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.
Method makes non-interpretable models more intervenable.
Bipolar disorder (BPD) is a chronic mental illness characterized by extreme mood and energy changes from mania to depression. These changes drive behaviors that often lead to devastating personal or social consequences. BPD is managed clinically with regular interactions with care providers, who assess mood, energy lev…
Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactu…
We model the default contagion process in a large heterogeneous financial network under the interventions of a regulator (a central bank) with only partial information which is a more realistic setting than most current literature. We provide the analytical results for the asymptotic optimal intervention policies and t…
In this paper we describe the homology and cohomology of some natural bimodules over the little discs operad, whose components are configurations of non--overlapping discs. At the end we briefly explain how this algebraic structure intervenes in the study of spaces of non--equal immersions.
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
Proves Bochner's identity on graphs using a new auxiliary graph.
In this paper, we construct smooth forward Ricci flow evolutions of singular initial metrics resulting from rotationally symmetric neckpinches on S^(n+1), without performing an intervening surgery. In the restrictive context of rotational symmetry, this construction gives evidence in favor of Perelman's hope for a "can…
Paper establishes identifiability and achievability for causal representation learning.
Adversarial CBO optimizes under interventions by adversaries and non-stationarities.
In a wide variety of applications, including personalization, we want to measure the difference in outcome due to an intervention and thus have to deal with counterfactual inference. The feedback from a customer in any of these situations is only 'bandit feedback' - that is, a partial feedback based on whether we chose…
COCA refactors training data to identify and erase unsafe concepts in LLMs.
Paper axiomatizes interventional probability distributions.
As machine learning models are increasingly used for high-stakes decision making, scholars have sought to intervene to ensure that such models do not encode undesirable social and political values. However, little attention thus far has been given to how values influence the machine learning discipline as a whole. How …
CaT-GNN improves credit card fraud detection by integrating causal reasoning into GNNs.
New method evaluates multiple social disparities using machine learning.
The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the do-calculus is required has been hotly debated, In this paper we demonstrate that, while it is critical to explic…
Model explanations based on pure observational data cannot compute the effects of features reliably, due to their inability to estimate how each factor alteration could affect the rest. We argue that explanations should be based on the causal model of the data and the derived intervened causal models, that represent th…
MIP framework improves urban flow prediction by adapting to distribution shifts.
This article attempts to place the emergence of probabilistic numerics as a mathematical-statistical research field within its historical context and to explore how its gradual development can be related both to applications and to a modern formal treatment. We highlight in particular the parallel contributions of Sul'…
Proposes Causal Loss to improve machine learning models' causal inference.
Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine …
We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph. We first show that this problem is NP-hard. We then prove…
This work investigates how GCNs should handle local structure discrepancies in testing nodes.
This paper was first written in 1990, but was never published. In it, the author presents a novel approach to the study of constant curvature spacetimes in 2+1 dimensions. A parameterization of flat 2+1-dimensional domains of dependence is given in terms of measured geodesic laminations. There is also an interesting re…
Study develops method for estimating causal effects in continuous variables.
The algorithmic trading comes from digitalisation of the processing of trading assets on financial markets. Since 1980 the computerization of the stock market offers real time processing of financial information. This technological revolution has offered processes and mathematic methods to identify best return on trans…
Stackelberg Games are gaining importance in the last years due to the raise of Adversarial Machine Learning (AML). Within this context, a new paradigm must be faced: in classical game theory, intervening agents were humans whose decisions are generally discrete and low dimensional. In AML, decisions are made by algorit…
Fine-tunes LLMs to correct bias in predictions.
We develop a general strategy, based on gauge theoretical methods, to prove existence of curves on class VII surfaces. We prove that, for , every minimal class VII surface has a cycle of rational curves hence, by a result of Nakamura, is a global deformation of a one parameter family of blown up primary Hopf sur…
New algorithm improves treatment effect estimation from observational data.
New clustering method uses Wasserstein distance to analyze simulation outputs.
We propose a new method to study the internal memory used by reinforcement learning policies. We estimate the amount of relevant past information by estimating mutual information between behavior histories and the current action of an agent. We perform this estimation in the passive setting, that is, we do not interven…
Method combines LD and Fermat Distance for neural network uncertainty.
New bounds assess policy evaluation under unobserved confounders, showing model-based methods are more effective.
Bayesian Power Steering fine-tunes large diffusion models for domain adaptation.
Temporal observations such as videos contain essential information about the dynamics of the underlying scene, but they are often interleaved with inessential, predictable details. One way of dealing with this problem is by focusing on the most informative moments in a sequence. We propose a model that learns to discov…
Study proposes a tax-based system to share disaster risk among regions.
New framework identifies causal models with arbitrary interventions, improving realism.
Given a set of experiments in which varying subsets of observed variables are subject to intervention, we consider the problem of identifiability of causal models exhibiting latent confounding. While identifiability is trivial when each experiment intervenes on a large number of variables, the situation is more complic…
Study applies market microstructure to Cuban informal currency market, finding market makers improve liquidity.
Empirical study on UEEs reveals liquidity's role and universal recovery patterns.
The performance of a reinforcement learning algorithm can vary drastically during learning because of exploration. Existing algorithms provide little information about the quality of their current policy before executing it, and thus have limited use in high-stakes applications like healthcare. We address this lack of …
Proposes a new Alzheimer's disease simulator for causal effect estimation.
New method reduces gender bias in language models without harming performance.
Much of scientific data is collected as randomized experiments intervening on some and observing other variables of interest. Quite often, a given phenomenon is investigated in several studies, and different sets of variables are involved in each study. In this article we consider the problem of integrating such knowle…
Bayesian data selection framework ensures fairness in machine learning models.
Estimates effects of multiple interventions with hidden confounders using single-variable interventions.