2022

Globally Injective ReLU Networks
, , , and
Journal of Machine Learning Research, 2022
@article{puthawala2020globally,
  author  = {Michael Puthawala and Konik Kothari and Matti Lassas and Ivan Dokmani{\'c} and Maarten de Hoop},
  title   = {Globally Injective ReLU Networks},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {105},
  pages   = {1--55},
  url     = {http://jmlr.org/papers/v23/21-0282.html}
}
Learning Sub-Patterns in Piecewise Continuous Functions
and
Neurocomputing, 2022
@article{KRATSIOS2022,
title = {Learning Sub-Patterns in Piecewise Continuous Functions},
journal = {Neurocomputing},
year = {2022},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2022.01.036},
url = {https://www.sciencedirect.com/science/article/pii/S092523122200056X},
author = {Anastasis Kratsios and Behnoosh Zamanlooy},
}
Universal Approximation Under Constraints is Possible with Transformers
, , and
(ICLR) The Tenth International Conference on Learning Representations, 2022
@inproceedings{kratsios2022universal,
title={Universal Approximation Under Constraints is Possible with Transformers},
author={Kratsios, Anastasis and Zamanlooy, Behnoosh and Ivan, Dokmanic and Tianlin, Liu},
booktitle={(ICLR) The Tenth International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=JGO8CvG5S9},
note={under review}
}
Conditional Injective Flows for Bayesian Imaging
, , , , and
ArXiv, 2022
@article{ctrumpets,
  title={Conditional Injective Flows for Bayesian Imaging},
  author={AmirEhsan Khorashadizadeh and Konik Kothari and Leonardo Salsi and Ali Aghababaei Harandi and Maarten de Hoop and Ivan Dokmani\'c},
  journal={ArXiv},
  year={2022},
  projectpage = {http://sada.dmi.unibas.ch/en/research/injective-flows},
  volume={abs/2204.07664},
  eprint={2204.07664},
  archivePrefix={arXiv}
}

