Improved Approximation Algorithms for Biconnected Subgraphs via Better Lower Bounding Techniques. - Naveen Garg 001; Santosh S. Vempala; Aman Singla (SODA, 1993)

A Limited-Backtrack Greedy Schema for Approximation Algorithms. - Vivek Arora; Santosh S. Vempala; Huzur Saran; Vijay V. Vazirani (FSTTCS, 1994)

Improved approximation guarantees for minimum-weight k-trees and prize-collecting salesmen. - Baruch Awerbuch; Yossi Azar; Avrim Blum; Santosh S. Vempala (STOC, 1995)

A constant-factor approximation for the k-MST problem in the plane. - Avrim Blum; Prasad Chalasani; Santosh S. Vempala (STOC, 1995)

A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions. - Avrim Blum; Alan M. Frieze; Ravi Kannan; Santosh S. Vempala (FOCS, 1996)

A Constant-factor Approximation Algorithm for the k MST Problem (Extended Abstract). - Avrim Blum; R. Ravi 001; Santosh S. Vempala (STOC, 1996)

A Random Sampling Based Algorithm for Learning the Intersection of Half-spaces. - Santosh S. Vempala (FOCS, 1997)

Random Sampling of Euler Tours. - Prasad Tetali; Santosh S. Vempala (RANDOM, 1997)

Simple Markov-Chain Algorithms for Generating Bipartite Graphs and Tournaments (Extended Abstract). - Ravi Kannan; Prasad Tetali; Santosh S. Vempala (SODA, 1997)

Locality-Preserving Hashing in Multidimensional Spaces. - Piotr Indyk; Rajeev Motwani 001; Prabhakar Raghavan; Santosh S. Vempala (STOC, 1997)

Sampling Lattice Points. - Ravi Kannan; Santosh S. Vempala (STOC, 1997)

The Colin de Verdière Number and Sphere Representations of a Graph. - Andrew Kotlov; László Lovász 001; Santosh S. Vempala (Comb., 1997)

Fast Monte-Carlo Algorithms for Finding Low-Rank Approximations. - Alan M. Frieze; Ravi Kannan; Santosh S. Vempala (FOCS, 1998)

Random Projection: A New Approach to VLSI Layout. - Santosh S. Vempala (FOCS, 1998)

Latent Semantic Indexing: A Probabilistic Analysis. - Christos H. Papadimitriou; Prabhakar Raghavan; Hisao Tamaki; Santosh S. Vempala (PODS, 1998)

Semi-Definite Relaxations for Minimum Bandwidth and other Vertex-Ordering Problems. - Avrim Blum; Goran Konjevod; R. Ravi 001; Santosh S. Vempala (STOC, 1998)

New Approximation Guarantees for Minimum-Weight k-Trees and Prize-Collecting Salesmen. - Baruch Awerbuch; Yossi Azar; Avrim Blum; Santosh S. Vempala (SIAM J. Comput., 1998)

A Constant-Factor Approximation Algorithm for the Geometric k-MST Problem in the Plane. - Joseph S. B. Mitchell; Avrim Blum; Prasad Chalasani; Santosh S. Vempala (SIAM J. Comput., 1998)

An Algorithmic Theory of Learning: Robust Concepts and Random Projection. - Rosa I. Arriaga; Santosh S. Vempala (FOCS, 1999)

Approximating Multicast Congestion. - Santosh S. Vempala; Berthold Vöcking (ISAAC, 1999)

Clustering in Large Graphs and Matrices. - Petros Drineas; Alan M. Frieze; Ravi Kannan; Santosh S. Vempala; V. Vinay (SODA, 1999)

A Convex Relaxation for the Asymmetric TSP. - Santosh S. Vempala; Mihalis Yannakakis (SODA, 1999)

A Constant-Factor Approximation Algorithm for the k-MST Problem. - Avrim Blum; R. Ravi 001; Santosh S. Vempala (J. Comput. Syst. Sci., 1999)

Simple Markov-chain algorithms for generating bipartite graphs and tournaments. - Ravi Kannan; Prasad Tetali; Santosh S. Vempala (Random Struct. Algorithms, 1999)

Factor 4/3 approximations for minimum 2-connected subgraphs. - Santosh S. Vempala; Adrian Vetta (APPROX, 2000)

Efficient Algorithms for Universal Portfolios. - Adam Kalai; Santosh S. Vempala (FOCS, 2000)

On Clusterings - Good, Bad and Spectral. - Ravi Kannan; Santosh S. Vempala; Adrian Vetta (FOCS, 2000)

Towards a 4/3 approximation for the asymmetric traveling salesman problem. - Robert D. Carr; Santosh S. Vempala; Jacques Mandler (SODA, 2000)

Randomized metarounding (extended abstract). - Robert D. Carr; Santosh S. Vempala (STOC, 2000)

