14 accepted papers
Estimating Individual Treatment Effects (ITE) in multi-treatment scenarios faces two critical challenges: the Hyperparameter Selection Dilemma for balancing weights and the Curse of Dimensionality in computational scalability. This paper derives a novel multi-treatment generalization bound and propo…
Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific content. In practice, these requests often arrive sequentially over time, giving rise to the challenging problem of *MLLM …
In-context learning (ICL) enables pre-trained transformers (TFs) to perform few-shot learning across diverse tasks, fostering growing research into its underlying mechanisms. However, existing studies typically assume a causally-sufficient regime, overlooking spurious correlations and prediction bia…
Strategic classification (SC) explores how individuals or entities modify their features strategically to achieve favorable classification outcomes. However, existing SC methods, which are largely based on linear models or shallow neural networks, face significant limitations in terms of scalability…
Time-varying counterfactual prediction (TCP) from observational data supports the answer of when and how to assign multiple sequential treatments, yielding importance in various applications. Despite the progress achieved by recent advances, e.g., LSTM or Transformer based causal approaches, their c…
The real world naturally has dimensions of time and space. Therefore, estimating the counterfactual outcomes with spatial-temporal attributes is a crucial problem. However, previous methods are based on classical statistical models, which still have limitations in performance and generalization. Thi…
Standing out as one of the most widely used tools in Cross-Domain Few-Shot Learning (CDFSL), data augmentation forms the bedrock of numerous recent advancements. However, the current augmentations in CDFSL are limited in their ability to modify high-level semantic attributes, resulting in a lack of…
Few-shot learning (FSL) aims to enable learning models with the ability to automatically adapt to novel (unseen) domains in open-world scenarios. Nonetheless, there exists a significant disparity between the vast number of new concepts encountered in the open world and the restricted available scale…
Causal reasoning capability is critical in advancing large language models (LLMs) towards artificial general intelligence (AGI). While versatile LLMs appear to have demonstrated capabilities in understanding contextual causality and providing responses that obey the laws of causality, it remains unc…
Generating unlabeled data has been recently shown to help address the few-shot hypothesis adaptation (FHA) problem, where we aim to train a classifier for the target domain with a few labeled target-domain data and a well-trained source-domain classifier (i.e., a source hypothesis), for the addition…
This paper explores Deployment Authorization (DPA) as a means of restricting the generalization capabilities of vision models on certain domains to protect intellectual property. Nevertheless, the current advancements in DPA are predominantly confined to fully supervised settings. Such settings requ…
In novel class discovery (NCD), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes. However, the implicit assumptions behind NCD are still unclear. In this paper, we demystify assumptions behind NCD and find that…
As the amount of data shared on the network increases, these data pose a threat to our privacy. This paper focuses on the privacy-preserving issues of action recognition for humans. Generally, the face is considered the most identifiable visual cue for a human. However, removing face information is…
In few-shot domain adaptation (FDA), classifiers for the target domain are trained with \emph{accessible} labeled data in the source domain (SD) and few labeled data in the target domain (TD). However, data usually contain private information in the current era, e.g., data distributed on personal ph…