Zobrazeno 1 - 10
of 8 336
pro vyhledávání: '"A, Dörfler"'
Maximum Likelihood Estimation of continuous variable models can be very challenging in high dimensions, due to potentially complex probability distributions. The existence of multiple interdependencies among variables can make it very difficult to es
Externí odkaz:
http://arxiv.org/abs/2409.03495
We consider a recommender system that takes into account the interaction between recommendations and the evolution of user interests. Users opinions are influenced by both social interactions and recommended content. We leverage online feedback optim
Externí odkaz:
http://arxiv.org/abs/2408.16899
Learning the unknown causal parameters of a linear structural causal model is a fundamental task in causal analysis. The task, known as the problem of identification, asks to estimate the parameters of the model from a combination of assumptions on t
Externí odkaz:
http://arxiv.org/abs/2407.12528
Autor:
Doerfler, Periwinkle, Turk, Kieron Ivy, Geeng, Chris, McCoy, Damon, Ackerman, Jeffrey, Dragiewicz, Molly
In this work, we analyze two large-scale surveys to examine how individuals think about sharing smartphone access with romantic partners as a function of trust in relationships. We find that the majority of couples have access to each others' devices
Externí odkaz:
http://arxiv.org/abs/2407.04906
Diffusion regulates a phenomenal number of natural processes and the dynamics of many successful generative models. Existing models to learn the diffusion terms from observational data rely on complex bilevel optimization problems and properly model
Externí odkaz:
http://arxiv.org/abs/2406.12616
We study optimization problems whereby the optimization variable is a probability measure. Since the probability space is not a vector space, many classical and powerful methods for optimization (e.g., gradients) are of little help. Thus, one typical
Externí odkaz:
http://arxiv.org/abs/2406.10676
We study the problem of nonepisodic reinforcement learning (RL) for nonlinear dynamical systems, where the system dynamics are unknown and the RL agent has to learn from a single trajectory, i.e., without resets. We propose Nonepisodic Optimistic RL
Externí odkaz:
http://arxiv.org/abs/2406.01175
Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision processes (MDP). However, various systems are inherently continuous in time, making discrete-time MDPs an inexact modeling choice. In many applications, such
Externí odkaz:
http://arxiv.org/abs/2406.01163
Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the opti
Externí odkaz:
http://arxiv.org/abs/2405.18070
In this paper, we implement an advanced safety filter to smoothly limit the current of an inverter-based Battery Energy Storage System. The task involves finding suitable Control Barrier Function and Control Lyapunov Function via Sum-of-Squares optim
Externí odkaz:
http://arxiv.org/abs/2405.14427