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Found 8 papers, 4 papers with code
Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems
We consider stochastic optimization problems with heavy-tailed noise with structured density.
Exploring Local Norms in Exp-concave Statistical Learning
We consider the problem of stochastic convex optimization with exp-concave losses using Empirical Risk Minimization in a convex class.
A Contrastive Approach to Online Change Point Detection
We suggest a novel procedure for online change point detection.
Exponential Savings in Agnostic Active Learning through Abstention
We show that in pool-based active classification without assumptions on the underlying distribution, if the learner is given the power to abstain from some predictions by paying the price marginally smaller than the average loss $1/2$ of a random guess, exponential savings in the number of label requests are possible whenever they are possible in the corresponding realizable problem.
Rates of convergence for density estimation with generative adversarial networks
In this work we undertake a thorough study of the non-asymptotic properties of the vanilla generative adversarial networks (GANs).
Manifold-based time series forecasting
Prediction for high dimensional time series is a challenging task due to the curse of dimensionality problem.
Denoising
Time Series Forecasting
Statistics Theory
Statistics Theory
Structure-adaptive manifold estimation
We consider a problem of manifold estimation from noisy observations.
An adaptive multiclass nearest neighbor classifier
We consider a problem of multiclass classification, where the training sample $S_n = \{(X_i, Y_i)\}_{i=1}^n$ is generated from the model $\mathbb P(Y = m | X = x) = \eta_m(x)$, $1 \leq m \leq M$, and $\eta_1(x), \dots, \eta_M(x)$ are unknown $\alpha$-Holder continuous functions. Given a test point $X$, our goal is to predict its label.
