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Found 14 papers, 1 papers with code
Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation
Furthermore, under a variant of the "no large sub-cluster'' condition from in prior work [BKK22], we show that our algorithm outputs an accurate clustering, not just a refinement, even for general-weight mixtures.
Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+α$ Moments
The state of the art results for mean estimation in $\mathbb{R}$ are 1) the optimal sub-Gaussian mean estimator by [LV22], with the tight sub-Gaussian constant for all distributions with finite but unknown variance, and 2) the analysis of the median-of-means algorithm by [BCL13] and a lower bound by [DLLO16], characterizing the big-O optimal errors for distributions for which only a $1+\alpha$ moment exists for $\alpha \in (0, 1)$.
Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints
We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework.
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.
Branch & Learn with Post-hoc Correction for Predict+Optimize with Unknown Parameters in Constraints
Combining machine learning and constrained optimization, Predict+Optimize tackles optimization problems containing parameters that are unknown at the time of solving.
High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors
In location estimation, we are given $n$ samples from a known distribution $f$ shifted by an unknown translation $\lambda$, and want to estimate $\lambda$ as precisely as possible.
Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions
We study the fundamental task of outlier-robust mean estimation for heavy-tailed distributions in the presence of sparsity.
Predict+Optimize for Packing and Covering LPs with Unknown Parameters in Constraints
First, we propose a novel and practically relevant framework for the Predict+Optimize setting, but with unknown parameters in both the objective and the constraints.
Finite-Sample Maximum Likelihood Estimation of Location
We consider 1-dimensional location estimation, where we estimate a parameter $\lambda$ from $n$ samples $\lambda + \eta_i$, with each $\eta_i$ drawn i. i. d.
Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize
This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving.
Optimal Sub-Gaussian Mean Estimation in $\mathbb{R}$
We revisit the problem of estimating the mean of a real-valued distribution, presenting a novel estimator with sub-Gaussian convergence: intuitively, "our estimator, on any distribution, is as accurate as the sample mean is for the Gaussian distribution of matching variance."
Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies
Next, we derive a new anomaly estimator using a mixture model, and we prove that our anomaly estimator is asymptotically unbiased regardless of the size of the anomaly family.
Uncertainty about Uncertainty: Optimal Adaptive Algorithms for Estimating Mixtures of Unknown Coins
Given a mixture between two populations of coins, "positive" coins that each have -- unknown and potentially different -- bias $\geq\frac{1}{2}+\Delta$ and "negative" coins with bias $\leq\frac{1}{2}-\Delta$, we consider the task of estimating the fraction $\rho$ of positive coins to within additive error $\epsilon$.
Augmenting Stream Constraint Programming with Eventuality Conditions
Stream constraint programming is a recent addition to the family of constraint programming frameworks, where variable domains are sets of infinite streams over finite alphabets.
