arXiv research

Empirical study shows carriers ignore past shippers' behavior, focusing only on current actions.

Investment strategy to minimize drawdown time in a financial market.

Proposes a model to incentivize exploration in web platforms with payments.

Global optimization in Bayesian inference yields little additional benefit.

Myopic procedures are shown to be asymptotically optimal in ranking and selection problems.

Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision problem. These tech…

Paper confirms Feldman's conjecture on two-armed bandit problem.

Myopic investors make suboptimal choices that benefit others, leading to market inefficiencies.

Myopic optimization outperforms reinforcement learning in portfolio management, leading to lower returns and higher risks.

Efficiently optimizes constrained problems with two-step lookahead BO.

Efficiently reduces computational burden of rollout acquisition functions in Bayesian optimization.

This paper optimizes sampling policies for Bayesian optimization to improve exploration and exploitation.

New RL algorithms find SNE in Markov games with myopic followers.

Efficiently recovers network community structure from clients' small subgraphs.

This study improves lookahead Bayesian optimization using rollout approximation.

We maximize the expected utility from terminal wealth for an HARA investor when the market price of risk is an unobservable random variable. We compute the optimal portfolio explicitly and explore the effects of learning by comparing it with the corresponding myopic policy. In particular, we show that, for a market pri…

This paper shows how diverse tasks can make inefficient exploration in MTRL efficient.

Algorithm learns optimal coordination for strategic agents in uncertain settings.

New strategy for DOE tasks using posterior sampling and probabilistic programming.

This paper derives a portfolio decomposition formula when the agent maximizes utility of her wealth at some finite planning horizon. The financial market is complete and consists of multiple risky assets (stocks) plus a risk free asset. The stocks are modelled as exponential Brownian motions with drift and volatility b…

Proposes ALRL for single shot active learning with pseudo annotators.

New $k$-step policy gradient method avoids local optima in restricted policy classes.

Portfolio turnpikes state that, as the investment horizon increases, optimal portfolios for generic utilities converge to those of isoelastic utilities. This paper proves three kinds of turnpikes. In a general semimartingale setting, the abstract turnpike states that optimal final payoffs and portfolios converge under …

Extends return extrapolation to nonlinear, asymmetric functions under stochastic volatility.

Study shows naive recommendation models are flawed due to user feedback.

We extend return extrapolation to incorporate asymmetry and saturation, finding that asymmetric nonlinear extrapolation leads to lower welfare loss.

The paper calculates how fast optimal investment strategies approach CRRA strategies in stochastic factor models.

NM-PPG optimizes adaptive feature acquisition in POMDPs for better predictions.

Develops a mathematical model for CLMM dynamics in DeFi.

New method optimizes costly functions with unknown costs and budget constraints.

GLASSES optimizes global optimization by considering dozens of future function evaluations.

This work tackles slate-based recommender systems using RL, optimizing long-term user engagement.

A model predicts financial agent behavior from rational to myopic decisions.

Study shows bifurcating price dynamics in ASME with traders.

New algorithm finds optimal decision trees for binary variables.

Study optimal treatment assignment policies under strategic agent responses.

BINOCULARS improves experimental design by balancing exploration and exploitation.

We consider two active binary-classification problems with atypical objectives. In the first, active search, our goal is to actively uncover as many members of a given class as possible. In the second, active surveying, our goal is to actively query points to ultimately predict the proportion of a given class. Numerous…

Study on predictable forward processes in trading without frequent evaluations.

We provide a new characterization of mean-variance hedging strategies in a general semimartingale market. The key point is the introduction of a new probability measure $P^{\star}$ which turns the dynamic asset allocation problem into a myopic one. The minimal martingale measure relative to $P^{\star}$ coincides with t…

PFNs4BO uses neural processes for flexible Bayesian Optimization.

Robo-advisor improves investment advice through client interaction.

This paper studies the properties of discrete time stochastic optimal control problems associated with portfolio selection. We investigate if optimal continuous time strategies can be used effectively for a discrete time market after a straightforward discretization. We found that Merton's strategy approximates the per…

This paper finds an MFE in large stochastic games using reinforcement learning.

A new Bayesian method optimizes time-dependent expensive functions with lookahead.

New concept of Blackwell regret for reinforcement learning with sparse rewards.

New algorithms optimize time series classification speed and accuracy.

Study optimal portfolio strategy with sporadic bankruptcy for isoelastic utility.