On the approximability of the traveling salesman problem (extended abstract). - Christos H. Papadimitriou; Santosh S. Vempala (STOC, 2000)

Edge Covers of Setpairs and the Iterative Rounding Method. - Joseph Cheriyan; Santosh S. Vempala (IPCO, 2001)

Fences Are Futile: On Relaxations for the Linear Ordering Problem. - Alantha Newman; Santosh S. Vempala (IPCO, 2001)

On Euclidean Embeddings and Bandwidth Minimization. - John Dunagan; Santosh S. Vempala (RANDOM-APPROX, 2001)

Optimal outlier removal in high-dimensional. - John Dunagan; Santosh S. Vempala (STOC, 2001)

A Spectral Algorithm for Learning Mixtures of Distributions. - Santosh S. Vempala; Grant Wang (FOCS, 2002)

Flow Metrics. - Claudson F. Bornstein; Santosh S. Vempala (LATIN, 2002)

Solving convex programs by random walks. - Dimitris Bertsimas; Santosh S. Vempala (STOC, 2002)

Approximation algorithms for minimum-cost k-vertex connected subgraphs. - Joseph Cheriyan; Santosh S. Vempala; Adrian Vetta (STOC, 2002)

Randomized metarounding. - Robert D. Carr; Santosh S. Vempala (Random Struct. Algorithms, 2002)

Efficient Algorithms for Online Decision Problems. - Adam Kalai; Santosh S. Vempala (COLT, 2003)

Simulated Annealing in Convex Bodies and an 0*(n4) Volume Algorithm. - László Lovász 001; Santosh S. Vempala (FOCS, 2003)

Logconcave Functions: Geometry and Efficient Sampling Algorithms - László Lovász 001; Santosh S. Vempala (FOCS, 2003)

An Approximation Algorithm for the Minimum-Cost k-Vertex Connected Subgraph. - Joseph Cheriyan; Santosh S. Vempala; Adrian Vetta (SIAM J. Comput., 2003)

On Kernels, Margins, and Low-Dimensional Mappings. - Maria-Florina Balcan; Avrim Blum; Santosh S. Vempala (ALT, 2004)

The Random Projection Method - Santosh S. Vempala (DIMACS/AMS, 2004)

Testing Geometric Convexity. - Luis Rademacher; Santosh S. Vempala (FSTTCS, 2004)

A simple polynomial-time rescaling algorithm for solving linear programs. - John Dunagan; Santosh S. Vempala (STOC, 2004)

Hit-and-run from a corner. - László Lovász 001; Santosh S. Vempala (STOC, 2004)

The Spectral Method for Mixture Models - Hadi Salmasian; Ravindran Kannan; Santosh S. Vempala (Electron. Colloquium Comput. Complex., 2004)

Optimal outlier removal in high-dimensional spaces. - John Dunagan; Santosh S. Vempala (J. Comput. Syst. Sci., 2004)

A spectral algorithm for learning mixture models. - Santosh S. Vempala; Grant Wang (J. Comput. Syst. Sci., 2004)

Clustering Large Graphs via the Singular Value Decomposition. - Petros Drineas; Alan M. Frieze; Ravi Kannan; Santosh S. Vempala; V. Vinay (Mach. Learn., 2004)

On the Held-Karp relaxation for the asymmetric and symmetric traveling salesman problems. - Robert D. Carr; Santosh S. Vempala (Math. Program., 2004)

The Spectral Method for General Mixture Models. - Ravindran Kannan; Hadi Salmasian; Santosh S. Vempala (COLT, 2005)

Nash Equilibria in Random Games. - Imre Bárány; Santosh S. Vempala; Adrian Vetta (FOCS, 2005)

A divide-and-merge methodology for clustering. - David Cheng; Santosh S. Vempala; Ravi Kannan; Grant Wang (PODS, 2005)

Tensor decomposition and approximation schemes for constraint satisfaction problems. - Wenceslas Fernandez de la Vega; Marek Karpinski; Ravi Kannan; Santosh S. Vempala (STOC, 2005)

Adaptive Sampling and Fast Low-Rank Matrix Approximation. - Amit Deshpande 001; Santosh S. Vempala (APPROX-RANDOM, 2006)

Fast Algorithms for Logconcave Functions: Sampling, Rounding, Integration and Optimization. - László Lovász 001; Santosh S. Vempala (FOCS, 2006)

Dispersion of Mass and the Complexity of Randomized Geometric Algorithms. - Luis Rademacher; Santosh S. Vempala (FOCS, 2006)

Local versus global properties of metric spaces. - Sanjeev Arora; László Lovász 001; Ilan Newman; Yuval Rabani; Yuri Rabinovich; Santosh S. Vempala (SODA, 2006)

Matrix approximation and projective clustering via volume sampling. - Amit Deshpande 001; Luis Rademacher; Santosh S. Vempala; Grant Wang (SODA, 2006)

