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.
Study relaxes identification assumptions for natural direct effects in non-randomized settings.
A new measure of model complexity based on Fisher Information.
New study finds environment significantly suppresses star formation in galaxies, contrary to previous beliefs.
Natural experiment dataset reveals inconsistent treatment effect estimators.
We propose a comprehensive treatment of the leverage effect, i.e. the relationship between returns and volatility of a specific asset, focusing on energy commodities futures, namely Brent and WTI crude oils, natural gas and heating oil. After estimating the volatility process without assuming any specific form of its b…
Unified perspective on natural gradient methods for GMMs, improving variational inference.
The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored. We examine how natural gradients can be used in non-conjugate stochastic settings, together with hyperparameter learning. We conclude that the natural gradient can signific…
New Random Forest variants estimate heterogeneous treatment effects using Wasserstein distances.
This essay discusses the advantages of a probabilistic agent-based approach to questions in theoretical economics, from the nature of economic agents, to the nature of the equilibria supported by their interactions. One idea we propose is that "agents" are meta-individual, hierarchically structured objects, that includ…
Square-root natural-gradient improves variational inference convergence.
Recent work on adversarial examples has demonstrated that most natural inputs can be perturbed to fool even state-of-the-art machine learning systems. But does this happen for humans as well? In this work, we investigate: what fraction of natural instances of speech can be turned into "illusions" which either alter hum…
Given the notion of suborbifold of the second author (based on ideas of Borzellino/Brunsden) and the classical correspondence (up to certain equivalences) between (effective) orbifolds via atlases and effective orbifold groupoids, we analyze which groupoid embeddings correspond to suborbifolds and give classes of subor…
This study investigates how much knowledge from natural images can be transferred to pathology images.
Natural gradient descent has proven effective at mitigating the effects of pathological curvature in neural network optimization, but little is known theoretically about its convergence properties, especially for \emph{nonlinear} networks. In this work, we analyze for the first time the speed of convergence of natural …
CATR rationalizes text data to stabilize causal effect estimation.
Modern deep learning models are often trained in parallel over a collection of distributed machines to reduce training time. In such settings, communication of model updates among machines becomes a significant performance bottleneck and various lossy update compression techniques have been proposed to alleviate this p…
Proposes a tabular transformer model to maintain feature effect intelligibility.
Generates natural product-like compounds using GPT models.
New method estimates heterogeneous treatment effects with improved guarantees.
BICauseTree improves causal effect estimation by identifying clusters and balancing treatment allocation.
NGD models have higher effective dimension than SGD models.
This study uses NLP to detect financial risks from documents.
The paper classifies geodesic orbit spaces with simple isotropy groups.
An arbitrary Lie groupoid gives rise to a groupoid of germs of local diffeomorphisms over its base manifold, known as its effect. The effect of any bundle of Lie groups is trivial. All quotients of a given Lie groupoid determine the same effect. It is natural to regard the effects of any two Morita equivalent Lie group…
DSL estimates heterogeneous treatment effects over time in survival settings.
NES optimizes discrete structured VAEs effectively without gradient propagation.
Natural gradient for Wasserstein metric approximated using kernel methods.
Energy-efficient detection of natural errors in deep networks.
Causal Interaction Trees identify treatment subgroup effects in observational data.
One important effect of price shocks in the United States has been increased political attention paid to the structure and performance of oil and natural gas markets, along with some governmental support for energy conservation. This paper describes how price changes helped lead the emergence of a political agenda acco…
Q-Learner estimates ratio-based treatment effects without imposing parametric structures.
We propose a learning-based filter that allows us to directly modify a synthetic speech waveform into a natural speech waveform. Speech-processing systems using a vocoder framework such as statistical parametric speech synthesis and voice conversion are convenient especially for a limited number of data because it is p…
Understanding the statistical properties of recurrence intervals of extreme events is crucial to risk assessment and management of complex systems. The probability distributions and correlations of recurrence intervals for many systems have been extensively investigated. However, the impacts of microscopic rules of a c…
The authors argue against the classification of forecasting methods as machine learning or statistical.
In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and state-action informatio…
Develops Palatini formalism in generalized geometry for string theory.
Study bandit problems under censorship, estimating performance loss.
In this article we describe cell decompositions of the moduli space of Riemann surfaces and their relationship to a Hurwitz problem. The cells possess natural linear structures and with respect to this they can be described as rational convex polytopes which come equipped with natural integer points and a volume form. …
Proposes integrating random effects into deep neural networks for better predictive performance.
Proposes a new k-NN algorithm to improve classification accuracy by removing noise and pseudo-neighbours.
Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective descent directions. For models with many parameters, the covariance matrix they are based on becomes gigantic, making them inapplicable in thei…
New analysis shows SNG's effectiveness in small samples.
We study invariant metrics on Ledger-Obata spaces . We give the classification and an explicit construction of all naturally reductive metrics, and also show that in the case , any invariant metric is naturally reductive. We prove that a Ledger-Obata space is a geodesic orbit space if and onl…
We complete the list of normal forms for effective 3-forms with constant coefficients with respect to the natural action of symplectomorphisms in \mathbb{R}^6. We show that the 3-form which corresponds to the Special Lagrangian equation is among the new members of the classification. The symplectic symmetry algebras an…
Decodes neural activity to detect context effects in natural settings.
DeepMed uses DNNs to estimate causal mediation effects without sparsity constraints.
Proposes a new VAE model to estimate treatment effects from confounded data.
We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read}, compose and write operations. NSE c…