3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.AI2025★ 1 cited
Who Judges the Judge? LLM Jury-on-Demand: Building Trustworthy LLM Evaluation Systems
Xiaochuan Li, Ke Wang, Girija Gouda +5
As Large Language Models (LLMs) become integrated into high-stakes domains, there is a growing need for evaluation methods that are both scalable for real-time deployment and relia…
cs.LG2024
Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations
Yueyang Shen, Agus Sudjianto, Arun Prakash R +5
We propose and study a minimalist approach towards synthetic tabular data generation. The model consists of a minimalistic unsupervised SparsePCA encoder (with contingent clusterin…
stat.ML2020★ 3 cited
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Akash Srivastava, Yamini Bansal, Yukun Ding +15
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the…