Symmetric network computation. - David Pritchard 001; Santosh S. Vempala (SPAA, 2006)

Network Design Via Iterative Rounding Of Setpair Relaxations. - Joseph Cheriyan; Santosh S. Vempala; Adrian Vetta (Comb., 2006)

On The Approximability Of The Traveling Salesman Problem. - Christos H. Papadimitriou; Santosh S. Vempala (Comb., 2006)

Simulated annealing in convex bodies and an O*(n4) volume algorithm. - László Lovász 001; Santosh S. Vempala (J. Comput. Syst. Sci., 2006)

Kernels as features: On kernels, margins, and low-dimensional mappings. - Maria-Florina Balcan; Avrim Blum; Santosh S. Vempala (Mach. Learn., 2006)

Simulated Annealing for Convex Optimization. - Adam Tauman Kalai; Santosh S. Vempala (Math. Oper. Res., 2006)

Filtering spam with behavioral blacklisting. - Anirudh Ramachandran; Nick Feamster; Santosh S. Vempala (CCS, 2007)

An Efficient Re-scaled Perceptron Algorithm for Conic Systems. - Alexandre Belloni; Robert M. Freund; Santosh S. Vempala (COLT, 2007)

Spectral Algorithms for Learning and Clustering. - Santosh S. Vempala (COLT, 2007)

Adaptive Simulated Annealing: A Near-optimal Connection between Sampling and Counting. - Daniel Stefankovic; Santosh S. Vempala; Eric Vigoda (FOCS, 2007)

Life (and routing) on the Wireless Manifold. - Varun Kanade; Santosh S. Vempala (HotNets, 2007)

Path Splicing: Reliable Connectivity with Rapid Recovery. - Murtaza Motiwala; Nick Feamster; Santosh S. Vempala (HotNets, 2007)

The geometry of logconcave functions and sampling algorithms. - László Lovász 001; Santosh S. Vempala (Random Struct. Algorithms, 2007)

Isotropic PCA and Affine-Invariant Clustering. - S. Charles Brubaker; Santosh S. Vempala (FOCS, 2008)

Path splicing. - Murtaza Motiwala; Megan Elmore; Nick Feamster; Santosh S. Vempala (SIGCOMM, 2008)

A discriminative framework for clustering via similarity functions. - Maria-Florina Balcan; Avrim Blum; Santosh S. Vempala (STOC, 2008)

Logconcave random graphs. - Alan M. Frieze; Santosh S. Vempala; Juan Vera 001 (STOC, 2008)

Algorithmic Prediction of Health-Care Costs. - Dimitris Bertsimas; Margrét V. Bjarnadóttir; Michael A. Kane; J. Christian Kryder; Rudra Pandey; Santosh S. Vempala; Grant Wang (Oper. Res., 2008)

Random Tensors and Planted Cliques. - S. Charles Brubaker; Santosh S. Vempala (APPROX-RANDOM, 2009)

Sampling s-Concave Functions: The Limit of Convexity Based Isoperimetry. - Karthekeyan Chandrasekaran; Amit Deshpande 001; Santosh S. Vempala (APPROX-RANDOM, 2009)

Design of a blood flow system. - Adebola Osuntogun; Stephen Thomas; John Pitman; Sridhar Basavaraju; Bright Mulenga; Santosh S. Vempala (ICTD, 2009)

Design and deployment of a blood safety monitoring tool. - Stephen Thomas; Adebola Osuntogun; John Pitman; Bright Mulenga; Santosh S. Vempala (ICTD, 2009)

Expanders via random spanning trees. - Navin Goyal; Luis Rademacher; Santosh S. Vempala (SODA, 2009)

Thin Partitions: Isoperimetric Inequalities and Sampling Algorithms for some Nonconvex Families - Karthekeyan Chandrasekaran; Daniel Dadush; Santosh S. Vempala (CoRR, 2009)

The Limit of Convexity Based Isoperimetry: Sampling Harmonic-Concave Functions - Karthekeyan Chandrasekaran; Amit Deshpande 001; Santosh S. Vempala (CoRR, 2009)

Spectral Algorithms. - Ravi Kannan; Santosh S. Vempala (Found. Trends Theor. Comput. Sci., 2009)

An Efficient Rescaled Perceptron Algorithm for Conic Systems. - Alexandre Belloni; Robert M. Freund; Santosh S. Vempala (Math. Oper. Res., 2009)

Corrigendum: A Random Sampling Algorithm for Learning an Intersection of Halfspaces. - Santosh S. Vempala (FOCS, 2010)

Learning Convex Concepts from Gaussian Distributions with PCA. - Santosh S. Vempala (FOCS, 2010)

Recent Progress and Open Problems in Algorithmic Convex Geometry. - Santosh S. Vempala (FSTTCS, 2010)

A New Approach to Strongly Polynomial Linear Programming. - Mihály Bárász; Santosh S. Vempala (ICS, 2010)

