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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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20416181 · May 202619922001200920172026
48 results for missing mass

Novel concentration inequalities are obtained for the missing mass, i.e. the total probability mass of the outcomes not observed in the sample. We derive distribution-free deviation bounds with sublinear exponents in deviation size for missing mass and improve the results of Berend and Kontorovich (2013) and Yari Saeed…

2015-03-20abs ↗pdf ↗

We are concerned with obtaining novel concentration inequalities for the missing mass, i.e. the total probability mass of the outcomes not observed in the sample. We not only derive - for the first time - distribution-free Bernstein-like deviation bounds with sublinear exponents in deviation size for missing mass, but …

2015-03-10abs ↗pdf ↗

Given nn samples from a population of individuals belonging to different types with unknown proportions, how do we estimate the probability of discovering a new type at the (n+1)(n+1)-th draw? This is a classical problem in statistics, commonly referred to as the missing mass estimation problem. Recent results by Ohannes…

2018-06-25abs ↗pdf ↗

This paper shows that one cannot learn the probability of rare events without imposing further structural assumptions. The event of interest is that of obtaining an outcome outside the coverage of an i.i.d. sample from a discrete distribution. The probability of this event is referred to as the "missing mass". The impo…

2015-03-12abs ↗pdf ↗

Estimates missing data points in classifier inputs based on training data.

problem Estimating the proportion of unseen data points in classifier inputs.
method Characterizes the expected missing mass in terms of the sample and uses optimization to find nearly unbiased estimators with minimized MSE.
result Found estimators with MSE roughly 80% of the Good-Turing estimator's, improving over 93% of runs.

GT estimator shows convergence for Markov samples, improving i.i.d. results.

problem Estimating missing mass in Markov samples.
method Analyzed convergence of Good-Turing estimator for Markov samples, considering spectral properties of transition matrices.
result The convergence of the GT estimator for Markov samples depends on the spectral properties of the transition matrices, leading to a new minimax rate of 1/(nβ5)1/(nβ^5) for rank-2 Markov chains.

We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates pathologies in existing adaptive methods. ITs adaptively sample from hierarchica…

2018-06-25abs ↗pdf ↗

The study optimizes distribution estimation from samples with relative entropy error, adapting to sparse distributions.

problem Estimating discrete distributions with high-probability accuracy in relative entropy.
method Analysis of Laplace estimator and confidence-dependent smoothing techniques, including data-dependent smoothing.
result Optimal high-probability risk bounds for various estimators, including a new data-dependent smoothing method.

The mass, or binding energy, is the basis property of the atomic nucleus. It determines its stability, and reaction and decay rates. Quantifying the nuclear binding is important for understanding the origin of elements in the universe. The astrophysical processes responsible for the nucleosynthesis in stars often take …

2018-06-01abs ↗pdf ↗

New method recovers relative rates in spatial compositional data from IMS.

problem Challenges in analyzing spatial data from IMS due to competitive sampling.
method Hierarchical Variational Graph Fused Lasso using heavy-tailed graphical lasso prior and automatic differentiation variational inference.
result Our method outperforms state-of-the-practice point estimate methodologies in IMS and has superior posterior coverage.

Bi-GAN model for imputing and predicting irregular time-series data.

problem Irregularly observed, varying length time-series data with missing entries.
method Bi-GAN model using a bidirectional recurrent network in a generative adversarial setting.
result Bi-GAN model can impute and predict missing values for time-series of varying length.

New tools quantify deep generative models' performance.

problem Measuring the quality-diversity trade-off in deep generative models.
method Established non-asymptotic bounds on sample complexity and introduced frontier integrals.
result Smoothed estimators improve convergence rates of divergence frontiers.

The paper studies nonlinear mass concepts in 3-manifolds with nonnegative scalar curvature.

problem Nonlinear isocapacitary mass in 3-manifolds with nonnegative scalar curvature.
method Derives positive mass theorems and shows mass coincides with ADM mass under mild conditions.
result Nonlinear masses coincide with ADM mass and prove the Penrose inequality.

