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Found 7 papers, 0 papers with code
Sharp bounds for population recovery
The population recovery problem is a basic problem in noisy unsupervised learning that has attracted significant research attention in recent years [WY12, DRWY12, MS13, BIMP13, LZ15, DST16].
Optimal mean-based algorithms for trace reconstruction
For any constant deletion rate $0 < \delta < 1$, we give a mean-based algorithm that uses $\exp(O(n^{1/3}))$ time and traces; we also prove that any mean-based algorithm must use at least $\exp(\Omega(n^{1/3}))$ traces.
Learning sparse mixtures of rankings from noisy information
We study the problem of learning an unknown mixture of $k$ rankings over $n$ elements, given access to noisy samples drawn from the unknown mixture.
Learning large-margin halfspaces with more malicious noise
We describe a simple algorithm that runs in time poly(n, 1/gamma, 1/eps) and learns an unknown n-dimensional gamma-margin halfspace to accuracy 1-eps in the presence of malicious noise, when the noise rate is allowed to be as high as Theta(eps gamma sqrt(log(1/gamma))).
Algorithms and hardness results for parallel large margin learning
Our main negative result deals with boosting, which is a standard approach to learning large-margin halfspaces.
Adaptive Martingale Boosting
In recent work Long and Servedio LS05short presented a ``martingale boosting'' algorithm that works by constructing a branching program over weak classifiers and has a simple analysis based on elementary properties of random walks.
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals
This lower bound is essentially best possible since an SQ algorithm of Klivans et al. (2008) agnostically learns this class to any constant excess error using $n^{O(\log k)}$ queries of tolerance $n^{-O(\log k)}$.
