Greedy Column Subset Selection: New Bounds and Distributed Algorithms. - Jason M. Altschuler; Aditya Bhaskara; Gang Fu; Vahab S. Mirrokni; Afshin Rostamizadeh; Morteza Zadimoghaddam (ICML, 2016)

Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. - Jason M. Altschuler; Jonathan Weed; Philippe Rigollet (NIPS, 2017)

Inclusion of forbidden minors in random representable matroids. - Jason M. Altschuler; Elizabeth Yang (Discret. Math., 2017)

Approximating the Quadratic Transportation Metric in Near-Linear Time. - Jason M. Altschuler; Francis R. Bach; Alessandro Rudi; Jonathan Weed (CoRR, 2018)

Massively scalable Sinkhorn distances via the Nyström method. - Jason M. Altschuler; Francis R. Bach; Alessandro Rudi; Jonathan Niles-Weed (NeurIPS, 2019)

Best Arm Identification for Contaminated Bandits. - Jason M. Altschuler; Victor-Emmanuel Brunel; Alan Malek (J. Mach. Learn. Res., 2019)

Random Osborne: a simple, practical algorithm for Matrix Balancing in near-linear time. - Jason M. Altschuler; Pablo A. Parrilo (CoRR, 2020)

High-precision Wasserstein barycenters in polynomial time. - Jason M. Altschuler; Enric Boix-Adserà (CoRR, 2020)

Lyapunov Exponent of Rank-One Matrices: Ergodic Formula and Inapproximability of the Optimal Distribution. - Jason M. Altschuler; Pablo A. Parrilo (SIAM J. Control. Optim., 2020)

Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent. - Jason M. Altschuler; Sinho Chewi; Patrik Gerber; Austin J. Stromme (NeurIPS, 2021)

Hardness results for Multimarginal Optimal Transport problems. - Jason M. Altschuler; Enric Boix-Adserà (Discret. Optim., 2021)

Wasserstein barycenters can be computed in polynomial time in fixed dimension. - Jason M. Altschuler; Enric Boix-Adserà (J. Mach. Learn. Res., 2021)

Online Learning over a Finite Action Set with Limited Switching. - Jason M. Altschuler; Kunal Talwar (Math. Oper. Res., 2021)

Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss. - Jason M. Altschuler; Kunal Talwar (NeurIPS, 2022)

Flows, Scaling, and Entropy Revisited: a Unified Perspective via Optimizing Joint Distributions. - Jason M. Altschuler (CoRR, 2022)

Concentration of the Langevin Algorithm's Stationary Distribution. - Jason M. Altschuler; Kunal Talwar (CoRR, 2022)

Asymptotics for Semidiscrete Entropic Optimal Transport. - Jason M. Altschuler; Jonathan Niles-Weed; Austin J. Stromme (SIAM J. Math. Anal., 2022)

Wasserstein Barycenters Are NP-Hard to Compute. - Jason M. Altschuler; Enric Boix-Adserà (SIAM J. Math. Data Sci., 2022)

Transport and Beyond: Efficient Optimization over Probability Distributions - Jason M. Altschuler (2022)

Approximating Min-Mean-Cycle for Low-Diameter Graphs in Near-Optimal Time and Memory. - Jason M. Altschuler; Pablo A. Parrilo (SIAM J. Optim., 2022)

Acceleration by Stepsize Hedging I: Multi-Step Descent and the Silver Stepsize Schedule. - Jason M. Altschuler; Pablo A. Parrilo (CoRR, 2023)

Near-linear convergence of the Random Osborne algorithm for Matrix Balancing. - Jason M. Altschuler; Pablo A. Parrilo (Math. Program., 2023)

Kernel Approximation on Algebraic Varieties. - Jason M. Altschuler; Pablo A. Parrilo (SIAM J. Appl. Algebra Geom., 2023)

Polynomial-time algorithms for multimarginal optimal transport problems with structure. - Jason M. Altschuler; Enric Boix-Adserà (Math. Program., 2023)

Shifted Interpolation for Differential Privacy. - Jinho Bok; Weijie J. Su; Jason M. Altschuler (ICML, 2024)

Shifted Composition II: Shift Harnack Inequalities and Curvature Upper Bounds. - Jason M. Altschuler; Sinho Chewi (CoRR, 2024)

Acceleration by Random Stepsizes: Hedging, Equalization, and the Arcsine Stepsize Schedule. - Jason M. Altschuler; Pablo A. Parrilo (CoRR, 2024)

Shifted Composition III: Local Error Framework for KL Divergence. - Jason M. Altschuler; Sinho Chewi (CoRR, 2024)

On the Privacy of Noisy Stochastic Gradient Descent for Convex Optimization. - Jason M. Altschuler; Jinho Bok; Kunal Talwar (SIAM J. Comput., 2024)

Faster High-accuracy Log-concave Sampling via Algorithmic Warm Starts. - Jason M. Altschuler; Sinho Chewi (J. ACM, 2024)

Accelerating Proximal Gradient Descent via Silver Stepsizes. - Jinho Bok; Jason M. Altschuler (COLT, 2025)

Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling. - Jason M. Altschuler; Kunal Talwar (SIAM J. Math. Data Sci., 2025)

Shifted Composition I: Harnack and Reverse Transport Inequalities. - Jason M. Altschuler; Sinho Chewi (IEEE Trans. Inf. Theory, 2025)

Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm. - Xufeng Cai; Jason M. Altschuler; Jelena Diakonikolas (CoRR, 2025)

Acceleration by Stepsize Hedging: Multi-Step Descent and the Silver Stepsize Schedule. - Jason M. Altschuler; Pablo A. Parrilo (J. ACM, 2025)

Negative Stepsizes Make Gradient-Descent-Ascent Converge. - Henry Shugart; Jason M. Altschuler (CoRR, 2025)

Shifted Composition IV: Underdamped Langevin and Numerical Discretizations with Partial Acceleration. - Jason M. Altschuler; Sinho Chewi; Matthew S. Zhang (CoRR, 2025)

Optimized methods for composite optimization: a reduction perspective. - Jinho Bok; Jason M. Altschuler (CoRR, 2025)

Acceleration by stepsize hedging: Silver Stepsize Schedule for smooth convex optimization. - Jason M. Altschuler; Pablo A. Parrilo (Math. Program., 2025)