Search Results for author:
Found 12 papers, 1 papers with code
Empirical Risk Minimization for Losses without Variance
This paper considers an empirical risk minimization problem under heavy-tailed settings, where data does not have finite variance, but only has $p$-th moment with $p \in (1, 2)$.
Epsilon*: Privacy Metric for Machine Learning Models
We introduce Epsilon*, a new privacy metric for measuring the privacy risk of a single model instance prior to, during, or after deployment of privacy mitigation strategies.
Adaptive Privacy Composition for Accuracy-first Mechanisms
In many practical applications of differential privacy, practitioners seek to provide the best privacy guarantees subject to a target level of accuracy.
A Cover Time Study of a non-Markovian Algorithm
In this work, we stand on a theoretical perspective and show that the negative feedback strategy (a count-based exploration method) is better than the naive random walk search.
Kernel PCA for multivariate extremes
We propose kernel PCA as a method for analyzing the dependence structure of multivariate extremes and demonstrate that it can be a powerful tool for clustering and dimension reduction.
On Penalization in Stochastic Multi-armed Bandits
We study an important variant of the stochastic multi-armed bandit (MAB) problem, which takes penalization into consideration.
Catoni-style Confidence Sequences under Infinite Variance
In this paper, we provide an extension of confidence sequences for settings where the variance of the data-generating distribution does not exist or is infinite.
Detection of Small Holes by the Scale-Invariant Robust Density-Aware Distance (RDAD) Filtration
A novel topological-data-analytical (TDA) method is proposed to distinguish, from noise, small holes surrounded by high-density regions of a probability density function.
Spectral learning of multivariate extremes
We propose a spectral clustering algorithm for analyzing the dependence structure of multivariate extremes.
A new look at fairness in stochastic multi-armed bandit problems
Under such a framework, we propose a hard-threshold UCB-like algorithm, which enjoys many merits including asymptotic fairness, nearly optimal regret, better tradeoff between reward and fairness.
Extreme Bandits using Robust Statistics
We consider a multi-armed bandit problem motivated by situations where only the extreme values, as opposed to expected values in the classical bandit setting, are of interest.
Sign Stable Projections, Sign Cauchy Projections and Chi-Square Kernels
The method of stable random projections is popular for efficiently computing the Lp distances in high dimension (where 0<p<=2), using small space.
