arXiv research

New MAB model incentivizes user arm-pulling with self-reinforcing preferences.

In many platforms, user arrivals exhibit a self-reinforcing behavior: future user arrivals are likely to have preferences similar to users who were satisfied in the past. In other words, arrivals exhibit positive externalities. We study multiarmed bandit (MAB) problems with positive externalities. We show that the self…

The study aims to prevent unfair content presentation in recommender systems.

Self-reinforcing feedback loops in personalization systems are typically caused by users choosing from a limited set of alternatives presented systematically based on previous choices. We propose a Bayesian choice model built on Luce axioms that explicitly accounts for users' limited exposure to alternatives. Our model…

A new HP model balances interpretability and flexibility for EHR event sequences.

Paper develops polynomial approximations for complex probability densities.

A model is presented of the market dynamics to emphasis the effects of increasing returns to scale, including the description of the born and death of the adaptive producers. The evolution of market structure and its behavior with the technological shocks are discussed. Its dynamics is in good agreement with some empir…

There is a growing concern in recent years over the potential formation of bubbles in the Chinese real estate market. This paper aims to conduct a series of bubble diagnostic analysis over nine representative Chinese cities from two aspects. First, we investigate whether the prices had been significantly deviating from…

Neural networks are easier to optimise when they have many more weights than are required for modelling the mapping from inputs to outputs. This suggests a two-stage learning procedure that first learns a large net and then prunes away connections or hidden units. But standard training does not necessarily encourage ne…

Optimizes molecular generation for chemist preferences.

New method adapts to user preferences dynamically, improving recommendation models.

The study explains how market-makers' hedging affects stock volatility during gamma-squeeze events.

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…

Enhances preference learning by incorporating response times into binary choices.

Bayesian optimization learns DM preferences for multi-outcome experiments.

New study shows personalized content recommendations can lead to polarization of user preferences.

A general theory of innovation and progress in human society is outlined, based on the combat between two opposite forces (conservatism/inertia and speculative herding "bubble" behavior). We contend that human affairs are characterized by ubiquitous ``bubbles'', which involve huge risks which would not otherwise be tak…

Bayesian optimization agent learns user preferences from pairwise comparisons.

New RLHF framework handles general preference oracles without reward functions.

This paper studies robust forward investment and consumption preferences within a zero-volatility context. Different from previous works, we consider an incomplete financial market model due to general investment portfolio constraints. We provide a new PDE characterization and a novel semi-explicit saddle-point constru…

In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Buil…

Study on identifying most preferred policy in bandits with vector-valued rewards.

Stable and consistent model alignment for language models without assuming human preference models.

Dropping a tiny fraction of preferences can significantly alter the rankings of top LLMs.

Paper explores limits and possibilities of aligning LLMs with human preferences.

Paper improves parameter estimation of continuous distributions using preference feedback.

Paper investigates monotonicity issues in AI preference learning.

DOPL learns from preference feedback to solve RMAB problems.

Direct Density Ratio Optimization aligns LLMs with human preferences without assuming specific models.

Bayesian optimization with preference learning identifies preferred solutions in multi-objective problems.

Training models to prefer certain responses can unintentionally shift probability to harmful ones.

This work proves win rate is key to understanding preference learning.

IDT learns human preferences from uncertain decisions, even when humans are suboptimal.

The paper proposes a method to infer multi-objective rewards from preferences.

The paper explores game-theoretic alignment of LLMs with human preferences, finding limitations and conditions.

Fashion preference is a fuzzy concept that depends on customer taste, prevailing norms in fashion product/style, henceforth used interchangeably, and a customer's perception of utility or fashionability, yet fashion e-retail relies on algorithmically generated search and recommendation systems that process structured d…

Bal-PM reduces preference labeling costs for LLMs.

DPA aligns LLMs with multi-objective rewards for diverse user preferences.

Tutorials on preference learning with Gaussian Processes.

Robot motions in the presence of humans should not only be feasible and safe, but also conform to human preferences. This, however, requires user feedback on the robot's behavior. In this work, we propose a novel approach to leverage the user's brain signals as a feedback modality in order to decode the judgment of rob…

Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items. In practical settings, a ranked list of top-$k$ items from the estimated preference order is recommended to the end user in the decreasing …

Improved DPO framework penalizes preference uncertainty to avoid overoptimization.

Response time improves alignment with diverse human preferences.

Derives a new objective to learn from human preferences without approximations.

Investor optimizes portfolio under dynamic risk preferences.

Study on tracking preference shifts in dueling bandits problems.

Paper analyzes finite-time guarantees for preference-based RL.

A new, computationally friendly formula for a class of risk-averse preferences.