:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Rajmohan, Prakash Palanivelu, Roosta, Fred
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2505.12353
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Geometric Wall: Manifold Structure Predicts Layerwise Sparse Autoencoder Scaling Laws
by: Zaher, Eslam, et al.
Published: (2026)

ESPO: Entropy Importance Sampling Policy Optimization
by: Sheng, Yuepeng, et al.
Published: (2025)

AIS: Adaptive Importance Sampling for Quantized RL
by: Zhou, Jiajun, et al.
Published: (2026)

GIPO: Gaussian Importance Sampling Policy Optimization
by: Lu, Chengxuan, et al.
Published: (2026)

Measuring Sample Importance in Data Pruning for Language Models based on Information Entropy
by: Kim, Minsang, et al.
Published: (2024)

Preference as Reward, Maximum Preference Optimization with Importance Sampling
by: Jiang, Zaifan, et al.
Published: (2023)

Beyond Importance Sampling: Rejection-Gated Policy Optimization
by: Sun, Ziwu, et al.
Published: (2026)

CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning
by: Hossen, Md Anwar, et al.
Published: (2026)

MISA: Memory-Efficient LLMs Optimization with Module-wise Importance Sampling
by: Liu, Yuxi, et al.
Published: (2025)

Partial Domain Adaptation via Importance Sampling-based Shift Correction
by: Guo, Cheng-Jun, et al.
Published: (2025)

Low Variance Off-policy Evaluation with State-based Importance Sampling
by: Bossens, David M., et al.
Published: (2022)

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
by: Xu, Jian, et al.
Published: (2024)

Adaptive-lambda Subtracted Importance Sampled Scores in Machine Unlearning for DDPMs and VAEs
by: Dini, MohammadParsa, et al.
Published: (2025)

Rethinking Importance Sampling in LLM Policy Optimization: A Cumulative Token Perspective
by: Zhang, Yuheng, et al.
Published: (2026)

Relative Importance Sampling for off-Policy Actor-Critic in Deep Reinforcement Learning
by: Humayoo, Mahammad, et al.
Published: (2018)

Path Planning for Masked Diffusion Model Sampling
by: Peng, Fred Zhangzhi, et al.
Published: (2025)

Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling
by: Aouali, Imad, et al.
Published: (2024)

Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows
by: Kruse, Liam A., et al.
Published: (2025)

Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation
by: Zhou, Hongyi, et al.
Published: (2025)

Quotient DAGs for Off-Policy Evaluation:Forward-Flow Importance Sampling and Exact Slate Propensities
by: Xie, Ziwen, et al.
Published: (2026)

Revisiting Transformer Layer Parameterization Through Causal Energy Minimization
by: Xu, Jin, et al.
Published: (2026)

CoPRIS: Efficient and Stable Reinforcement Learning via Concurrency-Controlled Partial Rollout with Importance Sampling
by: Qu, Zekai, et al.
Published: (2025)

Parameter Importance-Driven Continual Learning for Foundation Models
by: Wang, Lingxiang, et al.
Published: (2025)

Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits
by: Sun, Jiazheng, et al.
Published: (2025)

Combining Open-box Simulation and Importance Sampling for Tuning Large-Scale Recommenders
by: Paneri, Kaushal, et al.
Published: (2024)

Failure Probability Estimation for Black-Box Autonomous Systems using State-Dependent Importance Sampling Proposals
by: Delecki, Harrison, et al.
Published: (2024)

Simulating Environments with Reasoning Models for Agent Training
by: Li, Yuetai, et al.
Published: (2025)

LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
by: Pan, Rui, et al.
Published: (2024)

Weakly Supervised Learning on Large Graphs
by: Prakash, Aditya
Published: (2025)

Score-Regularized Joint Sampling with Importance Weights for Flow Matching
by: Liu, Xinshuang, et al.
Published: (2025)

TIP: Token Importance in On-Policy Distillation
by: Xu, Yuanda, et al.
Published: (2026)

The Importance of Time in Causal Algorithmic Recourse
by: Beretta, Isacco, et al.
Published: (2023)

From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?
by: Arshad, Muhammad Arbab, et al.
Published: (2024)

AMPO: Active Multi-Preference Optimization for Self-play Preference Selection
by: Gupta, Taneesh, et al.
Published: (2025)

REFA: Reference Free Alignment for multi-preference optimization
by: Gupta, Taneesh, et al.
Published: (2024)

From SHAP Scores to Feature Importance Scores
by: Letoffe, Olivier, et al.
Published: (2024)

Beyond Distribution Sharpening: The Importance of Task Rewards
by: Mittal, Sarthak, et al.
Published: (2026)

EDGE: A Theoretical Framework for Misconception-Aware Adaptive Learning
by: Verma, Ananda Prakash
Published: (2025)

Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations
by: Xu, Fred, et al.
Published: (2025)

Adaptive Machine Learning-Driven Multi-Fidelity Stratified Sampling for Failure Analysis of Nonlinear Stochastic Systems
by: Xu, Liuyun, et al.
Published: (2025)