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Found 14 papers, 0 papers with code
Tight Bounds for Quantum State Certification with Incoherent Measurements
When $\sigma$ is the maximally mixed state $\frac{1}{d} I_d$, this is known as mixedness testing.
Semi-Random Sparse Recovery in Nearly-Linear Time
We design a new iterative method tailored to the geometry of sparse recovery which is provably robust to our semi-random model.
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication
We ask whether it is possible to obtain optimal instance-dependent regret $\tilde{O}(1/\Delta)$ where $\Delta$ is the gap between the $m$-th and $m+1$-st best arms.
Robust Voting Rules from Algorithmic Robust Statistics
In this work we study the problem of robustly learning a Mallows model.
Clustering Mixtures with Almost Optimal Separation in Polynomial Time
We give the first algorithm which runs in polynomial time, and which almost matches this guarantee.
Margin-Independent Online Multiclass Learning via Convex Geometry
We consider the problem of multi-class classification, where a stream of adversarially chosen queries arrive and must be assigned a label online.
Sparsification for Sums of Exponentials and its Algorithmic Applications
In particular, we give the first algorithms for approximately (and robustly) determining the number of components in a Gaussian mixture model that work without a separation condition.
Algorithms from Invariants: Smoothed Analysis of Orbit Recovery over $SO(3)$
Our main result is a quasi-polynomial time algorithm for orbit recovery over $SO(3)$ in this model.
Learning GMMs with Nearly Optimal Robustness Guarantees
In this work we solve the problem of robustly learning a high-dimensional Gaussian mixture model with $k$ components from $\epsilon$-corrupted samples up to accuracy $\widetilde{O}(\epsilon)$ in total variation distance for any constant $k$ and with mild assumptions on the mixture.
Myersonian Regression
Motivated by pricing applications in online advertising, we study a variant of linear regression with a discontinuous loss function that we term Myersonian regression.
Settling the Robust Learnability of Mixtures of Gaussians
This work represents a natural coalescence of two important lines of work: learning mixtures of Gaussians and algorithmic robust statistics.
Tensor Completion Made Practical
We show strong provable guarantees, including showing that our algorithm converges linearly to the true tensors even when the factors are highly correlated and can be implemented in nearly linear time.
Optimal Contextual Pricing and Extensions
We provide a generic algorithm with $O(d^2)$ regret where $d$ is the covering dimension of this class.
Efficiently Learning Mixtures of Mallows Models
Mixtures of Mallows models are a popular generative model for ranking data coming from a heterogeneous population.
