Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow f…
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48 results for “latent preferences”
SARA uses similarity to learn rewards robustly and adaptively.
problem Robustness to labeler errors and adaptability to diverse feedback formats.
method Contrastive framework that learns latent representations and computes rewards as similarities.
result Strong performance on offline RL benchmarks and diverse applications.
Bayesian model identifies three types of travelers adapting to feedback.
problem Capturing adaptive, feedback-driven travel behavior in heterogeneous individuals.
method Latent Class Reinforcement Learning (LCRL) model with Variational Bayes estimation.
result Three distinct traveler classes identified: context-dependent, persistent exploitative, and exploratory.
Bayesian framework learns latent preference archetypes for many-objective optimization.
problem Expanding space of trade-offs and context-dependent human values.
method Dirichlet-process mixture model for latent preference archetypes, hybrid queries for efficient information.
result Mixture-aware Bayesian optimization outperforms standard methods on synthetic and real-world benchmarks.
LUQ-Learning adapts Q-learning for healthcare decisions considering patient preferences.
problem Optimizing treatment decisions for multivariate outcomes based on individual preferences.
method Latent Utility Q-Learning (LUQ-Learning) framework that adapts Q-learning for composite outcomes.
result LUQ-Learning achieves highly competitive performance compared to alternative methods in simulations.
Researchers improve Gaussian processes to model inconsistent preferences.
problem Model inconsistent preferences and clusters of comparable items.
method Generalized Gaussian processes with spectral decomposition and universal RKHS.
result Competitive with state-of-the-art methods on simulated and real-world data.
Decentralized learning for matching markets with time-varying preferences.
problem Matching between competing agents and supply arms with time-varying preferences.
method Linear contextual bandit framework, learning algorithms to identify latent environment and stable matchings.
result Achieve instance-dependent logarithmic regret, applicable for large markets.
Model learns metrics and preferences from user comparisons.
problem Simultaneous metric and preference learning from user comparisons.
method Jointly learns a metric and latent ideal points for each user.
result Model captures individual preferences and learns metrics efficiently.
PBO framework optimizes latent preferences over multiple objectives.
problem Optimizing latent preferences with multiple conflicting objectives.
method Proposes DSTS, a multi-objective generalization of dueling Thompson sampling.
result DSTS outperforms benchmarks and provides asymptotic consistency.
In this paper, we implement an information-theoretic approach to travel behaviour analysis by introducing a generative modelling framework to identify informative latent characteristics in travel decision making. It involves developing a joint tri-partite Bayesian graphical network model using a Restricted Boltzmann Ma…
Mitigates biases in reward models using variational inference.
problem Spurious correlations in reward models that align large language models with human preferences.
method Formulates data-generating process, identifies non-spurious latent variables, and uses variational inference to recover them.
result Effective mitigation of spurious correlation issues, yielding more robust reward models.
On Estimating Multi-Attribute Choice Preferences using Private Signals and Matrix Factorizationstat.ML
Revealed preference theory studies the possibility of modeling an agent's revealed preferences and the construction of a consistent utility function. However, modeling agent's choices over preference orderings is not always practical and demands strong assumptions on human rationality and data-acquisition abilities. Th…
Hierarchical Partial-Order Models for Ranking
problem Rank aggregation combining ordered lists
method Hierarchical partial-order models
result Bayesian inference for latent poset hierarchy
This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where res…
We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs). Through experimentation on synthetic and real datasets, we argue that random encoders should be preferred over deterministic encoders. We highlight the potential of WAEs for representation learning with promising results on a benchmar…
Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most collaborative filtering algorithms assume that these latent factors are static, although it has been shown that user preferen…
We propose a topic modeling approach to the prediction of preferences in pairwise comparisons. We develop a new generative model for pairwise comparisons that accounts for multiple shared latent rankings that are prevalent in a population of users. This new model also captures inconsistent user behavior in a natural wa…
The paper develops a method to estimate consumer preferences from observed rankings.
problem Estimating consumer preferences from partial ranking information.
method Interpreting observed rankings as pairwise comparisons, modeling latent utility, and correcting for selection bias.
result The method improves recommendation performance, especially for previously unconsumed products.
Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed preferences and behaviors of users are assumed to be ge…
Study recovers investor preferences from portfolio data using synthetic data and robust optimization.
problem Recovering latent investor preferences from observed portfolio allocations under uncertainty.
method Inverse portfolio optimization framework integrating robust optimization and regret-based inference.
result Accurate recovery of transaction cost parameters and partial identifiability of ESG penalties under preference misspecification and market shocks.
We introduce a novel latent grouping model for predicting the relevance of a new document to a user. The model assumes a latent group structure for both users and documents. We compared the model against a state-of-the-art method, the User Rating Profile model, where only users have a latent group structure. We estimat…
Social-based recommendation systems exploit the selections of friends to combat the data sparsity on user preferences, and improve the recommendation accuracy of the collaborative filtering strategy. The main challenge is to capture and weigh friends' preferences, as in practice they do necessarily match. In this paper…
CARE improves LLM aggregation by accounting for shared confounders.
problem LLM judges' correlated errors due to shared latent confounders.
method CARE explicitly models judges' scores as true quality and confounders.
result CARE reduces aggregation error by up to 26.8% across various benchmarks.
IArxiv sorts daily papers by user preference using LDA.
problem Harder for scientists to stay updated with diverse research.
method LDA on paper corpus to extract topics, user preference learning.
result Sorts papers by user preference on underlying topics.
