Image de couverture de livre

Entire Space Counterfactual Learning for Reliable Content Recommendations

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Wang, Hao, Chen, Zhichao, Liu, Zhaoran, Li, Haozhe, Yang, Degui, Liu, Xinggao, Li, Haoxuan
Format:	Preprint
Publié:	2022
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

FreDF: Learning to Forecast in the Frequency Domain
par: Wang, Hao, et autres
Publié: (2024)

Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation
par: Wang, Hao, et autres
Publié: (2024)

An Accurate and Interpretable Framework for Trustworthy Process Monitoring
par: Wang, Hao, et autres
Publié: (2023)

Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting
par: Pan, Licheng, et autres
Publié: (2025)

CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks
par: Wang, Hao, et autres
Publié: (2026)

DeepFilter: A Transformer-style Framework for Accurate and Efficient Process Monitoring
par: Wang, Hao, et autres
Publié: (2025)

ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation
par: Hou, Chaoqun, et autres
Publié: (2024)

Enabling Agents to Communicate Entirely in Latent Space
par: Du, Zhuoyun, et autres
Publié: (2025)

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization
par: Chen, Xinjie, et autres
Publié: (2025)

Deep Time-series Forecasting Needs Kernelized Moment Balancing
par: Pan, Licheng, et autres
Publié: (2026)

Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
par: Wang, Hao, et autres
Publié: (2025)

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining
par: Yang, Hao, et autres
Publié: (2025)

Task Priors: Enhancing Model Evaluation by Considering the Entire Space of Downstream Tasks
par: Patel, Niket, et autres
Publié: (2025)

CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning
par: Zheng, Congmin, et autres
Publié: (2025)

Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
par: Wang, Hao, et autres
Publié: (2025)

DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
par: Wang, Hao, et autres
Publié: (2025)

Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency
par: Liu, Zhihan, et autres
Publié: (2023)

Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
par: Huang, Yinqiu, et autres
Publié: (2024)

Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency
par: Wang, Lingxiao, et autres
Publié: (2022)

Self-Distilled Disentangled Learning for Counterfactual Prediction
par: Li, Xinshu, et autres
Publié: (2024)

IB-GRPO: Aligning LLM-based Learning Path Recommendation with Educational Objectives via Indicator-Based Group Relative Policy Optimization
par: Wang, Shuai, et autres
Publié: (2026)

FDRMFL:Multi-modal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning
par: Wu, Haozhe
Publié: (2025)

DDTime: Dataset Distillation with Spectral Alignment and Information Bottleneck for Time-Series Forecasting
par: Li, Yuqi, et autres
Publié: (2025)

Revisiting Counterfactual Regression through the Lens of Gromov-Wasserstein Information Bottleneck
par: Yang, Hao, et autres
Publié: (2024)

Reward-Augmented Data Enhances Direct Preference Alignment of LLMs
par: Zhang, Shenao, et autres
Publié: (2024)

Latent-Space Contrastive Reinforcement Learning for Stable and Efficient LLM Reasoning
par: Shan, Lianlei, et autres
Publié: (2026)

DKINet: Medication Recommendation via Domain Knowledge Informed Deep Learning
par: Liu, Sicen, et autres
Publié: (2023)

Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?
par: Yin, Yutong, et autres
Publié: (2025)

HiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents
par: Peng, Jiangweizhi, et autres
Publié: (2026)

Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation
par: Li, He, et autres
Publié: (2025)

Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
par: Kan, Ziwen, et autres
Publié: (2024)

Can a Single Tree Outperform an Entire Forest?
par: Mao, Qiangqiang, et autres
Publié: (2024)

LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching
par: Cao, Zhuo, et autres
Publié: (2025)

Budgeting Counterfactual for Offline RL
par: Liu, Yao, et autres
Publié: (2023)

Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
par: Liu, Zhihan, et autres
Publié: (2024)

Language Models for Controllable DNA Sequence Design
par: Su, Xingyu, et autres
Publié: (2025)

Contextual Dynamic Pricing with Strategic Buyers
par: Liu, Pangpang, et autres
Publié: (2023)

Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agents
par: Liu, Zhihan, et autres
Publié: (2026)

Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
par: Lu, Miao, et autres
Publié: (2022)

Decentralized Autoregressive Generation
par: Maschan, Stepan, et autres
Publié: (2026)