12 accepted papers
Conformal Prediction (CP) provides rigorous finite-sample coverage guarantees, yet its statistical efficiency hinges critically on the size of the calibration set. In data-scarce regimes, CP often suffers from volatile quantile estimation, leading to overly conservative and wide prediction intervals…
Vision-Language-Action (VLA) models typically bridge the gap between perceptual and action spaces by pre-training a large-scale Vision-Language Model (VLM) on robotic data. While this approach greatly enhances performance, it also incurs significant training costs. In this paper, we investigate how
In order to streamline the fine-tuning of foundation models, Low-Rank Adapters (LoRAs) have been substantially adopted across various fields, including instruction tuning and domain adaptation. The underlying concept of LoRA involves decomposing a full-rank matrix into the product of two lower-rank…
In education, analyzing student oral reading is critical to reading comprehension, vocabulary development, and fluency. Automating these assessments is a crucial part of language research– however, researchers often use a separate model for each assessment. Transferring knowledge between assessments…
Gaussian splatting enables fast novel view synthesis in static 3D environments. However, reconstructing real-world environments remains challenging as distractors or occluders break the multi-view consistency assumption required for accurate 3D reconstruction. Most existing methods rely on external…
Model extraction attack shows promising performance in revealing sequential recommendation (SeqRec) robustness, e.g., as an upstream task of transfer-based attack to provide optimization feedback for downstream attacks. However, existing work either heavily relies on impractical prior knowledge or h…
Oral reading fluency assessment is a process where a student reads a passage aloud and is scored against words read correctly by a human listener. Current automatic reading fluency systems match these words read using speech recognition models trained with clean speech data from native adult speaker…
Dialogue Aspect-based Sentiment Quadruple analysis (DiaASQ) extends ABSA to more complex real-world scenarios (i.e., dialogues), which makes existing generation methods encounter heightened noise and order bias challenges, leading to decreased robustness and accuracy.To address these, we propose the…
Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent years, due to its ability to detect object motion out of sight. Most previous works on NLOS tracking rely on active illumination, e.g., laser, and suffer from high cost and elaborate experimental conditions. Besides, these te…
ASTE (Aspect Sentiment Triplet Extraction) has gained increasing attention. Recent advancements in the ASTE task have been primarily driven by Natural Language Generation-based (NLG) approaches. However, most NLG methods overlook the supervision of the encoder-decoder hidden representations and fail…
Automated prosody classification in the context of oral reading fluency is a critical area for the objective evaluation of students’ reading proficiency. In this work, we present the largest dataset to date in this domain. It includes spoken phrases from over 1,300 students assessed by multiple trai…
Contrastive learning is emerging as a powerful technique for extracting knowledge from unlabeled data. This technique requires a balanced mixture of two ingredients: positive (similar) and negative (dissimilar) samples. This is typically achieved by maintaining a queue of negative samples during tra…