Research on nearest-neighbor methods tends to focus somewhat dichotomously either on the statistical or the computational aspects -- either on, say, Bayes consistency and rates of convergence or on techniques for speeding up the proximity search. This paper aims at bridging these realms: to reap the advantages of fast …

2019-10-07abs ↗pdf ↗

We prove directly without using a density theorem that (i) the ADM mass defined in the usual way on an asymptotically flat manifold is equal to the mass defined intrinsically using Ricci tensor; (ii) the Hamiltonian formulation of center of mass and the center of mass defined intrinsically using Ricci tensor are the sa…

2014-08-18abs ↗pdf ↗

The paper examines mass aspects at future null infinity and limits of quasilocal mass.

problem Understanding mass aspects and limits of quasilocal mass at future null infinity.
method Review and extension of Bondi mass and mass loss formula in Bondi-Sachs coordinate system.
result New results about the limit of quasilocal mass of unit spheres at null infinity.

Unified definition of mass aspect function for weakly regular hyperbolic manifolds.

problem Ambiguity in mass definition for asymptotically hyperbolic manifolds.
method Introduced an ADM-style mass aspect function for broad asymptotics and low regularity.
result Unified mass aspect function exhibits favorable covariance properties.

On asymptotically flat and asymptotically hyperbolic manifolds, by evaluating the total mass via the Ricci tensor, we show that the limits of certain Brown-York type and Hawking type quasi-local mass integrals equal the total mass of the manifold in all dimensions.

2015-10-27abs ↗pdf ↗

Paper studies identifiability and stability of drifting fields in generative modeling.

problem Identify and stabilize drifting fields in generative modeling.
method Introduces companion-elliptic kernel families to address limitations of Laplace kernel.
result Establishes field identifiability and demonstrates scalar observables for weak convergence.

Study on residual Monge-Ampère mass for symmetric plurisubharmonic functions.

problem Estimating the residual Monge-Ampère mass of symmetric plurisubharmonic functions with isolated singularities.
method Utilized Sasakian geometry to derive estimates on the residual mass in relation to Lelong numbers.
result Partially resolved the zero mass conjecture by Guedj and Rashkovskii.

Study on residual Monge-Ampère mass for symmetric plurisubharmonic functions.

problem Analyzing the residual Monge-Ampère mass of symmetric plurisubharmonic functions.
method Proved zero mass for functions with zero Lelong number at origin and S1S^1-invariance.
result Zero mass conjecture answered for symmetric functions.

The paper connects mass, harmonic functions, and capacity in asymptotically flat 3-manifolds.

problem Connections among ADM mass, harmonic functions, and capacity in asymptotically flat 3-manifolds.
method New formulae for ADM mass via harmonic functions, monotone quantities, and geometric inequalities.
result The mass-to-capacity ratio is bounded below by 1 - sqrt(normalized Willmore functional of the boundary).

Refines geometric center of mass analysis for Einstein field equations.

problem Analyzing the geometric center of mass of Willmore surfaces in initial data for Einstein field equations.
method Refined Lyapunov-Schmidt analysis to study geometric center of mass of area-constrained Willmore surfaces.
result The geometric center of mass agrees with the Hamiltonian center of mass under specific conditions.

New mass inequalities and proofs for causal variational principles.

problem Proving new mass inequalities for causal variational principles.
method Proved a new inequality for minimizers of causal variational principles and applied it to prove the positive mass theorem.
result Introduced a positive quasilocal mass and proved new mass inequalities.

Defines a new geometric quantity for hyperbolic manifolds, showing it's well-defined and invariant.

problem Defining a mass for asymptotically hyperbolic manifolds under weaker conditions.
method Volume-renormalized mass defined as a linear combination of ADM mass and renormalized volume.
result Volume-renormalized mass is well-defined and diffeomorphism invariant under weaker conditions.