First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper introduces a GP-Vol model to flexibly capture the time-dependent changes in variance, and develops a new online algorithm for fully Bayesian inference under the model. The paper is clearly written, the developed inference method seems technically sound, and the presented results look promising. My opinion on the model itself, using a non-parametric approach such as using the GP prior on the transition function (as in the paper), seems, though, a bit an obvious way of extending the prior work developed in the finance area. So, I wouldn't put too high grade on the paper in terms of its originality.

Search (PNS), are effective in playing and solving games. However, the understanding of their performance in industrial applications is still limited.

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.

Neural Information Processing Systems http://nips.cc/

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.