Paper connects Plackett-Luce and Cox models for preference estimation.
problem Estimating preferences from annotated data.
method Connects Plackett-Luce model to Cox Proportional Hazards model.
result Implications of the connection between the two models.
Proposes DATELINE for aggregating k-ary preferences with uncertainty.
problem Aggregating k-ary preferences with feature information and uncertainty.
method Employing deep neural networks and a weighted Plackett-Luce model with uncertainty vectors.
result Provides theoretical guarantees for robustness.
Optimal sample complexity for learning Plackett-Luce models.
problem PAC-learning good items from subsetwise feedback in Plackett-Luce models.
method Algorithm based on a wrapper around a PAC winner-finding algorithm, adapting to instance hardness.
result Optimal instance-dependent sample complexity for best arm identification.
Paper tackles non-identifiability of mixture models in partial order datasets.
problem Non-identifiability of mixture models in datasets with partial orders.
method Proved non-identifiability conditions and proposed GMM algorithms.
result GMM algorithms for learning mixtures of two Plackett-Luce models are consistent.
Proposes variance reduction for optimizing permutation models.
problem High variance in gradient estimates for discrete latent variables.
method Control variates for the Plackett-Luce distribution.
result Optimization of black-box functions over permutations using SGD.
Algorithm selects k arms from context-dependent options using Plackett-Luce model.
problem Selecting k arms from context-dependent options with Plackett-Luce feedback.
method Proposes CPPL algorithm inspired by UCB, evaluated on synthetic and real data.
result Demonstrates effectiveness of CPPL algorithm in online algorithm selection.
PLD distills knowledge using choice-theoretic Plackett-Luce model.
problem Model compression and knowledge transfer between large and small networks.
method PLD uses a weighted list-wise ranking loss based on the Plackett-Luce model.
result PLD achieves consistent gains across diverse architectures and distillation methods.
CRS model improves ranking data modeling with theoretical guarantees.
problem Lack of rich, multimodal models for ranking data.
method Contextual Repeated Selection (CRS) model for multimodal ranking data.
result CRS model significantly outperforms existing methods in various ranking contexts.
We propose a novel and flexible rank-breaking-then-composite-marginal-likelihood (RBCML) framework for learning random utility models (RUMs), which include the Plackett-Luce model. We characterize conditions for the objective function of RBCML to be strictly log-concave by proving that strict log-concavity is preserved…
New method reduces variance in estimating PL model expectations.
problem High variance in Monte Carlo estimates of PL model expectations.
method Combining Gumbel top-k trick with quasi-Monte Carlo sampling.
result More sample-efficient estimators of PL model expectations.
Random utility theory models an agent's preferences on alternatives by drawing a real-valued score on each alternative (typically independently) from a parameterized distribution, and then ranking the alternatives according to scores. A special case that has received significant attention is the Plackett-Luce model, fo…
Efficiently calculates PL model likelihood for partitioned preference data.
problem Computational infeasibility of calculating PL model likelihood for partitioned preference data.
method Random utility model formulation and efficient numerical integration approach.
result Proposed method outperforms existing LTR baselines and scales to real-world tasks.
Hierarchical Partial-Order Models for Ranking
problem Rank aggregation combining ordered lists
method Hierarchical partial-order models
result Bayesian inference for latent poset hierarchy
New algorithm learns human preferences from few comparisons efficiently.
problem Learning human preferences from limited comparison feedback.
method Formulated as D-optimal design for Plackett-Luce model, solved using randomized Frank-Wolfe algorithm.
result Proposed algorithm efficiently solves D-optimal design problem for Plackett-Luce objective.
Paper proposes efficient and accurate initialization and EM algorithm for PL mixture models.
problem Initialization issues and combinatorial complexity in PL likelihood maximization.
method Initialization algorithm and EM algorithm for true log-likelihood maximization.
result Proposed algorithm provides accurate initial estimates and efficiently maximizes true log-likelihood.
This paper optimizes slate decision systems for large action spaces.
problem Optimizing large-scale decision systems with arbitrary reward functions.
method A policy optimization framework with a novel relaxation of decision functions.
result Demonstrates the effectiveness of the proposed method on large action spaces.
