The paper classifies circle actions on 6D manifolds with isolated fixed points.
problem Classifying circle actions on 6D manifolds with isolated fixed points.
method Performing equivariant connected sums at fixed points with specific manifolds.
result A sequence of operations can reduce the fixed point data to the empty collection.
In this paper, we study a circle action on a compact oriented manifold with a discrete fixed point set. The fixed point data consists of the weights of the S1-representations at the fixed points. We prove various results and properties of the action, in terms of the fixed point data. We show that the manifold can be…
Causalfe estimates treatment effects in panel data with fixed effects.
problem Spurious heterogeneity in treatment effect estimates due to fixed effects in panel data.
method CFFE approach with node-level residualization during tree construction.
result Validates the estimator's performance through simulation studies.
New algorithms learn from fixed data without exploration.
problem Learning from fixed data without additional exploration.
method Introduces batch-constrained reinforcement learning.
result First continuous control RL algorithm for fixed data.
A new clustering framework using fixed points for data analysis.
problem Lack of unified understanding and application of clustering algorithms in data analysis.
method Restated model-based clustering using fixed point theory, iteratively constructing contraction maps to find cluster centers.
result Unified clustering framework reveals convergence mechanisms and interconnections among clustering algorithms.
Interpreting gradient methods as fixed-point iterations, we provide a detailed analysis of those methods for minimizing convex objective functions. Due to their conceptual and algorithmic simplicity, gradient methods are widely used in machine learning for massive data sets (big data). In particular, stochastic gradien…
The problem of recovering the asymptotics of a short range perturbation of the Euclidean metric on R^n from fixed energy scattering data is studied. It is shown that if two such metrics, g1, g2, have scattering data at some fixed energy which are equal up to smoothing, then there exists a diffeomorphism ψ`fixing infini…
Fixed-parameter tractability of private synthetic data generation
problem Generating synthetic data under differential privacy
method Linear programming and subsampled private multiplicative weights method
result Optimal error rates across all regimes
Let the circle act on a compact almost complex manifold M. In this paper, we classify the fixed point data of the action if there are 4 fixed points and the dimension of the manifold is at most 6. First, if dimM=2, then M is a disjoint union of rotations on two 2-spheres. Second, if dimM=4, we prove that th…
Classifies circle actions on 6D manifolds with 4 fixed points.
problem Classifying circle actions on 6D manifolds with specific fixed points.
method Analyzes fixed point data and proves agreement with known actions.
result Agrees with actions on 6-spheres or CP3. Classifies multigraphs for torus actions on 6D manifolds with isolated fixed points.
problem Classifying torus actions on 6D manifolds with isolated fixed points.
method Associate multigraphs to fixed point data, study operations, and prove classification.
result Classifies multigraphs for 6D manifolds by converting them into the empty graph.
Paper proves new method for constructing initial data in general relativity.
problem Proving the existence of solutions for initial data in general relativity.
method Using the Banach fixed point theorem to prove existence, with guarantees of uniqueness and explicit construction.
result Guaranteed uniqueness and explicit construction of solutions to the conformal method equations.
Improved convergence of fixed-point methods using windowed Anderson acceleration.
problem Improving convergence of fixed-point methods for symmetric operators.
method Windowed Anderson acceleration for symmetric fixed-point iterations.
result Windowed Anderson acceleration improves convergence over standard fixed-point methods.
Study circle actions on unitary manifolds with discrete fixed points.
problem Understanding circle actions on compact unitary manifolds with discrete fixed points.
method Prove relationships between weights at fixed points and derive results regarding the first equivariant Chern class and Hirzebruch χy-genus. result Derive a multigraph encoding fixed point data, leading to new insights into unitary S1-manifolds. R package xtdml uses DML for panel data models with fixed effects.
problem Estimating structural parameters in panel data models with fixed effects.
method Combines machine learning with statistical estimation for inference.
result Demonstrates improved performance in learning nuisance functions.
We express the index of the Dirac operator on symplectic quotients of a Hamiltonian loop group manifold with proper moment map in terms of fixed point data.
