arXiv Machine Learning
Jun 18
Wasserstein Policy Learning for Distributional Outcomes
arXiv:2606. 19117v1 Announce Type: cross Abstract: Offline policy learning has received growing attention in causal inference.
Additive Control Variates Dominate Self-Normalisation in Off-Policy Evaluation
arXiv:2602. 14914v3 Announce Type: replace Abstract: Off-policy evaluation (OPE) is essential for assessing ranking and recommendation systems without costly online interventions.
Related stories
arXiv Machine Learning
Jun 2
Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation
arXiv:2509. 03456v2 Announce Type: replace-cross Abstract: Off-policy evaluation (OPE) and off-policy learning (OPL) are foundational for decision-making in offline contextual bandits.
arXiv Machine Learning
Jun 3
Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
arXiv:2606. 03332v1 Announce Type: new Abstract: Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation.
Hugging Face Trending Papers
Jun 2
Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near $0$ and $1$ often lead to high bias and variance.
arXiv Machine Learning
Jul 17
Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap
arXiv:2607. 14604v1 Announce Type: new Abstract: Online controlled experiments are the gold standard for hypothesis testing in online platforms.
arXiv Machine Learning
Jun 8
Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions
arXiv:2606. 07399v1 Announce Type: cross Abstract: Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nuisance model misspecification.
arXiv Machine Learning
Jun 30
SP-CACW: Convergence-Aware Client Weighting for Selfish Personalized Learning
arXiv:2606. 29322v1 Announce Type: new Abstract: Collaborative learning is sustainable only when it benefits each participant.
arXiv Machine Learning
Jun 24
KLip-PPO: A per-sample KL perspective on PPO-Clip
arXiv:2606. 23932v1 Announce Type: new Abstract: Proximal Policy Optimization (PPO) is the standard policy-gradient algorithm for on-policy reinforcement learning.
arXiv AI
Jul 21
Normalized Rewards for Preference Optimization
arXiv:2607. 16240v1 Announce Type: cross Abstract: Direct Alignment Algorithms (DAAs) such as DPO have become a common way to post-train and align LLMs with human preferences.
Hugging Face Trending Papers
Jun 25
Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding
Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and debiasing under unobserved confounding scenarios. In this paper, we propose the Cross-Head Attention Uplift Network (CHAUN) and Robust Adversarial Inverse Propensity Score (RA-IPS) method to address these limitations.
arXiv AI
Jun 12
Boosting Direct Preference Optimization with Penalization
arXiv:2606. 12505v1 Announce Type: cross Abstract: Offline preference optimization has become a practical substitute for reinforcement learning from human feedback, but pairwise objectives such as Direct Preference Optimization (DPO) and its variants use only the chosen and rejected responses stored in a static dataset.
arXiv Machine Learning
Jun 26
Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding
arXiv:2606. 27114v1 Announce Type: new Abstract: Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and debiasing under unobserved confounding scenarios.