arXiv research

This work introduces COLA, a strategy to aggregate conformal prediction sets efficiently.

The paper evaluates index-based allocation policies using data from randomized control trials.

Framework for online resource allocation using social welfare functions.

We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers. Such an optimized worker assignment method allows us to boost the efficacy of any popular crowdsourcing estimation algorithm. We consider a mutual info…

Optimizes identifying top-k items from comparisons with minimal comparisons.

Optimal query allocation improves extractive QA efficiency with LLMs.

The paper improves PCS approximation for ranking and selection under limited simulation budgets.

Optimal online learning for joint pricing and resource allocation.

Adaptive AI delegation framework for dynamic decision authority allocation.

We study an extension of the classic stochastic multi-armed bandit problem which involves multiple plays and Markovian rewards in the rested bandits setting. In order to tackle this problem we consider an adaptive allocation rule which at each stage combines the information from the sample means of all the arms, with t…

If the probability of default parameters (PDs) fed as input into a credit portfolio model are estimated as through-the-cycle (TTC) PDs stressed market conditions have little impact on the results of the capital calculations conducted with the model. At first glance, this is totally different if the PDs are estimated as…

A new online learning problem, CAB, tackles matching platforms to maximize user satisfaction.

Berry et al. (1997) initiated the development of the infinite arms bandit problem. They derived a regret lower bound of all allocation strategies for Bernoulli rewards with uniform priors, and proposed strategies based on success runs. Bonald and Proutière (2013) proposed a two-target algorithm that achieves the regret…

We introduce a statistical model for operational losses based on heavy-tailed distributions and bipartite graphs, which captures the event type and business line structure of operational risk data. The model explicitly takes into account the Pareto tails of losses and the heterogeneous dependence structures between the…

New algorithm tackles resource allocation in multi-armed bandits to balance speed and throughput.

Study wSAA for contextual decisions, improving uncertainty quantification under computational constraints.

Paper proposes a new UCB approach for estimating maximum mean.

This study uses OPE methods to quickly assess auction policies.

StratPPI improves prediction-powered inference with stratified sampling.

Maximising the detection of intrusions is a fundamental and often critical aim of perimeter surveillance. Commonly, this requires a decision-maker to optimally allocate multiple searchers to segments of the perimeter. We consider a scenario where the decision-maker may sequentially update the searchers' allocation, lea…

New algorithm optimizes resource allocation in non-stationary networks.

Randomized Controlled Trials (RCTs) are the gold standard for comparing the effectiveness of a new treatment to the current one (the control). Most RCTs allocate the patients to the treatment group and the control group by uniform randomization. We show that this procedure can be highly sub-optimal (in terms of learnin…

Geometric approach combines asset returns and investor views for better portfolio optimization.

The paper studies how to allocate human validation in AI-assisted tasks to minimize errors.

In this paper we propose a new methodology for solving an uncertain stochastic Markovian control problem in discrete time. We call the proposed methodology the adaptive robust control. We demonstrate that the uncertain control problem under consideration can be solved in terms of associated adaptive robust Bellman equa…

Corrects bias in LLM-as-a-judge evaluations using adaptive calibration.

OPAL optimizes labeling strategy for precise inference from uncertain models.

We derive a closed form portfolio optimization rule for an investor who is diffident about mean return and volatility estimates, and has a CRRA utility. The novelty is that confidence is here represented using ellipsoidal uncertainty sets for the drift, given a volatility realization. This specification affords a simpl…

OpenAlpha validates decentralized capital strategies using game theory and market aggregation.

New study shows non-adaptive trials can be outperformed by adaptive designs in treatment selection.

New method improves treatment effect estimation in adaptive experiments with noncompliance.

EoS selectively shapes learning, affecting some groups more than others.

We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learning paradigm popular within Cognitive Network related topics (e.g., Spectrum Sensing and Allocation). We focus on the case where the rewards are exponentially distributed, which is common when dealing with Rayleigh fading c…

Develops a framework for distributional Granger causality

UCB exploration improves best arm identification in fixed-budget settings.

The paper studies calibration in ML models for wireless networks, showing key theoretical and practical insights.

An automatic machine learning (AutoML) task is to select the best algorithm and its hyper-parameters simultaneously. Previously, the hyper-parameters of all algorithms are joint as a single search space, which is not only huge but also redundant, because many dimensions of hyper-parameters are irrelevant with the selec…

We introduce the community exploration problem that has many real-world applications such as online advertising. In the problem, an explorer allocates limited budget to explore communities so as to maximize the number of members he could meet. We provide a systematic study of the community exploration problem, from off…

Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection…

The paper optimizes DIA purchase policies using lifecycle models and asset allocation.

Study optimizes resource allocation in noisy systems for better control.

The paper proposes an asset allocation strategy using the Sortino ratio for better performance.

We investigate statistical uncertainty quantification for reinforcement learning (RL) and its implications in exploration policy. Despite ever-growing literature on RL applications, fundamental questions about inference and error quantification, such as large-sample behaviors, appear to remain quite open. In this paper…

Non-parametric bootstrap improves robust portfolio and trading strategy optimization.

This paper tackles post-trade allocation inefficiencies and presents a uniform return allocation method.

This paper examines allocation mechanisms in markets with transfer costs, showing how these costs affect economic efficiency.

Paper introduces a new method for allocating capital based on risk measures from ruin theory.

The aims of this study are twofold. First, we consider an optimal risk allocation problem with non-convex preferences. By establishing an infimal representation for distortion risk measures, we give some necessary and sufficient conditions for the existence of optimal and asymptotic optimal allocations. We will show th…