On Nash-Equilibria of Approximation-Stable Games. - Pranjal Awasthi; Maria-Florina Balcan; Avrim Blum; Or Sheffet; Santosh S. Vempala (SAGT, 2010)

Circumventing censorship with collage. - Sam Burnett; Nick Feamster; Santosh S. Vempala (SIGCOMM, 2010)

Thin Partitions: Isoperimetric Inequalities and a Sampling Algorithm for Star Shaped Bodies. - Karthekeyan Chandrasekaran; Daniel Dadush; Santosh S. Vempala (SODA, 2010)

Chipping Away at Censorship Firewalls with User-Generated Content. - Sam Burnett; Nick Feamster; Santosh S. Vempala (USENIX Security Symposium, 2010)

Enumerative Algorithms for the Shortest and Closest Lattice Vector Problems in Any Norm via M-Ellipsoid Coverings - Daniel Dadush; Chris Peikert; Santosh S. Vempala (CoRR, 2010)

A random-sampling-based algorithm for learning intersections of halfspaces. - Santosh S. Vempala (J. ACM, 2010)

On Noise-Tolerant Learning of Sparse Parities and Related Problems. - Elena Grigorescu; Lev Reyzin; Santosh S. Vempala (ALT, 2011)

Semantic Communication for Simple Goals Is Equivalent to On-line Learning. - Brendan Juba; Santosh S. Vempala (ALT, 2011)

Algorithmic Extensions of Cheeger's Inequality to Higher Eigenvalues and Partitions. - Anand Louis; Prasad Raghavendra; Prasad Tetali; Santosh S. Vempala (APPROX-RANDOM, 2011)

Enumerative Lattice Algorithms in any Norm Via M-ellipsoid Coverings. - Daniel Dadush; Chris Peikert; Santosh S. Vempala (FOCS, 2011)

An FPTAS for #Knapsack and Related Counting Problems. - Parikshit Gopalan; Adam R. Klivans; Raghu Meka; Daniel Stefankovic; Santosh S. Vempala; Eric Vigoda (FOCS, 2011)

LifeNet: a flexible ad hoc networking solution for transient environments. - Hrushikesh Mehendale; Ashwin Paranjpe; Santosh S. Vempala (SIGCOMM, 2011)

Algorithms for Implicit Hitting Set Problems. - Karthekeyan Chandrasekaran; Richard M. Karp; Erick Moreno-Centeno; Santosh S. Vempala (SODA, 2011)

Deterministic Construction of an Approximate M-Ellipsoid and its Application to Derandomizing Lattice Algorithms - Daniel Dadush; Santosh S. Vempala (CoRR, 2011)

Structure from Local Optima: Learning Subspace Juntas via Higher Order PCA - Santosh S. Vempala; Ying Xiao 003 (CoRR, 2011)

A Discrepancy based Approach to Integer Programming - Karthekeyan Chandrasekaran; Santosh S. Vempala (CoRR, 2011)

The Cutting Plane Method Is Polynomial for Perfect Matchings. - Karthekeyan Chandrasekaran; László A. Végh; Santosh S. Vempala (FOCS, 2012)

Randomly-oriented k-d Trees Adapt to Intrinsic Dimension. - Santosh S. Vempala (FSTTCS, 2012)

Effective Principal Component Analysis. - Santosh S. Vempala (SISAP, 2012)

Deterministic construction of an approximate M-ellipsoid and its applications to derandomizing lattice algorithms. - Daniel Dadush; Santosh S. Vempala (SODA, 2012)

Many sparse cuts via higher eigenvalues. - Anand Louis; Prasad Raghavendra; Prasad Tetali; Santosh S. Vempala (STOC, 2012)

Modeling high-dimensional data: technical perspective. - Santosh S. Vempala (Commun. ACM, 2012)

The Complexity of Statistical Algorithms - Vitaly Feldman; Elena Grigorescu; Lev Reyzin; Santosh S. Vempala (CoRR, 2012)

Deterministic 2^{O(n)} Algorithms for M-Ellipsoids, Lattice Problems and Volume Estimation - Daniel Dadush; Santosh S. Vempala (CoRR, 2012)

The Approximate Rank of a Matrix and its Algorithmic Applications. - Noga Alon; Santosh S. Vempala (Electron. Colloquium Comput. Complex., 2012)

Statistical Algorithms and a Lower Bound for Planted Clique. - Vitaly Feldman; Elena Grigorescu; Lev Reyzin; Santosh S. Vempala; Ying Xiao 003 (Electron. Colloquium Comput. Complex., 2012)

A Deterministic Polynomial-Time Approximation Scheme for Counting Knapsack Solutions. - Daniel Stefankovic; Santosh S. Vempala; Eric Vigoda (SIAM J. Comput., 2012)

Complexity of learning subspace juntas and ICA. - Santosh S. Vempala; Ying Xiao 003 (ACSSC, 2013)

