3 citations · 14 across the 17 of their papers we have counts for
10 papers · 1 filter
Online Distribution Learning with Local Private Constraints
Jin Sima, Changlong Wu, Olgica Milenkovic +1
We study the problem of online conditional distribution estimation with \emph{unbounded} label sets under local differential privacy. Let be a distribution-valued fun…
Oracle-Efficient Hybrid Online Learning with Unknown Distribution
Changlong Wu, Jin Sima, Wojciech Szpankowski
We study the problem of oracle-efficient hybrid online learning when the features are generated by an unknown i.i.d. process and the labels are generated adversarially. Assuming ac…
Robust Online Classification: From Estimation to Denoising
Changlong Wu, Ananth Grama, Wojciech Szpankowski
We study online classification of features into labels with general hypothesis classes. In our setting, true labels are determined by some function within the hypothesis class but…
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression
Mohsen Heidari, Wojciech Szpankowski
Many conventional learning algorithms rely on loss functions other than the natural 0-1 loss for computational efficiency and theoretical tractability. Among them are approaches ba…
Online Learning in Dynamically Changing Environments
Changlong Wu, Ananth Grama, Wojciech Szpankowski
We study the problem of online learning and online regret minimization when samples are drawn from a general unknown non-stationary process. We introduce the concept of a dynamic c…
Expected Worst Case Regret via Stochastic Sequential Covering
Changlong Wu, Mohsen Heidari, Ananth Grama +1
We study the problem of sequential prediction and online minimax regret with stochastically generated features under a general loss function. We introduce a notion of expected wors…
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
Changlong Wu, Mohsen Heidari, Ananth Grama +1
We study the sequential general online regression, known also as the sequential probability assignments, under logarithmic loss when compared against a broad class of experts. We f…
On Agnostic PAC Learning using -polynomial Regression and Fourier-based Algorithms
Mohsen Heidari, Wojciech Szpankowski
We develop a framework using Hilbert spaces as a proxy to analyze PAC learning problems with structural properties. We consider a joint Hilbert space incorporating the relation bet…
Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning
Gil I. Shamir, Wojciech Szpankowski
Theoretical results show that Bayesian methods can achieve lower bounds on regret for online logistic regression. In practice, however, such techniques may not be feasible especial…
Statistical and computational thresholds for the planted -densest sub-hypergraph problem
Luca Corinzia, Paolo Penna, Wojciech Szpankowski +1
In this work, we consider the problem of recovery a planted -densest sub-hypergraph on -uniform hypergraphs. This fundamental problem appears in different contexts, e.g., com…