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.
Calculates local Granger causality for Gaussian and nonlinear systems.
There has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs. Gamarnik and Sudan (2014) showed that local algorithms are weaker than global algorithms for finding large independent sets in sparse random regular graphs. Montanari (2015) showed t…
We analyse the structure of local martingale deflators projected on smaller filtrations. In a general continuous-path setting, we show that the local martingale part in the multiplicative Doob-Meyer decomposition of projected local martingale deflators are themselves local martingale deflators in the smaller informatio…
Data processing inequalities link Fisher information to local differential privacy constraints.
We consider a financial market model with a single risky asset whose price process evolves according to a general jump-diffusion with locally bounded coefficients and where market participants have only access to a partial information flow. For any utility function, we prove that the partial information financial marke…
Proper holomorphic isometries between Bergman domains are biholomorphisms.
Proposes ContSup to boost local learning by supplying context between isolated modules.
A new method predicts links better across various networks.
This paper proposes an alternative to E2E training for deep networks, reducing memory footprint.
Proposes a method to identify causal relationships using background knowledge.
GOLFS selects features for clustering by combining global and local information.
LSB is a new MCMC method for discrete spaces that reduces target evaluations.
A new pricing controller handles resource constraints to infer target prices effectively.
New tensor framework connects Fisher information, hypergraphs, and multi-observable correlations.
We study locally differentially private algorithms for reinforcement learning to obtain a robust policy that performs well across distributed private environments. Our algorithm protects the information of local agents' models from being exploited by adversarial reverse engineering. Since a local policy is strongly bei…
We introduce a method, KL-LIME, for explaining predictions of Bayesian predictive models by projecting the information in the predictive distribution locally to a simpler, interpretable explanation model. The proposed approach combines the recent Local Interpretable Model-agnostic Explanations (LIME) method with ideas …
We study a basic private estimation problem: each of users draws a single i.i.d. sample from an unknown Gaussian distribution, and the goal is to estimate the mean of this Gaussian distribution while satisfying local differential privacy for each user. Informally, local differential privacy requires that each data …
Recent advances in deep learning theory have evoked the study of generalizability across different local minima of deep neural networks (DNNs). While current work focused on either discovering properties of good local minima or developing regularization techniques to induce good local minima, no approach exists that ca…
b-LOAD extends local causal discovery with prior knowledge, improving causal effect estimation.
Novel approach uses quasi-conformal geometry for OSA classification from cephalometry.
Biological and artificial neural systems are composed of many local processors, and their capabilities depend upon the transfer function that relates each local processor's outputs to its inputs. This paper uses a recent advance in the foundations of information theory to study the properties of local processors that u…
A framework compares image representations based on local geometry.
Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approache…
An approach to distributed machine learning is to train models on local datasets and aggregate these models into a single, stronger model. A popular instance of this form of parallelization is federated learning, where the nodes periodically send their local models to a coordinator that aggregates them and redistribute…
CAGES optimizes expensive RL problems by efficiently learning gradients from multiple sources.
The paper studies projections of asset prices under equivalent martingale measures.
Study compares local and global models for hierarchical forecasting accuracy.
Local graph clustering improves with noisy labels, enhancing accuracy and performance.
New meta-RL method avoids exploration-exploitation trade-off.
Paper finds local normal forms for wavefronts in flat coordinates.
Proposes a method for multi-view clustering that considers local structures and feature weights.
In this paper we investigate the local risk-minimization approach for a semimartingale financial market where there are restrictions on the available information to agents who can observe at least the asset prices. We characterize the optimal strategy in terms of suitable decompositions of a given contingent claim, wit…
In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying t…
A method to visualize multidimensional local subspaces using implicit differentiation.
Uncertainty principles such as Heisenberg's provide limits on the time-frequency concentration of a signal, and constitute an important theoretical tool for designing and evaluating linear signal transforms. Generalizations of such principles to the graph setting can inform dictionary design for graph signals, lead to …
Corrects an earlier theorem, establishing new facts about information structures and non-anticipative aggregation.
In the present work we address the problem of evaluating the historical performance of a trading strategy or a certain portfolio of assets. Common indicators such as the Sharpe ratio and the risk adjusted return have significant drawbacks. In particular, they are global indices, that is they do not preserve any 'local'…
Combining global and local explanations improves user understanding of RL agents.
Paper resolves decades-old problem about -spectra.
DNNs improve localization from channel estimates, overcoming practical impairments.
In this paper we investigate the local risk-minimization approach for a combined financial-insurance model where there are restrictions on the information available to the insurance company. In particular we assume that, at any time, the insurance company may observe the number of deaths from a specific portfolio of in…
New methods identify local clusters in graphs with few labels.
We discuss how minimal financial market models can be constructed by bridging the gap between two existing, but incomplete, market models: a model in which a population of virtual traders make decisions based on common global information but lack local information from their social network, and a model in which the tra…
In plant and animal breeding studies a distinction is made between the genetic value (additive + epistatic genetic effects) and the breeding value (additive genetic effects) of an individual since it is expected that some of the epistatic genetic effects will be lost due to recombination. In this paper, we argue that t…
Enhances graph neural networks with spectral and topological information.
Local surrogate explainers vary in objectives, leading to incomparable explanations.
In probabilistic approaches to classification and information extraction, one typically builds a statistical model of words under the assumption that future data will exhibit the same regularities as the training data. In many data sets, however, there are scope-limited features whose predictive power is only applicabl…
SGD generalization bounds derived from information theory.