5 papers
Group-realizable multi-group learning by minimizing empirical risk
Navid Ardeshir, Samuel Deng, Daniel Hsu +1
The sample complexity of multi-group learning is shown to improve in the group-realizable setting over the agnostic setting, even when the family of groups is infinite so long as i…
Time-Aware Synthetic Control
Saeyoung Rho, Cyrus Illick, Samhitha Narasipura +3
The synthetic control (SC) framework is widely used for observational causal inference with time-series panel data. SC has been successful in diverse applications, but existing met…
Panprediction: Optimal Predictions for Any Downstream Task and Loss
Sivaraman Balakrishnan, Nika Haghtalab, Daniel Hsu +2
Supervised learning is classically formulated as training a model to minimize a fixed loss function over a fixed distribution, or task. However, an emerging paradigm instead views…
Survey on Algorithms for multi-index models
Joan Bruna, Daniel Hsu
We review the literature on algorithms for estimating the index space in a multi-index model. The primary focus is on computationally efficient (polynomial-time) algorithms in Gaus…
Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
Berfin Şimşek, Amire Bendjeddou, Daniel Hsu
This work focuses on the gradient flow dynamics of a neural network model that uses correlation loss to approximate a multi-index function on high-dimensional standard Gaussian dat…