5 citations · 10 across the 11 of their papers we have counts for
7 papers · 1 filter
SleuthBench: Benchmarking Statistical LLM Evaluation Using Tabular Hidden Signals
Jingyun Jia, Antoine Remond-Tiedrez, Aaron Alvarez +3
Evaluating statistical discovery by large language model (LLM) agents requires verifiable analytical ground truth. Establishing such ground truth for real-world datasets is costly,…
RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification
Yanxuan Yu, Dong Liu, Shu Wang +9
Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis, where minority-class samples are scarce yet critical for…
Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare
Sazan Mahbub, Caleb N. Ellington, Caleb Ellington +5
We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesi…
Context-Adaptive Inference: A Unified Statistical and Foundation-Model View
Yue Yao, Caleb N. Ellington, Jingyun Jia +9
Modern predictive systems are expected to adapt their behavior to the specific situation they are facing. A clinical model should not treat every patient the same; a retrieval-augm…
OLIVE: Online Low-Rank Incremental Learning for Efficient Adaptive Exoskeletons
Dong Liu, Yanxuan Yu, Ben Lengerich +2
Wearable exoskeleton systems hold promise for restoring mobility in individuals with physical impairments, yet most existing controllers rely on static gait policies that cannot ad…
MKA: Memory-Keyed Attention for Efficient Long-Context Reasoning
Dong Liu, Yanxuan Yu, Ben Lengerich +1
As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in…
AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers
Dong Liu, Yanxuan Yu, Ben Lengerich +1
Diffusion Transformers (DiTs) achieve state-of-the-art performance in high-fidelity image and video generation but suffer from expensive inference due to their iterative denoising…