In this paper we propose a Bayesian nonparametric model for clustering partial ranking data. We start by developing a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the pr…
Researchers show mixtures of ranking models are generally identifiable.
problem Understanding when and how parameters of mixtures of ranking models can be uniquely determined.
method Algebraic geometry framework applied to verify the number of solutions in polynomial systems.
result Popular mixtures of ranking models with two components are generically identifiable.
A learner selects subsets of choices for a user who then picks from them, aiming to minimize regret.
problem Optimizing subset selection for user choices in a stochastic setting.
method Introduces a new problem and defines regret, then proposes algorithms with matching upper and lower bounds.
result Upper and lower bounds on expected regret match up to a logarithmic term, demonstrating algorithm efficiency.
A new ranking model with dynamic covariates improves statistical analysis.
problem Statistical ranking with varying covariates across comparisons.
method Introduced a Plackett--Luce framework for covariate-assisted ranking, providing conditions for model identifiability and MLE existence, and developing an alternating maximization algorithm.
result Uniform consistency of the Maximum Likelihood Estimation (MLE) under suitable assumptions on graph design and covariates.
Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict. Datasets often exhibit multimodality, intransitivity, or incomplete rankings---particularly when generated by humans---yet popular probabilistic models are often too rigid to capture such complexities. In…
The paper analyzes RLHF with human feedback and provides convergence results for MLE and pessimistic MLE.
problem Improving RLHF with human feedback from pairwise or K-wise comparisons. method Theoretical framework for RLHF with convergence analysis of MLE and pessimistic MLE.
result MLE fails but pessimistic MLE provides improved policies under certain coverage assumptions.
The paper tackles PAC ranking with subset-wise feedback, achieving optimal sample complexity.
problem Probably Approximately Correct (PAC) ranking of items with subset-wise preference feedback.
method Adaptive subset-wise preference feedback, Plackett-Luce model, pivot trick for score estimates.
result Achieves optimal sample complexity for PAC ranking with subset-wise feedback.
New method uses rank-conditioned Horvitz-Thompson estimation for unbiased sample reuse in Plackett-Luce best-of-K objective.
problem Estimating the expected maximum reward in Plackett-Luce draws without replacement.
method Rank-conditioned Horvitz-Thompson estimation with joint-score REINFORCE for unbiased sample reuse.
result Unbiased estimation of the Plackett-Luce best-of-K objective with finite second moment guarantees.
We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a gamma process. We derive a posterior characterization and a simple and effective Gibbs…
NeuralSort optimizes sorting networks using continuous relaxations.
problem Non-differentiability of sorting operator hinders gradient-based optimization.
method Continuous relaxation of sorting operator to unimodal row-stochastic matrices, enabling gradient-based optimization.
result Gradient-based stochastic optimization over permutations is achieved.
New RLHF approach mitigates bias in aligning LLMs with human preferences.
problem Algorithmic bias in RLHF leading to preference collapse.
method Preference Matching (PM) RLHF, using PM regularizer and conditional variant.
result 29% to 41% improvement in alignment with human preferences.
The paper improves spectral ranking methods for diverse comparison graphs.
problem Estimating preference scores from multiway comparisons with heterogeneous sizes.
method Develops a two-step spectral method for estimating preference scores and their uncertainties.
result The two-step spectral method achieves the same asymptotic efficiency as the Maximum Likelihood Estimator (MLE).
A new method reduces variance in training early-stage rankers for large-scale search systems.
problem Training early-stage rankers for large-scale search systems is challenging due to exploding variance in policy gradient methods.
method Proposes credit-assigned policy gradient (CA-PG) to mitigate variance in training early-stage rankers.
result CA-PG significantly reduces variance in training early-stage rankers compared to vanilla policy gradient.
We study the problem of collaborative filtering where ranking information is available. Focusing on the core of the collaborative ranking process, the user and their community, we propose new models for representation of the underlying permutations and prediction of ranks. The first approach is based on the assumption …
Improved sample efficiency in preference-based RL with multiple comparisons.
problem Sample inefficiency in preference-based reinforcement learning with pairwise comparisons.
method Proposes M-AUPO, an algorithm that selects multiple actions by maximizing average uncertainty within subsets.
result Achieves a suboptimality gap of $O\left( \frac{d}{T} \sqrt{ \sum_{t=1}^T \frac{1}{|S_t|}}
ight)$, improving performance with larger subsets.
