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.
New algorithm optimizes long-term user satisfaction in recommendation systems.
New algorithm optimizes for long-term user satisfaction in delayed reward settings.
DSPN predicts advertiser satisfaction and intent for e-commerce platforms.
In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cumulative constraints. We propose a novel first-order policy optimization method, Interior-point Policy Optimization (IPO), which augments the…
Study on electronic banking satisfaction in Nigeria.
Hybrid approach combines user feedback and machine learning for predicting user satisfaction.
An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and employ annotation schemes with limit…
In this paper we consider an interval portfolio selection problem with uncertain returns and introduce an inclusive concept of satisfaction index for interval inequality relation. Based on the satisfaction index, we propose an approach to reduce the interval programming problem with uncertain objective and constraints …
Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work which is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we use a reward based on user satisfa…
Study shows non-systematic bias in customer satisfaction surveys limits data value.
An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and rely on annotation schemes with low …
Enhanced neural network framework improves constraint satisfaction with topological conditioning.
A new online learning problem, CAB, tackles matching platforms to maximize user satisfaction.
Analyzes retail trends from sales, search, and reviews.
Researchers study how teachers' advising relationships influence their perceptions of satisfaction and students, not policy influence.
As known, attribute selection is a method that is used before the classification of data mining. In this study, a new data set has been created by using attributes expressing overall satisfaction in Turkey Statistical Institute (TSI) Life Satisfaction Survey dataset. Attributes are sorted by Ranking search method using…
Novel discretization of Euler equations for incompressible fluids.
We extend the classical risk minimization model with scalar risk measures to the general case of set-valued risk measures. The problem we obtain is a set-valued optimization model and we propose a goal programming-based approach with satisfaction function to obtain a solution which represents the best compromise betwee…
Paper presents a reinforcement learning framework for personalized music playlist generation.
Despite the growing importance of multilingual aspect of web search, no appropriate offline metrics to evaluate its quality are proposed so far. At the same time, personal language preferences can be regarded as intents of a query. This approach translates the multilingual search problem into a particular task of searc…
GACEM optimizes complex multi-modal problems using neural networks.
A new ML method teaches constraints directly to models.
This paper proposes a method to safely adjust exploration in RL to satisfy constraints.
The paper shows regularization can't always find all optimal solutions in constrained ML.
Improves generative models by optimizing rewards and sample editing.
TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.
LIMEADE improves AI advice for opaque models, enhancing accuracy and user satisfaction.
Several algorithms for solving constraint satisfaction problems are based on survey propagation, a variational inference scheme used to obtain approximate marginal probability estimates for variable assignments. These marginals correspond to how frequently each variable is set to true among satisfying assignments, and …
This paper presents a new approach for training artificial neural networks using techniques for solving the constraint satisfaction problem (CSP). The quotient gradient system (QGS) is a trajectory-based method for solving the CSP. This study converts the training set of a neural network into a CSP and uses the QGS to …
CRPO solves challenging SRL problems with convergence guarantee.
Little is known about how different types of advertising affect brand attitudes. We investigate the relationships between three brand attitude variables (perceived quality, perceived value and recent satisfaction) and three types of advertising (national traditional, local traditional and digital). The data represent t…
We introduce here for the first time the long-term swap rate, characterised as the fair rate of an overnight indexed swap with infinitely many exchanges. Furthermore we analyse the relationship between the long-term swap rate, the long-term yield, see Biagini et al. [2018], Biagini and Härtel [2014], and El Karoui et a…
Kernel method estimates long-term effects from short-term data.
In the last few years the systematic adoption of deep learning to visual generation has produced impressive results that, amongst others, definitely benefit from the massive exploration of convolutional architectures. In this paper, we propose a general approach to visual generation that combines learning capabilities …
New algorithm reduces robust optimization scale for better constraint satisfaction.
Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally. Coarse-graining methods aim to define simpler systems which are more amenable to analysis and exploration; most current methods, however, focus on a priori state aggregation based on similarities in transition rates, whi…
Scalable model checking for stochastic systems using Gaussian Processes and Bayesian Neural Networks.
Model combines long-term and short-term memory using conceptors.
New framework estimates long-term outcomes from short-term data.
TimeBridge addresses non-stationarity in long-term time series forecasting.
There have been recent efforts for incorporating Graph Neural Network models for learning full-stack solvers for constraint satisfaction problems (CSP) and particularly Boolean satisfiability (SAT). Despite the unique representational power of these neural embedding models, it is not clear how the search strategy in th…
Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics, and biology. In this paper we present a model-free RL algorithm to synthesize control policies that maximize the probability of satisfying h…
This paper balances short-term and long-term rewards in policy learning.
We study the long-term memory in diverse stock market indices and foreign exchange rates using the Detrended Fluctuation Analysis(DFA). For all daily and high-frequency market data studied, no significant long-term memory property is detected in the return series, while a strong long-term memory property is found in th…
The paper analyzes how ESG investors can prioritize green stocks without sacrificing overall wealth.
This paper uses Bayesian models to analyze CTA returns across short and long-term trends.
TQFT invariants are either easy or hard to compute, depending on the TQFT type.
Purpose - This paper seeks to take a cautionary stance to the impact of the marketing mix on customer satisfaction, via a case study deriving consensus rankings for benchmarking on selected retail stores in Malaysia. Design/methodology/approach - The ELECTRE I model is used in deriving consensus rankings via multicrite…