This article evaluates ratings systems for emerging markets.
problem Developing econometrical rating models for emerging market companies.
method Evaluates current ratings systems and develops econometrical models.
result Specific issues in developing rating models for emerging markets.
Optimizes binary rating systems for item ranking.
problem Designing efficient feedback systems for item ranking.
method Formalizes performance, provides algorithm, empirically designs and validates.
result Empirically designed and validated approximately optimal rating system.
Two privacy-preserving rating collection methods for recommender systems.
problem Collecting user ratings while maintaining privacy.
method Modified Laplace mechanism and randomized response.
result Both mechanisms are differentially private and preserve data utility.
The paper explores growth rates and Perron numbers in Coxeter systems with low-dimensional Davis complexes.
problem Investigating growth rates and specific types of numbers in Coxeter systems with Davis complexes of low dimension.
method Examining Coxeter systems with Davis complexes of dimension at most 2, focusing on growth rates and specific types of numbers.
result The growth rate of Coxeter systems with Davis complexes of dimension at most 2 are either Salem or Pisot numbers, depending on the Euler characteristic.
Bayesian rating system for large competitions improves prediction and efficiency.
problem Rating systems for large, competitive events like online programming contests.
method Developed a Bayesian rating system for many participants, proving robustness and runtime.
result The system outperforms existing systems in accuracy and computation speed.
This paper builds a recommendation system for borrowers on P2PL platforms to lower interest rates.
problem Help borrowers make wise decisions on P2PL platforms to achieve lower interest rates.
method Developed a recommendation system to suggest the type of loan borrowers should apply for.
result Borrowers can achieve lowered interest rates with a higher likelihood of getting funded using the recommendation system.
New method improves reliability of recommender systems in face of fake accounts and manipulation.
problem Improving reliability of recommender systems in the presence of fake accounts and manipulation.
method Robust Discrete Matrix Completion (RDMC) method designed to handle sparse rating data and manipulation.
result Evaluations show RDMC offers a statistically-sound blueprint for future studies on recommender systems.
Study identifies and validates a method for system identification of Markov jump linear systems.
problem System identification for autonomous Markov jump linear systems with complete state observations.
method Proposes switched least squares method for identification and derives rates of convergence.
result Data-independent rate of convergence is O ( log ( T ) / T ) \mathcal{O}\big(\sqrt{\log(T)/T} \big) O ( log ( T ) / T ) , showing strong consistency. We introduce a solvable model of randomly growing systems consisting of many independent subunits. Scaling relations and growth rate distributions in the limit of infinite subunits are analysed theoretically. Various types of scaling properties and distributions reported for growth rates of complex systems in a variety…
Reinforcement learning improves wireless systems' rate adaptation.
problem Optimizing rate adaptation in 4G/5G systems using ACK/NACK feedback.
method Formulated as a Multi-Armed Bandit problem, proposed binary search algorithm with PAC guarantees.
result Achieved PAC solution for OLLA with binary search, outperforming UCB methods.
Paper revisits set membership estimation for linear systems with relaxed disturbance bounds.
problem Set membership estimation for linear systems with disturbances bounded by convex sets.
method Adopted block-martingale small-ball condition and random perturbed control policies to establish convergence rates.
result Established convergence rates for disturbances bounded by general convex sets.
The paper explores using set-level ratings for better user-item preference prediction in recommender systems.
problem Capturing user preferences on individual items using set-level ratings.
method Developed collaborative filtering-based methods to model user behaviors in set-level ratings.
result Collaborative filtering-based models can recover and predict user preferences on individual items using set-level ratings.
Study growth rates of subgroups in groups with a constricting element.
problem Understanding growth rates of subgroups in groups with a constricting element.
method Examining the spectrum of relative and quotient exponential growth rates of quasi-convex subgroups.
result Determine when growth rates of subgroups are strictly smaller or coincide with the group's growth rate.
Dual frame-rate system improves real-time perception for autonomous vehicles.
problem Conflicting requirement of safety and efficiency in real-time perception systems.
method Proposes a dual frame-rate system with a modulator stream for robust features and a prediction stream for transient signals.
result Consistent improvements across various backbone architectures and input resolutions.
A new multi-task framework for recommender systems improves ranking and rating predictions.
problem Improving ranking and rating predictions in recommender systems.
method Exploits a two-phase decision process: first deciding to interact with an item (ranking task) and then rating it (rating prediction task).
result Superior performance compared to state-of-the-art methods on two benchmark datasets.
