With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-cell-level measurements. However, the individual gene expression levels of interest are not directly observable; instead, only repeated proxy measurements from each individual's cells are available, providing a derived outcome to estimate the underlying outcome for each of many genes. In this paper, we propose a generic semiparametric inference framework for doubly robust estimation with multiple derived outcomes, which also encompasses the usual setting of multiple outcomes when the response of each unit is available. To reliably quantify the causal effects of heterogeneous outcomes, we specialize the analysis to standardized average treatment effects and quantile treatment effects. Through this, we demonstrate the use of the semiparametric inferential results for doubly robust estimators derived from both Von Mises expansions and estimating equations. A multiple testing procedure based on Gaussian multiplier bootstrap is tailored for doubly robust estimators to control the false discovery exceedance rate. Applications in single-cell CRISPR perturbation analysis and individual-level differential expression analysis demonstrate the utility of the proposed methods and offer insights into the usage of different estimands for causal inference in genomics.

Variational inference (VI) has emerged as a popular method for approximate inference for high-dimensional Bayesian models. In this paper, we propose a novel VI method that extends the naive mean field via entropic regularization, referred to as $\Xi$-variational inference ($\Xi$-VI). $\Xi$-VI has a close connection to the entropic optimal transport problem and benefits from the computationally efficient Sinkhorn algorithm. We show that $\Xi$-variational posteriors effectively recover the true posterior dependency, where the dependence is downweighted by the regularization parameter. We analyze the role of dimensionality of the parameter space on the accuracy of $\Xi$-variational approximation and how it affects computational considerations, providing a rough characterization of the statistical-computational trade-off in $\Xi$-VI. We also investigate the frequentist properties of $\Xi$-VI and establish results on consistency, asymptotic normality, high-dimensional asymptotics, and algorithmic stability. We provide sufficient criteria for achieving polynomial-time approximate inference using the method. Finally, we demonstrate the practical advantage of $\Xi$-VI over mean-field variational inference on simulated and real data.

Weakly Supervised Object Localization (WSOL) allows for training deep learning models for classification and localization, using only global class-level labels. The lack of bounding box (bbox) supervision during training represents a considerable challenge for hyper-parameter search and model selection. Earlier WSOL works implicitly observed localization performance over a test set which leads to biased performance evaluation. More recently, a better WSOL protocol has been proposed, where a validation set with bbox annotations is held out for model selection. Although it does not rely on the test set, this protocol is unrealistic since bboxes are not available in real-world applications, and when available, it is better to use them directly to fit model weights. Our initial empirical analysis shows that the localization performance of a model declines significantly when using only image-class labels for model selection (compared to using bounding-box annotations). This suggests that adding bounding-box labels is preferable for selecting the best model for localization. In this paper, we introduce a new WSOL validation protocol that provides a localization signal without the need for manual bbox annotations. In particular, we leverage noisy pseudo boxes from an off-the-shelf ROI proposal generator such as Selective-Search, CLIP, and RPN pretrained models for model selection. Our experimental results with several WSOL methods on ILSVRC and CUB-200-2011 datasets show that our noisy boxes allow selecting models with performance close to those selected using ground truth boxes, and better than models selected using only image-class labels.

Rapid advancements in continual segmentation have yet to bridge the gap of scaling to large continually expanding vocabularies under compute-constrained scenarios. We discover that traditional continual training leads to catastrophic forgetting under compute constraints, unable to outperform zero-shot segmentation methods. We introduce a novel strategy for semantic and panoptic segmentation with zero forgetting, capable of adapting to continually growing vocabularies without the need for retraining or large memory costs. Our training-free approach, kNN-CLIP, leverages a database of instance embeddings to enable open-vocabulary segmentation approaches to continually expand their vocabulary on any given domain with a single-pass through data, while only storing embeddings minimizing both compute and memory costs. This method achieves state-of-the-art mIoU performance across large-vocabulary semantic and panoptic segmentation datasets. We hope kNN-CLIP represents a step forward in enabling more efficient and adaptable continual segmentation, paving the way for advances in real-world large-vocabulary continual segmentation methods. Figure 1: We propose kNN-CLIP to continually expand the vocabulary space of segmentation models.

