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Causal K-Means Clustering
arXiv:2405.03083v5 Announce Type: replace-cross Abstract: Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup structure is typically unknown, it is more challenging to identify…
WFR-FM: Simulation-Free Dynamic Unbalanced Optimal Transport
arXiv:2601.06810v2 Announce Type: replace Abstract: The Wasserstein-Fisher-Rao (WFR) metric extends dynamic optimal transport (OT) by coupling displacement with change of mass, providing a principled geometry for modeling unbalanced snapshot dynamics. Existing WFR solvers, however, are often unstable, computationally expensive, and…
Regret Bounds for Reinforcement Learning from Multi-Source Imperfect Preferences
arXiv:2603.20453v2 Announce Type: replace Abstract: Reinforcement learning from human feedback (RLHF) replaces hard-to-specify rewards with pairwise trajectory preferences, yet regret-oriented theory often assumes that preference labels are generated consistently from a single ground-truth objective. In practical RLHF systems, however, feedback…
DiffGradCAM: A Universal Class Activation Map Resistant to Adversarial Training
arXiv:2506.08514v3 Announce Type: replace Abstract: Class Activation Mapping (CAM) and its gradient-based variants (e.g., GradCAM) have become standard tools for explaining Convolutional Neural Network (CNN) predictions. However, these approaches typically focus on individual logits, while for neural networks using softmax,…
Partial VOROS: A Cost-aware Performance Metric for Binary Classifiers with Precision and Capacity Constraints
arXiv:2510.18520v2 Announce Type: replace Abstract: The ROC curve is widely used to assess binary classifiers. Yet for some applications, such as alert systems for monitoring hospitalized patients, conventional ROC analysis cannot meet two key deployment needs: enforcing a constraint on…
Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
arXiv:2604.01328v1 Announce Type: new Abstract: Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often wastes resources, yields inefficient designs, and misses critical insights. This tutorial presents Bayesian Optimisation…
Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing
arXiv:2208.02389v3 Announce Type: replace Abstract: Motivated by practical considerations in machine learning for financial decision-making, such as risk aversion and large action space, we consider risk-aware bandits optimization with applications in smart order routing (SOR). Specifically, based on preliminary observations…