Nearly Optimal NP-Hardness of Vertex Cover on k-Uniform k-Partite Hypergraphs. - Sushant Sachdeva; Rishi Saket (APPROX-RANDOM, 2011)

A Reformulation of the Arora-Rao-Vazirani Structure Theorem - Sanjeev Arora; James R. Lee; Sushant Sachdeva (CoRR, 2011)

Cuts in Cartesian Products of Graphs - Sushant Sachdeva; Madhur Tulsiani (CoRR, 2011)

Approximating the Exponential, the Lanczos Method and an tilde{O}(m)-Time Spectral Algorithm for Balanced Separator - Lorenzo Orecchia; Sushant Sachdeva; Nisheeth K. Vishnoi (CoRR, 2011)

On the Characterization and Selection of Diverse Conformational Ensembles with Applications to Flexible Docking. - Sébastien Loriot; Sushant Sachdeva; Karine Bastard; Chantal Prévost; Frédéric Cazals (IEEE ACM Trans. Comput. Biol. Bioinform., 2011)

"Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders". - Sanjeev Arora; Rong Ge 001; Ankur Moitra; Sushant Sachdeva (NIPS, 2012)

Finding overlapping communities in social networks: toward a rigorous approach. - Sanjeev Arora; Rong Ge 001; Sushant Sachdeva; Grant Schoenebeck (EC, 2012)

Approximating the exponential, the lanczos method and an Õ(m)-time spectral algorithm for balanced separator. - Lorenzo Orecchia; Sushant Sachdeva; Nisheeth K. Vishnoi (STOC, 2012)

Testing Permanent Oracles - Revisited. - Sanjeev Arora; Arnab Bhattacharyya 001; Rajsekar Manokaran; Sushant Sachdeva (Electron. Colloquium Comput. Complex., 2012)

Optimal Inapproximability for Scheduling Problems via Structural Hardness for Hypergraph Vertex Cover. - Sushant Sachdeva; Rishi Saket (CCC, 2013)

Approximation Theory and the Design of Fast Algorithms. - Sushant Sachdeva; Nisheeth K. Vishnoi (CoRR, 2013)

An Arithmetic Analogue of Fox's Triangle Removal Argument - Pooya Hatami; Sushant Sachdeva; Madhur Tulsiani (CoRR, 2013)

Matrix Inversion Is As Easy As Exponentiation - Sushant Sachdeva; Nisheeth K. Vishnoi (CoRR, 2013)

Inapproximability of Minimum Vertex Cover on k-uniform k-partite Hypergraphs. - Venkatesan Guruswami; Sushant Sachdeva; Rishi Saket (Electron. Colloquium Comput. Complex., 2013)

New Results in the Theory of Approximation: Fast Graph Algorithms and Inapproximability - Sushant Sachdeva (2013)

Greedy Geometric Algorithms for Collection of Balls, with Applications to Geometric Approximation and Molecular Coarse-Graining. - Frédéric Cazals; Tom Dreyfus; Sushant Sachdeva; N. Shah (Comput. Graph. Forum, 2014)

Simultaneous Approximation of Constraint Satisfaction Problems. - Amey Bhangale; Swastik Kopparty; Sushant Sachdeva (Electron. Colloquium Comput. Complex., 2014)

Faster Algorithms via Approximation Theory. - Sushant Sachdeva; Nisheeth K. Vishnoi (Found. Trends Theor. Comput. Sci., 2014)

Algorithms for Lipschitz Learning on Graphs. - Rasmus Kyng; Anup Rao 002; Sushant Sachdeva; Daniel A. Spielman (COLT, 2015)

Fast, Provable Algorithms for Isotonic Regression in all L_p-norms. - Rasmus Kyng; Anup Rao 002; Sushant Sachdeva (NIPS, 2015)

Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders. - Sanjeev Arora; Rong Ge 001; Ankur Moitra; Sushant Sachdeva (Algorithmica, 2015)

Fast, Provable Algorithms for Isotonic Regression in all ℓp-norms. - Rasmus Kyng; Anup Rao 002; Sushant Sachdeva (CoRR, 2015)

A Simple Analysis of the Dikin Walk. - Sushant Sachdeva; Nisheeth K. Vishnoi (CoRR, 2015)

Approximate Gaussian Elimination for Laplacians - Fast, Sparse, and Simple. - Rasmus Kyng; Sushant Sachdeva (FOCS, 2016)

Sparsified Cholesky and multigrid solvers for connection laplacians. - Rasmus Kyng; Yin Tat Lee; Richard Peng; Sushant Sachdeva; Daniel A. Spielman (STOC, 2016)

The mixing time of the Dikin walk in a polytope - A simple proof. - Sushant Sachdeva; Nisheeth K. Vishnoi (Oper. Res. Lett., 2016)

A Framework for Analyzing Resparsification Algorithms. - Rasmus Kyng; Jakub Pachocki; Richard Peng; Sushant Sachdeva (SODA, 2017)

Sampling random spanning trees faster than matrix multiplication. - David Durfee; Rasmus Kyng; John Peebles; Anup B. Rao; Sushant Sachdeva (STOC, 2017)

Electron-Proton Dynamics in Deep Learning. - Qiuyi Zhang 001; Rina Panigrahy; Sushant Sachdeva (CoRR, 2017)

Convergence Results for Neural Networks via Electrodynamics. - Rina Panigrahy; Ali Rahimi; Sushant Sachdeva; Qiuyi Zhang 001 (ITCS, 2018)

Near-optimal approximation algorithm for simultaneous Max-Cut. - Amey Bhangale; Subhash Khot; Swastik Kopparty; Sushant Sachdeva; Devanathan Thiruvenkatachari (SODA, 2018)