Statistical test verifies long-term rating system calibration with overlapping time windows.
problem Verifying supervisory requirements for overlapping time windows in rating systems.
method Analyzes long-run default rate distribution and correlation effects; presents conservative calibration test methods.
result Developed a test for individual and portfolio levels that can handle unknown variance.
Model shows how adding liquidity in banking system can lead to defaults.
problem Systemic risk and interbank lending in banking systems.
method Simple model using Feller diffusions and Markov Nash equilibrium.
result Adding liquidity leads to defaults in banking system.
Study learns dynamics of linear systems from multiple short trajectories.
problem Learning dynamics of autonomous linear systems from multiple short trajectories.
method Finite sample analysis for stable and unstable systems, adjusting trajectory length for marginally stable systems.
result Learning rate of O ( 1 N ) \mathcal{O}(\frac{1}{\sqrt{N}}) O ( N 1 ) for both stable and unstable systems. Enhances KWS in vehicles with multi-source fusion.
problem Improving precision and recall rates in vehicle keyword spotting.
method Integrates vehicle information into a DNN for speech classification and selects optimal sensitivity parameters.
result Significantly improved performance metrics (precision, recall, MSE) compared to baseline.
Algorithm learns linear systems from partial observations with near-optimal rate.
problem Identifying linear dynamical systems from partial observations, especially those with long-term memory.
method Multi-scale low-rank approximation using SVD on Hankel matrices of increasing sizes, combined with Fourier domain concentration bounds.
result Near-optimal rate of $\widetilde O\left(\sqrt\frac{d}{T}
ight)$ in H 2 \mathcal{H}_2 H 2 error, with logarithmic dependence on memory length. The paper proposes a new recommender system combining ratings and textual reviews.
problem Lack of using textual reviews in recommender systems.
method Combines Latent Factor Model with Latent Dirichlet Allocation for textual reviews.
result Combining textual reviews with ratings improves recommendation quality.
New method solves optimization problems on manifolds using symplectic integrators.
problem Optimization tasks on manifolds with nonlinear constraints.
method Dissipative extension of Dirac's theory of constrained Hamiltonian systems and geometric/symplectic numerical integrators.
result Developed algorithms achieve optimal convergence rates locally.
Paper anonymizes user ratings to protect privacy while improving recommendation accuracy.
problem Protecting user privacy while maintaining recommendation accuracy with anonymized ratings.
method Exhaustively lists recommender models using anonymized ratings and presents item-based collaborative filtering algorithms.
result Item-based collaborative filtering based on anonymized ratings outperforms non-anonymized ratings in some settings.
Neural models learn continuous-time Markov chain transition rates from data.
problem Learning transition rates for complex stochastic systems.
method Neural networks to model nonlinear transition rates from observed data.
result Neural models outperform traditional methods in accuracy.
Algorithm optimally estimates linear dynamical systems with active input selection.
problem Estimating parameters of linear dynamical systems efficiently.
method Active learning with adaptive input selection.
result Finite time bound and asymptotic optimality proven.
Deep RL tackles mmWave backhaul resource allocation challenges.
problem Limited backhaul capacity and dynamic data rates in mmWave systems.
method Deep reinforcement learning (DRL) to predict and allocate backhaul resources.
result Efficient utilization of backhaul resources through DRL.
Elo ratings learn model parameters quickly using Markov chains.
problem Ranking players in online settings.
method Bradley--Terry--Luce model and Markov chain theory.
result Elo learns model parameters at a competitive rate.
We study large deviations and rare default clustering events in a dynamic large heterogeneous portfolio of interconnected components. Defaults come as Poisson events and the default intensities of the different components in the system interact through the empirical default rate and via systematic effects that are comm…
This research improves DeFi interest rates using a PID control system.
problem Lack of adaptive interest rates in DeFi money markets.
method Introduces a time-weighted PID control system for interest rate management.
result Adaptive interest rates improve risk mitigation and market utilization.
Asynchronous SGD generalizes well with enough data, improving stability and reducing error.
problem Generalization performance of asynchronous distributed SGD systems.
method Algorithm stability framework, adaptive learning rate strategy.
result Distributed asynchronous SGD generalizes well with enough data samples.
Sparse curves on surfaces grow at a specific intermediate rate.
problem Understanding growth patterns of sparse curve systems on surfaces.
method Analyzing the intersection numbers and sizes of sparse curve systems.
result Sparse curve systems grow roughly like c g c^{\sqrt{g}} c g . The study analyzes momentum-based optimization algorithms from dynamical systems perspective.
problem Understanding convergence rates of momentum-based optimization algorithms.
method Exploits dynamical systems, control theory, and symplectic perspectives to analyze convergence rates.
result Provides closed-form expressions relating algorithm parameters to convergence rates.
