Dynamic prize linked savings aims to increase account sizes and manage risks.
problem Limited appeal and low account sizes in prize linked savings.
method Proposes a dynamic prize linked savings model and three risk management methods.
result Effective risk management methods were tested and shown to mitigate severe risks.
Model explains Netflix Prize success with structured correlation.
problem Understanding structured correlation in high-dimensional data.
method Developed a new statistical learning model.
result Characterized learnability in terms of VCNk,k-dimension. New method speeds up knot computations in 3D.
problem Computational complexity in knot theory.
method 3D representation of knots for faster computation.
result Savings in computational complexity for knot invariants.
Sullivan discusses his contributions to math and physics.
problem None explicitly stated in the abstract.
method Personal overview of Dennis Sullivan's work.
result Sullivan's work spans mathematics and physics.
Improves heuristics for routing problems using attention models.
problem Improving heuristics for combinatorial optimization problems, especially for routing problems.
method Proposed a model based on attention layers and trained it using REINFORCE with a simple greedy rollout.
result Significantly improved results for TSP and other routing problems, close to optimal or specialized algorithms.
Study on pooled annuity funds and how initial savings affect income stability.
problem Analyzing the stability of income payments in pooled annuity funds.
method Examining the influence of initial savings on income fluctuations and developing a criterion for pooling funds.
result Identification of a term, the 'implied number of homogeneous members', linking initial savings to income fluctuations.
Stochastic gradient descent on manifolds improves low-rank approximation.
problem Efficiently approximate large matrices with lower rank.
method Stochastic gradient descent on a manifold.
result Algorithm outperforms Euclidean space methods on Netflix Prize data.
Paper proposes an algorithm to reconstruct optimal model structure from graph adjacency matrix.
problem Optimal model structure reconstruction from weighted colored graph adjacency matrix.
method Uses prize-collecting Steiner tree algorithm to reconstruct minimum spanning tree.
result Demonstrates the effectiveness of the prize-collecting Steiner tree algorithm for model structure reconstruction.
Paper proposes a new method for valuing long-term annuities using real-world probability measure.
problem Valuation of long-term annuities using classical no-arbitrage methods.
method Real-world probability measure valuation, employing numéraire portfolio.
result Real-world valuation leads to lower values than classical approaches.
A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk, based on theories of decision making under uncertainty which won the 2002 Nobel prize in economics. First, the paper documents inaccuracy…
MILABOT is a chatbot trained to converse with humans using deep reinforcement learning.
problem Developing a conversational agent capable of handling open-domain small talk topics.
method Deep reinforcement learning applied to crowdsourced and real-world data.
result MILABOT outperformed other systems in A/B testing with real-world users.
MILABOT is a deep reinforcement learning chatbot trained on crowdsourced and user data.
problem Developing a conversational chatbot for small talk topics.
method Deep reinforcement learning applied to an ensemble of natural language models.
result MILABOT outperformed other chatbots in A/B testing with real users.
New analysis shows rational actors will deploy AGI despite negative social value due to catastrophic risk.
problem Rational actors will deploy AGI despite negative social value due to shared catastrophic risk.
method Continuous-time preemption game with shared catastrophic externalities, showing suicide region and welfare distortion.
result The suicide region widens as catastrophic risk grows, and two mechanisms can close it.
GeoShapley uses game theory to measure spatial effects in ML models.
problem Measuring the impact of location on machine learning model predictions.
method Extends Shapley value framework to quantify spatial effects in various ML models.
result Validated GeoShapley values against known processes and demonstrated utility in house price modeling.
Mixed models study optimal saving under risk.
problem Optimal saving in risky consumer environments.
method Two mixed models combining possibilistic and probabilistic approaches.
result Optimal saving strategies identified for risk types.
Sym-NCO leverages symmetricities to improve DRL-NCO performance.
problem Improving neural combinatorial optimization methods.
method Sym-NCO is a regularizer-based training scheme that exploits universal symmetricities in CO problems and solutions.
result Sym-NCO significantly improves DRL-NCO performance across various CO tasks.