The Complexity of Approximating Vertex Expansion. - Anand Louis; Prasad Raghavendra; Santosh S. Vempala (FOCS, 2013)

The approximate rank of a matrix and its algorithmic applications: approximate rank. - Noga Alon; Troy Lee; Adi Shraibman; Santosh S. Vempala (STOC, 2013)

Statistical algorithms and a lower bound for detecting planted cliques. - Vitaly Feldman; Elena Grigorescu; Lev Reyzin; Santosh S. Vempala; Ying Xiao 003 (STOC, 2013)

Fourier PCA. - Navin Goyal; Santosh S. Vempala; Ying Xiao 003 (CoRR, 2013)

Nimble Algorithms for Cloud Computing - Ravindran Kannan; Santosh S. Vempala (CoRR, 2013)

Near-optimal deterministic algorithms for volume computation via M-ellipsoids. - Daniel Dadush; Santosh S. Vempala (Proc. Natl. Acad. Sci. USA, 2013)

Principal Component Analysis and Higher Correlations for Distributed Data. - Ravi Kannan; Santosh S. Vempala; David P. Woodruff (COLT, 2014)

Integer feasibility of random polytopes: random integer programs. - Karthekeyan Chandrasekaran; Santosh S. Vempala (ITCS, 2014)

A Cubic Algorithm for Computing Gaussian Volume. - Ben Cousins; Santosh S. Vempala (SODA, 2014)

Fourier PCA and robust tensor decomposition. - Navin Goyal; Santosh S. Vempala; Ying Xiao 003 (STOC, 2014)

Efficient Representations for Life-Long Learning and Autoencoding. - Maria-Florina Balcan; Avrim Blum; Santosh S. Vempala (CoRR, 2014)

Bypassing KLS: Gaussian Cooling and an O*(n^3) Volume Algorithm. - Ben Cousins; Santosh S. Vempala (CoRR, 2014)

Subsampled Power Iteration: a New Algorithm for Block Models and Planted CSP's. - Vitaly Feldman; Will Perkins 001; Santosh S. Vempala (CoRR, 2014)

Unsupervised Learning through Prediction in a Model of Cortex. - Christos H. Papadimitriou; Santosh S. Vempala (CoRR, 2014)

Max vs Min: Independent Component Analysis with nearly Linear Sample Complexity. - Santosh S. Vempala; Ying Xiao 003 (CoRR, 2014)

Expanders via Random Spanning Trees. - Alan M. Frieze; Navin Goyal; Luis Rademacher; Santosh S. Vempala (SIAM J. Comput., 2014)

Efficient Representations for Lifelong Learning and Autoencoding. - Maria-Florina Balcan; Avrim Blum; Santosh S. Vempala (COLT, 2015)

Cortical Learning via Prediction. - Christos H. Papadimitriou; Santosh S. Vempala (COLT, 2015)

Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity. - Santosh S. Vempala; Ying Xiao 003 (COLT, 2015)

Publishable Humanly Usable Secure Password Creation Schemas. - Manuel Blum 001; Santosh Srinivas Vempala (HCOMP, 2015)

Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's. - Vitaly Feldman; Will Perkins 001; Santosh S. Vempala (NIPS, 2015)

Cortical Computation. - Christos H. Papadimitriou; Santosh S. Vempala (PODC, 2015)

Bypassing KLS: Gaussian Cooling and an O^*(n3) Volume Algorithm. - Benjamin Cousins; Santosh S. Vempala (STOC, 2015)

On the Complexity of Random Satisfiability Problems with Planted Solutions. - Vitaly Feldman; Will Perkins 001; Santosh S. Vempala (STOC, 2015)

Statistical Query Algorithms for Stochastic Convex Optimization. - Vitaly Feldman; Cristóbal Guzmán; Santosh S. Vempala (CoRR, 2015)

Accelerated Newton Iteration: Roots of Black Box Polynomials and Matrix Eigenvalues. - Anand Louis; Santosh S. Vempala (CoRR, 2015)

Query Complexity of Sampling and Small Geometric Partitions. - Navin Goyal; Luis Rademacher; Santosh S. Vempala (Comb. Probab. Comput., 2015)

Stochastic Billiards for Sampling from the Boundary of a Convex Set. - A. B. Dieker; Santosh S. Vempala (Math. Oper. Res., 2015)

Visual Categorization with Random Projection. - Rosa I. Arriaga; David Rutter; Maya Cakmak; Santosh S. Vempala (Neural Comput., 2015)

Cortical Computation via Iterative Constructions. - Christos H. Papadimitriou; Samantha Petti; Santosh S. Vempala (COLT, 2016)

Agnostic Estimation of Mean and Covariance. - Kevin A. Lai; Anup B. Rao; Santosh S. Vempala (FOCS, 2016)

