4 citations · 6 across the 12 of their papers we have counts for
13 papers
Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space
Yue Cao, Jianyang Gu, Vyacheslav Kungurtsev +4
Dataset distillation (DD) has proven to reduce training cost while preserving accuracy. While promising, the factors that make one distilled dataset more effective than another rem…
Symplectic Neural Networks for Learning Non-Separable Hamiltonians
Harsh Choudhary, Vyacheslav Kungurtsev, Chandan Gupta +2
Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample efficiency. Identifying the…
CONCORD: Concept-Informed Diffusion for Dataset Distillation
Jianyang Gu, Haonan Wang, Ruoxi Jia +4
Dataset distillation (DD) has witnessed significant progress in creating small datasets that encapsulate rich information from large original ones. Particularly, methods based on g…
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning
Vyacheslav Kungurtsev, Yuanfang Peng, Jianyang Gu +4
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 achi…
Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference
Vyacheslav Kungurtsev, Apaar, Aarya Khandelwal +3
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 da…
Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons
Vyacheslav Kungurtsev, Fadwa Idlahcen, Petr Rysavy +2
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.…
Spectral Methods for Quantum Optimal Control: Artificial Boundary Conditions
Ales Wodecki, Jakub Marecek, Vyacheslav Kungurtsev +3
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…
Group Distributionally Robust Dataset Distillation with Risk Minimization
Saeed Vahidian, Mingyu Wang, Jianyang Gu +3
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…
The Effects of Transmission-Rights Pricing on Multi-Stage Electricity Markets
Erwann de Belloy de Saint-Lienard, Jakub Marecek, Vyacheslav Kungurtsev
Cross-border transmission infrastructure is pivotal in balancing modern power systems, but requires fair allocation of cross-border transmission capacity, possibly via fair pricing…
Truss topology design under harmonic loads: Peak power minimization with semidefinite programming
Shenyuan Ma, Jakub Marecek, Vyacheslav Kungurtsev +1
Designing lightweight yet stiff structures that can withstand vibrations is a crucial task in structural optimization. Here, we present a novel framework for truss topology optimiz…
Scheduling a Multi-Product Pipeline: A Discretized MILP Formulation
Ales Wodecki, Pavel Rytir, Vyacheslav Kungurtsev +1
Multi-product pipelines are a highly efficient means of transporting liquids. Traditionally used to transport petroleum, its products and derivatives, they are now being repurposed…
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents
Yuqi Jia, Saeed Vahidian, Jingwei Sun +4
Data heterogeneity presents significant challenges for federated learning (FL). Recently, dataset distillation techniques have been introduced, and performed at the client level, t…
Efficient Dataset Distillation via Minimax Diffusion
Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev +4
Dataset distillation reduces the storage and computational consumption of training a network by generating a small surrogate dataset that encapsulates rich information of the origi…