Zobrazeno 1 - 10
of 9 753
pro vyhledávání: '"A, Siska"'
Autor:
Cao, Jialun, Šiška, David
We address the liquidation problem arising from the credit risk management in decentralised finance (DeFi) by formulating it as an ergodic optimal control problem. In decentralised derivatives exchanges, liquidation is triggered whenever the parties
Externí odkaz:
http://arxiv.org/abs/2411.19637
We investigate proximal descent methods, inspired by the minimizing movement scheme introduced by Jordan, Kinderlehrer and Otto, for optimizing entropy-regularized functionals on the Wasserstein space. We establish linear convergence under flat conve
Externí odkaz:
http://arxiv.org/abs/2411.15067
This paper explores the integration of advanced cryptographic techniques for secure computation in data spaces to enable secure and trusted data sharing, which is essential for the evolving data economy. In addition, the paper examines the role of da
Externí odkaz:
http://arxiv.org/abs/2410.16442
Autor:
Munoz, Gary D. Lopez, Minnich, Amanda J., Lutz, Roman, Lundeen, Richard, Dheekonda, Raja Sekhar Rao, Chikanov, Nina, Jagdagdorj, Bolor-Erdene, Pouliot, Martin, Chawla, Shiven, Maxwell, Whitney, Bullwinkel, Blake, Pratt, Katherine, de Gruyter, Joris, Siska, Charlotte, Bryan, Pete, Westerhoff, Tori, Kawaguchi, Chang, Seifert, Christian, Kumar, Ram Shankar Siva, Zunger, Yonatan
Generative Artificial Intelligence (GenAI) is becoming ubiquitous in our daily lives. The increase in computational power and data availability has led to a proliferation of both single- and multi-modal models. As the GenAI ecosystem matures, the nee
Externí odkaz:
http://arxiv.org/abs/2410.02828
Anomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. One of the primary approaches to anomaly detection are methods based on forecasting. Nevertheless, extensive real-
Externí odkaz:
http://arxiv.org/abs/2409.18874
We analyse the regret arising from learning the price sensitivity parameter $\kappa$ of liquidity takers in the ergodic version of the Avellaneda-Stoikov market making model. We show that a learning algorithm based on a regularised maximum-likelihood
Externí odkaz:
http://arxiv.org/abs/2409.02025
Entropy regularization has been extensively used in policy optimization algorithms to regularize the optimization landscape and accelerate convergence; however, it comes at the cost of introducing an additional regularization bias. This work quantifi
Externí odkaz:
http://arxiv.org/abs/2405.20250
Benchmarks have emerged as the central approach for evaluating Large Language Models (LLMs). The research community often relies on a model's average performance across the test prompts of a benchmark to evaluate the model's performance. This is cons
Externí odkaz:
http://arxiv.org/abs/2404.16966
This paper studies the convergence of the mirror descent algorithm for finite horizon stochastic control problems with measure-valued control processes. The control objective involves a convex regularisation function, denoted as $h$, with regularisat
Externí odkaz:
http://arxiv.org/abs/2401.01198
We study the global convergence of a Fisher-Rao policy gradient flow for infinite-horizon entropy-regularised Markov decision processes with Polish state and action space. The flow is a continuous-time analogue of a policy mirror descent method. We e
Externí odkaz:
http://arxiv.org/abs/2310.02951