Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations | Paper Citation Record

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This paper cites Polynomial time and private learning of unbounded gaussian mixture models.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Polynomial time and private learning of unbounded gaussian mixture models

Reference 1

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This paper cites Mixtures of gaussians are privately learnable with a polynomial number of samples.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Mixtures of gaussians are privately learnable with a polynomial number of samples

Reference 2

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This paper cites Nearly tight sample complexity bounds for learning mixtures of gaussians via sample compression schemes.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Nearly tight sample complexity bounds for learning mixtures of gaussians via sample compression schemes

Reference 3

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This paper cites Near-optimal sample complexity bounds for robust learning of gaussian mixtures via compression schemes.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Near-optimal sample complexity bounds for robust learning of gaussian mixtures via compression schemes

Reference 4

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This paper cites Efficient learning of simplices.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Efficient learning of simplices

Reference 5

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This paper cites Private and polynomial time algorithms for learning gaussians and beyond.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Private and polynomial time algorithms for learning gaussians and beyond

Reference 6

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This paper cites Private distribution learning with public data: The view from sample compression.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Private distribution learning with public data: The view from sample compression

Reference 7

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This paper cites Minimax rates for conditional density estimation via empirical entropy.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Minimax rates for conditional density estimation via empirical entropy

Reference 8

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This paper cites Learning smooth shapes by probing.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Learning smooth shapes by probing

Reference 9

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This paper cites Density estimation on an unknown submanifold.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Density estimation on an unknown submanifold

Reference 10

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This paper cites Sharp rate of average decay of the fourier transform of a bounded set.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Sharp rate of average decay of the fourier transform of a bounded set

Reference 11

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This paper cites Not all learnable distribution classes are privately learnable.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Not all learnable distribution classes are privately learnable

Reference 12

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Observation 887a3db6-91d9-411c-a7f5-65b67bb0518b · outbound

This paper cites Model-based learning using a mixture of mixtures of gaussian and uniform distributions.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Model-based learning using a mixture of mixtures of gaussian and uniform distributions

Reference 13

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This paper cites Efficiently learning ising models on arbitrary graphs.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Efficiently learning ising models on arbitrary graphs

Reference 14

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This paper cites A gaussian uniform mixture model for robust kalman filtering.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations A gaussian uniform mixture model for robust kalman filtering

Reference 15

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This paper cites Convex Optimization.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Convex Optimization

Reference 16

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This paper cites A functional approach to data structures and its use in multidimensional searching.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations A functional approach to data structures and its use in multidimensional searching

Reference 17

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This paper cites Learning from untrusted data.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Learning from untrusted data

Reference 18

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This paper cites Computational Geometry: Algorithms and Applications.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Computational Geometry: Algorithms and Applications

Reference 19

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This paper cites A probabilistic theory of pattern recognition , volume 31.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations A probabilistic theory of pattern recognition , volume 31

Reference 20

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This paper cites Combinatorial Methods in Density Estimation.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Combinatorial Methods in Density Estimation

Reference 21

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This paper cites The total variation distance between high-dimensional gaussians.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations The total variation distance between high-dimensional gaussians

Reference 22

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This paper cites The minimax learning rates of normal and ising undirected graphical models.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations The minimax learning rates of normal and ising undirected graphical models

Reference 23

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This paper cites Differential privacy.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Differential privacy

Reference 24

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This paper cites On the sample complexity of adversarial multi-source pac learning.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations On the sample complexity of adversarial multi-source pac learning

Reference 25

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This paper cites Learning in the presence of malicious errors.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Learning in the presence of malicious errors

Reference 26

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This paper cites Relating data compression and learnability.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Relating data compression and learnability

Reference 27

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This paper cites The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure

Reference 28

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This paper cites A brief history of generative models for power law and lognormal distributions.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations A brief history of generative models for power law and lognormal distributions

Reference 29

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This paper cites Differential privacy with higher utility by exploiting coordinate-wise disparity: Laplace mechanism can beat gaussian in high dimensions.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Differential privacy with higher utility by exploiting coordinate-wise disparity: Laplace mechanism can beat gaussian in high dimensions

Reference 30

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This paper cites On statistical learning of simplices: Unmixing problem revisited.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations On statistical learning of simplices: Unmixing problem revisited

Reference 31

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Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Unresolved cited work

Reference 32

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This paper cites Machine learning for anomaly detection: A systematic review.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Machine learning for anomaly detection: A systematic review

Reference 33

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This paper cites Minimax estimation of smooth densities in wasserstein distance.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Minimax estimation of smooth densities in wasserstein distance

Reference 34

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This paper cites New upper bounds in klee’s measure problem.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations New upper bounds in klee’s measure problem

Reference 35

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This paper cites A fourier approach to mixture learning.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations A fourier approach to mixture learning

Reference 36

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This paper cites Real and Complex Analysis.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Real and Complex Analysis

Reference 37

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This paper cites Certifying some distributional robustness with principled adversarial training.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Certifying some distributional robustness with principled adversarial training

Reference 38

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This paper cites Sample complexity bounds for learning high-dimensional simplices in noisy regimes.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Sample complexity bounds for learning high-dimensional simplices in noisy regimes

Reference 39

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This paper cites Special functions: An introduction to the classical functions of mathematical physics.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Special functions: An introduction to the classical functions of mathematical physics

Reference 40

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This paper cites Tsybakov.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Tsybakov

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:53.890583Z

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Observation 3397e481-3d7d-453c-9425-a64559340a8a · outbound

This paper cites Minimax rate of distribution estimation on unknown submanifolds under adversarial losses.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Minimax rate of distribution estimation on unknown submanifolds under adversarial losses

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:53.611538Z

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This paper cites A theory of the learnable.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations A theory of the learnable

Reference 43

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This paper cites On minimax density estimation via measure transport.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations On minimax density estimation via measure transport

Reference 44

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This paper cites @esa (Ref.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations @esa (Ref

Reference 45

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Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Unresolved cited work

Reference 46

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This paper cites Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning.

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning

Reference 47

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