Study a 10D symplectic manifold with 6 fixed points, linking to G2 orbit.
problem Understanding fixed points and Chern classes in Hamiltonian S1 actions. method Analyzing manifold data, comparing to G2 orbit. result Certain data uniquely determine others, showing similarities to G2 orbit. Develops DML for nonlinear panel data models with fixed effects.
problem Estimating causal effects in nonlinear panel data models with fixed effects.
method Double machine learning (DML) procedures for approximating nuisance functions.
result First-differencing yields the least constraints on fixed effects distribution.
New tensor approach models global fixed income risks across maturities and economies.
problem Lack of models capturing multi-dimensional data in global fixed income markets.
method Introduces tensor-valued approach to model shared risks among multiple interest rate curves.
result Estimates risk factors decomposable into maturity and country domains, enabling tailored portfolio management.
Blowing up a point p in a manifold M builds a new manifold M' in which p is replaced by the projectivization of the tangent space of M at p. This well-known operation also applies to fixed points of diffeomorphisms, yielding continuous homomorphisms between automorphism groups of M and M'. The construction for maps inv…
New method accelerates optimization in fixed time, improving convergence rates.
problem Optimization in large-scale data-driven problems.
method Gradient-based optimization framework with fixed-time stable dynamical systems.
result Achieves convergence to the optimizer in a fixed number of iterations, independent of initialization.
Study gradient flow of phase transitions with fixed contact angle.
problem Understanding phase transitions with fixed contact angle.
method Gradient flow of the Allen-Cahn equation with fixed boundary contact angle.
result Established interior and boundary convergence properties for solutions and energy measures.
Enhanced synthetic dataset improves asset allocation analysis.
problem Lack of realistic synthetic data for fixed income portfolio construction.
method Improved CorrGAN model for synthetic correlation matrices and Encoder-Decoder model for additional data conditioning.
result Synthetic dataset enhances portfolio construction and asset allocation analysis.
A faster method for estimating effects in large data using fixed-point trees.
problem Estimating heterogeneous effects in large dimensions with computational efficiency.
method Fixed-point approximation to eliminate Jacobian estimation and speed up GRFs.
result Significant computational efficiency improvement without sacrificing statistical accuracy.
New method handles unknown task boundaries in continual learning.
problem Catastrophic forgetting in neural networks.
method Fixed-point equations for online variational Bayes optimization.
result Approximates online Bayes update for non-stationary data.
Developed an efficient iterative algorithm for SVI model.
problem SVI model's optimizer's strong dependence on input starting point.
method Fixed-point and least-square optimizer.
result Convergence results for fixed-point iterative algorithm in certain situations.
Recurrent auto-encoder model summarises sequential data through an encoder structure into a fixed-length vector and then reconstructs the original sequence through the decoder structure. The summarised vector can be used to represent time series features. In this paper, we propose relaxing the dimensionality of the dec…
Study circle actions on 4-manifolds, deriving formulas and graphs.
problem Understanding circle actions on 4-dimensional manifolds.
method Derive the Atiyah-Hirzebruch formula and associate graphs to fixed point data.
result Show existence of 4D oriented S^1-manifolds from satisfying graphs.
Suppose one is given a discrete group G, a cocompact proper G-manifold M, and a G-self-map f of M. Then we introduce the equivariant Lefschetz class of f, which is globally defined in terms of cellular chain complexes, and the local equivariant Lefschetz class of f, which is locally defined in terms of fixed point data…
Conditions for equivariant bundles on 4-manifolds with cyclic actions.
problem Existence of equivariant bundles on 4-manifolds with cyclic actions.
method Conditions derived from the twisted signature formula and congruence relations between fixed point data and isotropy representations.
result Necessary and sufficient conditions for the existence of equivariant bundles.
The paper optimizes k-NN for distributed learning with minimax optimal performance.
problem Minimizing error rates in classification, regression, and density estimation.
method Optimal aggregation of fixed-k nearest neighbors from multiple subsets of data.
result Achieves minimax optimal error rates up to a logarithmic factor.