Improved Semi-Supervised Learning with Multiple Graphs. - Krishnamurthy Viswanathan; Sushant Sachdeva; Andrew Tomkins; Sujith Ravi (AISTATS, 2019)

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

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. - Guodong Zhang 006; Lala Li; Zachary Nado; James Martens; Sushant Sachdeva; George E. Dahl; Christopher J. Shallue; Roger B. Grosse (NeurIPS, 2019)

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

Short Cycles via Low-Diameter Decompositions. - Yang P. Liu; Sushant Sachdeva; Zejun Yu (SODA, 2019)

Flows in almost linear time via adaptive preconditioning. - Rasmus Kyng; Richard Peng; Sushant Sachdeva; Di Wang 005 (STOC, 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)

Special Section on the Fifty-First Annual ACM Sympositum on the Theory of Computing (STOC 2019). - Dana Moshkovitz; Sushant Sachdeva (SIAM J. Comput., 2019)

Faster Graph Embeddings via Coarsening. - Matthew Fahrbach; Gramoz Goranci; Richard Peng; Sushant Sachdeva; Chi Wang 001 (ICML, 2020)

Regularized linear autoencoders recover the principal components, eventually. - Xuchan Bao; James Lucas; Sushant Sachdeva; Roger B. Grosse (NeurIPS, 2020)

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

A Provably Convergent and Practical Algorithm for Min-max Optimization with Applications to GANs. - Oren Mangoubi; Sushant Sachdeva; Nisheeth K. Vishnoi (CoRR, 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)

A Convergent and Dimension-Independent Min-Max Optimization Algorithm. - Vijay Keswani; Oren Mangoubi; Sushant Sachdeva; Nisheeth K. Vishnoi (ICML, 2022)

Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. - Sally Dong; Yu Gao 001; Gramoz Goranci; Yin Tat Lee; Richard Peng; Sushant Sachdeva; Guanghao Ye (SODA, 2022)

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)

A Deterministic Almost-Linear Time Algorithm for Minimum-Cost Flow. - Jan van den Brand; Li Chen 028; Richard Peng; Rasmus Kyng; Yang P. Liu; Maximilian Probst Gutenberg; Sushant Sachdeva; Aaron Sidford (FOCS, 2023)

A New Approach to Estimating Effective Resistances and Counting Spanning Trees in Expander Graphs. - Lawrence Li; Sushant Sachdeva (SODA, 2023)

A Simple Framework for Finding Balanced Sparse Cuts via APSP. - Li Chen 028; Rasmus Kyng; Maximilian Probst Gutenberg; Sushant Sachdeva (SOSA, 2023)

A Simple and Efficient Parallel Laplacian Solver. - Sushant Sachdeva; Yibin Zhao 003 (SPAA, 2023)

Almost-Linear-Time Algorithms for Maximum Flow and Minimum-Cost Flow. - Li Chen 028; Rasmus Kyng; Yang P. Liu; Richard Peng; Maximilian Probst Gutenberg; Sushant Sachdeva (Commun. ACM, 2023)

Graph Sparsification, Spectral Sketches, and Faster Resistance Computation via Short Cycle Decompositions. - Timothy Chu; Yu Gao 001; Richard Peng; Sushant Sachdeva; Saurabh Sawlani; Junxing Wang (SIAM J. Comput., 2023)

Almost-Linear Time Algorithms for Decremental Graphs: Min-Cost Flow and More via Duality. - Jan van den Brand; Li Chen 028; Rasmus Kyng; Yang P. Liu; Simon Meierhans; Maximilian Probst Gutenberg; Sushant Sachdeva (FOCS, 2024)

Optimal Electrical Oblivious Routing on Expanders. - Cella Florescu; Rasmus Kyng; Maximilian Probst Gutenberg; Sushant Sachdeva (ICALP, 2024)

Better Sparsifiers for Directed Eulerian Graphs. - Sushant Sachdeva; Anvith Thudi; Yibin Zhao 003 (ICALP, 2024)

Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra. - Rajarshi Bhattacharjee; Gregory Dexter; Cameron Musco; Archan Ray; Sushant Sachdeva; David P. Woodruff (ITCS, 2024)

Electrical Flows for Polylogarithmic Competitive Oblivious Routing. - Gramoz Goranci; Monika Henzinger; Harald Räcke; Sushant Sachdeva; A. R. Sricharan (ITCS, 2024)

Incremental Approximate Maximum Flow on Undirected Graphs in Subpolynomial Update Time. - Jan van den Brand; Li Chen 028; Rasmus Kyng; Yang P. Liu; Richard Peng; Maximilian Probst Gutenberg; Sushant Sachdeva; Aaron Sidford (SODA, 2024)

Fast Algorithms for Separable Linear Programs. - Sally Dong; Gramoz Goranci; Lawrence Li; Sushant Sachdeva; Guanghao Ye (SODA, 2024)

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

PREM: Privately Answering Statistical Queries with Relative Error. - Badih Ghazi; Cristóbal Guzmán; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Sushant Sachdeva (COLT, 2025)

Eulerian Graph Sparsification by Effective Resistance Decomposition. - Arun Jambulapati; Sushant Sachdeva; Aaron Sidford; Kevin Tian; Yibin Zhao 003 (SODA, 2025)

Maximum Flow and Minimum-Cost Flow in Almost-Linear Time. - Li Chen 028; Rasmus Kyng; Yang P. Liu; Richard Peng; Maximilian Probst Gutenberg; Sushant Sachdeva (J. ACM, 2025)