24 citations · 68 across the 24 of their papers we have counts for
9 papers · 1 filter
From Selection to Generation: A Survey of LLM-based Active Learning
Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…
Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent
Junda Wu, Yuxin Xiong, Xintong Li +9
Recent MLLMs have shown emerging visual understanding and reasoning abilities after being pre-trained on large-scale multimodal datasets. Unlike pre-training, where MLLMs receive r…
Causal Discovery in Semi-Stationary Time Series
Shanyun Gao, Raghavendra Addanki, Tong Yu +2
Discovering causal relations from observational time series without making the stationary assumption is a significant challenge. In practice, this challenge is common in many areas…
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits
Yu Xia, Fang Kong, Tong Yu +4
Web-based applications such as chatbots, search engines and news recommendations continue to grow in scale and complexity with the recent surge in the adoption of LLMs. Online mode…
Leveraging Graph Diffusion Models for Network Refinement Tasks
Puja Trivedi, Ryan Rossi, David Arbour +7
Most real-world networks are noisy and incomplete samples from an unknown target distribution. Refining them by correcting corruptions or inferring unobserved regions typically imp…
Fairness-Aware Graph Neural Networks: A Survey
April Chen, Ryan A. Rossi, Namyong Park +6
Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks.…