We announce the following result and give several applications: A Hamiltonian T-space (for T a torus) with isolated fixed points is cobordant to a disjoint union of weighted projective spaces which are constructed from its fixed point data. The applications concern the Duistermaat-Heckman formula, the topological J…
There is significant recent interest to parallelize deep learning algorithms in order to handle the enormous growth in data and model sizes. While most advances focus on model parallelization and engaging multiple computing agents via using a central parameter server, aspect of data parallelization along with decentral…
SequenceR uses seq-to-seq learning to fix bugs in code.
problem Fixing bugs in code using machine learning.
method Sequence-to-sequence learning with copy mechanism, trained on curated code samples.
result SequenceR can perfectly predict and find correct patches for bugs.
A new restart criterion for k-means++ improves clustering quality and adapts to data difficulty.
problem Arbitrary restart counts in k-means++ lead to inconsistent results and wasted computation.
method GTRC combines Good-Turing estimates, bounds, and user-specified tolerance to dynamically decide restarts.
result GTRC achieves clustering quality comparable to fixed restart counts, varying restarts based on data difficulty.
APGAI identifies good arms anytime with fixed budget.
problem Identifying a good arm with a fixed sampling budget.
method An anytime algorithm for good arm identification in stochastic bandits.
result APGAI achieves efficient detection of good arms with upper bounds on probability of error and sampling complexity.
Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in the…
Stochastic Gradient Descent (SGD) is a central tool in machine learning. We prove that SGD converges to zero loss, even with a fixed (non-vanishing) learning rate - in the special case of homogeneous linear classifiers with smooth monotone loss functions, optimized on linearly separable data. Previous works assumed eit…
New method learns causal models from data efficiently.
problem Learning Structural Causal Models from data is challenging.
method Amortized inference via Conditional Fixed-Point Iterations with transformer embeddings.
result Single model predicts causal mechanisms conditioned on data and graph.
LMMVAE improves VAE for correlated data by separating latent variables into fixed and random parts.
problem Correlated data in tabular and image datasets.
method Integrates random effects into VAE architecture, separating latent variables into fixed and random parts.
result Significant improvement in reconstruction error and likelihood loss on unseen data.
Derives log-corrections in AdS4/CFT3 using supergravity localization.
problem Factorizing log-corrections in AdS4/CFT3.
method Supergravity localization, Atiyah-Singer index theorem, fixed points (NUTs), fixed two-manifolds (Bolts).
result General fixed-point formula for log-corrections in large N expansion.
Characterizes representations for complex projective structures with specific branch data.
problem Understanding representations of surface groups as holonomy of complex projective structures.
method Computing holonomies for spherical metrics and affine structures with prescribed conical angles.
result Computed holonomies for spherical metrics and affine structures with specific conical angles.
A new estimator reduces bias and improves efficiency for staggered adoption studies.
problem Bias in difference-in-differences estimates for staggered adoption studies.
method Fused Extended Two-Way Fixed Effects (FETWFE) estimator with automatic parameter selection.
result FETWFE identifies correct restrictions with probability tending to one, improving efficiency.
Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a cruc…
Modern ML methods show unexpected behaviors that contradict classical statistics.
problem Modern machine learning methods exhibit behaviors at odds with classical statistical intuitions.
method Comparison between fixed and random design settings in ML and statistics.
result Moving from fixed to random designs reveals new insights into bias-variance tradeoffs and overfitting.
Study on curvature image iterations converging to solutions of Minkowski problems.
problem Solving Lp Minkowski problems with prescribed data. method Iterations of curvature image operators Λpφ applied to convex bodies. result Iterations of curvature image operators converge to fixed points under certain conditions.
Paper extends Brouwer Fixed Point Theorem with amiable and almost amiable fixed sets.
problem Extending the Brouwer Fixed Point Theorem to approximate fixed sets.
method Introducing shape boundary regions in CW spaces as amiable and almost amiable fixed subsets of dpc maps.
result Variation of Jordan Curve Theorem and Fixed Cell Complex Theorem.
The Renormalisation Group (RG) provides a framework in which it is possible to assess whether a deep-learning network is sensitive to small changes in the input data and hence prone to error, or susceptible to adversarial attack. Distinct classification outputs are associated with different RG fixed points and sensitiv…