arXiv research

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

Global optimization in Bayesian inference yields little additional benefit.

Efficiently recovers network community structure from clients' small subgraphs.

We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specific goal. Our approach, Myopic Posterior Sampling (MPS), is inspired by the classical posterior (Thompson) sampling algorithm for multi-armed…

This study improves lookahead Bayesian optimization using rollout approximation.

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

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

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

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

We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of futu…

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

Efficiently optimizes constrained problems with two-step lookahead BO.

New algorithm finds optimal decision trees for binary variables.

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

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…

New algorithms optimize time series classification speed and accuracy.

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…

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

Algorithm learns optimal coordination for strategic agents in uncertain settings.

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 …

New algorithm learns optimal policies in strategic MDPs with private types.

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.

Many recommendation algorithms rely on user data to generate recommendations. However, these recommendations also affect the data obtained from future users. This work aims to understand the effects of this dynamic interaction. We propose a simple model where users with heterogeneous preferences arrive over time. Based…

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

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

We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high dimensional space. Unlike …

We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified. This is in some sense the second step after causal discovery. Taking a probabilistic approach to estimating these functions, we derive a natural myopic act…

New algorithms minimize MMD to approximate probability measures efficiently.

We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a set of potential maximizers (BO) or unclassified points (LSE), which…

We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF…

BINOCULARS improves experimental design by balancing exploration and exploitation.

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

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…

This paper combines LLMs with RL for better trading strategies.

Bayesian methods improve drug discovery experiment design.

Study on predictable forward processes in trading without frequent evaluations.

No-regret learning with strategic experts, incentivized.

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…

The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling. We …

PFNs4BO uses neural processes for flexible Bayesian Optimization.

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approach…

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.

Active learning (AL) aims to enable training high performance classifiers with low annotation cost by predicting which subset of unlabelled instances would be most beneficial to label. The importance of AL has motivated extensive research, proposing a wide variety of manually designed AL algorithms with diverse theoret…

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