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 deep reinforcement learning framework for dynamic multichannel access.
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…
Paper develops AGLD for MCMC with bounds for various data access strategies.
New policy reduces spectrum access regret in uncoordinated systems.
Paper uses machine learning to optimize UAV deployment for traffic offloading.
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…
MixDiff detects OOD samples in constrained access environments by comparing perturbed samples.
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…
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.
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…
Study shows non-wandering, partially hyperbolic systems are ergodic.
Model analyzes trading frictions in cap-and-trade markets, showing how they interact to affect market effectiveness.
Paper uses clustering to identify food assistance deserts in Ohio.
Estimates TV distance between autoregressive models under different access models.
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…
The study proves how groups can be split with limited complexity.
Parrot learns optimal cache replacement policies using imitation learning.
New measure predicts Dutch housing market downturns.
The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose. However, current access networks treat all packets identically, and lack the agility to determine which clients are most in need of service at a …
Algorithm optimizes spectrum access for dynamic multi-user environments.
Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum. We propose a kernel-based reinforcement learning approach coupled with a novel budget-constrained sparsification technique that efficiently captures the envir…
New algorithm tackles multi-player bandit problems with limited access to arms.
Deep learning improves SCMA decoding and codebook construction for 5G wireless networks.
Proposes a new method to selectively access privileged information in reinforcement learning.
Accessible groups with infinitely many ends have infinitely many twisted conjugacy classes.
In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solutio…
Study active learning of PTFs with derivative access.
This paper describes the Speech Technology Center (STC) antispoofing systems submitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended version of the previous challenges and includes 2 evaluation conditions: logical access use-case scenario with speech synthesis and voice conversion attack types and ph…
Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: avoiding causing harmful interference to primary users (PUs), and conducting effective interference coordination wi…
There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, or age. However, this raises two significant challenges: (1) protected attributes may not be available or it may not be legal to use…
Minimizing a function over an intersection of convex sets is an important task in optimization that is often much more challenging than minimizing it over each individual constraint set. While traditional methods such as Frank-Wolfe (FW) or proximal gradient descent assume access to a linear or quadratic oracle on the …
Study agnostic RL in large state spaces with weak function approximation.
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 …
GrateTile optimizes CNN feature map storage for efficient data access.
A novel Gradient-Based Multiple Access algorithm for distributed learning over fading channels.
Satellite imagery and ML improve livelihood measurements and estimate electrification's impact.
LLMs help less-resourced researchers access costly data.
Theorem converse to Jordan's curve theorem says that {\it if a compact set has two complementary domains in , from each of which it is at every point accessible, it is a simple closed curve}. We show that the requirement of this theorem that {\it all} points of were accessible from {\it both} complementa…
Myanmar needs to improve financial access for its unbanked population.
Study identifies spatial inequalities in urban services access based on income.
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…
Study on women entrepreneurs' access to finance in France.
In this paper we study a family of variance reduction methods with randomized batch size---at each step, the algorithm first randomly chooses the batch size and then selects a batch of samples to conduct a variance-reduced stochastic update. We give the linear convergence rate for this framework for composite functions…
New methods protect privacy while providing accurate prediction sets.
C-Learning estimates reachability over time to solve multi-goal tasks.
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 …
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of labelled training data, our methodology is capable of predicting the labels of the test data almost always even if the training data is entir…
Improved sample complexity for diffusion models without needing empirical risk minimizers.