1 citations · 1 across the 4 of their papers we have counts for
4 papers
One-Step Generative Modeling via Wasserstein Gradient Flows
Jiaqi Han, Puheng Li, Qiushan Guo +3
Diffusion models and flow-based methods have shown impressive generative capability, especially for images, but their sampling is expensive because it requires many iterative updat…
Enhancing a Risk Model by Adding Transient Statistical Factors
Alexandros E. Tzikas, Emmanuel J. Candès, Trevor Hastie +3
Estimating the covariance of asset returns, i.e., the risk model, is a key component of financial portfolio construction and evaluation. Most risk modeling approaches produce a fac…
Efficient Evaluation of LLM Performance with Statistical Guarantees
Skyler Wu, Yash Nair, Emmanuel J. Candès
Exhaustively evaluating many large language models (LLMs) on a large suite of benchmarks is expensive. We cast benchmarking as finite-population inference and, under a fixed query…
Overparameterized ReLU Neural Networks Learn the Simplest Models: Neural Isometry and Exact Recovery
Yifei Wang, Yixuan Hua, Emmanuel Candés +1
The practice of deep learning has shown that neural networks generalize remarkably well even with an extreme number of learned parameters. This appears to contradict traditional st…