Parameterized algorithms for stable matching with ties and incomplete lists. - Deeksha Adil; Sushmita Gupta; Sanjukta Roy 001; Saket Saurabh 001; Meirav Zehavi (Theor. Comput. Sci., 2018)

Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression. - Deeksha Adil; Richard Peng; Sushant Sachdeva (NeurIPS, 2019)

Iterative Refinement for ℓp-norm Regression. - Deeksha Adil; Rasmus Kyng; Richard Peng; Sushant Sachdeva (SODA, 2019)

Iterative Refinement for 𝓁p-norm Regression. - Deeksha Adil; Rasmus Kyng; Richard Peng; Sushant Sachdeva (CoRR, 2019)

Faster p-norm minimizing flows, via smoothed q-norm problems. - Deeksha Adil; Sushant Sachdeva (CoRR, 2019)

Faster p-norm minimizing flows, via smoothed q-norm problems. - Deeksha Adil; Sushant Sachdeva (SODA, 2020)

Almost-Linear-Time Weighted 𝓁p-Norm Solvers in Slightly Dense Graphs via Sparsification. - Deeksha Adil; Brian Bullins; Rasmus Kyng; Sushant Sachdeva (ICALP, 2021)

Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. - Deeksha Adil; Brian Bullins; Sushant Sachdeva (NeurIPS, 2021)

Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities. - Deeksha Adil; Brian Bullins; Arun Jambulapati; Sushant Sachdeva (CoRR, 2022)

Fast Algorithms for 𝓁p-Regression. - Deeksha Adil; Rasmus Kyng; Richard Peng; Sushant Sachdeva (CoRR, 2022)

Fast Algorithms for $ ell_p$-Regression and Other Problems - Deeksha Adil (2022)

Convex optimization with p-norm oracles. - Deeksha Adil; Brian Bullins; Arun Jambulapati; Aaron Sidford (CoRR, 2024)

Fast Algorithms for ℓp-Regression. - Deeksha Adil; Rasmus Kyng; Richard Peng; Sushant Sachdeva (J. ACM, 2024)

Acceleration Meets Inverse Maintenance: Faster ℓ∞-Regression. - Deeksha Adil; Shunhua Jiang; Rasmus Kyng (ICALP, 2025)

Decremental (1+ε)-Approximate Maximum Eigenvector: Dynamic Power Method. - Deeksha Adil; Thatchaphol Saranurak (ICALP, 2025)

Efficient and Provable Algorithms for Covariate Shift. - Deeksha Adil; Jaroslaw Blasiok (CoRR, 2025)