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.
A new approach for learning from expert demonstrations using multiple perspectives.
We study a generalized setup for learning from demonstration to build an agent that can manipulate novel objects in unseen scenarios by looking at only a single video of human demonstration from a third-person perspective. To accomplish this goal, our agent should not only learn to understand the intent of the demonstr…
Guarantees for third-person imitation learning from offline data.
Through a short sale, a person borrows a share of stock from a lender, sells the borrowed share to a third person at the current price, and purchases an identical share in the market at a future date and at a future price to replace the borrowed share of stock. This only makes sense if the short seller anticipates a do…
Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter is particularly problematic when applying RL to robotics, where detecting whether the desired configuration is reached might require considerable sup…
Machine learning models predict which ideas will be innovated based on subjective perspectives.
New approach to convex hulls for low-rank problems.
This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the final ensemble classifier model as a static state, which can be estimated using a Kalman filter that combines noisy estimates made by indivi…
Smooth metric measure spaces have been studied from the two different perspectives of Bakry-Émery and Chang-Gursky-Yang, both of which are closely related to work of Perelman on the Ricci flow. These perspectives include a generalization of the Ricci curvature and the associated quasi-Einstein metrics, which include Ei…
Paper classifies institutions based on credit, debit, and funding adjustment paradigms.
Currently, there starts a research trend to leverage neural architecture for recommendation systems. Though several deep recommender models are proposed, most methods are too simple to characterize users' complex preference. In this paper, for a fine-grain analysis, users' ratings are explained from multiple perspectiv…
Survey explores translation surfaces from geometric and topological perspectives.
This work analyzes tree-based methods from a ranking perspective, providing insights and new statistics.
The present, partly expository, monograph consists of three parts. The first part treats Spin- and Pin-structures from three different perspectives and shows them to be suitably equivalent. It also introduces an intrinsic perspective on the relative Spin- and Pin-structures of Fukaya-Oh-Ohta-Ono and Solomon, establishe…
This paper analyzes self-supervised learning from a multi-view perspective.
This paper treats the theory of Mukai duality on K3 surfaces from the differential geometric perspective, taylored to the need of the author's companion paper about Mukai duality of adiabatic coassociative K3 fibrations.
We discuss some of the key ideas of Perelman's proof of Poincaré's conjecture via the Hamilton program of using the Ricci flow, from the perspective of the modern theory of nonlinear partial differential equations.
A tutorial on variational inference for high-dimensional models.
P3I learns holistic scene representations from a single image.
Deep learning has sparked a network of mutual interactions between different disciplines and AI. Naturally, each discipline focuses and interprets the workings of deep learning in different ways. This diversity of perspectives on deep learning, from neuroscience to statistical physics, is a rich source of inspiration t…
Closed strings can be seen either as one-dimensional objects in a target space or as points in the free loop space. Correspondingly, a B-field can be seen either as a connection on a gerbe over the target space, or as a connection on a line bundle over the loop space. Transgression establishes an equivalence between th…
Gradient descent optimization improved by circuit perspective.
GANs analyzed for performance and training issues.
Unified framework for Gaussian process methods in differential equations.
New method calculates volume-renormalized mass from Hamiltonian perspective.
Studies report that firms do not invest in cost-effective green technologies. While economic barriers can explain parts of the gap, behavioural aspects cause further under-valuation. This could be partly due to systematic deviations of decision-making agents' perceptions from normative benchmarks, and partly due to the…
New perspective on CNNs using Hessian maps reveals their structure.
This paper develops a pricing model for data assets from the buyer's perspective.
Examines challenges and proposes new approaches in machine learning theory.
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine l…
We take a Hamiltonian-based perspective to generalize Nesterov's accelerated gradient descent and Polyak's heavy ball method to a broad class of momentum methods in the setting of (possibly) constrained minimization in Euclidean and non-Euclidean normed vector spaces. Our perspective leads to a generic and unifying non…
In this paper, we study some algebraic topology aspects of String structures, more precisely, from the perspective of Whitehead tower and the perspective of the loop group of . We also extend the generalized Witten genera constructed for the first time in \cite{CHZ11} to correspond to String structur…
Enhances price sentiment index using survey comments.
Proposes a probabilistic method for generating semantically-aware adversarial examples.
A note on learning with agents having global perspectives and a principal optimizing their performance.
The guaranteed minimum withdrawal benefit (GMWB) rider, as an add on to a variable annuity (VA), guarantees the return of premiums in the form of peri- odic withdrawals while allowing policyholders to participate fully in any market gains. GMWB riders represent an embedded option on the account value with a fee structu…
New progress on frame flow ergodicity for nearly pinched manifolds.
Bayesian deep learning improves neural network accuracy and generalization.
The paper analyzes privacy leakage in federated learning using linear algebra and optimization theory.
Explores new perspectives in transverse index theory for Lie group actions.
Machine fairness is impossible to achieve fully due to historical biases.
Researchers use LLMs to judge other LLMs, but this study provides a new geometric perspective to understand when it works.
The statistical analysis of discrete data has been the subject of extensive statistical research dating back to the work of Pearson. In this survey we review some recently developed methods for testing hypotheses about high-dimensional multinomials. Traditional tests like the test and the likelihood ratio test ca…
We discuss social network analysis from the perspective of economics. We organize the presentaion around the theme of externalities: the effects that one's behavior has on others' well-being. Externalities underlie the interdependencies that make networks interesting. We discuss network formation, as well as interactio…
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectiv…
The paper analyzes diffusion condensation for data geometry and topology.
New perspective on APS indices preserves orientations and gradings through bordisms.
We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functions. We leverage this perspective to provide formal evidence regarding the usefulness of value functions as auxiliary tasks. Our formulation considers adapting the representation t…