We introduce the probably approximately correct (PAC) \emph{Battling-Bandit} problem with the Plackett-Luce (PL) subset choice model--an online learning framework where at each trial the learner chooses a subset of k arms from a fixed set of n arms, and subsequently observes a stochastic feedback indicating prefere…
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features. Given a budget, our framework iteratively computes the most cost-effective elicitation questions in order to help the agents make a better group decision. We illustrate the viability of the fram…
We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives. In contrast to most existing methods, we explicitly model the process of turning a full ranking into an incomplete on…
This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with n agents (users) {xi}i∈[n] and m alternatives (items) {yj}j∈[m], each of which is associated with a latent feat…
This paper studies the problem of inferring a global preference based on the partial rankings provided by many users over different subsets of items according to the Plackett-Luce model. A question of particular interest is how to optimally assign items to users for ranking and how many item assignments are needed to a…
Paper extends RUMs with features to handle incomplete preferences and proves identifiability.
problem Learning preferences from incomplete and possibly noisy data.
method Generalized Mixture of Random Utility Models with features, proving identifiability and consistency of MLE.
result Proves consistency of Maximum Likelihood Estimation (MLE) for PL with features under certain conditions.
This paper explores the adaptive (active) PAC (probably approximately correct) top-k ranking (i.e., top-k item selection) and total ranking problems from l-wise (l≥2) comparisons under the multinomial logit (MNL) model. By adaptively choosing sets to query and observing the noisy output of the most favored …
New algorithm predicts ranked stock lists for long-short portfolios.
problem Constructing effective long-short stock portfolios using machine learning.
method Proposes a new listwise learn-to-rank loss function to emphasize top and bottom of a rank list.
result Demonstrates superior performance in constructing long-short portfolios with a 38% annual return.
CoarsenRank improves robustness in rank aggregation despite model misspecification.
problem Rank aggregation under model misspecification in real-world scenarios.
method CoarsenRank designs a neighborhood of ideal preferences to handle agnostic noise-corrupted data.
result CoarsenRank achieves robustness against model misspecification within a defined neighborhood.
The paper ranks items based on top choices in multiway comparisons.
problem Ranking items based on top choices in multiway comparisons.
method Uniform sampling scheme, statistical rates of convergence, asymptotic normality, maximum likelihood estimator, Gaussian multiplier bootstrap.
result Proposed inference framework for ranking items through maximum pairwise difference statistic.
Paper accelerates MM algorithm for faster inference of ranking scores from comparison data.
problem Inference of Bradley-Terry model parameters from comparison data.
method Developed and analyzed MM algorithm for maximum likelihood and Bayesian estimation, proposed an accelerated version.
result Accelerated MM algorithm achieves faster convergence rates compared to classical MM algorithm.
Proposes new listwise learning-to-rank models to address rating ties and document relevance.
problem Rating ties and document relevance in existing listwise learning-to-rank models.
method Models ranking as selecting documents from a candidate set based on unique rating levels. Uses a new loss function and adapted RNN model for refining prediction scores.
result Models notably outperform state-of-the-art learning-to-rank models on four public datasets.
New algorithms learn MNL weights efficiently for any slate size.
problem Efficiently learn weights for MNL models given query access.
method Two algorithms: adaptive and non-adaptive, with specific query complexities.
result Optimal query complexities for both adaptive and non-adaptive cases.
Study develops time-continuous models and probabilistic descriptions for agent-based economic market models.
problem Formulating and describing agent-based economic market models in a time-continuous and probabilistic manner.
method Derived time-continuous formulations, discussed impact of time-scaling, proved stability, presented probabilistic descriptions using kinetic theory.
result Time-continuous formulations and probabilistic descriptions for agent-based economic market models.
Hybrid model combines interpretable and black-box models for better transparency and performance.
problem Balancing interpretability and predictive performance in machine learning models.
method Proposes a Hybrid Predictive Model (HPM) integrating an interpretable model with a black-box model, using principled objective functions and customized training algorithms.
result Hybrid models achieve an efficient trade-off between transparency and predictive performance.
Boosts generative models by combining multiple meta-models.
problem Challenges in creating a single generative model that accurately represents complex data.
method Cascades multiple meta-models (like RBM and VAE) to create a stronger generative model.
result Derives a decomposable variational lower bound for training and evaluating the boosted model.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.