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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.

168,694 papers · 148 categories

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306191121 · May 202619922001200920172026
48 results for Implication Rules

TransINT embeds KGs by preserving implication rules, outperforming existing methods.

problem Embedding KGs while preserving relation implications for better access and analysis.
method Isomorphic intersections of linear subspaces with shared parameters for missing facts.
result Significant performance improvement in link prediction and triple classification.

We consider the problem of function estimation in the case where an underlying causal model can be inferred. This has implications for popular scenarios such as covariate shift, concept drift, transfer learning and semi-supervised learning. We argue that causal knowledge may facilitate some approaches for a given probl…

2012-06-27abs ↗pdf ↗

A framework for anonymized risk sharing without revealing identities or preferences.

problem Risk sharing without revealing individual identities or preferences.
method Axiomatic framework with four key axioms: actuarial fairness, risk fairness, risk anonymity, and operational anonymity.
result The conditional mean risk sharing rule is uniquely characterized by these axioms.

We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independ…

2015-09-30abs ↗pdf ↗

The paper investigates the effects of invalid action masking in policy gradient algorithms.

problem Invalid actions in policy gradient algorithms can lead to suboptimal performance.
method The paper provides theoretical justification and empirical demonstrations of the importance of invalid action masking.
result Invalid action masking is crucial as the number of invalid actions increases.

IGGP learns game rules from varying quality game play, finding no overall trend.

problem Learn game rules from varying quality game play.
method Used Sancho's intelligent game traces and ILP systems (Metagol, Aleph, ILASP) to induce game rules from traces of varying quality and volume.
result No overall trend in accuracy of learned game rules from varying quality and volume of training data.

New findings on how certain functionals behave in random variable spaces.

problem Understanding when law-invariant convex functionals simplify to the mean.
method Analyzing a broad class of random variable spaces and mild semicontinuity assumptions.
result The expectation functional is the only law-invariant convex functional that collapses to the mean under certain conditions.

Study on how reparametrization affects neural nets' parameter spaces from a geometric perspective.

problem Inconsistencies in flatness measures, optimization, and probability densities under reparametrization.
method Riemannian geometry to study invariance of neural nets under reparametrization.
result Invariance of neural nets is an inherent property if the metric is explicitly represented and transformation rules are correct.

Graphical models have proven to be powerful tools for representing high-dimensional systems of random variables. One example of such a model is the undirected graph, in which lack of an edge represents conditional independence between two random variables given the rest. Another example is the bidirected graph, in whic…

2013-10-09abs ↗pdf ↗

New research shows shrinkage methods re-scale portfolio efficient frontiers under distributional misspecification.

problem Poor performance of mean-variance portfolio decisions under distributional assumptions.
method Investigation of shrinkage methods under different distributional assumptions (auto-correlation, skewness, excess kurtosis).
result Shrinkage methods re-scale the sample efficient frontier, implying standard comparison methods are flawed.

Investors adjust spending based on a social norm, spending less during losses and more during gains.

problem Managing spending and portfolio decisions while adhering to a social norm.
method Formulated a preference ordering with two CRRA preference orderings, solved analytically and numerically.
result Annual spending should be lower than expected financial return and procyclical, with spending cuts following losses.

A new hierarchy quantifies agency in systems based on information processing.

problem Lack of a measurable, universal definition for agency in intelligent systems.
method Developed a bottom-up framework based on information processing hierarchy.
result Identified three orders of information processing (I, II, III) as necessary for agency.

New pivoting strategy improves trace norm contraction in low-rank approximation.

problem Finding good low-rank approximations of symmetric, positive-definite matrices.
method Choosing rows with likelihood proportional to Aii2A_{ii}^2 for randomly pivoted partial Cholesky algorithm.
result Same trace norm contraction result in Frobenius norm for improved pivoting strategy.

Paper investigates differentiable fuzzy implications and their suitability for learning.

problem Analyzing differentiable fuzzy implications and their suitability for learning.
method Investigates the properties of fuzzy implications in a differentiable setting and introduces a new family of fuzzy implications.
result Various fuzzy implications are unsuitable for differentiable learning, and a new family of fuzzy implications is introduced.

