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Found 16 papers, 7 papers with code
Explainable k-Means and k-Medians Clustering
In terms of negative results, we show that popular top-down decision tree algorithms may lead to clusterings with arbitrarily large cost, and we prove that any explainable clustering must incur an \Omega(\log k) approximation compared to the optimal clustering.
Benchmarking Robustness to Adversarial Image Obfuscations
We evaluate 33 pretrained models on the benchmark and train models with different augmentations, architectures and training methods on subsets of the obfuscations to measure generalization.
A Theoretical View on Sparsely Activated Networks
After representing LSH-based sparse networks with our model, we prove that sparse networks can match the approximation power of dense networks on Lipschitz functions.
Lower Bounds on the Total Variation Distance Between Mixtures of Two Gaussians
Mixtures of high dimensional Gaussian distributions have been studied extensively in statistics and learning theory.
Average-Case Communication Complexity of Statistical Problems
Our motivation is to understand the statistical-computational trade-offs in streaming, sketching, and query-based models.
Approximate Trace Reconstruction
In the usual trace reconstruction problem, the goal is to exactly reconstruct an unknown string of length $n$ after it passes through a deletion channel many times independently, producing a set of traces (i. e., random subsequences of the string).
Probing Predictions on OOD Images via Nearest Categories
Overall, adversarially robust networks resemble a nearest neighbor classifier when it comes to OOD data.
Unsupervised Embedding of Hierarchical Structure in Euclidean Space
To learn the embedding, we revisit using a variational autoencoder with a Gaussian mixture prior, and we show that rescaling the latent space embedding and then applying Ward's linkage-based algorithm leads to improved results for both dendrogram purity and the Moseley-Wang cost function.
Trace Reconstruction Problems in Computational Biology
The problem of reconstructing a string from its error-prone copies, the trace reconstruction problem, was introduced by Vladimir Levenshtein two decades ago.
Vector-Matrix-Vector Queries for Solving Linear Algebra, Statistics, and Graph Problems
We consider the general problem of learning about a matrix through vector-matrix-vector queries.
ExKMC: Expanding Explainable $k$-Means Clustering
To allow flexibility, we develop a new explainable $k$-means clustering algorithm, ExKMC, that takes an additional parameter $k' \geq k$ and outputs a decision tree with $k'$ leaves.
LSF-Join: Locality Sensitive Filtering for Distributed All-Pairs Set Similarity Under Skew
Theoretically, we show that LSF-Join efficiently finds most close pairs, even for small similarity thresholds and for skewed input sets.
A Closer Look at Accuracy vs. Robustness
Current methods for training robust networks lead to a drop in test accuracy, which has led prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning.
Explainable $k$-Means and $k$-Medians Clustering
In terms of negative results, we show, first, that popular top-down decision tree algorithms may lead to clusterings with arbitrarily large cost, and second, that any tree-induced clustering must in general incur an $\Omega(\log k)$ approximation factor compared to the optimal clustering.
Robustness for Non-Parametric Classification: A Generic Attack and Defense
To test our defense, we provide a novel attack that applies to a wide range of non-parametric classifiers.
Clustering Billions of Reads for DNA Data Storage
We provide empirical justification of the accuracy, scalability, and convergence of our algorithm on real and synthetic data.
