Graph Limits and Homomorphism Density Inequalities
Explore graph limits theory, homomorphism density inequalities, and their applications in extremal combinatorics. Gain insights into undecidability and randomness in graph theory.
A Morse Model for Stable Homotopy - ICBS 2024
Explore stable homotopy groups, framed bordism, and Morse theory in topological spaces. Gain insights into Floer homotopy foundations and the Pontryagin-Thom construction.
Filling Radius, Positive Scalar Curvature and Quantitative Operator K-Theory
Explore techniques in quantitative operator theory to estimate filling radius, connecting positive scalar curvature and asymptotic dimension in large-scale geometry.
Representations of Simple Affine Vertex Algebras at Elliptic Levels and Affine Springer Fibres
Explore connections between affine vertex algebras and elliptic affine Springer fibres, delving into representation theory and geometric insights.
Towards a Mod p Langlands Correspondence for GL2
Explore the mod $p$ Langlands correspondence for $\mathrm{GL}_2(K)$, examining conjectures and recent progress in this open area of mathematical research.
New Minimal Surfaces in S^3 and B^3 via Eigenvalue Optimization
Explore new methods for producing minimal surfaces in $S^3$ and $B^3$ through spectral geometry optimization, addressing topological realization and low-area surface growth.
Properties of the Unit Distance Graph of the Plane
Explore properties of the unit distance graph of the plane, including the Hadwiger-Nelson problem, Erdos' conjecture, and fractional chromatic number.
Progression-Free Sets in Arithmetic Sequences
Explore lower and upper bounds for progression-free sets in $\mathbb{Z}_m^n$, examining the polynomial method's application to $r_k(\mathbb{Z}_m^n)$ and related problems.
Total Positivity: Combinatorics, Geometry and Representation Theory
Explore total positivity theory in matrices, its applications, and recent developments in combinatorics, geometry, and representation theory.
Universally Counting Curves in Calabi-Yau Threefolds - ICBS 2024
Explore enumerative geometry through a new perspective on curve counting in complex threefolds, focusing on the Grothendieck group of 1-cycles and universal curve enumeration invariants.
The Tale of Math and Scalability - Predicting System Performance
Explore how mathematical principles can predict and enhance software system scalability, uncovering the hidden math behind efficient engineering practices.
Automated Scalable Bayesian Inference via Data Summarization - 2018
Explore scalable Bayesian inference techniques using data summarization. Learn about coresets, theoretical guarantees, and practical applications in large-scale data settings.
A Bayesian View of Inductive Learning in Humans and Machines - 2004
Explore how humans make successful generalizations from limited evidence through Bayesian inferences constrained by implicit domain theories, with applications to machine learning.
Causal AI for Web and Healthcare
Explore causal AI applications in web and healthcare using knowledge graphs for intervention and counterfactual reasoning, model-based explainability, and domain-specific insights.
The Signal and the Noise by Nate Silver - Part 2
Explore Nate Silver's insights on data analysis and prediction in "The Signal and the Noise" through an expert review of key concepts and applications.
The Signal and the Noise by Nate Silver - Part 1
Explore key insights from Nate Silver's "The Signal and the Noise" in this comprehensive review, focusing on statistical analysis and prediction techniques.
Some Approaches for Solving the Discretely-Constrained Mixed Complementarity Problem (DC-MCP)
Explore approaches for solving discretely-constrained mixed complementarity problems in energy and transportation, combining integer restrictions with MCP systems and considering equity aspects.
Gradient and Hessian Approximations for Model-based Blackbox Optimization
Explore gradient and Hessian approximations for blackbox optimization, covering theoretical foundations, novel techniques, and practical applications in medical physics.
Foundations of Neo-Bayesian Statistics - Part 2
Explore Neo-Bayesian Statistics foundations, examining alternative probability theories and their implications for statistical inference, dynamic consistency, and capacity updating.
MAgNet: Mesh Agnostic Neural PDE Solver
Explore innovative neural PDE solver for accurate, scalable climate predictions. Learn about mesh-agnostic architecture combining Implicit Neural Representations and Graph Neural Networks.
Linear Stochastic Graphon Systems with Q-Noise
Explore linear stochastic graphon systems with Q-noise, extending classical theory to large networks. Learn about LQG control problems, finite rank operators, and Nash Equilibrium approximations in graphon-based systems.