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 paper defines strong emergence in field theories and proves it exists between certain theories.
The paper axiomatizes strong emergence in parameterized field theories and proves existence theorems.
Neural networks learn task-specific features, influenced by nonlinearity.
This note continues investigation of randomness-type properties emerging in idealized financial markets with continuous price processes. It is shown, without making any probabilistic assumptions, that the strong variation exponent of non-constant price processes has to be 2, as in the case of continuous martingales.
Graphs help agents learn emergent communication.
Study detects emerging trends in financial news articles about Microsoft.
Paper analyzes weak-to-strong generalization in CNNs, identifying data-scarce and data-abundant regimes.
The paper explains how language models acquire complex skills through scaling laws and statistical analysis.
This paper ranks Latin American countries based on AI potential.
LSTM model predicts stock prices with high accuracy in stable sectors but struggles with volatile ones.
The paper reviews MARL and its causal challenges, advocating for a 'causality first' approach.
To identify emerging interdependencies between traded stocks we investigate the behavior of the stocks of FTSE 100 companies in the period 2000-2015, by looking at daily stock values. Exploiting the power of information theoretical measures to extract direct influences between multiple time series, we compute the infor…
To investigate the universality of the structure of interactions in different markets, we analyze the cross-correlation matrix C of stock price fluctuations in the National Stock Exchange (NSE) of India. We find that this emerging market exhibits strong correlations in the movement of stock prices compared to developed…
Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the dispatcher and work of special automatic devices proves to be ins…
The paper connects two clustering methods by showing gradient ascent flow can move up the cluster tree.
New research shows label refinement and weak training have limitations for aligning LLMs.
A toy model shows how locality can emerge in the universe's Hamiltonian and initial state.
A hierarchical model shows how scaling laws emerge from sequential feature recovery.
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over many user devices, and the learning algorithm must run on-device, …
Indirect competition emerged from the complex organization of human societies, and knowledge of the existing network topology may aid in developing effective strategies for success. Here, we propose an agent-based model of competition with systems co-existing in a `small-world' social network. We show that within the r…
Improves statistical learning bounds with self-concordant losses.
One of the principal statistical features characterizing the activity in financial markets is the distribution of fluctuations in market indicators such as the index. While the developed stock markets, e.g., the New York Stock Exchange (NYSE) have been found to show heavy-tailed return distribution with a characteristi…
The economical world consists of a highly interconnected and interdependent network of firms. Here we develop temporal and structural network tools to analyze the state of the economy. Our analysis indicates that a strong clustering can be a warning sign. Reduction in diversity, which was an essential aspect of the dyn…
Active inference implemented for high-dimensional tasks shows efficient exploration and improved sample efficiency.
The goal of the paper is to give an optimal transport formulation of the full Einstein equations of general relativity, linking the (Ricci) curvature of a space-time with the cosmological constant and the energy-momentum tensor. Such an optimal transport formulation is in terms of convexity/concavity properties of the …
Traditional centralized energy systems have the disadvantages of difficult management and insufficient incentives. Blockchain is an emerging technology, which can be utilized in energy systems to enhance their management and control. Integrating token economy and blockchain technology, token economic systems in energy …
Survey on threats to federated learning models.
CNN accurately reconstructs lattice topology with strong thermal fluctuations.
New methods solve complex optimization problems without strong convexity assumptions.
New framework boosts neural network performance and resilience.
Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form --- where form could be a tree, ring, chain, grid, etc. [Kemp & Tenenbaum (2008…
Emergent misalignment is influenced by training dynamics, model priors, and data.
Contrastive learning estimates transition kernels for continuous-time stochastic processes.
High-dimensional geometry makes adversarial examples easier to construct.
New framework recovers reward and rationality parameters from game behavior.
Bayesian optimization has emerged as a strong candidate tool for global optimization of functions with expensive evaluation costs. However, due to the dynamic nature of research in Bayesian approaches, and the evolution of computing technology, using Bayesian optimization in a parallel computing environment remains a c…
Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle, emerging irregularities. Additionally, recent dete…
An analysis of the Japanese credit market in 2004 between banks and quoted firms is done in this paper using the tools of the networks theory. It can be pointed out that: (i) a backbone of the credit channel emerges, where some links play a crucial role; (ii) big banks privilege long-term contracts; the "minimal spanni…
Paper analyzes transfer risk in transfer learning for finance.
Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time-varying sparse signals. Of the existing algorithms, fewer still provide strong performance guarantees. This paper examines two algorithms f…
Framework uses human judgment to distinguish algorithmically indistinguishable cases.
Noise causes learning plateaus in neural networks.
The paper provides consistency results for KDE on manifolds with irregular kernels.
Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass hu…
Flexible approach for normal approximations in geometric and topological statistics.
GrokAlign aligns Jacobians to accelerate grokking in deep networks.
Proves that emergent algebras right-distributivity implies left-distributivity.
Our analysis of financial data, in terms of super-exponential growth, suggests that the seed of the 2002/03 crisis of the Dutch supermarket giant AHOLD was planted in 1996. It became quite visible in 1999 when the post-bubble destabilization regime was well-developed and acted as the precursor of an inevitable collapse…