Argentum is a crypto coin for saving and investment in unstable countries.
problem Stable purchasing power for savings in unstable economies.
method Designing a crypto coin backed by investment instruments.
result Provides a stabilization instrument for savings in unstable economies.
We present several models to describe the stochastic evolution of stocks that show some strong resistance at some level and generalize to this situation the evolution based upon geometric Brownian motion. If volatility and drift are related in a certain way we show that our model can be integrated in an exact way. The …
Estimates cost savings from early cancer diagnosis.
problem Improving early cancer diagnosis to reduce treatment costs.
method Combining published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis, and extrapolating to other cancer sites.
result Estimates U.S. national annual treatment cost-savings from early cancer diagnosis in the trillions.
Improved cash flow forecasts lead to significant cost savings.
problem The impact of forecast accuracy on cash management costs.
method Analysis of two real data sets from the textile industry.
result Predictive accuracy is highly correlated with cost savings.
The effects of saving and spending patterns on holding time distribution of money are investigated based on the ideal gas-like models. We show the steady-state distribution obeys an exponential law when the saving factor is set uniformly, and a power law when the saving factor is set diversely. The power distribution c…
Data science reveals co-evolution of income inequality and savings across countries.
problem Understanding the co-evolution of income inequality and savings across countries.
method Time series data for Gini indices and Gross Domestic Savings (% of GDP) were used to construct correlation and similarity matrices, and a multi-dimensional scaling technique was applied. Linear regression was used to test the empirical linkage between income inequality and savings.
result The empirical model proposed by Chakraborti-Chakrabarti (2000) holds reasonably true for many economies of the world, showing a moderate relationship between income inequality and savings.
We analyze the ideal gas like models of markets and review the different cases where a `savings' factor changes the nature and shape of the distribution of wealth. These models can produce similar distribution of wealth as observed across varied economies. We present a more realistic model where the saving factor can v…
Smart home system saves energy based on consumer behavior.
problem Energy wastage in smart homes.
method Machine learning algorithm using consumer behavior data.
result The system successfully saves energy without compromising comfort.
Neural network improves K-factor estimation for OFDM systems.
problem Estimating Ricean K factor for link quality in OFDM systems.
method Classified as a classification problem, neural network estimates K factor at transmitter side.
result High accuracy in K factor estimation with reduced feedback bandwidth.
Using a model of wealth distribution where traders are characterized by quenched random saving propensities and trade among themselves by bipartite transactions, we mimic the enhanced rates of trading of the rich by introducing the preferential selection rule using a pair of continuously tunable parameters. The biparti…
We consider a simple model of a closed economic system where the total money is conserved and the number of economic agents is fixed. In analogy to statistical systems in equilibrium, money and the average money per economic agent are equivalent to energy and temperature, respectively. We investigate the effect of the …
We review a simple model of closed economy, where the economic agents make money transactions and a saving criterion is present. We observe the Gibbs distribution for zero saving propensity, and non-Gibbs distributions otherwise. While the exact solution in the case of zero saving propensity is already known to be give…
SAVED safely learns robot tasks with sparse rewards.
problem Challenges in reinforcement learning for robotics, especially sparse rewards and complex constraints.
method SAVED uses supervision to constrain exploration and learn efficiently, handling complex constraints.
result SAVED outperforms state-of-the-art methods in success rate, constraint satisfaction, and sample efficiency.
Study explores factors influencing saving behavior among Dhaka employees.
problem Factors influencing saving behavior among Dhaka employees.
method Quantitative approach with cross-sectional survey design, structured questionnaire, descriptive statistics, reliability analysis, regression analysis.
result Only financial management practices had a significant positive relationship with saving behavior.
A hybrid CNN with shared parameters learns to incorporate recurrent structures.
problem Designing efficient recurrent neural networks.
method Parameter sharing scheme combining CNN and recurrent layers.
result Substantial parameter savings with competitive accuracy.
