arXiv research

Optimal contracts are found for agents with quadratic effort costs.

Schrödinger bridge solved with Weyl calculus for quadratic state cost.

Optimal linear contracts are possible even with memory in Gaussian settings.

Sparsity-constrained optimization has wide applicability in machine learning, statistics, and signal processing problems such as feature selection and compressive Sensing. A vast body of work has studied the sparsity-constrained optimization from theoretical, algorithmic, and application aspects in the context of spars…

Optimal dynamic allocation of carbon allowances reduces emissions efficiently.

The paper proposes a method to measure fairness through equality of effort using algorithmic recourse.

We solve a Schrödinger bridge with a quadratic state cost, finding a closed-form solution.

The paper solves a utility-based hedging problem with quadratic costs.

Study how transaction costs impact stock returns and holdings in equilibrium.

We reconsider the problem of optimal trading in the presence of linear and quadratic costs, for arbitrary linear costs but in the limit where quadratic costs are small. Using matched asymptotic expansion techniques, we find that the trading speed vanishes inside a band that is narrower than in the absence of quadratic …

We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs. Our algorithm, COAL, makes predictions by regressing to each label's cost and predicting the smallest. On a new example, it uses a set of regressors that perform well on past data t…

A new method speeds up computation of Sinkhorn divergences to linear time.

In this short note, we consider mean-variance optimized portfolios with transaction costs. We show that introducing quadratic transaction costs makes the optimization problem more difficult than using linear transaction costs. The reason lies in the specification of the budget constraint, which is no longer linear. We …

The paper explores how ReLU DNNs can represent MPC policies and vice versa.

A new method steers Gaussian distributions with minimal effort.

Proposes a new framework for invariant quadratic P&L predictions in option books.

Federated Averaging (FedAvg) has emerged as the algorithm of choice for federated learning due to its simplicity and low communication cost. However, in spite of recent research efforts, its performance is not fully understood. We obtain tight convergence rates for FedAvg and prove that it suffers from `client-drift' w…

The paper extends a variance gamma model to quadratic functions, reducing arbitrage and computational costs.

OMGD algorithm optimizes online convex optimization with switching costs and delayed gradients.

NOT learns optimal transport plans, kernel costs improve performance.

Study examines how market dynamics affect emissions trading prices and abatement efforts.

Study cost-driven state representation learning for control from partial observations.

We consider a class of learning problems that involve a structured sparsity-inducing norm defined as the sum of $\ell_\infty$-norms over groups of variables. Whereas a lot of effort has been put in developing fast optimization methods when the groups are disjoint or embedded in a specific hierarchical structure, we add…

Model liquidity premia using a risk-sharing economy with quadratic costs.

Model shows how discount rates affect intergenerational equity in climate mitigation.

Paper optimizes broker performance by estimating execution costs.

RL and DTSOC for final quadratic hedging performance studied.

Study analyzes market equilibrium returns with price impact and transaction costs.

We present an expansion for portfolio optimization in the presence of small, instantaneous, quadratic transaction costs. Specifically, the magnitude of transaction costs has a coefficient that is of the order $ε$ small, which leads to the optimization problem having an asymptotically-singular Hamilton-Jacobi-Bellman eq…

We make several improvements to the mean-variance framework for optimal pre-trade algorithmic execution, by working with volume measures and generic price dynamics. Volume measures are the continuum analogies for discrete volume profiles commonly implemented in the execution industry. Execution then becomes an absolute…

Pushing a little forward an approach proposed by Villani, we are going to prove that in the Riemannian setting the condition $\nabla^2 f< g$ implies that $f$ is $c$-concave with respect to the quadratic cost as soon as it has a sufficiently small $C^1$-norm. From this, we deduce a sufficient condition for the optimalit…

Non-bilinear observations make optimal control harder, showing non-convex costs and non-affine optimal controllers.

AI task delegation faces incentive collapse with unbounded payments as AI accuracy rises.

We study super--replication of European contingent claims in an illiquid market with insider information. Illiquidity is captured by quadratic transaction costs and insider information is modeled by an investor who can peek into the future. Our main result describes the scaling limit of the super--replication prices wh…

We study risk-sharing equilibria with general convex costs on the agents' trading rates. For an infinite-horizon model with linear state dynamics and exogenous volatilities, we prove that the equilibrium returns mean-revert around their frictionless counterparts - the deviation has Ornstein-Uhlenbeck dynamics for quadr…

This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function. We present a method, based on convex optimization, that accomplishes this task robustly: i.e., we minimize the worst-case cost, accounting for system uncertainty given the observed …

A new scheme reduces global search cost by a square root factor.

Accurate annotation of medical image is the crucial step for image AI clinical application. However, annotating medical image will incur a great deal of annotation effort and expense due to its high complexity and needing experienced doctors. To alleviate annotation cost, some active learning methods are proposed. But …

Study on regularity of optimal transport maps on convex domains with quadratic cost.

Study learns state representations from observations for control, proving guarantees.

Poyiadjis et al. (2011) show how particle methods can be used to estimate both the score and the observed information matrix for state space models. These methods either suffer from a computational cost that is quadratic in the number of particles, or produce estimates whose variance increases quadratically with the am…

Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received …

The study sets limits on how well systems can be controlled adaptively.

New model-free algorithm achieves similar LQR regret guarantees.

A new method matches measures across different spaces using cost-regularized optimal transport.

In this article we consider the Merton problem in a market with a single risky asset and transaction costs. We give a complete solution of the problem up to the solution of a free-boundary problem for a first-order differential equation, and find that the form of the solution (whether the problem is well-posed, whether…

We consider a class of learning problems regularized by a structured sparsity-inducing norm defined as the sum of l_2- or l_infinity-norms over groups of variables. Whereas much effort has been put in developing fast optimization techniques when the groups are disjoint or embedded in a hierarchy, we address here the ca…

Solves steering problem with continuous time, Hilbert-Schmidt cost, and matrix ODEs.