2026
  1. Muon does not converge on convex Lipschitz functions
    T. Parshakova, A. Khaled, M. Crawshaw, G. Garrigos, and R. M. Gower
    arXiv preprint arXiv:2605.08980, 2026
    paper Slides
2025
  1. Multiple approximate-response agents (MARA): fast near-optimal primal recovery for distributed optimization
    T. Parshakova, Y. Bai, G. Ryzin, and S. Boyd
    arXiv preprint arXiv:2503.12221, 2025
    paper Code Poster
  2. Fitting multilevel factor models
    T. Parshakova, T. Hastie, and S. Boyd
    SIAM Journal on Matrix Analysis and Applications, 2025
    paper Code
2024
  1. Optimization algorithm design via electric circuits
    S. Boyd, T. Parshakova, E. Ryu, and J. Suh
    Advances in Neural Information Processing System (Spotlight), 2024
    paper Code Poster Slides
  2. Multilevel low rank matrices and applications
    T. Parshakova
    Stanford University, 2024
    thesis Slides
  3. Factor fitting, rank allocation, and partitioning in multilevel low rank matrices
    T. Parshakova, T. Hastie, E. Darve, and S. Boyd
    Optimization, Discrete Mathematics, and Applications to Data Sciences, Springer Optimization and Its Applications, 2024
    paper Code Poster
2023
  1. Efficient graph field integrators meet point clouds
    K. Choromanski, A. Sehanobish, H. Lin, Y. Zhao, E. Berger, T. Parshakova, and  others
    International Conference on Machine Learning, 2023
    paper Code Poster
  2. Implementation of an oracle-structured bundle method for distributed optimization
    T. Parshakova, F. Zhang, and S. Boyd
    Optimization and Engineering, 2023
    paper Code Slides
2022
  1. Interpolation method and apparatus for arithmetic functions
    W. Athas, Z. Nadeem, and T. Parshakova
    2022
    US Patent App. 17/085,971
    website
  2. Methods and systems for producing neural sequential models
    T. Parshakova, M. Dymetman, and J.-M. Andreoli
    2022
    US Patent App. 17/018,754
    website
2019
  1. Distributional reinforcement learning for energy-based sequential models
    T. Parshakova, J.-M. Andreoli, and M. Dymetman
    NeurIPS 2019 Optimization Foundations of Reinforcement Learning Workshop, 2019
    paper Code Poster
  2. Global autoregressive models for data-efficient sequence learning
    T. Parshakova, J.-M. Andreoli, and M. Dymetman
    The SIGNLL Conference on Computational Natural Language Learning, 2019
    paper Code Poster
  3. Latent question interpretation through variational adaptation
    T. Parshakova, F. Rameau, A. Serdega, I. Kweon, and D.-S. Kim
    IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019
    paper Code
  4. Latent Question Interpretation: Parameter Adaptation Using Interpretation Policy
    T. Parshakova
    KAIST, 2019
    paper
2018
  1. Latent question interpretation through parameter adaptation using stochastic neuron
    T. Parshakova, and D.-S. Kim
    In MRC@IJCAI, 2018
    paper Code
  2. UMorph: Self-change tracker to reflect yourself to the future and past
    T. Parshakova, and D. Saakes
    In Proceedings of the 2018 ACM Conference Companion Publication on Designing Interactive Systems, 2018
    paper
2017
  1. Furniture that learns to move itself
    T. Parshakova, M. Cho, A. Cassinelli, and D. Saakes
    In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 2017
    paper
2016
  1. Ratchair: Furniture learns to move itself with vibration
    T. Parshakova, M. Cho, A. Cassinelli, and D. Saakes
    In ACM SIGGRAPH 2016 Emerging Technologies, 2016
    paper