超越种族主义文本的二元分类，我们的研究从社会科学理论中获取线索，以开发一种用于种族主义检测的多维模型，即污名化，进攻性，责备和排斥。在BERT和主题建模的帮助下，这种分类检测可以洞悉Covid-19期间数字平台上种族主义讨论的基本细节。我们的研究有助于丰富有关社交媒体上种族主义行为的学术讨论。首先，采用阶段分析来捕捉在Covid-19的早期阶段的主题变化的动态，该阶段从国内流行病转变为国际公共卫生紧急情况，后来转变为全球大流行。此外，映射这一趋势可以更准确地预测有关离线世界中种族主义的公众舆论发展，同时，制定了规定的干预策略，以打击像Covid-19这样的全球公共卫生危机期间的种族主义兴起。此外，这项跨学科研究还指出了关于社交网络分析和采矿的未来研究的方向。将社会科学观点整合到计算方法的发展中，为更准确的数据检测和分析提供了见解。

最近，已经开发了许多自动白细胞（WBC）或白细胞分类技术。但是，所有这些方法仅利用单个模态显微图像，即基于血液涂片或荧光，因此缺少从多模式图像中学习更好的潜力。在这项工作中，我们基于WBC分类任务的第一个多模式WBC数据集开发了有效的多模式体系结构。具体而言，我们提出的想法是通过两个步骤开发的 - 1）首先，我们仅在单个网络中学习模式特定的独立子网； 2）我们通过从高复杂性独立教师网络中提取知识来进一步增强独立子网的学习能力。因此，我们提出的框架可以实现高性能，同时保持多模式数据集的复杂性较低。我们的独特贡献是两倍-1）我们提出了用于WBC分类的同类多模式WBC数据集的第一个； 2）我们开发了高性能的多模式体系结构，同时也有效且复杂性低。

近年来，自动化方法迅速发展了皮肤病变和分类的方法。由于此类系统在诊所中的部署越来越多，因此很重要的是，为各种分布（OOD）样品（未知的皮肤病变和状况）开发更强大的系统。但是，当前对皮肤病变分类训练的深度学习模型倾向于将这些OOD样品错误地分类为他们学习的皮肤病变类别之一。为了解决这个问题，我们提出了一种简单而战略的方法，可以改善OOD检测性能，同时维持已知皮肤病变类别的多类分类精度。要说明，这种方法建立在皮肤病变图像的长尾且细粒度检测任务的现实情况之上。通过这种方法，1）首先，我们针对中间和尾巴之间的混合，以解决长尾问题。 2）后来，我们将上述混合策略与原型学习结合在一起，以解决数据集的细粒度。本文的独特贡献是两倍，这是通过广泛的实验证明的。首先，我们提出了针对皮肤病变的OOD任务的现实问题。其次，我们提出了一种针对问题设置的长尾且细粒度方面的方法，以提高OOD性能。

State-of-the-art automatic augmentation methods (e.g., AutoAugment and RandAugment) for visual recognition tasks diversify training data using a large set of augmentation operations. The range of magnitudes of many augmentation operations (e.g., brightness and contrast) is continuous. Therefore, to make search computationally tractable, these methods use fixed and manually-defined magnitude ranges for each operation, which may lead to sub-optimal policies. To answer the open question on the importance of magnitude ranges for each augmentation operation, we introduce RangeAugment that allows us to efficiently learn the range of magnitudes for individual as well as composite augmentation operations. RangeAugment uses an auxiliary loss based on image similarity as a measure to control the range of magnitudes of augmentation operations. As a result, RangeAugment has a single scalar parameter for search, image similarity, which we simply optimize via linear search. RangeAugment integrates seamlessly with any model and learns model- and task-specific augmentation policies. With extensive experiments on the ImageNet dataset across different networks, we show that RangeAugment achieves competitive performance to state-of-the-art automatic augmentation methods with 4-5 times fewer augmentation operations. Experimental results on semantic segmentation, object detection, foundation models, and knowledge distillation further shows RangeAugment's effectiveness.

