Tatiana Konstantinova

According to our database, Tatiana Konstantinova authored at least 10 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

A Fully Convolutional Approach to Denoising Structural Dynamics Data from X-Ray Photon Correlation Spectroscopy.

Nisar Nellikunnummel, Andi M. Barbour, Lutz Wiegart, Tatiana Konstantinova, Anthony M. DeGennaro

CoRR, May, 2026

Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables.

Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova, Shankar Ramasubramanian, Malcolm Wolff, Kin G. Olivares, Ruijun Ma, Mengfei Cao, Michael W. Mahoney, Andrew Gordon Wilson, Boris N. Oreshkin, Dmitry Efimov

CoRR, March, 2026

Zero-shot Forecasting by Simulation Alone.

Boris N. Oreshkin, Mayank Jauhari, Ravi Kiran Selvam, Malcolm Wolff, Wenhao Pan, Shankar Ramasubramanian, Kin G. Olivares, Tatiana Konstantinova, Andres Potapczynski, Mengfei Cao, Dmitry Efimov, Michael W. Mahoney, Andrew Gordon Wilson

CoRR, January, 2026

2025

Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models.

Ethan Baron, Boris N. Oreshkin, Ruijun Ma, Hanyu Zhang, Kari Torkkola, Michael W. Mahoney, Andrew Gordon Wilson, Tatiana Konstantinova

CoRR, October, 2025

A Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models.

Kin G. Olivares, Malcolm Wolff, Tatiana Konstantinova, Shankar Ramasubramanian, Andrew Gordon Wilson, Andres Potapczynski, Willa Potosnak, Mengfei Cao, Boris N. Oreshkin, Dmitry Efimov

CoRR, September, 2025

2024

Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings.

Mariya Bessonov, Ilia Ilmer, Tatiana Konstantinova, Alexey Ovchinnikov, Gleb Pogudin, Pedro Soto

Proceedings of the 2024 International Symposium on Symbolic and Algebraic Computation, 2024

2022

Obtaining weights for Gröbner basis computation in parameter identifiability problems.

Mariya Bessonov, Ilia Ilmer, Tatiana Konstantinova, Alexey Ovchinnikov, Gleb Pogudin, Pedro Soto

CoRR, 2022

Machine Learning Enhances Algorithms for Quantifying Non-Equilibrium Dynamics in Correlation Spectroscopy Experiments to Reach Frame-Rate-Limited Time Resolution.

Tatiana Konstantinova, Lutz Wiegart, Maksim S. Rakitin, Anthony M. DeGennaro, Andi M. Barbour

CoRR, 2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities.

Tatiana Konstantinova, Phillip M. Maffettone, Bruce Ravel, Stuart I. Campbell, Andi M. Barbour, Daniel Olds

CoRR, 2022

2021

Noise Reduction in X-ray Photon Correlation Spectroscopy with Convolutional Neural Networks Encoder-Decoder Models.

Tatiana Konstantinova, Lutz Wiegart, Maksim S. Rakitin, Anthony M. DeGennaro, Andi M. Barbour

CoRR, 2021