Update rules for learning in dynamic time warping spaces are based on optimal warping paths between parameter and input time series. In general, optimal warping paths are not unique resulting in adverse effects in theory and practice. Under the assumption of squared error local costs, we show that no two warping paths …

2017-05-16abs ↗pdf ↗

Early stopping improves generalization in overparameterized diffusion models.

problem Understanding and optimizing generalization in overparameterized diffusion models.
method Revisiting diffusion models, showing generalization occurs before memorization, and developing a phase diagram.
result Generalization time scales with dataset size, supporting early-stopping criteria.

The paper analyzes how low-rank layers in neural networks improve generalization.

problem Understanding how low-rank layers affect generalization in neural networks.
method Applying Maurer's chain rule for Gaussian complexity to analyze rank and spectral norm constraints.
result Deep networks with low-rank layers achieve better generalization than those with full-rank layers.

Paper shows large models are vulnerable to poisoning attacks.

problem Vulnerability of large machine learning models to poisoning attacks.
method Analyzed isotropic random feature vectors and geometric median as robust gradient aggregator rule.
result Linear and logistic regressions with D169H2/P2D \geq 169 H^2/P^2 parameters are vulnerable to arbitrary manipulation by poisoners.

The Leibniz rule for derivations is invariant under cyclic permutations of co-multiples within the arguments of derivations. We explore the implications of this principle: in effect, we construct a class of noncommutative bundles in which the sheaves of algebras of walks along a tesselated affine manifold form the base…

2012-10-02abs ↗pdf ↗

Relational data in its most basic form is a static collection of known facts. However, by learning to infer and deduct additional information and structure, we can massively increase the usefulness of the underlying data. One common form of inferential reasoning in knowledge bases is implication discovery. Here, by lea…

2019-07-28abs ↗pdf ↗

The paper clarifies the approximation of SGD with Ito SDEs for finite learning rates.

problem Theoretical justification and experimental verification of the Ito SDE approximation for finite learning rates in SGD.
method An efficient simulation algorithm SVAG and a necessary condition test for the SDE approximation.
result The Ito SDE approximation can meaningfully capture training and generalization properties of deep nets with finite learning rates.

The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions. Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which p…

2019-06-18abs ↗pdf ↗

Backtests of structured strategies lose much of their predictive power in live trading.

problem Uncertainty in how marketed backtests predict live performance of structured strategies.
method Analysis of 1,726 structured strategies from ten global institutions.
result Raw backtests have limited portability into live trading and deteriorate sharply.

DeepCTRL integrates rules into deep learning models, allowing flexible control at inference.

problem Lack of flexibility in incorporating rules into deep learning models.
method Integrates rule representations into deep neural networks, enabling flexible control at inference.
result Improves rule verification ratio and accuracy gains at downstream tasks.

New methods prune unpromising rules from KGs, improving scalability and runtime.

problem Scalability issues in walk-based rule learning from KGs.
method Rule Hierarchy Framework (RHF) and Hierarchical Pruning (HPMs).
result Significant reductions in runtime and number of learned rules without compromising predictive performance.

Peters (2011a) defined an optimal leverage which maximizes the time-average growth rate of an investment held at constant leverage. It was hypothesized that this optimal leverage is attracted to 1, such that, e.g., leveraging an investment in the market portfolio cannot yield long-term outperformance. This places a str…

2011-01-24abs ↗pdf ↗

In this study, we give the relationships between the conical curvatures of ruled surfaces drawn by the unit vectors of the ruling, central normal and central tangent of a regular ruled surface in the Euclidean -space. We obtain the differential equations characterizing slant ruled surfaces and if the reference ruled su…

2013-11-27abs ↗pdf ↗

New framework learns interpretable rule ensembles without sacrificing accuracy.

problem Trade-off between accuracy and interpretability in rule ensembles.
method Introduces local interpretability and a regularizer to promote it, using coordinate descent with local search.
result Learns rule ensembles with fewer rules to explain individual predictions, maintaining comparable accuracy.

Paper classifies ruled surfaces in Lorentz-Minkowski space for a specific flow.

problem Classifying ruled surfaces in Lorentz-Minkowski space.
method Examining homothetic self-similar solutions of the inverse mean curvature flow.
result Existence of two classes of non-cylindrical homothetic solitons.

In this study, we define some new types of ruled surfaces called slant ruled surfaces. We give some characterizations for a regular ruled surface to be a slant ruled surface in Euclidean 3- space. We show that if the slant ruled surface is developable then the striction curve is a general helix or a slant helix accordi…

2013-11-04abs ↗pdf ↗