This paper studies a recent proposal to use randomized value functions to drive exploration in reinforcement learning.

Then, we have that 1. y Fact 2. Let z:= [ x; y ] and z This can be easily proven using the AM-GM inequality. Fact 3. Let z:= [ x; y ] R It is a crucial building block for the algorithms in this work. The following classical theorem holds for AGD. We will start by giving a precise statement of Algorithm 1.Algorithm 1 Alternating Best Response (ABR)Require: g (,), Initial point z The basic idea is the following. The following two lemmas about the inexact APP A algorithm follow from the proof of Theorem 4.1 [ Here we provide their proofs for completeness.

This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting. To this extent, we present a novel approach reconciling classical state space models with deep learning methods.

This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting. To this extent, we present a novel approach reconciling classical state space models with deep learning methods.

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President Donald Trump holds charts as he speaks about the economy in the Oval Office, August 2025. President Donald Trump holds charts as he speaks about the economy in the Oval Office, August 2025. If the first term of Donald Trump provoked anxiety over the fate of objective knowledge, the second has led to claims we live in a world-historical age of stupid, accelerated by big tech. But might there be a way out? T he first and second Trump administrations have provoked markedly different critical reactions. The shock of 2016 and its aftermath saw a wave of liberal anxiety about the fate of objective knowledge, not only in the US but also in Britain, where the Brexit referendum that year had been won by a campaign that misrepresented key facts and figures.

We present a method for learning Bayesian networks from data sets containing thousands of variables without the need for structure constraints. Our approach is made of two parts. The first is a novel algorithm that effectively explores the space of possible parent sets of a node. It guides the exploration towards the most promising parent sets on the basis of an approximated score function that is computed in constant time. The second part is an improvement of an existing ordering-based algorithm for structure optimization. The new algorithm provably achieves a higher score compared to its original formulation. Our novel approach consistently outperforms the state of the art on very large data sets.

Finally, we prove that the algorithm's