Zobrazeno 1 - 10
of 18 074
pro vyhledávání: '"Alfano, A"'
We present an empirical study investigating how specific properties of preference datasets, such as mixed-quality or noisy data, affect the performance of Preference Optimization (PO) algorithms. Our experiments, conducted in MuJoCo environments, rev
Externí odkaz:
http://arxiv.org/abs/2411.06568
Even though the Dark Energy Spectroscopic Instrument (DESI) mission does not exclude a dynamical dark energy evolution, the concordance paradigm, i.e., the $\Lambda$CDM model, remains statistically favored, as it depends on the fewest number of free
Externí odkaz:
http://arxiv.org/abs/2411.04878
Autor:
Alfano, Anja, Evans, Nick
In gauge theories, the running of the anomalous dimension of a fermion bilinear operator is believed to lead to chiral symmetry breaking when gamma=1. Naively using perturbative results to judge when gamma=1 leads to the possibility of large dynamica
Externí odkaz:
http://arxiv.org/abs/2409.07977
Dung's Abstract Argumentation Framework (AF) has emerged as a key formalism for argumentation in Artificial Intelligence. It has been extended in several directions, including the possibility to express supports, leading to the development of the Bip
Externí odkaz:
http://arxiv.org/abs/2408.08916
Recent outcomes by the DESI Collaboration have shed light on a possible slightly evolving dark energy, challenging the standard $\Lambda$CDM paradigm. To better understand dark energy nature, high-redshift observations like gamma-ray burst data becom
Externí odkaz:
http://arxiv.org/abs/2408.02536
Explainable Artificial Intelligence and Formal Argumentation have received significant attention in recent years. Argumentation-based systems often lack explainability while supporting decision-making processes. Counterfactual and semifactual explana
Externí odkaz:
http://arxiv.org/abs/2405.04081
The redshift $z_t$ and the jerk parameter $j_t$ of the transition epoch are constrained by using two model-independent approaches involving the direct expansion of the Hubble rate and the expansion of the deceleration parameter around $z=z_t$. To ext
Externí odkaz:
http://arxiv.org/abs/2402.18967
Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived through the selection of a mirror map and enjoy finite-time convergence
Externí odkaz:
http://arxiv.org/abs/2402.05187
Autor:
Alfano, Gianvincenzo, Greco, Sergio, Mandaglio, Domenico, Parisi, Francesco, Shahbazian, Reza, Trubitsyna, Irina
EXplainable AI has received significant attention in recent years. Machine learning models often operate as black boxes, lacking explainability and transparency while supporting decision-making processes. Local post-hoc explainability queries attempt
Externí odkaz:
http://arxiv.org/abs/2401.10938
We propose a model-independent \textit{B\'ezier parametric interpolation} to alleviate the degeneracy between baryonic and dark matter abundances by means of intermediate-redshift data. To do so, we first interpolate the observational Hubble data to
Externí odkaz:
http://arxiv.org/abs/2311.05324