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Found 4 papers, 1 papers with code
Lightweight Knowledge Representations for Automating Data Analysis
The principal goal of data science is to derive meaningful information from data.
On Robustness to Adversarial Examples and Polynomial Optimization
In particular, we leverage this connection to (a) design computationally efficient robust algorithms with provable guarantees for a large class of hypothesis, namely linear classifiers and degree-2 polynomial threshold functions (PTFs), (b) give a precise characterization of the price of achieving robustness in a computationally efficient manner for these classes, (c) design efficient algorithms to certify robustness and generate adversarial attacks in a principled manner for 2-layer neural networks.
Clustering Stable Instances of Euclidean k-means
We design efficient algorithms that provably recover the optimal clustering for instances that are additive perturbation stable.
Clustering Stable Instances of Euclidean k-means.
To address this disconnect, we study the following question: what properties of real-world instances will enable us to design efficient algorithms and prove guarantees for finding the optimal clustering?
