arXiv research

This paper improves risk bounds and calibration for smart predict-then-optimize method.

Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is predict-then-optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predict…

A new autoregressive SPO method improves decision-making for dependent data.

Paper proposes SPO paradigm for better portfolio optimization in real markets.

Decision trees improve decision-making by optimizing predictions of unknown parameters.

New loss function handles uncertain constraints in CSLO problems.

Develops active learning method for linear optimization with margin-based criterion.

dboost optimizes prediction models for convex cone problems.

The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem, and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions induced by the predicted…

A smart method predicts and optimizes decisions online with resource constraints.

This study explains and mitigates inflated returns and turnover in SPO-based portfolio optimization.

DFFL tackles federated learning with heterogeneous objectives and constraints.

A scalable method for training prediction models in predict-then-optimize

New approach optimizes decisions based on uncertainty in predictions.

Proposes a probabilistic framework for smart contract risk quantification.

Proposes a new method for decision-aware learning in optimization.

SPO optimizes LLMs by eliminating group-based baselines and variance issues.

Stochastic particle-optimization sampling (SPOS) is a recently-developed scalable Bayesian sampling framework that unifies stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) algorithms based on Wasserstein gradient flows. With a rigorous non-asymptotic convergence theory developed recently…

We consider the space of differential operators $\mathcal{D}_{λμ}$ acting between $λ$- and $μ$-densities defined on $S^{1|2}$ endowed with its standard contact structure. This contact structure allows one to define a filtration on $\mathcal{D}_{λμ}$ which is finer than the classical one, obtained by writting a differen…

DVA framework attributes value of predictive models to features, configurations, and interactions.

Proposes BU-SPO method to improve text classification robustness.

New PG losses improve decision optimization in misspecified models.

The paper optimizes portfolios by selecting financial ratios via PCA for better value investment.

Tensor models decode human perception and memory using SPO triples.

In this paper, we show that the Lie superalgebra $\mathfrak{spo}(2l+2|n)$ is into the intersection of Lie superalgebra of contact vector fields $\mathcal{K}(2l+1|n)$ and the Lie superalgebra of projective vector fields $\mathfrak{pgl}(2l+2|n)$. We use mainly the embedding used by P. Mathonet and F. Radoux in "\textit{ …

Proposes a method to learn adaptive ambiguity sets for robust optimization.

We propose a new model of minority game with so-called smart agents such that the standard deviation and the total loss in this model reach the theoretical minimum values in the limit of long time. The smart agents use trail and error method to make a choice but bring global optimization to the system, which suggests t…

A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional met…

In an economy with asymmetric information, the smart contract in the blockchain protocol mitigates uncertainty. Since, as a new trading platform, the blockchain triggers segmentation of market and differentiation of agents in both the sell and buy sides of the market, it recomposes the asymmetric information and genera…

Investigates the cost-effectiveness of security features in smart card chips.

Distribution grids are currently challenged by frequent voltage excursions induced by intermittent solar generation. Smart inverters have been advocated as a fast-responding means to regulate voltage and minimize ohmic losses. Since optimal inverter coordination may be computationally challenging and preset local contr…

Derives metrics for DeFi vaults, addressing credit risk.

Bayesian framework reduces online optimization regret.

Paper proposes DP-PASGD for efficient, private IoT learning.

In this paper, we generalize the known results on the super circles $S^{1|1}$ and $S^{1|2}$. We construct the fine equivariant quantization on the super circle $S^{1|n}$ for $n\geqslant 3$. The equivariant Lie superalgebra is $\spo(2|n)$ which is constituted of the contact projective vector fields on $S^{1|n}$. In orde…

Proposes CPO framework for robust decision-making with explainable uncertainty regions.

Particle-optimization-based sampling (POS) is a recently developed effective sampling technique that interactively updates a set of particles. A representative algorithm is the Stein variational gradient descent (SVGD). We prove, under certain conditions, SVGD experiences a theoretical pitfall, {\it i.e.}, particles te…

Paper proposes auction method for smart derivatives to avoid disputes.

Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elder…

The paper establishes axioms for AMMs to ensure fair pricing and fee structures.

New method identifies latent treatment effects from proxy models.

A new method for decision-focused learning reduces computational cost.

In order to keep track of the operational state of power grid, the world's largest sensor systems, smart grid, was built by deploying hundreds of millions of smart meters. Such system makes it possible to discover and make quick response to any hidden threat to the entire power grid. Non-technical losses (NTLs) have al…

FinTech uses data science and AI to transform finance.

The paper categorizes four types of scale-up: smart, dumb, forced, and fumbled.

Adversarial attacks can fool ML energy theft detection models.

Unified approach for optimizing predictions in linear programming and inverse problems.

Improved security of smart contracts by classifying them into four categories.