no code implementations • 1 Feb 2024 • Jin Sima, Changlong Wu, Olgica Milenkovic, Wojciech Szpankowski
We study the problem of online conditional distribution estimation with \emph{unbounded} label sets under local differential privacy.
no code implementations • 27 Jan 2024 • 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.
no code implementations • 31 Jan 2023 • 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.
no code implementations • 9 Sep 2022 • Changlong Wu, Mohsen Heidari, Ananth Grama, Wojciech Szpankowski
We show that for a hypothesis class of VC-dimension $\mathsf{VC}$ and $i. i. d.$ generated features of length $T$, the cardinality of the stochastic global sequential covering can be upper bounded with high probability (whp) by $e^{O(\mathsf{VC} \cdot \log^2 T)}$.
no code implementations • 7 May 2022 • Changlong Wu, Mohsen Heidari, Ananth Grama, Wojciech Szpankowski
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.
no code implementations • 10 Dec 2019 • Changlong Wu, Narayana Prasad Santhanam
Given a length $n$ sample from $\mathbb{R}^d$ and a neural network with a fixed architecture with $W$ weights, $k$ neurons, linear threshold activation functions, and binary outputs on each neuron, we study the problem of uniformly sampling from all possible labelings on the sample corresponding to different choices of weights.
