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.
MixDiff detects OOD samples in constrained access environments by comparing perturbed samples.
Role mining tackles the problem of finding a role-based access control (RBAC) configuration, given an access-control matrix assigning users to access permissions as input. Most role mining approaches work by constructing a large set of candidate roles and use a greedy selection strategy to iteratively pick a small subs…
Estimates TV distance between autoregressive models under different access models.
Model analyzes trading frictions in cap-and-trade markets, showing how they interact to affect market effectiveness.
Paper uses machine learning to optimize UAV deployment for traffic offloading.
Access to food assistance programs such as food pantries and food banks needs focus in order to mitigate food insecurity. Accessibility to the food assistance programs is impacted by demographics of the population and geography of the location. It hence becomes imperative to define and identify food assistance deserts …
New measure predicts Dutch housing market downturns.
To make efficient use of limited spectral resources, we in this work propose a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. We employ the proposed framework …
Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches to saliency map generation are available, but most require access to model parameters. This work proposes an approach for saliency map gene…
Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as-a-service ("predictive analytics") systems are an example: Some …
New algorithm tackles multi-player bandit problems with limited access to arms.
This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have \emph{partial} and \emph{noisy} access to the hidden state sequence as side information. This access can be seen as "partial labeling" of …
Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to validate. This makes it challenging for non-technical collaborators and endpoint users (e.g. physicians) to easily provide feedback on model devel…
LLMs help less-resourced researchers access costly data.
Improved sample complexity for diffusion models without needing empirical risk minimizers.
We use an accessibility result of Delzant and Potyagailo to prove Swarup's Strong Accessibility Conjecture for Gromov hyperbolic groups with no 2-torsion. It follows that, if M is an irreducible, orientable, compact 3-manifold with hyperbolic fundamental group, then any hierarchy in which M is decomposed alternately al…
New methods protect privacy while providing accurate prediction sets.
Proves bounds on copyright risk for generative models.
ModHiFi identifies critical components for model modification without gradients or loss function.
Framework reuses pre-trained models for data-free transfer learning.
The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement amon…
Query access significantly speeds up learning Multi-Index Models under Gaussian distribution.
Quantum models can approximate any function if data encoding allows for a rich enough frequency spectrum.
Enlargement of filtrations is a classical topic in the general theory of stochastic processes. This theory has been applied to stochastic finance in order to analyze models with insider information. In this paper we study initial enlargement in a Markov chain market model, introduced by R. Norberg. In the enlargened fi…
New attacks improve privacy audits by analyzing model updates.
Paper tackles efficient HMM learning with conditional samples.
We perform the first study of the tradeoff space of access methods and replication to support statistical analytics using first-order methods executed in the main memory of a Non-Uniform Memory Access (NUMA) machine. Statistical analytics systems differ from conventional SQL-analytics in the amount and types of memory …
Machine learning (ML) is probably the first and foremost used technique to deal with the size and complexity of the new generation of data. In this paper, we analyze one of the means to increase the performances of ML algorithms which is exploiting data locality. Data locality and access patterns are often at the heart…
Generative models accelerate molecular dynamics by four orders of magnitude.
Procedure verifies if machine learning models assign fixed predictions that preclude access.
In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated. By considering the unknown spatio-temporal content popularity and user preference, a user request model based on hidden Markov process is proposed to characterize the fluctuant spatio-temporal traffic demands in F…
Paper proposes a graph model for optimal AP deployment in indoor optical wireless networks.
In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling. We investigate the nonasymptotic convergence of AGLD with a unified analysis for different data accessing (e.g. random access, cyclic access and random reshuffle) and snapshot upd…
New complexity measure helps in agnostic reinforcement learning with or without access to MDP dynamics.
We bound the size of -dimensional cubulations of finitely presented groups. We apply this bound to obtain acylindrical accessibility for actions on CAT(0) cube complexes and bounds on curves on surfaces.
Study shows non-wandering, partially hyperbolic systems are ergodic.
RelEx explains relational models without gradient access.
Study agnostic RL in large state spaces with weak function approximation.
New algorithms sample convex bodies using Markov chains and restricted Gaussian oracles.
WAFFLE embeds watermarks in federated learning models without access to training data.
Multi-user multi-armed bandits have emerged as a good model for uncoordinated spectrum access problems. In this paper we consider the scenario where users cannot communicate with each other. In addition, the environment may appear differently to different users, , the mean rewards as observed by different users…
Research on interference has provided evidence that the formation of dependencies between non-adjacent words relies on a cue-based retrieval mechanism. Two different models can account for one of the main predictions of interference, i.e., a slowdown at a retrieval site, when several items share a feature associated wi…
Developing efficient and scalable algorithms for Latent Dirichlet Allocation (LDA) is of wide interest for many applications. Previous work has developed an O(1) Metropolis-Hastings sampling method for each token. However, the performance is far from being optimal due to random accesses to the parameter matrices and fr…
FinGPT is an open-source financial LLM for democratizing financial data.
Develops methods to measure and reduce fairness in datasets with limited protected attribute labels.
The study proves how groups can be split with limited complexity.
Parrot learns optimal cache replacement policies using imitation learning.
Credit scores misclassify borrowers, especially minorities, leading to inequitable access.