arXiv research

New algorithm adapts to unknown demand smoothness for dynamic pricing.

Improved algorithm reduces regret in NRM with unknown demand.

This paper addresses privacy issues in personalized pricing using nonparametric demand models.

This paper addresses dynamic price discrimination with fairness constraints.

Paper develops PGMM framework for debiased inference on nonparametric IV estimators.

New method estimates consumer surplus from randomized pricing data.

New adaptive test for NPIV models controls size and has superior power.

Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections …

A new method uses GANs for robust optimization under uncertain data.

New method reduces bias in estimating causal effects from discretized variables.

We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Ho…

In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations. However, they demand large quantities of simulation calls. Critically, hyperpara…

We consider the problem of providing nonparametric confidence guarantees for undirected graphs under weak assumptions. In particular, we do not assume sparsity, incoherence or Normality. We allow the dimension $D$ to increase with the sample size $n$. First, we prove lower bounds that show that if we want accurate infe…

Demand variance can result in a mismatch between planned supply and actual demand. Demand shaping strategies such as pricing can be used to shift elastic demand to reduce the imbalance. In this work, we propose to consider elastic demand in the forecasting phase. We present a method to reallocate the historical elastic…

Modeling shared mobility demand considering supply limitations.

The paper proposes a new model to better estimate demand from censored data.

Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand foreca…

This paper improves computational efficiency in kernel ridge regression under covariate shift.

Study improves cross-modal bike-share and transit demand prediction.

New Bayesian method for sparse multidimensional item response theory.

A new metric optimizes forecasts for lumpy, intermittent demand.

Paper develops deep models for forecasting intermittent demand.

There is a growing demand for nonparametric conditional density estimators (CDEs) in fields such as astronomy and economics. In astronomy, for example, one can dramatically improve estimates of the parameters that dictate the evolution of the Universe by working with full conditional densities instead of regression (i.…

Statistical arbitrageurs have inelastic demand, contrary to classical models.

Implementing a set of microeconomic criteria, we develop price dynamics equations using a function of demand/supply with key symmetry properties. The function of demand/supply can be linear or nonlinear. The type of function determines the nature of the tail of the distribution based on the randomness in the supply and…

Develops methods for dynamic pricing in incomplete data settings.

Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values. Users' demands, implying strong purchase intents, can be the most useful way to promote products sales if well …

Bayesian nonparametrics adapt model complexity to diverse datasets.

NP-iMCMC algorithm for nonparametric models in universal PPLs.

A boosting method improves nonparametric density estimation without smoothing assumptions.

Novel probabilistic models forecast residential heating and electricity demand at hourly resolution.

Two neural network models analyze bus system efficiency and demand.

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…

Proposes a new model for more accurate demand forecasting considering dynamic contextual information.

The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.

The study examines Fisher-Riemann geodesics for nonparametric probability densities.

Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of …

We consider a firm that sells products over $T$ periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. A natural heuristic for this problem, commonly used in practice, is greedy iterative least squares (GILS). At each ti…

The Neyman-Pearson (NP) paradigm in binary classification seeks classifiers that achieve a minimal type II error while enforcing the prioritized type I error controlled under some user-specified level $α$. This paradigm serves naturally in applications such as severe disease diagnosis and spam detection, where people h…

Surveying nonparametric inference with shape constraints, past and future.

Study on inventory control with changing demand, proposing adaptive algorithms.

The volatility of financial instruments is rarely constant, and usually varies over time. This creates a phenomenon called volatility clustering, where large price movements on one day are followed by similarly large movements on successive days, creating temporal clusters. The GARCH model, which treats volatility as a…

Optimal nonparametric regression estimator adapts to unknown smoothness.

Model predicts trading strategies based on latent demand and price impact.

Paper optimizes demand aggregation for low-level electricity markets.

Bayesian nonparametric models improve OOD detection, especially with complex covariance structures.

MaxCOSD algorithm tackles non-i.i.d. demands and stateful dynamics in online inventory control.

Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger deman…