomin, et al.
Published: (2024)
by: Wang, Yaomin, et al.
Published: (2024)
MCNC: Manifold-Constrained Reparameterization for Neural Compression
by: Thrash, Chayne, et al.
Published: (2024)
by: Thrash, Chayne, et al.
Published: (2024)
Extending Kernel Trick to Influence Functions
by: Sun, Zhenhuan, et al.
Published: (2026)
by: Sun, Zhenhuan, et al.
Published: (2026)
One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning
by: Le, Minh, et al.
Published: (2025)
by: Le, Minh, et al.
Published: (2025)
GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization
by: Cai, Yaohui, et al.
Published: (2026)
by: Cai, Yaohui, et al.
Published: (2026)
Bag of Tricks to Boost Adversarial Transferability
by: Zhang, Zeliang, et al.
Published: (2024)
by: Zhang, Zeliang, et al.
Published: (2024)
Never Saddle for Reparameterized Steepest Descent as Mirror Flow
by: Jacobs, Tom, et al.
Published: (2026)
by: Jacobs, Tom, et al.
Published: (2026)
Learning Intractable Multimodal Policies with Reparameterization and Diversity Regularization
by: Wang, Ziqi, et al.
Published: (2025)
by: Wang, Ziqi, et al.
Published: (2025)
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
by: Ye, Zhenzhang, et al.
Published: (2024)
by: Ye, Zhenzhang, et al.
Published: (2024)
Performative Prediction with Bandit Feedback: Learning through Reparameterization
by: Chen, Yatong, et al.
Published: (2023)
by: Chen, Yatong, et al.
Published: (2023)
Scaling Quantum Machine Learning without Tricks: High-Resolution and Diverse Image Generation
by: Jäger, Jonas, et al.
Published: (2026)
by: Jäger, Jonas, et al.
Published: (2026)
Similar Items
-
Learning with Importance Weighted Variational Inference
by: Daudel, Kamélia, et al.
Published: (2024) -
Bures-Wasserstein Importance-Weighted Evidence Lower Bound: Exposition and Applications
by: Jiang, Peiwen, et al.
Published: (2026) -
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
by: Lin, Wu, et al.
Published: (2019) -
End-to-End Training for Back-Translation with Categorical Reparameterization Trick
by: Heo, DongNyeong, et al.
Published: (2022) -
DiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick
by: Vali, Mohammad Hassan, et al.
Published: (2025)