Bayesian optimization (BO) is a popular framework for optimizing black-box functions. In many applications, the objective function can be evaluated at multiple fidelities toenableatrade-offbetween thecostandaccuracy.

In the embedding phase, NIERT uniformly embeds both observed and target points. A learnable mask vector is introduced for target points lacking value data. The NIERT interpolator's core is a Transformer encoder with a masked self-attention mechanism, uniformly encoding observed and The NIERT, a Transformer encoder-only architecture that uniformly encodes observed points and models their correlations, exhibits superior interpolation accuracy. Our proposed architecture, specifically adapted to HINT's overall framework, introduces HINT employs residuals on observed points to estimate residuals on target points. Table 1: Statistics of the interpolation tasks used for training in each dataset.Dataset d Theoretical dataset II: Perlin is another synthetic assembly of interpolation tasks, specifically designed for the numerical interpolation of two-dimensional rough functions.

HINT consists of several sequentially arranged lightweight interpolation blocks.

In real-world decision-making tasks, learning an optimal policy without a trialand-error process is an appealing challenge. When expert demonstrations are available, imitation learning that mimics expert actions can learn a good policy efficiently.

Despite strong empirical performance for image classification, deep neural networks are often regarded as"black boxes" and theyare difficult tointerpret.

Reconstructing a continuous surface from a raw 3D point cloud is a challenging task.

Intheexamplein Figure 2, the queryqi is "Loadalltablesfromdataset". Conferenceon Reverse Engineering (WCRE 2002), 28 October - 1 November 2002, VA, USAIEEE Computer Society, 2002.

Semi-supervised learning aims to leverage alarge amount of unlabeled data for performance boosting. Existing works primarily focus on image classification. Inthispaper,wedelveintosemi-supervised learning forobject detection, where labeled data are more labor-intensive to collect.

For a given set of points, define a value called "weight" for each point in (a),

A fundamental question in the theory of reinforcement learning is: suppose the optimalQ-function lies inthe linear span ofagivenddimensional feature mapping, is sample-efficient reinforcement learning (RL) possible? The recent and remarkable result of Weisz et al. (2020) resolves this question in the negative, providinganexponential(ind)samplesizelowerbound,whichholdsevenifthe agent has access to a generative model of the environment. One may hope that such a lower can be circumvented with an even stronger assumption that there isaconstant gapbetween the optimalQ-value ofthe best action and that ofthe second-best action (for allstates); indeed, the construction inWeisz etal.