Accelerated Newton Iteration for Roots of Black Box Polynomials. - Anand Louis; Santosh S. Vempala (FOCS, 2016)

C4G BLIS: Health Care Delivery via Iterative Collaborative Design in Resource-constrained Settings. - Santosh S. Vempala; Naomi Chopra; Aishwarya Rajagopal; John Nkengasong; Sidney Akuro (ICTD, 2016)

Variable Binding through Assemblies in Spiking Neural Networks. - Robert Legenstein; Christos H. Papadimitriou; Santosh S. Vempala; Wolfgang Maass 001 (CoCo@NIPS, 2016)

Beyond Spectral: Tight Bounds for Planted Gaussians. - Ravi Kannan; Santosh S. Vempala (CoRR, 2016)

A practical volume algorithm. - Ben Cousins; Santosh S. Vempala (Math. Program. Comput., 2016)

A note on non-degenerate integer programs with small sub-determinants. - Stephan Artmann; Friedrich Eisenbrand; Christoph Glanzer; Timm Oertel; Santosh S. Vempala; Robert Weismantel (Oper. Res. Lett., 2016)

The Hidden Hubs Problem. - Ravindran Kannan; Santosh S. Vempala (COLT, 2017)

Eldan's Stochastic Localization and the KLS Hyperplane Conjecture: An Improved Lower Bound for Expansion. - Yin Tat Lee; Santosh Srinivas Vempala (FOCS, 2017)

Towards Human Computable Passwords. - Jeremiah Blocki; Manuel Blum 001; Anupam Datta; Santosh S. Vempala (ITCS, 2017)

On the Complexity of Learning Neural Networks. - Le Song; Santosh S. Vempala; John Wilmes; Bo Xie 002 (NIPS, 2017)

Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization. - Vitaly Feldman; Cristóbal Guzmán; Santosh S. Vempala (SODA, 2017)

Adaptive Matrix Vector Product. - Santosh S. Vempala; David P. Woodruff (SODA, 2017)

Geodesic walks in polytopes. - Yin Tat Lee; Santosh S. Vempala (STOC, 2017)

Randomized algorithms in numerical linear algebra. - Ravindran Kannan; Santosh S. Vempala (Acta Numer., 2017)

CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models. - Hulda S. Haraldsdóttir; Ben Cousins; Ines Thiele; Ronan M. T. Fleming; Santosh S. Vempala (Bioinform., 2017)

The Complexity of Human Computation: A Concrete Model with an Application to Passwords. - Manuel Blum 001; Santosh S. Vempala (CoRR, 2017)

Random Overlapping Communities: Approximating Motif Densities of Large Graphs. - Samantha Petti; Santosh S. Vempala (CoRR, 2017)

Geometric random edge. - Friedrich Eisenbrand; Santosh S. Vempala (Math. Program., 2017)

Efficient Convex Optimization with Membership Oracles. - Yin Tat Lee; Aaron Sidford; Santosh S. Vempala (COLT, 2018)

Continuous Algorithms (Invited Paper). - Santosh S. Vempala (FSTTCS, 2018)

Usability of Humanly Computable Passwords. - Samira Samadi; Santosh S. Vempala; Adam Tauman Kalai (HCOMP, 2018)

Long Term Memory and the Densest K-Subgraph Problem. - Robert Legenstein; Wolfgang Maass 001; Christos H. Papadimitriou; Santosh S. Vempala (ITCS, 2018)

Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons. - Nima Anari; Constantinos Daskalakis; Wolfgang Maass 001; Christos H. Papadimitriou; Amin Saberi; Santosh S. Vempala (NeurIPS, 2018)

The Price of Fair PCA: One Extra dimension. - Samira Samadi; Uthaipon Tao Tantipongpipat; Jamie Morgenstern; Mohit Singh; Santosh S. Vempala (NeurIPS, 2018)

Convergence rate of riemannian Hamiltonian Monte Carlo and faster polytope volume computation. - Yin Tat Lee; Santosh S. Vempala (STOC, 2018)

Stochastic localization + Stieltjes barrier = tight bound for log-Sobolev. - Yin Tat Lee; Santosh S. Vempala (STOC, 2018)

Polynomial Convergence of Gradient Descent for Training One-Hidden-Layer Neural Networks. - Santosh S. Vempala; John Wilmes (CoRR, 2018)

The Kannan-Lovász-Simonovits Conjecture. - Yin Tat Lee; Santosh S. Vempala (CoRR, 2018)

Algorithmic Theory of ODEs and Sampling from Well-conditioned Logconcave Densities. - Yin Tat Lee; Zhao Song 002; Santosh S. Vempala (CoRR, 2018)

Gaussian Cooling and O*(n3) Algorithms for Volume and Gaussian Volume. - Ben Cousins; Santosh S. Vempala (SIAM J. Comput., 2018)

