Quotient Sparsification for Submodular Functions
Explores sparsification of matroids and submodular functions, unifying graph and hypergraph cut sparsifiers with applications to set systems and algorithmic efficiency.
Discrepancy Minimization via Regularization
Explore a novel algorithmic framework for discrepancy minimization, reinterpreting key breakthroughs and proving conjectures for pseudorandom instances through regularization techniques.
Breaking the Quadratic Gap for Strongly Polynomial Solvers to Combinatorial Linear Programs
Explore advanced optimization techniques for combinatorial linear programs, focusing on breaking the quadratic gap in strongly polynomial solvers using interior-point methods.
Short-Flat Decompositions and Faster Algorithms for Linear Inverse Problems
Explore algorithmic applications of short-flat decompositions in solving undercomplete linear inverse problems, with focus on sparse recovery and low-rank matrix completion techniques.
IPM and Nested Dissection for Planar and Separable LPs
Exploring efficient LP solvers using nested dissection in IPM framework for planar min-cost flow, multicommodity flow, low treewidth LPs, and separable LPs. Focuses on optimization techniques and algorithm design.
Robust Gradient Descent - Agnostically Estimating an Unknown Affine Transformation
Polynomial-time algorithm for robust learning of unknown affine transformations in ICA, achieving optimal TV-distance recovery guarantees using Robust Gradient Descent and a new geometric certificate.
Sketching Techniques for Kernel Density Estimation or for Optimal Transport Computations
Explore advanced sketching techniques for kernel density estimation and optimal transport computations, enhancing algorithmic efficiency in data analysis and machine learning.
Sketching Techniques for Kernel Density Estimation
Explore advanced sketching techniques for efficient kernel density estimation, focusing on algorithmic design and computational improvements in high-dimensional data analysis.
Fast Attention Requires Bounded Entries
Explore the relationship between fast attention mechanisms and bounded entries in machine learning, with insights from Josh Alman's research on sketching and algorithm design.
Improved Frequency Estimation Algorithms With and Without Predictions
Explore advanced frequency estimation algorithms for data streams, including novel approaches outperforming classical methods and further improvements using heavy-hitter predictions.
Provably Secure Machine Learning Algorithms - Designing Reliable AI Systems
Exploring vulnerabilities in machine learning systems and developing provably secure algorithms to defend against statistical attacks in high-dimensional settings and non-convex models.
Insights from Engineering Sketches for Production and Using Sketches at Scale
Explore insights on engineering sketches for large-scale production, covering Yahoo's journey in developing and implementing sketching algorithms across multiple languages and platforms.
Streaming Lower Bounds for the Needle Problems
Exploring tight lower bounds for frequency estimation in random order data streams, focusing on the needle problem and its implications for streaming algorithm design.
Streaming Euclidean K-Median and K-Means With O-Log N Space
Innovative algorithm for Euclidean k-median and k-means clustering in data streams, achieving (1+ε)-approximation with significantly reduced memory usage, breaking long-standing space complexity barriers.
Pseudorandom Hashing for Space-Bounded Computation with Applications in Streaming
Explores a new pseudorandom generator for space-bounded computation, improving update time in streaming algorithms without sacrificing space. Applications in Fp estimation and CountSketch are discussed.
Resizable Sketches
Explore techniques for maintaining fixed accuracy in sketches while allowing size growth, addressing challenges in data stream processing and algorithm design.
A Simple Quantum Sketch With Applications to Graph Algorithms
Explore quantum sketching for set analysis and its applications in graph algorithms, including triangle counting and Max-Dicut, with potential for significant space efficiency gains.
A Near-Linear Time Algorithm for the Chamfer Distance
Explore a groundbreaking near-linear time algorithm for computing the Chamfer distance, a crucial metric in computer vision and image processing.
Robustness of Sketches to Adaptive Inputs
Explore the resilience of sketching algorithms against adaptive inputs, examining their effectiveness in dynamic data environments and algorithmic design.
Tight Bounds for Volumetric Spanners in All Norms
Explores optimal bounds for volumetric spanners across all norms, discussing applications in optimization and matrix approximation. Presents a local search method for construction and its implications for coresets in MVEE problems.
A Simple -Approximation Adaptive Sketching Algorithm for Maximum Weighted Matching
Explore a simple algorithm for approximating weighted matching in graphs using adaptive sketching, featuring a primal-dual analysis and multiplicative weight update method.