Amin Jalali

Affiliations:
  • Technicolor AI Lab (former)
  • University of Wisconsin, Madison, WI, USA (former)
  • University of Washington, Department of Electrical Engineering, USA (former)


According to our database1, Amin Jalali authored at least 12 papers between 2013 and 2019.

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Bibliography

2019
New Computational and Statistical Aspects of Regularized Regression with Application to Rare Feature Selection and Aggregation.
CoRR, 2019

2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs.
CoRR, 2019

2018
Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes.
CoRR, 2018

Sparse Subspace Clustering with Missing and Corrupted Data.
Proceedings of the 2018 IEEE Data Science Workshop, 2018

Sparse Transition Matrix Estimation for Sub-Gaussian Autoregressive Processes with Missing Data.
Proceedings of the 2018 Annual American Control Conference, 2018

2017
Variational Gram Functions: Convex Analysis and Optimization.
SIAM J. Optim., 2017

Subspace Clustering via Tangent Cones.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Error bounds for Bregman denoising and structured natural parameter estimation.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

2016
Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices.
IEEE Trans. Inf. Theory, 2015

2013
A convex method for learning d-valued models.
Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013

Noisy estimation of simultaneously structured models: Limitations of convex relaxation.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013


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