Deeksha Adil

22 papers A* 7Journal 14
YearRankTypeTitle / Venue / Authors
2025 A* conf
Acceleration Meets Inverse Maintenance: Faster ℓ
ICALP
Deeksha Adil, Shunhua Jiang, Rasmus Kyng
2025 J jnl
Balancing Gradient and Hessian Queries in Non-Convex Optimization.
CoRR
Deeksha Adil, Brian Bullins, Aaron Sidford, Chenyi Zhang
2025 A* conf
Decremental (1+ε)-Approximate Maximum Eigenvector: Dynamic Power Method.
ICALP
Deeksha Adil, Thatchaphol Saranurak
2025 J jnl
Efficient and Provable Algorithms for Covariate Shift.
CoRR
Deeksha Adil, Jaroslaw Blasiok
2024 J jnl
Acceleration Meets Inverse Maintenance: Faster ℓ
CoRR
Deeksha Adil, Shunhua Jiang, Rasmus Kyng
2024 J jnl
Convex optimization with
CoRR
Deeksha Adil, Brian Bullins, Arun Jambulapati, Aaron Sidford
2024 J jnl
Decremental (1+ε)-Approximate Maximum Eigenvector: Dynamic Power Method.
CoRR
Deeksha Adil, Thatchaphol Saranurak
2024 J jnl
Fast Algorithms for
J. ACM
Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva
2022 — —
Fast Algorithms for $\ell_p$-Regression and Other Problems
Deeksha Adil
2022 J jnl
Fast Algorithms for 𝓁
CoRR
Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva
2022 J jnl
Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities.
CoRR
Deeksha Adil, Brian Bullins, Arun Jambulapati, Sushant Sachdeva
2021 A* conf
Almost-Linear-Time Weighted 𝓁
ICALP
Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva
2021 J jnl
Almost-linear-time Weighted 𝓁
CoRR
Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva
2021 A* conf
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization.
NeurIPS
Deeksha Adil, Brian Bullins, Sushant Sachdeva
2021 J jnl
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization.
CoRR
Deeksha Adil, Brian Bullins, Sushant Sachdeva
2020 A* conf
Faster
SODA
Deeksha Adil, Sushant Sachdeva
2019 A* conf
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression.
NeurIPS
Deeksha Adil, Richard Peng, Sushant Sachdeva
2019 J jnl
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression.
CoRR
Deeksha Adil, Richard Peng, Sushant Sachdeva
2019 J jnl
Faster p-norm minimizing flows, via smoothed q-norm problems.
CoRR
Deeksha Adil, Sushant Sachdeva
2019 A* conf
Iterative Refinement for ℓp-norm Regression.
SODA
Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva
2019 J jnl
Iterative Refinement for 𝓁
CoRR
Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva
2018 J jnl
Parameterized algorithms for stable matching with ties and incomplete lists.
Theor. Comput. Sci.
Deeksha Adil, Sushmita Gupta, Sanjukta Roy, Saket Saurabh, Meirav Zehavi