Bayesian optimization (BO) has emerged during the last few years as an effective approach to optimizing black-box functions where direct queries of the objective are expensive. In this paper we consider the case where direct access to the function is not possible, but information about user preferences is. Such scenari…
With the proliferation of social media platforms and e-commerce sites, several cross-domain collaborative filtering strategies have been recently introduced to transfer the knowledge of user preferences across domains. The main challenge of cross-domain recommendation is to weigh and learn users' different behaviors in…
Optimal recommendation system using user and item clustering.
problem Maximizing recommendation accuracy with limited feedback.
method Latent variable model with user and item clustering, exploiting i.i.d. structure.
result Near-optimal algorithm that combines item and user structures.
New model improves recommendation systems by analyzing user-item interactions.
problem Improving recommendation systems for better user-item interactions.
method Sliced Anti-symmetric Decomposition (SAD) model using tensor decomposition.
result SAD produces the most consistent personalized preferences compared to SOTA models.
New method optimizes policies without assuming known link functions between preferences and rewards.
problem Policy alignment with unknown and unrestricted link functions.
method Formulates an -divergence-constrained reward maximization problem, learning policies directly.
result Induces a semiparametric single-index binary choice model for policy alignment.
SUMO provides unbiased log marginal likelihood estimation for latent variable models.
problem Biased estimates of log marginal likelihood in latent variable models.
method Randomized truncation of infinite series for unbiased estimation.
result Models trained with SUMO give better test-set likelihoods than standard methods.
Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry. Despite their value, however, they still suffer from the challenges of capturing complicated user preferences and fine-grained user…
Best-of-N sampling reveals reward targets from preference data, influencing N and base distribution choices.
problem Understanding reward extraction from Best-of-N preference data and optimal N and base distribution choices.
method Specialized analysis of preference data via induced conditional distribution, deriving reward targets and design principles.
result Reward targets are explicit functions of N and base distribution, and bounded-class minimizers approach these targets as N grows.
Unified framework to bridge human and LLM judgments.
problem Systematic discrepancies between human and LLM evaluations.
method Latent human preference score and linear transformations of covariates.
result Higher agreement with human ratings and exposure of systematic gaps.
TALBO optimizes latent spaces for evolving design objectives.
problem Temporal drift in design objectives.
method GP-prior variational autoencoder for time-varying latent space.
result Consistently outperforms LSBO baselines across varying drift speeds and objectives.
LLM safety alignment explained as divergence estimation.
problem Aligning large language models to avoid harmful outputs.
method Presented a theoretical framework showing alignment methods as divergence estimators.
result KLDO method improves safety alignment using compliance-refusal datasets.
This paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as "this is better than that" between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimiz…
Study metric learning from limited preference comparisons, showing how low-dimensional structure can still reveal metric information.
problem Learning metric from limited pairwise preference comparisons.
method Ideal point model, divide-and-conquer approach for low-dimensional structure.
result Metric can be jointly identified even with limited comparisons when items exhibit low-dimensional structure.
We propose a novel parameterized family of Mixed Membership Mallows Models (M4) to account for variability in pairwise comparisons generated by a heterogeneous population of noisy and inconsistent users. M4 models individual preferences as a user-specific probabilistic mixture of shared latent Mallows components. Our k…
We are interested in learning customers' video preferences from their historic viewing patterns and geographical location. We consider a Bayesian latent factor modeling approach for this task. In order to tune the complexity of the model to best represent the data, we make use of Bayesian nonparameteric techniques. We …
Improved VAE representations lead to better image classification.
problem VAE representations are inferior to non-latent models for image classification.
method Used a decoder that prefers local features, improving global feature capture in latent variables.
result Significant improvement in downstream semantic classification tasks.
Choice functions accept a set of alternatives as input and produce a preferred subset of these alternatives as output. We study the problem of learning such functions under conditions of context-dependence of preferences, which means that the preference in favor of a certain choice alternative may depend on what other …
A new method improves recommendation accuracy by learning from multiple networks and time-dependent user preferences.
problem Incomplete user profiles and dynamic user preferences degrade recommender quality.
method A cross-network time-aware recommender that learns from multiple source networks and develops current user models.
result The proposed solution achieves superior performance in accuracy, novelty, and diversity.
This paper explores the preference-based top- rank aggregation problem. Suppose that a collection of items is repeatedly compared in pairs, and one wishes to recover a consistent ordering that emphasizes the top- ranked items, based on partially revealed preferences. We focus on the Bradley-Terry-Luce (BTL) model…
Limit Order Book Dynamics in Matching Markets: Microstructure, Spread, and Execution Slippageq-fin.TR
The paper models market dynamics using a limit order book system to explain slippage and inefficiency.
problem Inefficiency in matching markets due to structural liquidity constraints and slippage.
method Introduces a market microstructure framework with a latent preference state matrix and a dynamic discrete choice execution model.
result Persistent slippage and regional invariance of preference orderings are explained by liquidity thresholds.
The task of image generation started to receive some attention from artists and designers to inspire them in new creations. However, exploiting the results of deep generative models such as Generative Adversarial Networks can be long and tedious given the lack of existing tools. In this work, we propose a simple strate…
The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback data are created along with user-item online interactions in a variety of ways, such as browsing, purchasing, and sharing. These multiple typ…
New method learns decisions from collective preferences without individual covariates.
problem Making decisions online without individual covariates.
method Collaborative filtering, matrix completion bandit, ε-greedy policy, online gradient descent, inverse propensity weighting.
result Method outperforms benchmarks and reveals new discoveries.
In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with…