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Jason Altschuler
Email: ja4775 [at] nyu [dot] edu |
I am currently a Faculty Fellow at the NYU Center for Data Science. Previously, I received my PhD in Electrical Engineering and Computer Science from MIT, where I was fortunate to be advised by Pablo Parrilo. Before that, I received my undergrad degree from Princeton.
My research interests are broadly at the interface of optimization, probability, and machine learning. One recent focus is computational aspects of problems related to optimal transport (see e.g., this expository article here and my thesis here). Another recent focus is algorithmic connections between optimization, sampling, and differential privacy (see e.g., these recent papers here and here).
I am interested in practical implementations as well as theory, and have previously interned in both tech (Apple Research, Google Research, Google) and Wall Street (DE Shaw, Tower Research).
In my free time, I like to ski, play tennis, and eat too many cookies. I also like chess and, in a past life, I obtained an International Master norm in Spain.
Concentration of the Langevin Algorithm's stationary distribution
Jason Altschuler, Kunal Talwar.
Resolving the mixing time of the Langevin Algorithm to its stationary distribution for log-concave sampling
Jason Altschuler, Kunal Talwar.
Flows, Scaling, and Entropy Revisited: a Unified Perspective via Optimizing Joint Distributions
Jason Altschuler
Privacy of Noisy Stochastic Gradient Descent: more iterations without more privacy loss
Jason Altschuler, Kunal Talwar.
Kernel approximation on algebraic varieties
Jason Altschuler, Pablo Parrilo.
Polynomial-time algorithms for Multimarginal Optimal Transport problems with structure
Jason Altschuler, Enric Boix-Adsera.
Asymptotics for semi-discrete entropic optimal transport
Jason Altschuler, Jonathan Niles-Weed, Austin Stromme.
Approximating Min-Mean-Cycle for low-diameter graphs in near-optimal time and memory
Jason Altschuler, Pablo Parrilo.
Near-linear convergence of the Random Osborne algorithm for Matrix Balancing
Jason Altschuler, Pablo Parrilo.
Wasserstein barycenters are NP-hard to compute
Jason Altschuler, Enric Boix-Adsera.
Wasserstein barycenters can be computed in polynomial time in fixed dimension
[poster, talk, slides]
Jason Altschuler, Enric Boix-Adsera.
Hardness results for Multimarginal Optimal Transport problems
Jason Altschuler, Enric Boix-Adsera.
Online learning over a finite action set with limited switching
[poster, talk]
Jason Altschuler, Kunal Talwar.
Lyapunov Exponent of Rank One Matrices: Ergodic Formula and Inapproximability of the Optimal Distribution
[slides]
Jason Altschuler, Pablo Parrilo.
Best arm identification for contaminated bandits
Jason Altschuler, Victor-Emmanuel Brunel, Alan Malek.
Inclusion of forbidden minors in random representable matroids
Jason Altschuler, Elizabeth Yang.
Rapid analysis and exploration of fluorescence microscopy images
Benjamin Pavie, Satwik Rajaram, Austin Ouyang, Jason Altschuler, Robert Steininger, Lani Wu, Steven Altschuler.
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent
Jason Altschuler, Sinho Chewi, Patrik Gerber, Austin Stromme.
Lyapunov Exponent of Rank One Matrices: Ergodic Formula and Inapproximability of the Optimal Distribution
[slides]
Jason Altschuler, Pablo Parrilo.
Massively scalable Sinkhorn distances via the Nyström method
Jason Altschuler, Francis Bach, Alessandro Rudi, Jonathan Niles-Weed.
Online learning over a finite action set with limited switching
[poster, talk]
Jason Altschuler, Kunal Talwar.
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
[poster]
Jason Altschuler, Jonathan Weed, Philippe Rigollet.
Greedy column subset selection: new bounds and distributed algorithms
[poster, slides]
Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh, Morteza Zadimoghaddam.
Transport and Beyond: Efficient Optimization over Probability Distributions
Minimax rates for online learning with limited decision changes (superseded by this paper in MOR/COLT)
Probabilistic linear Boolean classification