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.
Proposes a method to reconcile count time series forecasts.
In this paper, we aim at introducing a new machine learning model, namely reconciled polynomial machine, which can provide a unified representation of existing shallow and deep machine learning models. Reconciled polynomial machine predicts the output by computing the inner product of the feature kernel function and va…
Quite a number of distinct versions of Bartnik's definition of quasi-local mass appear in the literature, and it is not a priori clear that any of them produce the same value in general. In this paper we make progress on reconciling these definitions. The source of discrepancies is two-fold: the choice of boundary cond…
The definition of quasi-local mass for a bounded space-like region in space-time is essential in several major unsettled problems in general relativity. The quasi-local mass is expected to be a type of flux integral on the boundary two-surface and should be independent of whichever space-like region it bounds. An impor…
The paper reconciles two conflicting fairness criteria in algorithmic risk scores.
Evidential clustering is an approach to clustering in which cluster-membership uncertainty is represented by a collection of Dempster-Shafer mass functions forming an evidential partition. In this paper, we propose to construct these mass functions by bootstrapping finite mixture models. In the first step, we compute b…
It is essential to incorporate the impact of investor behavior when modeling the dynamics of asset returns. In this paper, we reconcile behavioral finance and rational finance by incorporating investor behavior within the framework of dynamic asset pricing theory. To include the views of investors, we employ the method…
A new method uses preference relations to reconcile contradictory trading signals from multiple securities.
A new method calculates fractional moments using the moment-generating function.
We revisit logistic regression and its nonlinear extensions, including multilayer feedforward neural networks, by showing that these classifiers can be viewed as converting input or higher-level features into Dempster-Shafer mass functions and aggregating them by Dempster's rule of combination. The probabilistic output…
Proposes a neural network for accurate and reconciled hierarchical time series forecasting.
Identifies conditions for multiple invariant probabilities in Markov kernels.
Local mappings relate dual and primal factor graphs for efficient marginal probability estimation.
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…
The study uses statistical methods to analyze nuclear mass models.
New method calibrates classifier probabilities with guaranteed coverage.
This review explores entropy applications in data analysis and machine learning.
We introduce the concept of forward rank-dependent performance processes, extending the original notion to forward criteria that incorporate probability distortions. A fundamental challenge is how to reconcile the time-consistent nature of forward performance criteria with the time-inconsistency stemming from probabili…
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…
Solves probabilistic Lambert problem connecting astrodynamics with optimal mass transport.
RPN unifies various models with a reconciled polynomial network.
Given 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 -th draw? This is a classical problem in statistics, commonly referred to as the missing mass estimation problem. Recent results by Ohannes…
Unified definition of mass aspect function for weakly regular hyperbolic manifolds.
Study on residual Monge-Ampère mass for symmetric plurisubharmonic functions.
The maximum likelihood approach is adapted to the problem of estimation of drift and diffusion functions of stochastic processes from measured time series. We reconcile a previously devised iterative procedure [Kleinhans et al., Physics Letters A (346), 2005] and put the application of the method on a firm theoretical …
The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory as it involves extreme extrapolations of nuclear masses beyond the regions where experimenta…
New method for summarizing ranking distributions using consensus ranking distributions.
Study on residual Monge-Ampère mass for symmetric plurisubharmonic functions.
In this article, we classify the set of asymptotic mass-like invariants for asymptotically hyperbolic metrics. It turns out that the standard mass is just one example (but probably the most important one) among the two families of invariants we find. These invariants are attached to finite-dimensional representations o…
The paper connects mass, harmonic functions, and capacity in asymptotically flat 3-manifolds.
A new method for averaging probability distributions based on optimal weak mass transport.
Expected centre of mass for random embeddings is constant.
Global existence and geometry of constant mass aspect function foliation in perturbed Schwarzschild spacetime studied.
In financial markets, the order flow, defined as the process assuming value one for buy market orders and minus one for sell market orders, displays a very slowly decaying autocorrelation function. Since orders impact prices, reconciling the persistence of the order flow with market efficiency is a subtle issue. A poss…
The paper defines a new mass quantity for 3-manifolds and proves a positive mass theorem.
When forecasting time series with a hierarchical structure, the existing state of the art is to forecast each time series independently, and, in a post-treatment step, to reconcile the time series in a way that respects the hierarchy (Hyndman et al., 2011; Wickramasuriya et al., 2018). We propose a new loss function th…
We propose a correlated stochastic process of which the novel non-Gaussian probability mass function is constructed by exactly solving moment generating function. The calculation of cumulants and auto-correlation shows that the process is convergent and scale invariant in the large but finite number limit. We demonstra…
Proves Green function rigidity for specific operators and obtains new ADM mass formula.
Study calculates mass of special polyhedra in hyperbolic space.
Study on residual Monge-Ampère mass of complex functions with directional Lipschitz continuity.
New positive mass theorem for hyperbolic 3-manifolds using Green functions.
Unified framework for analyzing gradient flows of measures with exponential decay of entropy.
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 …
New formulae identify discrete probability laws without needing normalization constants.
We derive a positive mass theorem for asymptotically flat manifolds with boundary whose mean curvature satisfies a sharp estimate involving the conformal Green's function. The theorem also holds if the conformal Green's function is replaced by the standard Green's function for the Laplacian operator. As an application,…
Defines a new quasi-local mass related to spacetime harmonic functions.
A mass-type invariant for smooth metric measure spaces and its relation with the fractional Yamabe problem
Proposes a new framework for uncertainty evaluation in ML classification models.