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Found 6 papers, 0 papers with code
Learning-Augmented Algorithms for Online Linear and Semidefinite Programming
We show that if the predictor is accurate, we can efficiently bypass these impossibility results and achieve a constant-factor approximation to the optimal solution, i. e., consistency.
Hardness of Maximum Likelihood Learning of DPPs
In seminal work on DPPs in Machine Learning, Kulesza conjectured in his PhD Thesis (2011) that the problem of finding a maximum likelihood DPP model for a given data set is NP-complete.
Differentially-Private Sublinear-Time Clustering
Clustering is an essential primitive in unsupervised machine learning.
List Learning with Attribute Noise
We introduce and study the model of list learning with attribute noise.
Communication-Efficient Distributed Learning of Discrete Distributions
For the case of structured distributions, such as k-histograms and monotone distributions, we design distributed learning algorithms that achieve significantly better communication guarantees than the naive ones, and obtain tight upper and lower bounds in several regimes.
Testing $k$-Monotonicity
Our results include the following: - We demonstrate a separation between testing $k$-monotonicity and testing monotonicity, on the hypercube domain $\{0, 1\}^d$, for $k\geq 3$; - We demonstrate a separation between testing and learning on $\{0, 1\}^d$, for $k=\omega(\log d)$: testing $k$-monotonicity can be performed with $2^{O(\sqrt d \cdot \log d\cdot \log{1/\varepsilon})}$ queries, while learning $k$-monotone functions requires $2^{\Omega(k\cdot \sqrt d\cdot{1/\varepsilon})}$ queries (Blais et al. (RANDOM 2015)).
