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.
Proposes a framework for generating explainable AI exemplars.
GAICF proposes a framework for governing generative AI in banking.
We present generalization bounds for the TS-MKL framework for two stage multiple kernel learning. We also present bounds for sparse kernel learning formulations within the TS-MKL framework.
We give a combinatorial characterization of generic minimally rigid reflection frameworks. The main new idea is to study a pair of direction networks on the same graph such that one admits faithful realizations and the other has only collapsed realizations. In terms of infinitesimal rigidity, realizations of the former…
We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framework devised to support these opportunities. Derivations of general classes of our control-based RL methods are presented, together with form…
GAICF proposes a framework for managing generative AI risks in banking.
Framework generates multimodal datasets with known MI for benchmarking.
Unified framework for deriving generalization bounds in supervised learning.
Framework generates personalized insulin treatment strategies using deep models.
New framework generalizes neural network parameters to -algebra for more efficient feature learning.
Unified view of generative models using GFlowNet framework.
Generative framework unifies and improves personalized learning and estimation methods.
We extend our generic rigidity theory for periodic frameworks in the plane to frameworks with a broader class of crystallographic symmetry. Along the way we introduce a new class of combinatorial matroids and associated linear representation results that may be interesting in their own right. The same techniques immedi…
Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks. However, typical GANs require fully-observed data during training. In this paper, we present a GAN-based framework for learning from comple…
In this paper we propose a general framework for modeling an insurance liability cash flow in continuous time, by generalizing the reduced-form framework for credit risk and life insurance. In particular, we assume a nontrivial dependence structure between the reference filtration and the insurance internal filtration.…
Proposes a Bayesian framework for causal inference without explicit likelihood modeling.
Unified approach unites GANs and diffusion models using particle methods.
Novel framework for policy optimization with general parameterization and linear convergence.
Paper introduces a statistical framework for watermarking LLM-generated text.
The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring …
Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quadratic in the number of examples in the data which makes them quickly infeasible for moderate to large scale datasets. A significant effort t…
Develops a generic two-layer framework for adaptive ABMs.
A framework models order book dynamics using point processes and mass transport.
Novel framework for quantifying data distribution values.
Anomaly detection is challenging, especially for large datasets in high dimensions. Here we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. We release DRAMA, a general python package that implements the general framework with a wide range of built-in options.…
Collateralization with daily margining has become a new standard in the post-crisis market. Although there appeared vast literature on a so-called multi-curve framework, a complete picture of a multi-currency setup with cross-currency basis can be rarely found since our initial attempts. This work gives its extension r…
A framework clusters vehicle motion trajectories efficiently.
The paper develops a cross-validation method for improving signal denoising techniques.
Unified framework for proving generalization bounds in machine learning.
New algorithm expands FTRL framework with improved worst-case regret bounds.
New framework reduces cost of financial option pricing simulations on FPGAs.
We contribute a pop-song automation framework for lead melody generation and accompaniment arrangement. The framework reflects the major procedures of human music composition, generating both lead melody and piano accompaniment by a unified strategy. Specifically, we take chord progression as an input and propose three…
Conditional domain generation is a good way to interactively control sample generation process of deep generative models. However, once a conditional generative model has been created, it is often expensive to allow it to adapt to new conditional controls, especially the network structure is relatively deep. We propose…
We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data and privately train a model for generating synthetic data that …
Framework for ensuring fairness in machine learning models across multiple groups.
We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction. Our framework is general and it subsumes several well-known SMF formulations in the literature. We perform a non-asymptotic convergence analysis of our framework and derive computational and …
Unified framework for risk evaluation under uncertainty.
Improved compressed sensing using a generator that learns from measurements.
Unified framework for learning function representations using INRs and Transformers.
Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are related. A unified view on these methods has been missing. We present the generalized SIPA (sampling, intervention, prediction, aggregation) …
Paper proposes a DNN-driven AF framework for improved generalization.
The paper introduces a new framework to assess generative model uncertainty.
New framework relaxes independence assumption for graph-mixing dependencies.
Paper generalizes bipolar theorems for non-negative random variables.
Proposes a framework for compositional generalization in language models.
Generative Adversarial Networks have been crucial in the developments made in unsupervised learning in recent times. Exemplars of image synthesis from text or other images, these networks have shown remarkable improvements over conventional methods in terms of performance. Trained on the adversarial training philosophy…
A modular framework for knowledge distillation simplifies experiments and reproducibility.
This paper provides a generic framework of component analysis (CA) methods introducing a new expression for scatter matrices and Gram matrices, called Generalized Pairwise Expression (GPE). This expression is quite compact but highly powerful: The framework includes not only (1) the standard CA methods but also (2) sev…