arXiv research

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

Designs for allocating resources to prioritize needy applicants while estimating treatment effects.

Study explores strategies for randomized allocation in delayed rewards bandits.

The paper tackles resource allocation for arms with unknown and random rewards, achieving optimal regret bounds.

The paper proposes a new portfolio allocation method combining RMT and machine learning.

This paper explains CART random forests using stochastic control theory.

Machine learning improves portfolio allocation between index and risk-free assets.

New estimator improves policy evaluation in resource allocation RCTs.

New risk-sharing rules induced by capital allocation principles.

Framework optimizes targeting high-need individuals while estimating treatment effects.

Some online advertising offers pay only when an ad elicits a response. Randomness and uncertainty about response rates make showing those ads a risky investment for online publishers. Like financial investors, publishers can use portfolio allocation over multiple advertising offers to pursue revenue while controlling r…

We consider the problem of how to assign treatment in a randomized experiment, in which the correlation among the outcomes is informed by a network available pre-intervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation structure among the outcomes. …

Study resource allocation strategies in sequential decisions with unknown rewards.

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…

Method controls treatment risk in learning beneficial allocations.

Paper improves asset allocation using machine learning for regime detection.

The paper shows vector-valued risk measures ignore dependence structures.

The paper identifies a mesoscopic market structure and uses it to improve portfolio optimization.

ATA optimizes task allocation in distributed machine learning.

Optimizes asset allocation with illiquid assets using MPC.

Optimizes resource allocation in a network with random job requests.

In this paper, we study censored Semi-Bandits, a novel variant of the semi-bandits problem. The learner is assumed to have a fixed amount of resources, which it allocates to the arms at each time step. The loss observed from an arm is random and depends on the amount of resources allocated to it. More specifically, the…

Algorithm allocates perishable resources online to minimize envy and inefficiency.

RL agents outperform baselines in asset allocation.

Optimal algorithm for identifying best-arm with minimal regret.

We present a consensus Monte Carlo algorithm that scales existing Bayesian nonparametric models for clustering and feature allocation to big data. The algorithm is valid for any prior on random subsets such as partitions and latent feature allocation, under essentially any sampling model. Motivated by three case studie…

Designs efficient algorithms to maximize the expectation of Gaussian random variables.

We study a multi-armed bandit problem with covariates in a setting where there is a possible delay in observing the rewards. Under some mild assumptions on the probability distributions for the delays and using an appropriate randomization to select the arms, the proposed strategy is shown to be strongly consistent.

New framework for fair online allocation in continuous time with deadlines.

The paper introduces new portfolio rules beyond mean-variance, addressing asymmetry and uncertainty.

Paper shows faster core identification in matching markets.

RL optimizes resource allocation in MG by balancing experience and exploration.

Develops adaptive framework for estimating survival effects with censoring.

Study allocates resources to strategic agents while balancing cost and incentives.

In our previous paper, "A Unified Approach to Systemic Risk Measures via Acceptance Set" (\textit{Mathematical Finance, 2018}), we have introduced a general class of systemic risk measures that allow for random allocations to individual banks before aggregation of their risks. In the present paper, we prove the dual re…

Graph neural networks improve systemic risk measures for financial networks.

New algorithm optimizes online network resource allocation with long-term constraints.

Developing efficient and scalable algorithms for Latent Dirichlet Allocation (LDA) is of wide interest for many applications. Previous work has developed an O(1) Metropolis-Hastings sampling method for each token. However, the performance is far from being optimal due to random accesses to the parameter matrices and fr…

We characterize the class of exchangeable feature allocations assigning probability $V_{n,k}\prod_{l=1}^{k}W_{m_{l}}U_{n-m_{l}}$ to a feature allocation of $n$ individuals, displaying $k$ features with counts $(m_{1},\ldots,m_{k})$ for these features. Each element of this class is parametrized by a countable matrix $V$…

Register allocation, which is a crucial phase of a good optimizing compiler, relies on graph coloring. Hence, an efficient graph coloring algorithm is of paramount importance. In this work we try to learn a good heuristic for coloring interference graphs that are used in the register allocation phase. We aim to handle …

Optimizes trade execution with reinforcement learning for limit orders.

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station …

RL learns to ignore factors in factor investing portfolios.

We present a simulation-and-regression method for solving dynamic portfolio allocation problems in the presence of general transaction costs, liquidity costs and market impacts. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and t…

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

New method for privacy amplification without sampling for matrix factorization.

Algorithm allocates budgets to tasks with semi-bandit feedback, achieving near-optimal regret bounds.

This work demonstrates the potential of deep reinforcement learning techniques for transmit power control in wireless networks. Existing techniques typically find near-optimal power allocations by solving a challenging optimization problem. Most of these algorithms are not scalable to large networks in real-world scena…