2021

Total least squares phase retrieval
and
IEEE Transactions on Signal Processing, 2021
@article{gupta2021total,
  title={Total least squares phase retrieval},
  author={Gupta, Sidharth and Dokmani{\'c}, Ivan},
  journal={IEEE Transactions on Signal Processing},
  volume={70},
  pages={536--549},
  year={2021},
  publisher={IEEE}
}
Trumpets: Injective flows for inference and inverse problems
, , and
Uncertainty in Artificial Intelligence, 2021
@inproceedings{kothari2021trumpets,
  title={Trumpets: Injective flows for inference and inverse problems},
  projectpage = {http://sada.dmi.unibas.ch/en/research/injective-flows},
  author={Kothari, Konik and Khorashadizadeh, AmirEhsan and de Hoop, Maarten and Dokmani{\'c}, Ivan},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={1269--1278},
  year={2021},
  organization={PMLR}
}
Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling
and
55th Asilomar Conference on Signals, Systems, and Computers, 2021
@inproceedings{Chaman_2021_equivariant,
  title={Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling},
  author={Chaman, Anadi and Dokmani{\'c}, Ivan},
  booktitle={55th Asilomar Conference on Signals, Systems, and Computers},
  year={2021}
}
On Procrustes Analysis in Hyperbolic Space
and
arXiv preprint arXiv:2102.03723, 2021
@article{tabaghi2021procrustes,
  title={On Procrustes Analysis in Hyperbolic Space},
  author={Tabaghi, Puoya and Dokmani{\'c}, Ivan},
  journal={arXiv preprint arXiv:2102.03723},
  year={2021}
}
Learning Multiscale Convolutional Dictionaries for Image Reconstruction
, , and
@misc{Liu2021learning,
Author = {Tianlin Liu and Anadi Chaman and David Belius and Ivan Dokmani{\'c}},
Title={Learning Multiscale Convolutional Dictionaries for Image Reconstruction},
Year = {2021},
Eprint = {arXiv:2011.12815},
}
Optimizing Optimizers: Regret-optimal gradient descent algorithms
and
Proceedings of Thirty Fourth Conference on Learning Theory (Mikhail Belkin, Samory Kpotufe, eds.), 2021
@InProceedings{pmlr-v134-kratsios21a,
  title =    {Optimizing Optimizers: Regret-optimal gradient descent algorithms},
  author =       {Casgrain, Philippe and Kratsios, Anastasis},
  booktitle =    {Proceedings of Thirty Fourth Conference on Learning Theory},
  pages =    {883--926},
  year =    {2021},
  editor =    {Belkin, Mikhail and Kpotufe, Samory},
  volume =    {134},
  series =    {Proceedings of Machine Learning Research},
  month =    {15--19 Aug},
  publisher =    {PMLR},
  pdf =    {http://proceedings.mlr.press/v134/casgrain21a/casgrain21a.pdf},
  url =    {https://proceedings.mlr.press/v134/casgrain21a.html},
  abstract =    {This paper treats the task of designing optimization algorithms as an optimal control problem. Using regret as a metric for an algorithm’s performance, we study the existence, uniqueness and consistency of regret-optimal algorithms. By providing first-order optimality conditions for the control problem, we show that regret-optimal algorithms must satisfy a specific structure in their dynamics which we show is equivalent to performing \emph{dual-preconditioned gradient descent} on the value function generated by its regret. Using these optimal dynamics, we provide bounds on their rates of convergence to solutions of convex optimization problems. Though closed-form optimal dynamics cannot be obtained in general, we present fast numerical methods for approximating them, generating optimization algorithms which directly optimize their long-term regret. These are benchmarked against commonly used optimization algorithms to demonstrate their effectiveness.}
}
Universal Approximation Theorems for Differentiable Geometric Deep Learning
and
@article{kratsios2021universal,
      title={Universal Approximation Theorems for Differentiable Geometric Deep Learning},
      author={Anastasis Kratsios and Leonie Papon},
      year={2021},
      eprint={2101.05390},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
Neural Link Prediction with Walk Pooling
, and
arXiv preprint arXiv:2110.04375, 2021
@article{pan2021neural,
  title={Neural Link Prediction with Walk Pooling},
  author={Pan, Liming and Shi, Cheng and Dokmani{\'c}, Ivan},
  journal={arXiv preprint arXiv:2110.04375},
  year={2021}
}
Truly Shift-Invariant Convolutional Neural Networks
and
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
@inproceedings{Chaman_2021_CVPR,
    author    = {Chaman, Anadi and Dokmani{\'c}, Ivan},
    title     = {Truly Shift-Invariant Convolutional Neural Networks},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {3773-3783}
}

2020

Learning the Geometry of Wave-Based Imaging
, and
Advances in Neural Information Processing Systems, 2020
@inproceedings{kothari2020fionet,
 author = {Kothari, Konik and de Hoop, Maarten and Dokmani{\'c}, Ivan},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {Learning the Geometry of Wave-Based Imaging},
 projectpage = {http://sada.dmi.unibas.ch/en/research/imaging-with-waves},
 url = {https://proceedings.neurips.cc/paper/2020/file/5e98d23afe19a774d1b2dcbefd5103eb-Paper.pdf},
 volume = {33},
 year = {2020}
}
Solving complex quadratic systems with full-rank random matrices
, and
IEEE Transactions on Signal Processing, 2020
@article{huang2020solving,
  title={Solving complex quadratic systems with full-rank random matrices},
  author={Huang, Shuai and Gupta, Sidharth and Dokmani{\'c}, Ivan},
  journal={IEEE Transactions on Signal Processing},
  volume={68},
  pages={4782--4796},
  year={2020},
  publisher={IEEE}
}
Fast optical system identification by numerical interferometry
, , and
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
@inproceedings{gupta2020fast,
  title={Fast optical system identification by numerical interferometry},
  author={Gupta, Sidharth and Gribonval, R{\'e}mi and Daudet, Laurent and Dokmani{\'c}, Ivan},
  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1474--1478},
  year={2020},
  organization={IEEE}
}
Parsimonious seismic tomography with Poisson Voronoi projections: Methodology and validation
, , , , and
Seismological Research Letters, 2020
@article{fang2020parsimonious,
  title={Parsimonious seismic tomography with Poisson Voronoi projections: Methodology and validation},
  author={Fang, Hongjian and Van Der Hilst, Robert D and de Hoop, Maarten V and Kothari, Konik and Gupta, Sidharth and Dokmani{\'c}, Ivan},
  journal={Seismological Research Letters},
  volume={91},
  number={1},
  pages={343--355},
  year={2020},
  publisher={Seismological Society of America}
}
Hyperbolic distance matrices
and
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
@inproceedings{tabaghi2020hyperbolic,
  title={Hyperbolic distance matrices},
  author={Tabaghi, Puoya and Dokmani{\'c}, Ivan},
  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={1728--1738},
  year={2020}
}
Geometry of Comparisons
and
arXiv preprint arXiv:2006.09858, 2020
@article{tabaghi2020geometry,
  title={Geometry of Comparisons},
  author={Tabaghi, Puoya and Dokmani{\'c}, Ivan},
  journal={arXiv preprint arXiv:2006.09858},
  year={2020}
}