Special Section on the Fifty-Sixth Annual IEEE Symposium on Foundations of Computer Science (FOCS 2015). - Cristopher Moore; Santosh S. Vempala (SIAM J. Comput., 2018)

Optimal Convergence Rate of Hamiltonian Monte Carlo for Strongly Logconcave Distributions. - Zongchen Chen; Santosh S. Vempala (APPROX-RANDOM, 2019)

Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds. - Santosh S. Vempala; John Wilmes (COLT, 2019)

Random Projection in the Brain and Computation with Assemblies of Neurons. - Christos H. Papadimitriou; Santosh S. Vempala (ITCS, 2019)

Redesigning a Basic Laboratory Information System for the Global South. - Jung Wook Park; Aditi Shah; Rosa I. Arriaga; Santosh S. Vempala (ITU Kaleidoscope, 2019)

Multi-Criteria Dimensionality Reduction with Applications to Fairness. - Uthaipon Tantipongpipat; Samira Samadi; Mohit Singh; Jamie Morgenstern; Santosh S. Vempala (NeurIPS, 2019)

Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices. - Santosh S. Vempala; Andre Wibisono (NeurIPS, 2019)

Fair Dimensionality Reduction and Iterative Rounding for SDPs. - Jamie Morgenstern; Samira Samadi; Mohit Singh; Uthaipon Tao Tantipongpipat; Santosh S. Vempala (CoRR, 2019)

Rapid Convergence of the Unadjusted Langevin Algorithm: Log-Sobolev Suffices. - Santosh S. Vempala; Andre Wibisono (CoRR, 2019)

Human-Usable Password Schemas: Beyond Information-Theoretic Security. - Elan Rosenfeld; Santosh S. Vempala; Manuel Blum 001 (CoRR, 2019)

Matrix Decompositions and Sparse Graph Regularity. - Greg Bodwin; Santosh S. Vempala (CoRR, 2019)

A 4/3-Approximation Algorithm for the Minimum 2-Edge Connected Subgraph Problem. - Christoph Hunkenschröder; Santosh S. Vempala; Adrian Vetta (ACM Trans. Algorithms, 2019)

Brain Computation: A Computer Science Perspective. - Wolfgang Maass 001; Christos H. Papadimitriou; Santosh S. Vempala; Robert Legenstein (Computing and Software Science, 2019)

The Communication Complexity of Optimization. - Santosh S. Vempala; Ruosong Wang; David P. Woodruff (SODA, 2020)

Strong self-concordance and sampling. - Aditi Laddha; Yin Tat Lee; Santosh S. Vempala (STOC, 2020)

Fair k-Means Clustering. - Mehrdad Ghadiri; Samira Samadi; Santosh S. Vempala (CoRR, 2020)

Reducing Isotropy and Volume to KLS: An O(n3ψ2) Volume Algorithm. - He Jia; Aditi Laddha; Yin Tat Lee; Santosh S. Vempala (CoRR, 2020)

Robustly Learning Mixtures of k Arbitrary Gaussians. - Ainesh Bakshi; Ilias Diakonikolas; He Jia; Daniel M. Kane; Pravesh K. Kothari; Santosh S. Vempala (CoRR, 2020)

The complexity of human computation via a concrete model with an application to passwords. - Manuel Blum 001; Santosh S. Vempala (Proc. Natl. Acad. Sci. USA, 2020)

Convergence of Gibbs Sampling: Coordinate Hit-And-Run Mixes Fast. - Aditi Laddha; Santosh S. Vempala (SoCG, 2021)

Socially Fair k-Means Clustering. - Mehrdad Ghadiri; Samira Samadi; Santosh S. Vempala (FAccT, 2021)

Solving Sparse Linear Systems Faster than Matrix Multiplication. - Richard Peng; Santosh S. Vempala (SODA, 2021)

Reducing isotropy and volume to KLS: an o*(n3ψ2) volume algorithm. - He Jia; Aditi Laddha; Yin Tat Lee; Santosh S. Vempala (STOC, 2021)

Tutorial on the Robust Interior Point Method. - Yin Tat Lee; Santosh S. Vempala (CoRR, 2021)

Sparse Regression Faster than dω. - Mehrdad Ghadiri; Richard Peng; Santosh S. Vempala (CoRR, 2021)

Assemblies of neurons can learn to classify well-separated distributions. - Max Dabagia; Christos H. Papadimitriou; Santosh S. Vempala (CoRR, 2021)

Provable Lifelong Learning of Representations. - Xinyuan Cao; Weiyang Liu; Santosh S. Vempala (AISTATS, 2022)

How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization. - Shivam Garg; Santosh S. Vempala (AISTATS, 2022)

The Mirror Langevin Algorithm Converges with Vanishing Bias. - Ruilin Li; Molei Tao; Santosh S. Vempala; Andre Wibisono (ALT, 2022)

A Unified Approach to Discrepancy Minimization. - Nikhil Bansal 001; Aditi Laddha; Santosh S. Vempala (APPROX/RANDOM, 2022)

