Zobrazeno 1 - 10
of 203
pro vyhledávání: '"Kungurtsev, Vyacheslav"'
Publikováno v:
38th Conference on Neural Information Processing Systems (NeurIPS 2024)
Federated Learning has emerged as a promising paradigm for collaborative machine learning, while preserving user data privacy. Despite its potential, standard FL lacks support for diverse heterogeneous device prototypes, which vary significantly in m
Externí odkaz:
http://arxiv.org/abs/2409.18461
For statistical modeling wherein the data regime is unfavorable in terms of dimensionality relative to the sample size, finding hidden sparsity in the ground truth can be critical in formulating an accurate statistical model. The so-called "l0 norm"
Externí odkaz:
http://arxiv.org/abs/2409.01413
Autor:
Kungurtsev, Vyacheslav, Peng, Yuanfang, Gu, Jianyang, Vahidian, Saeed, Quinn, Anthony, Idlahcen, Fadwa, Chen, Yiran
Dataset distillation (DD) is an increasingly important technique that focuses on constructing a synthetic dataset capable of capturing the core information in training data to achieve comparable performance in models trained on the latter. While DD h
Externí odkaz:
http://arxiv.org/abs/2409.01410
In this paper, we present a guide to the foundations of learning Dynamic Bayesian Networks (DBNs) from data in the form of multiple samples of trajectories for some length of time. We present the formalism for a generic as well as a set of common typ
Externí odkaz:
http://arxiv.org/abs/2406.17585
Autor:
Kungurtsev, Vyacheslav, Apaar, Khandelwal, Aarya, Rastogi, Parth Sandeep, Chatterjee, Bapi, Mareček, Jakub
In this work, we demonstrate the Empirical Bayes approach to learning a Dynamic Bayesian Network. By starting with several point estimates of structure and weights, we can use a data-driven prior to subsequently obtain a model to quantify uncertainty
Externí odkaz:
http://arxiv.org/abs/2406.17831
We consider an optimal control problem where the state is governed by a free boundary problem called the two-phase membrane problem and the control appears in the coefficients of the characteristic function of the positivity and negativity parts of t
Externí odkaz:
http://arxiv.org/abs/2405.10704
Optimal control of closed quantum systems is a well studied geometrically elegant set of computational theory and techniques that have proven pivotal in the implementation and understanding of quantum computers. The design of a circuit itself corresp
Externí odkaz:
http://arxiv.org/abs/2404.15828
Autor:
Wodecki, Ales, Marecek, Jakub, Kungurtsev, Vyacheslav, Eichler, Pavel, Korpas, Georgios, Intallura, Philip
The problem of quantum state preparation is one of the main challenges in achieving the quantum advantage. Furthermore, classically, for multi-level problems, our ability to solve the corresponding quantum optimal control problems is rather limited.
Externí odkaz:
http://arxiv.org/abs/2403.14436
In a real Hilbert space domain setting, we study the convergence properties of the stochastic Ravine accelerated gradient method for convex differentiable optimization. We consider the general form of this algorithm where the extrapolation coefficien
Externí odkaz:
http://arxiv.org/abs/2403.04860
Dataset distillation (DD) has emerged as a widely adopted technique for crafting a synthetic dataset that captures the essential information of a training dataset, facilitating the training of accurate neural models. Its applications span various dom
Externí odkaz:
http://arxiv.org/abs/2402.04676