A multitude of (dis)similarity measures between neural network representations have been proposed, resulting in a fragmented research landscape. Most of these measures fall into one of two categories. First, measures such as linear regression, canonical correlations analysis (CCA), and shape distances, all learn explicit mappings between neural units to quantify similarity while accounting for expected invariances. Second, measures such as representational similarity analysis (RSA), centered kernel alignment (CKA), and normalized Bures similarity (NBS) all quantify similarity in summary statistics, such as stimulus-by-stimulus kernel matrices, which are already invariant to expected symmetries. Here, we take steps towards unifying these two broad categories of methods by observing that the cosine of the Riemannian shape distance (from category 1) is equal to NBS (from category 2). We explore how this connection leads to new interpretations of shape distances and NBS, and draw contrasts of these measures with CKA, a popular similarity measure in the deep learning literature.

Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data. However, most of these methods rely on pairwise relations in the graph and do not capture the underling higher-order relations between entities. Hypergraphs are versatile and expressive structures that can effectively model higher-order relationships among entities in the data. Despite the efforts to adapt GNNs to hypergraphs (HyperGNN), there are currently no fully self-supervised pretraining methods for HyperGNN on heterogeneous hypergraphs. In this paper, we present SPHH, a novel self-supervised pretraining framework for heterogeneous HyperGNNs. Our method is able to effectively capture higher-order relations among entities in the data in a self-supervised manner. SPHH is consist of two self-supervised pretraining tasks that aim to simultaneously learn both local and global representations of the entities in the hypergraph by using informative representations derived from the hypergraph structure. Overall, our work presents a significant advancement in the field of self-supervised pretraining of HyperGNNs, and has the potential to improve the performance of various graph-based downstream tasks such as node classification and link prediction tasks which are mapped to hypergraph configuration. Our experiments on two real-world benchmarks using four different HyperGNN models show that our proposed SPHH framework consistently outperforms state-of-the-art baselines in various downstream tasks. The results demonstrate that SPHH is able to improve the performance of various HyperGNN models in various downstream tasks, regardless of their architecture or complexity, which highlights the robustness of our framework.

State-of-the-art methods for conditional average treatment effect (CATE) estimation make widespread use of representation learning. Here, the idea is to reduce the variance of the low-sample CATE estimation by a (potentially constrained) low-dimensional representation. However, low-dimensional representations can lose information about the observed confounders and thus lead to bias, because of which the validity of representation learning for CATE estimation is typically violated. In this paper, we propose a new, representation-agnostic framework for estimating bounds on the representation-induced confounding bias that comes from dimensionality reduction (or other constraints on the representations) in CATE estimation. First, we establish theoretically under which conditions CATEs are non-identifiable given low-dimensional (constrained) representations. Second, as our remedy, we propose to perform partial identification of CATEs or, equivalently, aim at estimating of lower and upper bounds of the representation-induced confounding bias. We demonstrate the effectiveness of our bounds in a series of experiments. In sum, our framework is of direct relevance in practice where the validity of CATE estimation is of importance.

With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. Accurate geospatial predictions are vital for domain research based on ecosystem monitoring and quality assessment and for policy-making and action planning, considering effective management of natural resources. The accuracy and computation speed of ML has generally proved efficient. However, many questions have yet to be addressed to obtain precise and reproducible results suitable for further use in both research and practice. A better understanding of the ML concepts applicable to geospatial problems enhances the development of data science tools providing transparent information crucial for making decisions on global challenges such as biosphere degradation and climate change. This survey reviews common nuances in geospatial modelling, such as imbalanced data, spatial autocorrelation, prediction errors, model generalisation, domain specificity, and uncertainty estimation. We provide an overview of techniques and popular programming tools to overcome or account for the challenges. We also discuss prospects for geospatial Artificial Intelligence in environmental applications. To date, obtaining spatial predictions is an essential step in the monitoring, assessment, and prognosis tasks applicable to all kinds of Earth systems on both local and global scales (Figure 1).

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatio-temporal invariant patterns. Specifically, we first design the environment-aware EA-DGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

Electroencephalography (EEG) and Natural Language Processing (NLP) can be applied for education to measure students' comprehension in classroom lectures; currently, the two measures have been used separately. In this work, we propose a classification framework for predicting students' lecture comprehension in two tasks: (i) students' confusion after listening to the simulated lecture and (ii) the correctness of students' responses to the post-lecture assessment. The proposed framework includes EEG and NLP feature extraction, processing, and classification. EEG and NLP features are extracted to construct integrated features obtained from recorded EEG signals and sentence-level syntactic analysis, which provide information about specific biomarkers and sentence structures. An ensemble stacking classification method -- a combination of multiple individual models that produces an enhanced predictive model -- is studied to learn from the features to make predictions accurately. Furthermore, we also utilized subjective confusion ratings as another integrated feature to enhance classification performance. By doing so, experiment results show that this framework performs better than the baselines, which achieved F1 up to 0.65 for predicting confusion and 0.78 for predicting correctness, highlighting that utilizing this has helped improve the classification performance.

