Testing distributional assumptions of learning algorithms

Cites Work

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Learning intersections and thresholds of halfspaces
Sublinear time algorithms for earth mover's distance
Almost \(k\)-wise independence versus \(k\)-wise independence
A geometric inequality and the complexity of computing volume
Computing the volume is difficult
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Toward efficient agnostic learning
An \(O(n^{\log \log n})\) learning algorithm for DNF under the uniform distribution
Lower bounds on learning decision lists and trees
Polynomial regression under arbitrary product distributions
Optimal hypothesis testing for high dimensional covariance matrices
Bounding the average sensitivity and noise sensitivity of polynomial threshold functions
An invariance principle for polytopes
On Testing Expansion in Bounded-Degree Graphs
Constant depth circuits, Fourier transform, and learnability
The Power of Localization for Efficiently Learning Linear Separators with Noise
Learning Monotone Decision Trees in Polynomial Time
Agnostically Learning Halfspaces
On Agnostic Learning of Parities, Monomials, and Halfspaces
Hardness of Learning Halfspaces with Noise
Learning and Lower Bounds for AC 0 with Threshold Gates
A Coincidence-Based Test for Uniformity Given Very Sparsely Sampled Discrete Data
Learning Halfspaces with Malicious Noise
Optimal Identity Testing with High Probability
Bounded Independence Fools Halfspaces
Collision-based Testers are Optimal for Uniformity and Closeness
Learning circuits with few negations
Complexity theoretic limitations on learning halfspaces
Approximate resilience, monotonicity, and the complexity of agnostic learning
Testing Closeness of Discrete Distributions
Topics and Techniques in Distribution Testing: A Biased but Representative Sample

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