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.
Bayesian methods often misinterpret data and asymptotic concepts.
Everyone misunderstands the Sharpe ratio, which measures risk in finance.
This article examines five common misunderstandings about case-study research: (1) Theoretical knowledge is more valuable than practical knowledge; (2) One cannot generalize from a single case, therefore the single case study cannot contribute to scientific development; (3) The case study is most useful for generating …
In line with the recent research and debates about econophysics and financial economics, this article discusses on usual misunderstandings between the two disciplines in terms of modelling and basic hypotheses. In the literature devoted to econophysics, the methodology used by financial economists is frequently conside…
Corrected a misunderstanding about generalized Lie algebroids.
The paper highlights legal misunderstandings in ML fairness definitions.
The heuristic identification of peaks from noisy complex spectra often leads to misunderstanding of the physical and chemical properties of matter. In this paper, we propose a framework based on Bayesian inference, which enables us to separate multipeak spectra into single peaks statistically and consists of two steps.…
The concept of an objective spatial direction in special relativity is investigated and theories assuming light-speed isotropy while accepting the existence of a privileged spatial direction are classified. A natural generalization of the proper time principle is introduced which makes it possible to devise experimenta…
Counterfactual fairness not equivalent to demographic parity, finds study.
In a recent paper, "Why does deep and cheap learning work so well?", Lin and Tegmark claim to show that the mapping between deep belief networks and the variational renormalization group derived in [arXiv:1410.3831] is invalid, and present a "counterexample" that claims to show that this mapping does not hold. In this …
We study recursive-cube-of-rings (RCR), a class of scalable graphs that can potentially provide rich inter-connection network topology for the emerging distributed and parallel computing infrastructure. Through rigorous proof and validating examples, we have corrected previous misunderstandings on the topological prope…
The Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with finite-time singular crash hazard rates has been developed to describe the dynamics of financial bubbles and crashes. It has been applied successfully to a large variety of financial bubbles in many different markets. Having been developed fo…
Paper bridges generative models and explainability.
Policy gradient methods do not optimize the discounted objective, leading to suboptimal results.
Survey on principles and challenges of interpretable machine learning.
Data science redefines causal inference from observational data, classifying tasks into description, prediction, and counterfactual prediction.
Paper discusses conditions for global injectivity of semi-algebraic local diffeomorphisms.
Paper analyzes venture capital exit decisions under inconsistent preferences.
Traditional models of macroeconomic dynamics are fundamentally incorrect. The reason lies in a misunderstanding of peculiarities of the analysis of infinitesimal quantities. However, even those types of solutions that are envisaged by the above-mentioned models are nonrepresentative in the sense of the reflection of re…
Proves error bounds for state representation in RL using graph spectral features.
New Shapley values reveal non-linear feature dependencies.
Interactive EMA combines multiple explainability methods to improve model understanding.
Paper clarifies unary vs binary AV approaches and evaluates their performance.