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Explore advanced mathematical physics connecting gapped quantum phases to bordism theory, investigating the Kapustin-Kitaev conjecture through category theory and topological methods.
Explore advanced mathematical concepts connecting Kitaev pairings with coarse geometry, extending topological invariants to higher dimensions and Berry curvature theory.
Explore advanced topological quantum field theory through Kontsevich's quantization of finite homotopy types and its enhancement to fully extended TQFTs in iterated algebras.
Explore moduli spaces of 3D topological quantum field theories and their connection to gapped quantum systems through homotopy theory and ribbon category automorphisms.
Explore new techniques from mirror symmetry and quantum cohomology to prove irrationality of 4-dimensional unirational varieties using Hodge atoms theory.
Explore the geometric Langlands equivalence and its implications for automorphic functions, including connections to Ramanujan and Arthur multiplicity conjectures.
Explore how geometric Langlands equivalence reveals automorphic functions through algebraic geometry, connecting to Ramanujan and Arthur multiplicity conjectures.
Explore discretization techniques and distribution learning in diffusion models, covering randomized midpoints, score matching, and applications to parameter estimation and density learning.
Discover how side information in unlabeled data improves machine learning models through iterative pseudo-labeling and error decorrelation analysis.
Discover how negative stepsizes enable Gradient-Descent-Ascent convergence on min-max problems through innovative time-varying, asymmetric stepsize schedules and slingshot dynamics.
Explore self-play reinforcement learning for theorem proving where LLMs act as both conjecturers and provers, achieving state-of-the-art results on mathematical benchmarks.
Discover how to safely combine synthetic and real data to enhance statistical inference power while maintaining error bounds without distributional assumptions on synthetic data quality.
Explore theoretical foundations of diffusion models, focusing on how DDPM achieves efficient sampling by exploiting intrinsic data dimensionality and mixture structures.
Explore mathematical frameworks analyzing deep ResNet training dynamics, revealing how infinite-depth networks behave as infinitely wide and optimal Transformer scaling laws.
Explore interactive decision making frameworks covering multi-armed bandits, contextual bandits, and reinforcement learning with statistical learning theory and algorithmic primitives.
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