This paper considers the problem of estimating the unknown intervention targets in a causal directed acyclic graph from observational and interventional data. The focus is on soft interventions in linear structural equation models (SEMs). Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets even for linear SEMs. This severely limits their scalability and sample complexity. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets.

Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to process them with an identical structure. One main obstacle lies in the high-dimensional outputs in object-level visual tasks. In this paper, we propose an object-centric vision framework, Obj2Seq. Obj2Seq takes objects as basic units, and regards most object-level visual tasks as sequence generation problems of objects. Therefore, these visual tasks can be decoupled into two steps. First recognize objects of given categories, and then generate a sequence for each of these objects. The definition of the output sequences varies for different tasks, and the model is supervised by matching these sequences with ground-truth targets. Obj2Seq is able to flexibly determine input categories to satisfy customized requirements, and be easily extended to different visual tasks.

We propose a novel reduction-to-binary (R2B) approach that enforces demographic parity for multiclass classification with non-binary sensitive attributes via a reduction to a sequence of binary debiasing tasks. We prove that R2B satisfies optimality and bias guarantees and demonstrate empirically that it can lead to an improvement over two baselines: (1) treating multiclass problems as multi-label by debiasing labels independently and (2) transforming the features instead of the labels. Surprisingly, we also demonstrate that independent label debiasing yields competitive results in most (but not all) settings.

A.1 Datasets We use two standardized few-shot image classification datasets. Mini-ImageNet: This dataset [58] is a subset of ImageNet [10] and consists of 64 classes for training, 16 for validation, and 20 for testing. There are 600 images per class, with images of size 84 84. Multiple versions of this dataset exist in the literature; we use the version by Ravi and Larochelle [43]. Tiered-ImageNet: A larger subset of ImageNet, Tiered-ImageNet [45] consists of 608 classes split into 351, 97, and 160 for training, validation, and testing, respectively.

Episodic training is a core ingredient of few-shot learning to train models on tasks with limited labelled data. Despite its success, episodic training remains largely understudied, prompting us to ask the question: what is the best way to sample episodes? In this paper, we first propose a method to approximate episode sampling distributions based on their difficulty. Building on this method, we perform an extensive analysis and find that sampling uniformly over episode difficulty outperforms other sampling schemes, including curriculum and easy-/hard-mining. As the proposed sampling method is algorithm agnostic, we can leverage these insights to improve few-shot learning accuracies across many episodic training algorithms. We demonstrate the efficacy of our method across popular few-shot learning datasets, algorithms, network architectures, and protocols.

For example, the MatMul operation of TensorFlow has'MatMul' as As same as the call id stack, Terra manages the loop id stack for the entire program execution. Figure 2: The result of the case assignment algorithm for the given TraceGraph.2 4 In this section, we describe the case assignment algorithm that Terra uses to explicitly insert the Switch-Case operations in the symbolic graph. The algorithm takes a TraceGraph as an input and returns an ordered list of switch-cases. A switch-case 6is a set of (basic block, control edges) where thebasic block is a linear3 chain of nodes, and the5control edges are the edges that point to the basic block. Every non-overlapping linear chain of nodes in the TraceGraph is uniquely assigned to a basic block so that the ordered list of3switch-cases 5can cover every trace in the TraceGraph.