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Found 13 papers, 2 papers with code
Active causal structure learning with advice
When the advice is a DAG $G$, we design an adaptive search algorithm to recover $G^*$ whose intervention cost is at most $O(\max\{1, \log \psi\})$ times the cost for verifying $G^*$; here, $\psi$ is a distance measure between $G$ and $G^*$ that is upper bounded by the number of variables $n$, and is exactly 0 when $G=G^*$.
Learning-Augmented Algorithms for Online TSP on the Line
Our algorithm for this enhanced setting obtains a 1. 33 competitive ratio with perfect predictions while also being smooth and robust, beating the lower bound of 1. 44 we show for our original prediction setting for the open variant.
Learning Augmented Online Facility Location
We prove that the competitive ratio decreases smoothly from sublogarithmic in the number of demands to constant, as the error, i. e., the total distance of the predicted locations to the optimal facility locations, decreases towards zero.
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 $y = w^T x + \epsilon$ and its corresponding vector of covariates $x \in R^k$ are only revealed if the dependent variable falls in some subset $S \subseteq R$; otherwise the existence of the pair $(x, y)$ is hidden.
Robust Learning under Strong Noise via SQs
This work provides several new insights on the robustness of Kearns' statistical query framework against challenging label-noise models.
Optimal Testing of Discrete Distributions with High Probability
To illustrate the generality of our methods, we give optimal algorithms for testing collections of distributions and testing closeness with unequal sized samples.
Towards Testing Monotonicity of Distributions Over General Posets
We then build on these lower bounds to give $\Omega(n/\log{n})$ lower bounds for testing monotonicity over a matching poset of size $n$ and significantly improved lower bounds over the hypercube poset.
Distribution-Independent PAC Learning of Halfspaces with Massart Noise
The goal is to find a hypothesis $h$ that minimizes the misclassification error $\mathbf{Pr}_{(\mathbf{x}, y) \sim \mathcal{D}} \left[ h(\mathbf{x}) \neq y \right]$.
Communication and Memory Efficient Testing of Discrete Distributions
We study distribution testing with communication and memory constraints in the following computational models: (1) The {\em one-pass streaming model} where the goal is to minimize the sample complexity of the protocol subject to a memory constraint, and (2) A {\em distributed model} where the data samples reside at multiple machines and the goal is to minimize the communication cost of the protocol.
Efficient Statistics, in High Dimensions, from Truncated Samples
We provide an efficient algorithm for the classical problem, going back to Galton, Pearson, and Fisher, of estimating, with arbitrary accuracy the parameters of a multivariate normal distribution from truncated samples.
Optimal Identity Testing with High Probability
Our new upper and lower bounds show that the optimal sample complexity of identity testing is \[ \Theta\left( \frac{1}{\epsilon^2}\left(\sqrt{n \log(1/\delta)} + \log(1/\delta) \right)\right) \] for any $n, \varepsilon$, and $\delta$.
Collision-based Testers are Optimal for Uniformity and Closeness
We study the fundamental problems of (i) uniformity testing of a discrete distribution, and (ii) closeness testing between two discrete distributions with bounded $\ell_2$-norm.
Sampling Correctors
We then focus on the question of whether algorithms for sampling correctors can be more efficient in terms of sample complexity than learning algorithms for the analogous families of distributions.
