A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New estimator improves ATT estimation efficiency with external controls.
A method for logistic regression inference using both internal and external data.
Framework for estimating treatment effects using external control data.
Study uses multi-agent reinforcement learning to control self-assembly with high-resolution external control.
Estimates non-parametric logistic model using case-control data and external summary info.
This paper offers a framework for FX dealers to decide between internalizing and externalizing their market making to balance risk control and costs.
Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems. These feedback control methods typically compute the next control input by solving an online Quadratic Program (QP). Solving QP in real-time can be a computationally expensive process …
A new reinforcement learning method uses mutual information to encourage agents to control their environment.
Study optimal paths in Zermelo's navigation problem using geometric equations.
MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio
A predictor improves power grid frequency forecasts up to one hour.
We propose a decentralized Maximum Likelihood solution for estimating the stochastic renewable power generation and demand in single bus Direct Current (DC) MicroGrids (MGs), with high penetration of droop controlled power electronic converters. The solution relies on the fact that the primary control parameters are se…
RB-Modulation trains free diffusion models without external adapters.
Perceptual capabilities of artificial systems have come a long way since the advent of deep learning. These methods have proven to be effective, however they are not as efficient as their biological counterparts. Visual attention is a set of mechanisms that are employed in biological visual systems to ease computationa…
Causal ML methods failed to validate their personalized treatment effects in two large trials.
A close relationship between the classical Hamilton-Jacobi theory and the kinematic reduction of control systems by decoupling vector fields is shown in this paper. The geometric interpretation of this relationship relies on new mathematical techniques for mechanics defined on a skew-symmetric algebroid. This geometric…
NCT simplifies one-step generator adaptation to new controls.
Optimizes control interventions in real-world networks using deep-learning and network science.
Dissipative SymODEN learns dynamics with dissipation and control from data.
A-TMLE estimates ATE from RCT and RWD, achieving super-efficiency.
We show how to train a quantum network of pairwise interacting qubits such that its evolution implements a target quantum algorithm into a given network subset. Our strategy is inspired by supervised learning and is designed to help the physical construction of a quantum computer which operates with minimal external cl…
In this paper we solve the following problem in the affirmative: Let be a continuum in the plane $\complex$ and suppose that $h:Z\times [0,1]\to\complex$ is an isotopy starting at the identity. Can be extended to an isotopy of the plane? We will provide a new characterization of an accessible point in a planar …
In this paper, we first study the Poisson reductions of controlled Hamiltonian (CH) system and symmetric CH system by controllability distributions. These reductions are the extension of Poisson reductions by distribution for Poisson manifolds to that for phase spaces of CH systems with external force and control. We g…
This study designs a financial risk control platform using big data and machine learning.
Optimal reinsurance strategy analyzed for dynamic risk model with self- and externally-excited jumps.
Paper uses deep reinforcement learning for better control of rocket engines during start-up phases.
This paper examines the dividend and investment policies of a cash constrained firm that has access to costly external funding. We depart from the literature by allowing the firm to issue collateralized debt to increase its investment in productive assets resulting in a performance sensitive interest rate on debt. We f…
TACE unifies scalar and tensorial modeling in Cartesian space for accurate, stable, and efficient atomistic predictions.
Simple online monitor detects unsafe LLM outputs.
We study the dynamics of the batch minority game, with random external information, using generating functional techniques a la De Dominicis. The relevant control parameter in this model is the ratio of the number of possible values for the external information over the number of trading agents. In the …
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such an adaptation property relies heavily on cellular neuromodulation,…
CARL controls a quadruped to move naturally in complex environments.
Causal effect estimation relies on separating the variation in the outcome into parts due to the treatment and due to the confounders. To achieve this separation, practitioners often use external sources of randomness that only influence the treatment called instrumental variables (IVs). We study variables constructed …
Perfect tracking control for real-world Euler-Lagrange systems is challenging due to uncertainties in the system model and external disturbances. The magnitude of the tracking error can be reduced either by increasing the feedback gains or improving the model of the system. The latter is clearly preferable as it allows…
A new method learns from multi-modal sequences with external memory.
KG-WDRO optimizes transfer learning with external knowledge.
A reliable controller is critical and essential for the execution of safe and smooth maneuvers of an autonomous vehicle.The controller must be robust to external disturbances, such as road surface, weather, and wind conditions, and so on.It also needs to deal with the internal parametric variations of vehicle sub-syste…
Paper proposes methods to reduce financial contagion by targeted cash injections.
Ultra-cold atomic gases are unique in terms of the degree of controllability, both for internal and external degrees of freedom. This makes it possible to use them for the study of complex quantum many-body phenomena. However in many scenarios, the prerequisite condition of faithfully preparing a desired quantum state …
Neural networks powered with external memory simulate computer behaviors. These models, which use the memory to store data for a neural controller, can learn algorithms and other complex tasks. In this paper, we introduce a new memory to store weights for the controller, analogous to the stored-program memory in modern…
PFPN uses particle filtering to improve character control in physics-based simulations.
New test ensures quality of shared data in machine learning.
Paper presents neural network controllers for offset-free setpoint tracking.
Bayesian inference reconstructs external potentials in DFT for many-particle systems.
Typical neural networks with external memory do not effectively separate capacity for episodic and working memory as is required for reasoning in humans. Applying knowledge gained from psychological studies, we designed a new model called Differentiable Working Memory (DWM) in order to specifically emulate human workin…
Paper analyzes tech adoption in financial networks, finding key leadership and diffusion dynamics.
We extend existing models in the financial literature by introducing a cluster-derived canonical vine (CDCV) copula model for capturing high dimensional dependence between financial time series. This model utilises a simplified market-sector vine copula framework similar to those introduced by Heinen and Valdesogo (200…
The article provides a modest survey of the absolute theory of general systems of (partial) differential equations. The equations are relieved of all additional structures and subject to quite arbitrary change of the variables. An abstract mathematical theory in the Bourbaki sense with its own concepts and technical to…