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.
The assumption of positivity in causal inference (also known as common support and co-variate overlap) is necessary to obtain valid causal estimates. Therefore, confirming it holds in a given dataset is an important first step of any causal analysis. Most common methods to date are insufficient for discovering non-posi…
CVTMLE improves statistical inference in settings of positivity or Donsker class violations.
TMLE improves causal effect estimation in missing data scenarios with various positivity violations.
The study proves strong cosmic censorship violation for spherically symmetric dust clouds.
New algorithms control loss and constraints in uncertain, changing environments.
The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of pr…
CATR rationalizes text data to stabilize causal effect estimation.
Proves positive mass theorem for spin initial data sets with arbitrary ends and dominant energy shields.
Study functional confounders in causal inference, enabling estimable effects.
The study provides a theory for causal machine learning with generalization bounds.
Submodularity is studied for convex risk measures, including Expected Shortfall.
Given only positive (P) and unlabeled (U) data, PU learning can train a binary classifier without any negative data. It has two building blocks: PU class-prior estimation (CPE) and PU classification; the latter has been well studied while the former has received less attention. Hitherto, the distributional-assumption-f…
Paper introduces MVS to detect non-Markovian observations in reinforcement learning.
Hybrid LLM generates synthetic data preserving causal parameters.
A consistent theory of quantum gravity (QG) at Planck scale almost sure contains manifestations of Lorentz local symmetry violations (LV) which may be detected at observable scales. This can be effectively described and classified by models with nonlinear dispersions and related Finsler metrics and fundamental geometri…
Bayesian methods detect significant IIA violations in similarity choice data.
Deep-MIL models fail to respect key MIL assumption, leading to incorrect learning.
The paper addresses fairness in online learning by extending auditing schemes and presenting efficient algorithms.
The correspondence between Riemann-Finsler geometries and effective field theories with spin-independent Lorentz violation is explored. We obtain the general quadratic action for effective scalar field theories in any spacetime dimension with Lorentz-violating operators of arbitrary mass dimension. Classical relativist…
This contribution to the CPT'13 meeting briefly introduces Lorentz and CPT violation and outlines two recent developments in the field.
Bipartite Riemann-Finsler geometries with complementary Finsler structures are constructed. Calculable examples are presented based on a bilinear-form coefficient for explicit Lorentz violation.
Detecting faults and SLA violations in a timely manner is critical for telecom providers, in order to avoid loss in business, revenue and reputation. At the same time predicting SLA violations for user services in telecom environments is difficult, due to time-varying user demands and infrastructure load conditions. In…
We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate the probability of the event that the property is violated under an input model. Our approach critically varies from the formal verification f…
Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection. However, these networks have been shown to often mispredict on inputs with minor adversarial or even accidental perturbations. Consequences of such errors can …
New algorithm reduces sample complexity for safe reinforcement learning.
We show that any open subset of a contact manifold of dimension greater than three contains a certain non-convex hypersurface violating the Thurston-Bennequin inequality.
This paper considers online convex optimization over a complicated constraint set, which typically consists of multiple functional constraints and a set constraint. The conventional online projection algorithm (Zinkevich, 2003) can be difficult to implement due to the potentially high computation complexity of the proj…
NucleusDiff models atomic nuclei interactions to prevent separation violations in drug design.
New algorithm reduces regret and constraint violation in online convex optimization with complex constraints.
Optimistic algorithm reduces regret and constraint violations in online convex optimization with adversarial constraints.
Algorithm minimizes loss and constraint violations in online convex optimization with smooth penalties.
CausalCompass evaluates TSCD robustness under violations of modeling assumptions.
Paper tackles constrained bandit problems with a new learning framework.
Causality violations are typically seen as unrealistic and undesirable features of a physical model. The following points out three reasons why causality violations, which Bonnor and Steadman identified even in solutions to the Einstein equation referring to ordinary laboratory situations, are not necessarily undesirab…
Paper relaxes faithfulness assumption for causal discovery using interventions.
Measures policy-violating content prevalence with ML-assisted sampling and LLM labeling.
This paper detects Markov violations in RL with noise, improving policy development.
We study primal-dual type stochastic optimization algorithms with non-uniform sampling. Our main theoretical contribution in this paper is to present a convergence analysis of Stochastic Primal Dual Coordinate (SPDC) Method with arbitrary sampling. Based on this theoretical framework, we propose Optimality Violation-ba…
Higher-dimensional Schwarzschild spacetimes violate the Penrose property.
A new method tests Expected Shortfall by analyzing both duration and severity of VaR violations.
We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes." We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding…
Effective field theories with explicit Lorentz violation are intimately linked to Riemann-Finsler geometry. The quadratic single-fermion restriction of the Standard-Model Extension provides a rich source of pseudo-Riemann-Finsler spacetimes and Riemann-Finsler spaces. An example is presented that is constructed from a …
De Sitter spacetime can be separated into two parts along two kinds of hypersurfaces and the half-de Sitter spacetimes are covered by the planar and hyperbolic coordinates respectively. Two positive energy theorems were proved previously for certain -asymptotically de Sitter and $\H$-asymptotically de Sitter initial…
Quantum theory challenges traditional cause-effect relations, showing causal influences even without Bell inequality violations.
Certain momentum-dependent terms in the fermion sector of the Lorentz-violating Standard Model Extension (SME) yield solvable classical lagrangians of a type not mentioned in the literature. These cases yield new relatively simple examples of Finsler and pseudo-Finsler structures. One of the cases involves antisymmetri…
Machine learning forecasts show bias at long horizons, contrary to standard tests.
The physics of classical particles in a Lorentz-breaking spacetime has numerous features resembling the properties of Finsler geometry. In particular, the Lagrange function plays a role similar to that of a Finsler structure function. A summary is presented of recent results, including new calculable Finsler structures…
Differentiable causal discovery methods perform robustly under model violations.