Federated learning (FL) is a popular solution for distributed machine learning (ML). While FL has traditionally been studied for supervised ML tasks, in many applications, it is impractical to assume availability of labeled data across devices. To this end, we develop Cooperative Federated unsupervised Contrastive Learning ({\tt CF-CL)} to facilitate FL across edge devices with unlabeled datasets. {\tt CF-CL} employs local device cooperation where either explicit (i.e., raw data) or implicit (i.e., embeddings) information is exchanged through device-to-device (D2D) communications to improve local diversity. Specifically, we introduce a \textit{smart information push-pull} methodology for data/embedding exchange tailored to FL settings with either soft or strict data privacy restrictions. Information sharing is conducted through a probabilistic importance sampling technique at receivers leveraging a carefully crafted reserve dataset provided by transmitters. In the implicit case, embedding exchange is further integrated into the local ML training at the devices via a regularization term incorporated into the contrastive loss, augmented with a dynamic contrastive margin to adjust the volume of latent space explored. Numerical evaluations demonstrate that {\tt CF-CL} leads to alignment of latent spaces learned across devices, results in faster and more efficient global model training, and is effective in extreme non-i.i.d. data distribution settings across devices.

Alzheimer's disease is a severe neurological brain disorder. It is not curable, but earlier detection can help improve symptoms in a great deal. The machine learning-based approaches are popular and well-motivated models for many medical image processing tasks such as computer-aided diagnosis. These techniques can vastly improve the process for accurate diagnosis of Alzheimer's disease. In this paper, we investigate the performance of these techniques for Alzheimer's disease detection and classification using brain MRI and PET images from the OASIS database. The proposed system takes advantage of the powerful artificial neural network and support vector machines as classifiers, as well as principal component analysis as a feature extraction technique. The results indicate that the combined scheme achieves good accuracy and offers a significant advantage over the other approaches.

Robots that assist in daily life are required to locate specific instances of objects that match the user's desired object in the environment. This task is known as Instance-Specific Image Goal Navigation (InstanceImageNav), which requires a model capable of distinguishing between different instances within the same class. One significant challenge in robotics is that when a robot observes the same object from various 3D viewpoints, its appearance may differ greatly, making it difficult to recognize and locate the object accurately. In this study, we introduce a method, SimView, that leverages multi-view images based on a 3D semantic map of the environment and self-supervised learning by SimSiam to train an instance identification model on-site. The effectiveness of our approach is validated using a photorealistic simulator, Habitat Matterport 3D, created by scanning real home environments. Our results demonstrate a 1.7-fold improvement in task accuracy compared to CLIP, which is pre-trained multimodal contrastive learning for object search. This improvement highlights the benefits of our proposed fine-tuning method in enhancing the performance of assistive robots in InstanceImageNav tasks. The project website is https://emergentsystemlabstudent.github.io/MultiViewRetrieve/.

Activity cliff (AC) is a phenomenon that a pair of similar molecules differ by a small structural alternation but exhibit a large difference in their biochemical activities. The AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in peptides with canonical amino acids. This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the metric minimum inhibitory concentration (MIC), and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language ESM2 model demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient=0.50 for the regression task of the MIC values on the benchmark dataset.

Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences. Various approaches to suppress model reliance on harmful features have been proposed that can be applied post-hoc without additional training. Whereas those methods can be applied with efficiency, they also tend to harm model performance by globally shifting the distribution of latent features. To mitigate unintended overcorrection of model behavior, we propose a reactive approach conditioned on model-derived knowledge and eXplainable Artificial Intelligence (XAI) insights. While the reactive approach can be applied to many post-hoc methods, we demonstrate the incorporation of reactivity in particular for P-ClArC (Projective Class Artifact Compensation), introducing a new method called R-ClArC (Reactive Class Artifact Compensation). Through rigorous experiments in controlled settings (FunnyBirds) and with a real-world dataset (ISIC2019), we show that introducing reactivity can minimize the detrimental effect of the applied correction while simultaneously ensuring low reliance on spurious features.

Understanding and predicting pedestrian crossing behavior is essential for enhancing automated driving and improving driving safety. Predicting gap selection behavior and the use of zebra crossing enables driving systems to proactively respond and prevent potential conflicts. This task is particularly challenging at unsignalized crossings due to the ambiguous right of way, requiring pedestrians to constantly interact with vehicles and other pedestrians. This study addresses these challenges by utilizing simulator data to investigate scenarios involving multiple vehicles and pedestrians. We propose and evaluate machine learning models to predict gap selection in non-zebra scenarios and zebra crossing usage in zebra scenarios. We investigate and discuss how pedestrians' behaviors are influenced by various factors, including pedestrian waiting time, walking speed, the number of unused gaps, the largest missed gap, and the influence of other pedestrians. This research contributes to the evolution of intelligent vehicles by providing predictive models and valuable insights into pedestrian crossing behavior.