diakonikola
A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise
In this paper, we study the same problem in the Massart noise model that we now define.
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Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
Finding an approximate second-order stationary point (SOSP) is a well-studied and fundamental problem in stochastic nonconvex optimization with many applications in machine learning.
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Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
In fact, without such assumptions, this problem is NP-hard [Sim02; MR18].
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Outlier-robustestimationofasparselinearmodel using`1-penalized Huber'sM-estimator
We study the problem of estimating ap-dimensional s-sparse vector in a linear model with Gaussian design and additivenoise.
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Testing for Families of Distributions via the Fourier Transform
Alistair Stewart, Ilias Diakonikolas, Clement Canonne
Neural Information Processing Systems http://nips.cc/
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Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Eric Price, Alistair Stewart
Neural Information Processing Systems http://nips.cc/
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Learning
For additional motivation, it is reasonable to consider Massart noise to be a more realistic model of real-life noise (even when benign) when compared to the RCN model, as it allows for some amount of non-uniformity. This made Definition 1 a possibly tractable way to relax the noise assumption, without running intotheaforementioned computational barriers foragnostic learning.
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Private Testing of Distributions via Sample Permutations
Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld
Neural Information Processing Systems http://nips.cc/
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