arXiv research

New risk control method for non-monotonic losses in complex parameters.

Paper solves a control problem with robust methods.

The paper investigates quantitative rigidity using Colding's monotonicity formulas for Ricci curvature.

Study non-monotonic loss functions in CRC, achieving valid risk control with large calibration samples.

Extends conformal prediction for controlling expected risk of monotone loss functions.

New method solves nonseparable stochastic control problems.

Stochastic control problems in finance often involve complex controls at discrete times. As a result numerically solving such problems, for example using methods based on partial differential or integro-differential equations, inevitably give rise to low order accuracy, usually at most second order. In many cases one c…

A new method solves complex financial equations efficiently.

A new ML algorithm solves complex economic control problems.

Proposes mCS for multivariate selection with FDR control.

We give a singular control approach to the problem of minimizing an energy functional for measures with given total mass on a compact real interval, when energy is defined in terms of a completely monotone kernel. This problem occurs both in potential theory and when looking for optimal financial order execution strate…

Investigates time-inconsistent portfolio selection under MMV preferences.

New method for optimizing risk in financial models using Fourier transforms.

Developed a monotone numerical method for MV portfolio optimization under jump-diffusion models.

In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied. In order to provide the desired performance levels to the end-users in real-time video transmissions while using the energy resources efficiently, we assume that power c…

Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in failure-prone and adversarial environments, sensors get attacked, data get deleted, and a…

We present techniques, inspired by monodromy considerations, for constructing compact monotone Lagrangians in certain affine hypersurfaces, chiefly of Brieskorn-Pham type. We focus on dimensions 2 and 3, though the constructions generalise to higher ones. The techniques give significant latitude in controlling the homo…

Study causal inference under specific sampling methods with monotonicity assumptions.

Develops methods to select informative conformal prediction sets with FCR control.

We study a multiplicative transient price impact model for an illiquid financial market, where trading causes price impact which is multiplicative in relation to the current price, transient over time with finite rate of resilience, and non-linear in the order size. We construct explicit solutions for the optimal contr…

Proves flows of two-convex Lagrangians are regular, global, and converge.

A new family of momentum coefficients improves the convergence rate of accelerated algorithms.

Controller-Augmented Hidden Markov Models (CHMMs) are a framework for constrained sequential inference.

The control and sensing of large-scale systems results in combinatorial problems not only for sensor and actuator placement but also for scheduling or observability/controllability. Such combinatorial constraints in system design and implementation can be captured using a structure known as matroids. In particular, the…

Proposes non-exchangeable conformal risk control for better uncertainty bounds.

Motivated by recent work of Choquet-Bruhat, Chrusciel, and Martin-Garcia, we prove monotonicity properties and comparison results for the area of slices of the null cone of a point in a Lorentzian manifold. We also prove volume comparison results for subsets of the null cone analogous to the Bishop-Gromov relative volu…

Study tackles criterion collapse in learning criteria, showing conditions for loss minimization.

Generalising the idea of the classical EM algorithm that is widely used for computing maximum likelihood estimates, we propose an EM-Control (EM-C) algorithm for solving multi-period finite time horizon stochastic control problems. The new algorithm sequentially updates the control policies in each time period using Mo…

Derivative estimates for pluriclosed flow control curvature and torsion.

Self-referential meta learning avoids explicit optimization by modifying itself.

We analyze an $N+1$-player game and the corresponding mean field game with state space $\{0,1\}$. The transition rate of $j$-th player is the sum of his control $α^j$ plus a minimum jumping rate $η$. Instead of working under monotonicity conditions, here we consider an anti-monotone running cost. We show that the mean …

By means of a space-time Wasserstein control, we show the monotonicity of the W-entropy functional in time along heat flows on possibly singular metric measure spaces with non-negative Ricci curvature and a finite upper bound of dimension in an appropriate sense. The associated rigidity result on the rate of dissipatio…

Introduces SMMV preferences to avoid inconsistency in portfolio selection.

This paper analyzes and improves monotonic accelerated algorithms like M-NAG and M-FISTA.

Article addresses practical challenges in conformal prediction.

Study optimal control strategy for hedge funds managers with PSAHARA utility family.

A faster EM algorithm for unsupervised Gaussian mixture models.

Study tackles nonlinear factor models with unknown monotone links from incomplete and noisy data.

New adaptive test for NPIV models controls size and has superior power.

Convolutional neural networks (CNNs) are commonly used for image classification tasks, raising the challenge of their application on data flows. During their training, adaptation is often performed by tuning the learning rate. Usual learning rate strategies are time-based i.e. monotonously decreasing. In this paper, we…

Study optimal execution in financial markets with constraints.

This paper controls a boundary term in Huisken's formula for entropy.

The paper proposes a method to align AI models using conformal risk control.

BCD algorithm finds global minima in neural networks.

We show a concise extension of the monotone stability approach to backward stochastic differential equations (BSDEs) that are jointly driven by a Brownian motion and a random measure for jumps, which could be of infinite activity with a non-deterministic and time inhomogeneous compensator. The BSDE generator function c…

Study on convex ordering in stochastic control for swing contracts, proving value function convexity.

We study the utility maximization problem for power utility random fields in a semimartingale financial market, with and without intermediate consumption. The notion of an opportunity process is introduced as a reduced form of the value process of the resulting stochastic control problem. We show how the opportunity pr…

Researchers develop a new framework to control neural network sensitivity.