2019

Random mesh projectors for inverse problems
, , and
International Conference on Learning Representations, 2019
@inproceedings{kothari2018random,
  title={Random mesh projectors for inverse problems},
  author={Konik Kothari* and Sidharth Gupta* and Maarten v. de Hoop and Ivan Dokmani{\'c}},
  booktitle={International Conference on Learning Representations},
  year={2019},
  url={https://openreview.net/forum?id=HyGcghRct7},
}
Don't take it lightly: Phasing optical random projections with unknown operators
, , and
(NeurIPS) Advances in Neural Information Processing Systems, 2019
@article{gupta2019don,
  title={Don't take it lightly: Phasing optical random projections with unknown operators},
  author={Gupta, Sidharth and Gribonval, Remi and Daudet, Laurent and Dokmani{\'c}, Ivan},
  projectpage = {http://sada.dmi.unibas.ch/en/research/pr-optical-computing},
  journal={(NeurIPS) Advances in Neural Information Processing Systems},
  volume={32},
  pages={14855--14865},
  year={2019}
}
Solving complex quadratic equations with full-rank random Gaussian matrices
, and
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
@inproceedings{huang2019solving,
  title={Solving complex quadratic equations with full-rank random Gaussian matrices},
  author={Huang, Shuai* and Gupta, Sidharth* and Dokmani{\'c}, Ivan},
  booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={5596--5600},
  year={2019},
  organization={IEEE}
}
On the move: Localization with kinetic Euclidean distance matrices
, and
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
@inproceedings{tabaghi2019move,
  title={On the move: Localization with kinetic Euclidean distance matrices},
  author={Tabaghi, Puoya and Dokmani{\'c}, Ivan and Vetterli, Martin},
  booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={4893--4897},
  year={2019},
  organization={IEEE}
}
Kinetic Euclidean distance matrices
, and
IEEE Transactions on Signal Processing, 2019
@article{tabaghi2019kinetic,
  title={Kinetic Euclidean distance matrices},
  author={Tabaghi, Puoya and Dokmani{\'c}, Ivan and Vetterli, Martin},
  journal={IEEE Transactions on Signal Processing},
  volume={68},
  pages={452--465},
  year={2019},
  publisher={IEEE}
}
Permutations unlabeled beyond sampling unknown
IEEE Signal Processing Letters, 2019
@article{dokmanic2019permutations,
  title={Permutations unlabeled beyond sampling unknown},
  author={Dokmani{\'c}, Ivan},
  journal={IEEE Signal Processing Letters},
  volume={26},
  number={6},
  pages={823--827},
  year={2019},
  publisher={IEEE}
}
Multipath-enabled private audio with noise
, , and
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
@inproceedings{chaman2019multipath,
  title={Multipath-enabled private audio with noise},
  author={Chaman, Anadi and Liu, Yu-Jeh and Casebeer, Jonah and Dokmani{\'c}, Ivan},
  booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={685--689},
  year={2019},
  organization={IEEE}
}

2018

Reconstructing point sets from distance distributions
and
arXiv preprint arXiv:1804.02465, 2018
@article{huang2018reconstructing,
  title={Reconstructing point sets from distance distributions},
  author={Huang, Shuai and Dokmani{\'c}, Ivan},
  journal={arXiv preprint arXiv:1804.02465},
  year={2018}
}