arXiv research

We explore a new method for discrete-time control problems using randomization and entropy.

Study Transformer layers under cross-entropy training using mean field control.

Optimal control in latent factor models uses Tsallis entropy for exploration.

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

Improved CEM for fast real-time planning in high-dimensional control tasks.

This work optimizes RL algorithms using entropy regularisation for continuous-time LQ problems.

An energy based approach for stabilizing a mechanical system has offered a simple yet powerful control scheme. However, since it does not impose such strong constraints on parameter space of the controller, finding appropriate parameter values for an optimal controller is known to be hard. This paper intends to generat…

A new reinforcement learning method reduces action complexity for robust control.

Ricci flow controls curvature on manifolds with bounds.

Enhances RL by controlling policy stochasticity through trajectory entropy constraints.

Paper proposes a policy-search algorithm to learn entropy-maximizing exploration policies in reward-free environments.

Study uses actor-critic method for continuous-time mean-field control with entropy regularisation.

HCLM framework uses entropy regularization for open learning systems.

Entrocraft addresses RL performance saturation in LLMs by customizing entropy curves.

Transfer entropy shows abnormal brain connectivity in depression.

In this paper, we present a new class of Markov decision processes (MDPs), called Tsallis MDPs, with Tsallis entropy maximization, which generalizes existing maximum entropy reinforcement learning (RL). A Tsallis MDP provides a unified framework for the original RL problem and RL with various types of entropy, includin…

RL solves discrete LQ control with Gaussian optimal policy.

Using the definition of entropy of a family of increasing distances on a compact metric set given in [10] we introduce a notion of Finsler entropy for smooth distributions and Stefan-Sussmann foliations. This concept generalizes most of classical topological entropy on a compact Riemannian manifold : the entropy of a f…

MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio

ECLIPSE detects AI hallucinations in finance with high accuracy.

We adapt tools from information theory to analyze how an observer comes to synchronize with the hidden states of a finitary, stationary stochastic process. We show that synchronization is determined by both the process's internal organization and by an observer's model of it. We analyze these components using the conve…

We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us to differentiate the output of CEM with respect to the objective function's parameters. In the machine learning setting this brings CEM insi…

New method controls classifier guidance in diffusion models.

Improved control approach for correlated bandits with better performance.

Paper introduces RCaI, a risk-sensitive control method using Rényi divergence.

New approach to portfolio optimization shows entropy regularization is ineffective.

Reinforcement learning for continuous-time risk-sensitive asset allocation

Bayesian Markowitz portfolio problem shows entropy regularization is ineffective.

Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural gradient policy update. We show that the natural gradient and trust region optimization are equivalent if we use the natural parameterization of a st…

This paper uses normalizing flows to approximate transport maps between densities.

This paper optimizes trading strategies to minimize risk and maximize profit while accounting for market uncertainty.

We develop a technique based on Malliavin-Bismut calculus ideas, for asymptotic expansion of dual control problems arising in connection with exponential indifference valuation of claims, and with minimisation of relative entropy, in incomplete markets. The problems involve optimisation of a functional of Brownian path…

A new method for stochastic optimal control improves accuracy over existing techniques.

Characterizes metrics with finite total Q-curvature and introduces new volume entropy.

Study optimal investment strategies with entropy regularization in volatile markets.

A new active learning method for Gaussian process models.

Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that entropy minimization only may result into collapsed trivial solutions. In this paper, we propose to avoid trivial solutions by further introducing diversity maximization. In order to achieve the possib…

Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data. An alternative is to use sample-efficient episodic control methods: neuro-inspired algorithms which use non-/semi-parametric models that predict valu…

Hybridizes CEM and gradient descent for efficient model-predictive control.

We introduce a pathwise approach to analyze the relative performance of an equity portfolio with respect to a benchmark market portfolio. In this energy-entropy framework, the relative performance is decomposed into three components: a volatility term, a relative entropy term measuring the distance between the portfoli…

Fine-tunes diffusion models to generate diverse samples with high genuine rewards.

Study positive entropy actions by higher-rank lattices, proving rigidity and conjugacy results.

Paper characterizes embeddability of function spaces into $L_p$-type RKBS via metric entropy.

ED-VAE improves VAEs by explicitly including entropy components in ELBO.

New measures for causal entropy and information gain studied.

AR-DAE approximates entropy gradient for machine learning models.

Entropy regularization is used to get improved optimization performance in reinforcement learning tasks. A common form of regularization is to maximize policy entropy to avoid premature convergence and lead to more stochastic policies for exploration through action space. However, this does not ensure exploration in th…

Accounting for the non-normality of asset returns remains challenging in robust portfolio optimization. In this article, we tackle this problem by assessing the risk of the portfolio through the "amount of randomness" conveyed by its returns. We achieve this by using an objective function that relies on the exponential…