Assemblies of neurons learn to classify well-separated distributions. - Max Dabagia; Santosh S. Vempala; Christos H. Papadimitriou (COLT, 2022)

The Manifold Joys of Sampling (Invited Talk). - Yin Tat Lee; Santosh S. Vempala (ICALP, 2022)

Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space. - Yunbum Kook; Yin Tat Lee; Ruoqi Shen; Santosh S. Vempala (NeurIPS, 2022)

Robustly learning mixtures of k arbitrary Gaussians. - Ainesh Bakshi; Ilias Diakonikolas; He Jia; Daniel M. Kane; Pravesh K. Kothari; Santosh S. Vempala (STOC, 2022)

Convergence of the Riemannian Langevin Algorithm. - Khashayar Gatmiry; Santosh S. Vempala (CoRR, 2022)

Constant-Factor Approximation Algorithms for Socially Fair k-Clustering. - Mehrdad Ghadiri; Mohit Singh; Santosh S. Vempala (CoRR, 2022)

A Slightly Improved Bound for the KLS Constant. - Arun Jambulapati; Yin Tat Lee; Santosh S. Vempala (CoRR, 2022)

Socially fair network design via iterative rounding. - Aditi Laddha; Mohit Singh; Santosh S. Vempala (Oper. Res. Lett., 2022)

A unified view of graph regularity via matrix decompositions. - Greg Bodwin; Santosh S. Vempala (Random Struct. Algorithms, 2022)

Approximating sparse graphs: The random overlapping communities model. - Samantha Petti; Santosh S. Vempala (Random Struct. Algorithms, 2022)

Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators. - Yunbum Kook; Yin Tat Lee; Ruoqi Shen; Santosh S. Vempala (COLT, 2023)

Is Planted Coloring Easier than Planted Clique? - Pravesh Kothari; Santosh S. Vempala; Alexander S. Wein; Jeff Xu (COLT, 2023)

The k-Cap Process on Geometric Random Graphs. - Mirabel E. Reid; Santosh S. Vempala (COLT, 2023)

The Bit Complexity of Efficient Continuous Optimization. - Mehrdad Ghadiri; Richard Peng; Santosh S. Vempala (FOCS, 2023)

Beyond Moments: Robustly Learning Affine Transformations with Asymptotically Optimal Error. - He Jia; Pravesh K. Kothari; Santosh S. Vempala (FOCS, 2023)

Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time. - Xinyuan Cao; Santosh S. Vempala (NeurIPS, 2023)

Sampling with Barriers: Faster Mixing via Lewis Weights. - Khashayar Gatmiry; Jonathan A. Kelner; Santosh S. Vempala (CoRR, 2023)

Computation with Sequences in the Brain. - Max Dabagia; Christos H. Papadimitriou; Santosh S. Vempala (CoRR, 2023)

Efficiently Sampling the PSD Cone with the Metric Dikin Walk. - Yunbum Kook; Santosh S. Vempala (CoRR, 2023)

Computation with Sequences of Assemblies in a Model of the Brain. - Max Dabagia; Christos H. Papadimitriou; Santosh S. Vempala (ALT, 2024)

Sampling Polytopes with Riemannian HMC: Faster Mixing via the Lewis Weights Barrier. - Khashayar Gatmiry; Jonathan A. Kelner; Santosh S. Vempala (COLT, 2024)

Gaussian Cooling and Dikin Walks: The Interior-Point Method for Logconcave Sampling. - Yunbum Kook; Santosh S. Vempala (COLT, 2024)

In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies. - Yunbum Kook; Santosh S. Vempala; Matthew Shunshi Zhang (NeurIPS, 2024)

Calibrated Language Models Must Hallucinate. - Adam Tauman Kalai; Santosh S. Vempala (STOC, 2024)

Coin-Flipping In The Brain: Statistical Learning with Neuronal Assemblies. - Max Dabagia; Daniel Mitropolsky; Christos H. Papadimitriou; Santosh S. Vempala (CoRR, 2024)

Does GPT Really Get It? A Hierarchical Scale to Quantify Human vs AI's Understanding of Algorithms. - Mirabel Reid; Santosh S. Vempala (CoRR, 2024)

Does GPT Really Get It? A Hierarchical Scale to Quantify Human and AI's Understanding of Algorithms. - Mirabel Reid; Santosh S. Vempala (AAAI, 2025)

Sampling and Integration of Logconcave Functions by Algorithmic Diffusion. - Yunbum Kook; Santosh S. Vempala (STOC, 2025)

Faster logconcave sampling from a cold start in high dimension. - Yunbum Kook; Santosh S. Vempala (CoRR, 2025)

Why Language Models Hallucinate. - Adam Tauman Kalai; Ofir Nachum; Santosh S. Vempala; Edwin Zhang (CoRR, 2025)