Differentiable Search Indices (DSIs) encode a corpus of documents in the parameters of a model and use the same model to map queries directly to relevant document identifiers. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents (+12\%). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting by a significant margin. Concretely, it improves the average Hits@10 by $+21.1\%$ over competitive baselines for NQ and requires $6$ times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.

Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where the transition dynamics can be readily evaluated at specified states (e.g., via a simulator), agents can operate in what is often referred to as planning with a \emph{generative model}. We propose the AE-LSVI algorithm for best-policy identification, a novel variant of the kernelized least-squares value iteration (LSVI) algorithm that combines optimism with pessimism for active exploration (AE). AE-LSVI provably identifies a near-optimal policy \emph{uniformly} over an entire state space and achieves polynomial sample complexity guarantees that are independent of the number of states. When specialized to the recently introduced offline contextual Bayesian optimization setting, our algorithm achieves improved sample complexity bounds. Experimentally, we demonstrate that AE-LSVI outperforms other RL algorithms in a variety of environments when robustness to the initial state is required.

Spurious correlations in training data often lead to robustness issues since models learn to use them as shortcuts. For example, when predicting whether an object is a cow, a model might learn to rely on its green background, so it would do poorly on a cow on a sandy background. A standard dataset for measuring state-of-the-art on methods mitigating this problem is Waterbirds. The best method (Group Distributionally Robust Optimization - GroupDRO) currently achieves 89\% worst group accuracy and standard training from scratch on raw images only gets 72\%. GroupDRO requires training a model in an end-to-end manner with subgroup labels. In this paper, we show that we can achieve up to 90\% accuracy without using any sub-group information in the training set by simply using embeddings from a large pre-trained vision model extractor and training a linear classifier on top of it. With experiments on a wide range of pre-trained models and pre-training datasets, we show that the capacity of the pre-training model and the size of the pre-training dataset matters. Our experiments reveal that high capacity vision transformers perform better compared to high capacity convolutional neural networks, and larger pre-training dataset leads to better worst-group accuracy on the spurious correlation dataset.

A reliable critic is central to on-policy actor-critic learning. But it becomes challenging to learn a reliable critic in a multi-agent sparse reward scenario due to two factors: 1) The joint action space grows exponentially with the number of agents 2) This, combined with the reward sparseness and environment noise, leads to large sample requirements for accurate learning. We show that regularising the critic with spectral normalization (SN) enables it to learn more robustly, even in multi-agent on-policy sparse reward scenarios. Our experiments show that the regularised critic is quickly able to learn from the sparse rewarding experience in the complex SMAC and RWARE domains. These findings highlight the importance of regularisation in the critic for stable learning.

Spectral risk objectives - also called $L$-risks - allow for learning systems to interpolate between optimizing average-case performance (as in empirical risk minimization) and worst-case performance on a task. We develop stochastic algorithms to optimize these quantities by characterizing their subdifferential and addressing challenges such as biasedness of subgradient estimates and non-smoothness of the objective. We show theoretically and experimentally that out-of-the-box approaches such as stochastic subgradient and dual averaging are hindered by bias and that our approach outperforms them.

Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regression in which a network is trained by minimizing the squared or absolute error of output labels. Prior work has shown that solving a regression problem with a set of binary classifiers can improve accuracy by utilizing well-studied binary classification algorithms. We introduce binary-encoded labels (BEL), which generalizes the application of binary classification to regression by providing a framework for considering arbitrary multi-bit values when encoding target values. We identify desirable properties of suitable encoding and decoding functions used for the conversion between real-valued and binary-encoded labels based on theoretical and empirical study. These properties highlight a tradeoff between classification error probability and error-correction capabilities of label encodings. BEL can be combined with off-the-shelf task-specific feature extractors and trained end-to-end. We propose a series of sample encoding, decoding, and training loss functions for BEL and demonstrate they result in lower error than direct regression and specialized approaches while being suitable for a diverse set of regression problems, network architectures, and evaluation metrics. BEL achieves state-of-the-art accuracies for several regression benchmarks. Code is available at https://github.com/ubc-aamodt-group/BEL_regression.