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Adversarial examples are not bugs, they are features
Adversarial examples have attracted significant attention in machine learning, but the
reasons for their existence and pervasiveness remain unclear. We demonstrate that …
reasons for their existence and pervasiveness remain unclear. We demonstrate that …
Interactive proofs for verifying machine learning
We consider the following question: using a source of labeled data and interaction with an
untrusted prover, what is the complexity of verifying that a given hypothesis is" approximately …
untrusted prover, what is the complexity of verifying that a given hypothesis is" approximately …
Sgd learns one-layer networks in wgans
Generative adversarial networks (GANs) are a widely used framework for learning
generative models. Wasserstein GANs (WGANs), one of the most successful variants of …
generative models. Wasserstein GANs (WGANs), one of the most successful variants of …
On the limits of language generation: Trade-offs between hallucination and mode collapse
Specifying all desirable properties of a language model is challenging, but certain
requirements seem essential. Given samples from an unknown language, the trained model …
requirements seem essential. Given samples from an unknown language, the trained model …
Understanding adversarial robustness against on-manifold adversarial examples
Deep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-
trained model can be easily attacked by adding small perturbations to the original data. One …
trained model can be easily attacked by adding small perturbations to the original data. One …
Transfer learning beyond bounded density ratios
We study the fundamental problem of transfer learning where a learning algorithm collects
data from some source distribution $ P $ but needs to perform well with respect to a different …
data from some source distribution $ P $ but needs to perform well with respect to a different …
Computationally and statistically efficient truncated regression
We provide a computationally and statistically efficient estimator for the classical problem of
truncated linear regression, where the dependent variable $ y=\vec {w}^{\rm T}\vec …
truncated linear regression, where the dependent variable $ y=\vec {w}^{\rm T}\vec …
Efficient truncated statistics with unknown truncation
We study the problem of estimating the parameters of a Gaussian distribution when samples
are only shown if they fall in some (unknown) set. This core problem in truncated statistics …
are only shown if they fall in some (unknown) set. This core problem in truncated statistics …
Finite-sample symmetric mean estimation with fisher information rate
The mean of an unknown variance-$\sigma^ 2$ distribution $ f $ can be estimated from $ n $
samples with variance $\frac {\sigma^ 2}{n} $ and nearly corresponding subgaussian rate …
samples with variance $\frac {\sigma^ 2}{n} $ and nearly corresponding subgaussian rate …
Learning exponential families from truncated samples
Missing data problems have many manifestations across many scientific fields. A
fundamental type of missing data problem arises when samples are\textit {truncated}, ie …
fundamental type of missing data problem arises when samples are\textit {truncated}, ie …