Santosh Vempala
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| title | conference | date |
|---|---|---|
| Red Points and Blue Points | The Role of TCS in Modern Machine Learning | 2026-05-26 |
Given independent n‑dimensional points labeled by an unknown intersection of k halfspaces with a soft margin ρ, the work gives an algorithm that PAC‑learns the… | ||
| Coin-Flipping in the Brain: Probabilistic Computation and Learning in the Assembly Model | Understanding Higher-Level Intelligence from AI, Psychology, and Neuroscience Perspectives | 2024-06-24 |
Model uses a directed Erdős–Rényi connectome with constant brain areas of n neurons where k out of n fire (k ≈ √n) via winner‑take‑all inhibition, Hebbian… | ||
| Robust Gradient Descent: Agnostically Estimating an Unknown Affine Transformation... | Optimization and Algorithm Design 2023 | 2023-11-27 |
Claims a polynomial-time algorithm that, from an ε‑corrupted sample of an unknown affine transform of the d‑cube, outputs ĤA, Ĥb with total variation O(ε)… | ||
| Fast Algorithms for Multivariate ODEs arising in Sampling and Learning | Randomized Numerical Linear Algebra and Applications 2018 | 2018-09-24 |
Proposes a practical algorithm that solves the multivariate ODEs behind Hamiltonian Monte Carlo by approximating trajectories with low‑degree polynomials and… | ||
| High Dimensional Geometry and Concentration I | Foundations of Data Science Boot Camp 2018 | 2018-08-27 |
High-dimensional sets have most of their volume near boundary (shrinking by 1−ε multiplies volume by (1−ε)^n) and Euclidean balls put half their mass inside a… | ||
| High Dimensional Geometry and Concentration II | Foundations of Data Science Boot Camp 2018 | 2018-08-27 |
Proves the functional Brunn–Minkowski (Prékopa–Leindler) inequality by induction on dimension, using Brunn–Minkowski only in the one‑dimensional base case… | ||
| Unsupervised Learning | The Brain and Computation Boot Camp | 2018-01-16 |
Proven results and concrete limits: polynomial algorithms recover Gaussian-mixture parameters but sample complexity currently blows up with number of… | ||
| The Convergence of Hamiltonian Monte Carlo | Optimization, Statistics and Uncertainty | 2017-11-28 |
Proves that Hamiltonian Monte Carlo run with the Hessian manifold metric (log barrier) mixes far faster on polytopes — roughly MN^{2/3} mixing steps (with… | ||
| Subsampled Power Iteration: A Unified Algorithm for Block Models and Planted CSP's | Fast Algorithms via Spectral Methods 2014 | 2014-12-01 |
Proposes Subsampled Power Iteration that partitions edges into ~10 log n independent subgraphs and runs power iteration on each, taking independent subsamples… | ||
| Independent Component Analysis: From Theory to Practice and Back | Spectral Algorithms: From Theory to Practice 2014 | 2014-10-27 |
Problem: recover unknown linear mixing A from samples X = A S where S has independent coordinates and at most one Gaussian. Algorithm: make samples isotropic… | ||
| An SDP-based Algorithmic Cheeger Inequality for Vertex Expansion | Semidefinite Optimization, Approximation and Applications 2014 | 2014-09-24 |
An SDP relaxation for symmetric vertex expansion is formulated and shown to round to a cut by solving for vertex vectors, picking a random Gaussian projection… | ||
| Nimble Algorithms for Cloud Computing | Workshop on Succinct Data Representations and Applications (Simons Institute Big Data 2013) | 2013-09-16 |
Defines sublinear communication algorithms that use polynomial time/space but exchange far less than the data. Shows frequency moments across S servers by… | ||
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