In real-world continual learning scenarios, tasks often exhibit intricate and unpredictable semantic shifts, posing challenges for fixed prompt management strategies. We identify the inadequacy of universal and specific prompting in handling these dynamic shifts. Universal prompting is ineffective for tasks with abrupt semantic changes, while specific prompting struggles with overfitting under mild semantic shifts. To overcome these limitations, we propose an adaptive prompting approach that tailors minimal yet sufficient prompts based on the task semantics. Our methodology, SemPrompt, incorporates a two-level semantic grouping process: macroscopic semantic assignment and microscopic semantic refinement. This process ensures optimal prompt utilization for varying task semantics, improving the efficiency and effectiveness of learning in real-world CL settings. Our experimental results demonstrate that SemPrompt consistently outperforms existing methods in adapting to diverse semantic shifts in tasks.

Machine Unlearning is the process of removing specific training data samples and their corresponding effects from an already trained model. It has significant practical benefits, such as purging private, inaccurate, or outdated information from trained models without the need for complete re-training. Unlearning within a multimodal setting presents unique challenges due to the intrinsic dependencies between different data modalities and the expensive cost of training on large multimodal datasets and architectures. Current approaches to machine unlearning have not fully addressed these challenges. To bridge this gap, we introduce MMUL, a machine unlearning approach specifically designed for multimodal data and models. MMUL formulates the multimodal unlearning task by focusing on three key properties: (a): modality decoupling, which effectively decouples the association between individual unimodal data points within multimodal inputs marked for deletion, rendering them as unrelated data points within the model's context, (b): unimodal knowledge retention, which retains the unimodal representation capability of the model post-unlearning, and (c): multimodal knowledge retention, which retains the multimodal representation capability of the model post-unlearning. MMUL is efficient to train and is not constrained by the requirement of using a strongly convex loss. Experiments on two multimodal models and four multimodal benchmark datasets, including vision-language and graph-language datasets, show that MMUL outperforms existing baselines, gaining an average improvement of +17.6 points against the best-performing unimodal baseline in distinguishing between deleted and remaining data. In addition, MMUL can largely maintain pre-existing knowledge of the original model post unlearning, with a performance gap of only 0.3 points compared to retraining a new model from scratch.

Language models have shown promise in various tasks but can be affected by undesired data during training, fine-tuning, or alignment. For example, if some unsafe conversations are wrongly annotated as safe ones, the model fine-tuned on these samples may be harmful. Therefore, the correctness of annotations, i.e., the credibility of the dataset, is important. This study focuses on the credibility of real-world datasets, including the popular benchmarks Jigsaw Civil Comments, Anthropic Harmless & Red Team, PKU BeaverTails & SafeRLHF, that can be used for training a harmless language model. Given the cost and difficulty of cleaning these datasets by humans, we introduce a systematic framework for evaluating the credibility of datasets, identifying label errors, and evaluating the influence of noisy labels in the curated language data, specifically focusing on unsafe comments and conversation classification. With the framework, we find and fix an average of 6.16% label errors in 11 datasets constructed from the above benchmarks. The data credibility and downstream learning performance can be remarkably improved by directly fixing label errors, indicating the significance of cleaning existing real-world datasets. Open-source: https://github.com/Docta-ai/docta.

The power-law distribution plays a crucial role in complex networks as well as various applied sciences. Investigating whether the degree distribution of a network follows a power-law distribution is an important concern. The commonly used inferential methods for estimating the model parameters often yield biased estimates, which can lead to the rejection of the hypothesis that a model conforms to a power-law. In this paper, we discuss improved methods that utilize Bayesian inference to obtain accurate estimates and precise credibility intervals. The inferential methods are derived for both continuous and discrete distributions. These methods reveal that objective Bayesian approaches return nearly unbiased estimates for the parameters of both models. Notably, in the continuous case, we identify an explicit posterior distribution. This work enhances the power of goodness-of-fit tests, enabling us to accurately discern whether a network or any other dataset adheres to a power-law distribution. We apply the proposed approach to fit degree distributions for more than 5,000 synthetic networks and over 3,000 real networks. The results indicate that our method is more suitable in practice, as it yields a frequency of acceptance close to the specified nominal level.