Improving the accuracy of medical diagnosis with causal machine learning
Abstract Machine learning promises to revolutionize clinical decision making and diagnosis.
In medical diagnosis a doctor aims to explain a patient's symptoms by determining the …
In medical diagnosis a doctor aims to explain a patient's symptoms by determining the …
Causal effect inference with deep latent-variable models
Learning individual-level causal effects from observational data, such as inferring the most
effective medication for a specific patient, is a problem of growing importance for policy …
effective medication for a specific patient, is a problem of growing importance for policy …
Recovery guarantees for one-hidden-layer neural networks
In this paper, we consider regression problems with one-hidden-layer neural networks
(1NNs). We distill some properties of activation functions that lead to local strong convexity …
(1NNs). We distill some properties of activation functions that lead to local strong convexity …
SGD learns the conjugate kernel class of the network
A Daniely - Advances in neural information processing …, 2017 - proceedings.neurips.cc
We show that the standard stochastic gradient decent (SGD) algorithm is guaranteed to
learn, in polynomial time, a function that is competitive with the best function in the conjugate …
learn, in polynomial time, a function that is competitive with the best function in the conjugate …
Learning one-hidden-layer relu networks via gradient descent
We study the problem of learning one-hidden-layer neural networks with Rectified Linear
Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian …
Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian …
Relative error tensor low rank approximation
We consider relative error low rank approximation of tensors with respect to the Frobenius
norm. Namely, given an order-q tensor A∊ ℝ∏ i= 1 q ni, output a rank-k tensor B for which …
norm. Namely, given an order-q tensor A∊ ℝ∏ i= 1 q ni, output a rank-k tensor B for which …
Learning two layer rectified neural networks in polynomial time
We consider the following fundamental problem in the study of neural networks: given input
examples $ x\in\mathbb {R}^ d $ and their vector-valued labels, as defined by an underlying …
examples $ x\in\mathbb {R}^ d $ and their vector-valued labels, as defined by an underlying …
High dimensional estimation via sum-of-squares proofs
P Raghavendra, T Schramm… - Proceedings of the …, 2018 - World Scientific
Estimation is the computational task of recovering a hidden parameter x associated with a
distribution D x, given a measurement y sampled from the distribution. High dimensional …
distribution D x, given a measurement y sampled from the distribution. High dimensional …
Spectral learning on matrices and tensors
Spectral methods have been the mainstay in several domains such as machine learning,
applied mathematics and scientific computing. They involve finding a certain kind of spectral …
applied mathematics and scientific computing. They involve finding a certain kind of spectral …
An adaptive kernel approach to federated learning of heterogeneous causal effects
TV Vo, A Bhattacharyya, Y Lee… - Advances in Neural …, 2022 - proceedings.neurips.cc
We propose a new causal inference framework to learn causal effects from multiple,
decentralized data sources in a federated setting. We introduce an adaptive transfer …
decentralized data sources in a federated setting. We introduce an adaptive transfer …