We discuss the ideal gas like models of a trading market. The effect of savings on the distribution have been thoroughly reviewed. The market with fixed saving factors leads to a Gamma-like distribution. In a market with quenched random saving factors for its agents we show that the steady state income (m) distributi…
SAVE combines Q-learning and MCTS with amortized value estimates for improved performance.
problem Combining model-free Q-learning and model-based MCTS for efficient learning and planning.
method SAVE uses a learned prior to guide MCTS, which estimates improved state-action values. These estimates are used to update the prior, creating a cooperative relationship between learning and search.
result SAVE achieves higher rewards with fewer training steps and strong performance with small search budgets.
We mathematically analyze a simple market model where trading at each point in time involves only two agents with the sum of their money being conserved and with neither parties resulting with negative money after the interaction process. The exchange involves random re-distribution among the two players of a fixed fra…
Study on optimal portfolio selection with varying borrowing and saving rates in continuous-time markets.
problem Optimal portfolio selection in markets with different borrowing and saving rates.
method Hamilton-Jacobi-Bellman equation, partial differential equation, verification argument.
result Existence and smoothness of the value function, identification of trading regions and strategies.
New algorithms secure IoT edge computing from jamming attacks.
problem Secure mobile edge computing in IoT under jamming attacks.
method Online learning tools for developing SAVE-S and SAVE-A algorithms.
result Achieves sublinear regret without extra resources.
New Bitcoin coin selection method improves cost savings.
problem Improving cost savings in Bitcoin coin selection.
method Coin selection with leverage, allowing user-tunable parameters.
result Natural replacement for standard knapsack method.
Unified framework sparsifies GNNs for faster inference on large graphs.
problem Space and computational bottlenecks in GNNs due to graph size and connectivity.
method Unified GNN sparsification (UGS) framework that prunes graph adjacency matrix and model weights.
result Graph lottery tickets (GLTs) can be trained in isolation to match full model performance.
We have studied numerically the statistical mechanics of the dynamic phenomena, including money circulation and economic mobility, in some transfer models. The models on which our investigations were performed are the basic model proposed by A. Dragulescu and V. Yakovenko [1], the model with uniform saving rate develop…
Active learning helps save experiments in drug-target prediction by stopping when predictions are accurate enough.
problem Determining when to stop active learning to save experiments and costs.
method Computed active learning traces on simulated data to learn a regression model for accuracy. Designed stopping criteria based on model performance.
result Up to 40% savings in experiments for highly accurate predictions.
RL agent learns to save costs by managing household energy storage.
problem Maximizing cost savings in smart grids with household energy storage.
method Data-driven RL agent learns from tariff structures and storage capacity.
result RL agent explains its learning process and strategies.
Model trains agents to optimize saving and investment strategies for diverse retirement needs.
problem Optimal saving and investment strategies for individuals in varied employment and income profiles.
method Deep reinforcement learning to train intelligent agents with heterogeneous profiles.
result Flexible methodology estimates lifetime consumption and investment choices for different profiles.
SAVeD detects dataset versions without metadata, improving accuracy and separation.
problem Difficulty in identifying similar versions of structured datasets.
method Contrastive learning with modified SimCLR pipeline, generating and contrasting augmented table views.
result SAVeD achieves higher accuracy and separation scores on unseen tables.
Some general features of kinetic multi-agent models are reviewed, with particular attention to the relation between the agent saving propensities and the form of the equilibrium wealth distribution. The effect of a finite cutoff of the saving propensity distribution on the corresponding wealth distribution is studied. …
Various multi-agent models of wealth distributions defined by microscopic laws regulating the trades, with or without a saving criterion, are reviewed. We discuss and clarify the equilibrium properties of the model with constant global saving propensity, resulting in Gamma distributions, and their equivalence to the Ma…
Kinetic exchange models have been successful in explaining the shape of the income/wealth distribution in the economies. However, such models usually make some ad-hoc assumptions when it comes to determining the savings factor. Here, we examine a few models in and out of the domain of standard neo-classical economics t…
MATCHA speeds up decentralized SGD by parallelizing communication.
problem Error-runtime trade-off in decentralized SGD.
method MATCHA decomposes network topology into matchings for efficient communication.
result MATCHA reduces communication time by up to 5x compared to vanilla decentralized SGD.
We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity λ of agents, such that each agent saves a fraction λ of its money and trades with t…