arXiv research

Optimizes pension mix of PAYGO, EET, and individual savings.

In this article we solve the problem of maximizing the expected utility of future consumption and terminal wealth to determine the optimal pension or life-cycle fund strategy for a cohort of pension fund investors. The setup is strongly related to a DC pension plan where additionally (individual) consumption is taken i…

Accurate diagnosis is crucial for preventing the progression of Parkinson's, as well as improving the quality of life with individuals with Parkinson's disease. In this paper, we develop a sex-specific and age-dependent classification method to diagnose the Parkinson's disease using the online handwriting recorded from…

The paper optimizes pension policies with guarantees and sustainability constraints.

The paper analyzes optimal retirement timing considering age-dependent mortality risk.

Optimal annuitization strategy depends on age, labor income, and mortality risk.

Unified framework explains retirement and annuitization decisions under age-dependent mortality.

This paper studies pricing derivatives in an age-dependent semi-Markov modulated market. We consider a financial market where the asset price dynamics follow a regime switching geometric Brownian motion model in which the coefficients depend on finitely many age-dependent semi-Markov processes. We further allow the vol…

This project attempts to address the problem of asset pricing in a financial market, where the interest rates and volatilities exhibit regime switching. This is an extension of the Black-Scholes model. Studies of Markov-modulated regime switching models have been well-documented. This project extends that notion to a c…

This article studies a portfolio optimization problem, where the market consisting of several stocks is modeled by a multi-dimensional jump-diffusion process with age-dependent semi-Markov modulated coefficients. We study risk sensitive portfolio optimization on the finite time horizon. We study the problem by using a …

This paper assesses the hedge effectiveness of an index-based longevity swap and a longevity cap. Although swaps are a natural instrument for hedging longevity risk, derivatives with non-linear pay-offs, such as longevity caps, also provide downside protection. A tractable stochastic mortality model with age dependent …

Paper proposes a method to model health outcomes using varying-coefficients and KNN-based LASSO.

Optimizes molecular generation for chemist preferences.

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

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.

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.

Model trains agents to optimize saving and investment strategies for diverse retirement needs.

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.