New training method for ASR models reduces word error rate.
problem Traditional ASR training methods do not directly optimize word error rate.
method Training attention-based models to minimize expected word error rate using N-best lists.
result Improved performance by up to 8.2% compared to baseline.
RLCache uses reinforcement learning to optimize cache management decisions.
problem Optimizing cache hit rate and storage size in computer systems.
method Designing three reinforcement learning agents for cache manager tasks and two advanced architectures.
result Reinforcement learning agents achieve higher cache hit rates and minimize storage space compared to heuristics.
New method preserves convergence rates in gradient-based optimization.
problem How to discretize gradient-based optimization systems while preserving stability and convergence rates.
method Geometric framework for dissipative symplectic integration.
result Dissipative symplectic integrators preserve rates of convergence up to a controlled error.
The paper introduces a new short rate model with memory components.
problem Modeling short rate dynamics with past values.
method Integrates memory (delay) components into Merton or Vasiček models.
result Analytical solutions for bond prices and forward rates.
New model predicts user ratings using chains of evidence.
problem Predicting user ratings from sparse data.
method Recursive evidence chains linking users and items.
result Competitive results in accuracy and speed.
We investigate the dynamics of growth models in terms of dynamical system theory. We analyse some forms of knowledge and its influence on economic growth. We assume that the rate of change of knowledge depends on both the rate of change of physical and human capital. First, we study model with constant savings. The mod…
Decentralized learning ensures stability in online queuing systems with packet rates above 1.
problem Ensuring stability in online queuing systems with decentralized learning.
method Proposed cooperative queues and a learning algorithm for packet rates above 1.
result Decentralized learning strategies guarantee stability in queuing systems with packet rates above 1.
Paper proposes a combined model for better recommendation by integrating explicit and implicit feedbacks.
problem Improve recommendation accuracy by considering both explicit and implicit feedbacks.
method Developed three models (RHC-PMF, RV-PMF, RHCV-PMF) that incorporate users' explicit and implicit feedbacks for better rating prediction.
result RHCV-PMF model outperforms other models in cold start scenarios for both users and items.
A new model improves click-through rate prediction for recommendation systems.
problem Improving accuracy of click-through rate prediction in recommendation systems.
method Combines traditional feature engineering with deep neural networks to automate feature combinations.
result The model (FNFM) outperforms current deep learning feature combination models.
New algorithm estimates partially-observed linear systems with better rates than previous methods.
problem Estimating parameters of partially-observed linear systems with long-term dependencies and semi-parametric noise.
method Prefiltered least squares estimator with semi-parametric noise model.
result First algorithm provably estimates parameters of partially-observed linear systems with rates not dependent on dependency decay rate.
New methods improve recommendation accuracy for users and items with few ratings.
problem Skewed distribution and low ratings affect recommendation accuracy.
method Four matrix completion-based approaches: FARP, TMF, TMF + Dropout, IFWMF.
result Improved prediction accuracy for users and items with few ratings.
Derives relationship between interest rates and inflation in a two-component system.
problem Understanding the relationship between interest rates and inflation in a two-component economic system.
method Used the Fisher relation to derive a delay differential equation and provided computer simulations.
result Obtained a delay differential equation and provided solutions for it over different interest regimes.
Proposes a multi-perspective neural model for better user preference analysis in recommendation systems.
problem Complex user preferences not well captured by existing recommendation models.
method Uses multiple perspectives to encode user and item features, then combines these representations to predict ratings.
result Significant improvements over baseline models in predicting user ratings.
This paper elaborates on the validation requirements for rating systems and probabilities of default (PDs) which were introduced with the New Capital Standards (Basel II). We start in Section 2 with some introductory remarks on the topics and approaches that will be discussed later on. Then we have a view on the develo…
A fast method estimates correlations in hybrid systems using observable market data.
problem Estimating instantaneous correlations in hybrid systems from observable data.
method Empirical correlations between observable market quantities are used to estimate state variables' correlations. Linear systems are involved, and the matrix is converted to positive semidefinite if necessary.
result The estimates are reasonably accurate, especially with more than 1,000 data points.
A new method for multi-criteria recommender systems using graph attention networks.
problem Lack of nuanced relationships between users and items based on specific criteria.
method MDGAT, a multi-edge bipartite graph with dual attention networks and contrastive learning.
result MDGAT achieves higher accuracy in predicting item ratings compared to baseline methods.