The paper presents a linear reparametrization of PROMETHEE II net flow scores that incorporates both criteria weights and preference functions, enabling a clearer understanding of their interaction. Using this representation, the authors formulate linear optimization models for three inverse‑analysis scenarios: (1) minimizing weight deviations while allowing preference functions to vary, (2) minimizing deviations over all preference parameters, and (3) achieving consensus among multiple decision makers with objectives based on total or maximal individual deviation to promote fairness. An illustrative example demonstrates that permitting preference functions to vary can substantially alter inverse‑analysis conclusions compared to weight‑only models, and that the group formulations can distinguish efficient consensuses from fairer ones.
arXiv:2606. 17756v1 Announce Type: new Abstract: Fairness has become a central concern in ranking problems involving individuals or social groups, particularly under the Responsible Artificial Intelligence agenda.
The paper introduces Adaptive Doubly Robust (ADR), an off‑policy evaluation method for ranking policies that blends adaptive importance weighting with reward regression to reduce variance. ADR is unbiased when the true user behavior model is known and, under a sufficient condition, achieves lower variance than the prior Adaptive Inverse Propensity Scoring (AIPS) approach. Experiments on synthetic data show that ADR consistently improves mean squared error over AIPS and other ranking OPE estimators across various data sizes and ranking lengths.
arXiv:2603. 04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention.
The paper introduces multi-expert Conformal Risk Control (CRC) algorithms for pairwise LLM-as-a-Judge evaluation in open-ended dialogue. Two initial methods—Score Averaging and Decision Voting—aggregate at the score and decision levels, respectively, and outperform single-expert approaches on homogeneous expert panels. To address limited coverage on heterogeneous panels, the authors propose Marginal‑Calibrated Conformal Consensus (MC3), which captures distinct per‑expert scoring scales through threshold ratios while maintaining a unified decision function, and demonstrate its effectiveness on a new 1,800‑pair human pairwise‑preference benchmark called Panel.
arXiv:2608. 12302v1 Announce Type: new Abstract: We present a formal process to enable non-experts to instantiate and iterate on human-aligned reward functions, i.
arXiv:2607. 25136v1 Announce Type: new Abstract: Research on preference optimization often varies the training objective while holding the data fixed.
arXiv:2604.17805v3 Announce Type: replace-cross Abstract: Maximum-likelihood pairwise ranking is a com- mon computational mechanism for prioritization, reputation estimation, and comparison-driven de...
arXiv:2606. 07988v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on reward models to align their outputs with diverse user preferences.
Neural networks increasingly combine data across populations, time periods, and operating conditions to improve generalization. This raises a reliability question: whether a model refitted on pooled data preserves an action ordering supported by both sources.
arXiv:2607. 27255v1 Announce Type: cross Abstract: Neural networks increasingly combine data across populations, time periods, and operating conditions to improve generalization.
arXiv:2604. 18239v4 Announce Type: replace-cross Abstract: Preference optimization is widely used to align large language models (LLMs) with human preferences.
arXiv:2610.07128v1 Announce Type: cross Abstract: Aggregating the preferences of diverse user groups into a collective outcome raises fundamental challenges of equity, diversity, and inclusion (EDI):...