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.
Foundation models improve wage gap decomposition by capturing omitted career history factors.
Hurd's career overview and publications listed.
Job transitions and upskilling are common actions taken by many industry working professionals throughout their career. With the current rapidly changing job landscape where requirements are constantly changing and industry sectors are emerging, it is especially difficult to plan and navigate a predetermined career pat…
Prior work finds a diversity paradox: diversity breeds innovation, and yet, underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-population of ~1.2 million US doctoral recipients from 1977…
We introduce a model in which a regulator employs mechanism design to embed her human capital beta signal(s) in a firm's capital structure, in order to enhance the value of her post career change indexed executive stock option contract with the firm. We prove that the agency cost of this revolving door behavior increas…
Framework orders categories and measures differences from real data.
Professional baseball players are increasingly guaranteed expensive long-term contracts, with over 70 deals signed in excess of \$90 million, mostly in the last decade. These are substantial sums compared to a typical franchise valuation of \$1-2 billion. Hence, the players to whom a team chooses to give such a contrac…
Higher CEO career breadth correlates with better firm performance.
We study the gap between the state pension provided by the Italian pension system pre-Dini reform and post-Dini reform. The goal is to fill the gap between the old and the new pension by joining a defined contribution pension scheme and adopting an optimal investment strategy that is target-based. We find that it is po…
Discussing curvature flows and their applications.
Survey of contact homology for contact manifolds.
Bayesian inference over admissible histories leads to irreversible kinetics.
The credit crisis roiling the world's financial markets will likely take years and entire careers to fully understand and analyze. A short empirical investigation of the current trends, however, demonstrates that the losses in certain markets, in this case the US equity markets, follow a cascade or epidemic flow like m…
Sharp bounds on negative impact identified from observational data.
HS-FNO models non-Markovian PDEs by learning history and future states.
Overview of affine surface area and its history.
Solves Brezis' first open problem on ball solutions.
Survey on DDVV-type inequalities, their history, and recent developments.
SRMC framework reduces Monte Carlo variance by history-based sampling in high-dimensional spaces.
Paper analyzes history-based RL methods for MDPs, introduces a theoretical framework and practical algorithm.
Neural networks improve cancer risk prediction from family history data.
The paper explains why estimating a history-dependent policy can reduce MSE in reinforcement learning.
It is shown how the generating functional method of De Dominicis can be used to solve the dynamics of the original version of the minority game (MG), in which agents observe real as opposed to fake market histories. Here one again finds exact closed equations for correlation and response functions, but now these are de…
This paper suggests claim history will be deprecated in future auto insurance rates.
We present in this chapter (Chapter II) the history of ideas which lead up to the development of modern knot theory. We are more detailed when pre-XX century history is reported. With more recent times we are more selective, stressing developments related to Jones type invariants of links. In the Appendix, A.Przybyszew…
Paper tackles reinforcement learning for STL specifications with state history.
We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds i.i.d. pseudo-rewards to its history in round and then pulls the arm with the highest average reward in its perturbed history. Therefore, we call it perturbed-history exploration (PHE). Th…
We propose a new online algorithm for cumulative regret minimization in a stochastic linear bandit. The algorithm pulls the arm with the highest estimated reward in a linear model trained on its perturbed history. Therefore, we call it perturbed-history exploration in a linear bandit (LinPHE). The perturbed history is …
Develops NFCF to reduce gender bias in social media recommendation systems.
We give an explicit definition of decentralization and show you that decentralization is almost impossible for the current stage and Bitcoin is the first truly noncentralized currency in the currency history. We propose a new framework of noncentralized cryptocurrency system with an assumption of the existence of a wea…
Cold-start PV forecasting uses synthetic histories to train time-series foundation models.
Epsilon-machines are minimal, unifilar presentations of stationary stochastic processes. They were originally defined in the history machine sense, as hidden Markov models whose states are the equivalence classes of infinite pasts with the same probability distribution over futures. In analyzing synchronization, though…
Teaches deep learning to statisticians.
Combining deep learning and ensemble smoothers for better history matching.
New framework handles dynamic contexts in reinforcement learning.
A dangerously brief history of the developments of the main ideas in economics, as observed by a physicist, is given. This was published in 'Econophysics of Stock and Other Markets', Eds. A. Chatterjee, B. K. Chakrabarti, New Economic Windows Series, Springer, Milan, 2006, pp~219-224.
Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.
Marden's Tameness Conjecture predicts that every hyperbolic 3-manifold with finitely generated fundamental group is homeomorphic to the interior of a compact 3-manifold. It was recently established by Agol and Calegari-Gabai. We will survey the history of work on this conjecture and discuss its many applications.
Network growth processes can be understood as generative models of the structure and history of complex networks. This point of view naturally leads to the problem of network archaeology: reconstructing all the past states of a network from its structure---a difficult permutation inference problem. In this paper, we in…
The article explains the probabilistic method of default probability estimation by Pluto and Tasche.
Deep learning calibrates CO2 storage formations from seismic and well data.
A new method uses a frozen language model to improve sample efficiency in reinforcement learning.
HDT improves MCMC on graphs with history-dependent sampling.
Rhino learns causal relationships from time series data with history-dependent noise.
Proposes using frequent sequences to improve sequential recommendation models.
Explains the history and challenges of minimal surfaces.
We tackle the problem of algorithmic fairness, where the goal is to avoid the unfairly influence of sensitive information, in the general context of regression with possible continuous sensitive attributes. We extend the framework of fair empirical risk minimization to this general scenario, covering in this way the wh…
In the light of contemporary discussions of inter and transdisciplinarity, this paper approaches econophysics and sociophysics to seek a response to the question -- whether these interdisciplinary fields could contribute to physics and economics. Drawing upon the literature on history and philosophy of science, the pap…