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Found 16 papers, 1 papers with code
Efficient Gradient Estimation of Variational Quantum Circuits with Lie Algebraic Symmetries
Hybrid quantum-classical optimization and learning strategies are among the most promising approaches to harnessing quantum information or gaining a quantum advantage over classical methods.
Online Distribution Learning with Local Private Constraints
We study the problem of online conditional distribution estimation with \emph{unbounded} label sets under local differential privacy.
Oracle-Efficient Hybrid Online Learning with Unknown Distribution
We study the problem of oracle-efficient hybrid online learning when the features are generated by an unknown i. i. d.
Quantum Shadow Gradient Descent for Quantum Learning
We propose a new technique for generating quantum shadow samples (QSS), which generates quantum shadows as opposed to classical shadows used in existing works.
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression
We derive an elegant upper bound on the 0-1 loss in terms of the MMSE error and show that the sign of the MMSE is a PAC learner for any concept class containing it.
Online Learning in Dynamically Changing Environments
We study the problem of online learning and online regret minimization when samples are drawn from a general unknown non-stationary process.
Expected Worst Case Regret via Stochastic Sequential Covering
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 $e^{O(\mathsf{VC} \cdot \log^2 T)}$.
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
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.
Toward Physically Realizable Quantum Neural Networks
Current solutions for QNNs pose significant challenges concerning their scalability, ensuring that the postulates of quantum mechanics are satisfied and that the networks are physically realizable.
On Agnostic PAC Learning using $\mathcal{L}_2$-polynomial Regression and Fourier-based Algorithms
We develop a framework using Hilbert spaces as a proxy to analyze PAC learning problems with structural properties.
Learning k-qubit Quantum Operators via Pauli Decomposition
Our approach is based on the Pauli decomposition of quantum operators and a technique that we name Quantum Shadow Sampling (QSS) to reduce the sample complexity exponentially.
Quantum Physics Data Structures and Algorithms
Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning
Various approximations that, for huge sparse feature sets, diminish the theoretical advantages, must be used.
On maximum-likelihood estimation in the all-or-nothing regime
The result follows from two main technical points: (i) the connection established between the MLE and the MMSE, using the first and second-moment methods in the constrained signal space, (ii) a recovery regime for the MMSE stricter than the simple error vanishing characterization given in the standard AoN, that is here proved as a general result.
Statistical and computational thresholds for the planted $k$-densest sub-hypergraph problem
In this work, we consider the problem of recovery a planted $k$-densest sub-hypergraph on $d$-uniform hypergraphs.
Temporal Ordered Clustering in Dynamic Networks: Unsupervised and Semi-supervised Learning Algorithms
In temporal ordered clustering, given a single snapshot of a dynamic network in which nodes arrive at distinct time instants, we aim at partitioning its nodes into $K$ ordered clusters $\mathcal{C}_1 \prec \cdots \prec \mathcal{C}_K$ such that for $i<j$, nodes in cluster $\mathcal{C}_i$ arrived before nodes in cluster $\mathcal{C}_j$, with $K$ being a data-driven parameter and not known upfront.
Toward Universal Testing of Dynamic Network Models
Numerous networks in the real world change over time, in the sense that nodes and edges enter and leave the networks.
