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Found 22 papers, 6 papers with code
Towards Better Statistical Understanding of Watermarking LLMs
We consider the trade-off between model distortion and detection ability and formulate it as a constrained optimization problem based on the green-red algorithm of Kirchenbauer et al. (2023a).
Transformer Choice Net: A Transformer Neural Network for Choice Prediction
Discrete-choice models, such as Multinomial Logit, Probit, or Mixed-Logit, are widely used in Marketing, Economics, and Operations Research: given a set of alternatives, the customer is modeled as choosing one of the alternatives to maximize a (latent) utility function.
Learning to Make Adherence-Aware Advice
As artificial intelligence (AI) systems play an increasingly prominent role in human decision-making, challenges surface in the realm of human-AI interactions.
A Neural Network Based Choice Model for Assortment Optimization
Discrete-choice models are used in economics, marketing and revenue management to predict customer purchase probabilities, say as a function of prices and other features of the offered assortment.
When No-Rejection Learning is Consistent for Regression with Rejection
This paper studies the regression with rejection (RwR) problem and investigates a no-rejection learning strategy that uses all the data to learn the predictor.
Understanding Uncertainty Sampling
Uncertainty sampling is a prevalent active learning algorithm that queries sequentially the annotations of data samples which the current prediction model is uncertain about.
Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
More importantly, our new approach only needs the observations of the optimal solution in the training data rather than the objective function, which makes it a new and natural approach to the inverse linear programming problem under both contextual and context-free settings; we also analyze the proposed method under both offline and online settings, and demonstrate its performance using numerical experiments.
Deep Learning for Choice Modeling
Choice modeling has been a central topic in the study of individual preference or utility across many fields including economics, marketing, operations research, and psychology.
A Unified Image Preprocessing Framework For Image Compression
With the development of streaming media technology, increasing communication relies on sound and visual information, which puts a massive burden on online media.
Learning to Sell a Focal-ancillary Combination
A number of products are sold in the following sequence: First a focal product is shown, and if the customer purchases, one or more ancillary products are displayed for purchase.
Non-stationary Bandits with Knapsacks
Finally, we also extend the non-stationarity measure to the problem of online convex optimization with constraints and obtain new regret bounds accordingly.
On Dynamic Pricing with Covariates
In this paper, we show that UCB and Thompson sampling-based pricing algorithms can achieve an $O(d\sqrt{T}\log T)$ regret upper bound without assuming any statistical structure on the covariates $x_t$.
Fairer LP-based Online Allocation via Analytic Center
On a high level, we define the fairness in a way that a fair online algorithm should treat similar agents/customers similarly, and the decision made for similar agents/customers should be consistent over time.
The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks
In this paper, we study the bandits with knapsacks (BwK) problem and develop a primal-dual based algorithm that achieves a problem-dependent logarithmic regret bound.
Online Stochastic Optimization with Wasserstein Based Non-stationarity
We propose a unified Wasserstein-distance based measure to quantify the inaccuracy of the prior estimate in setting (i) and the non-stationarity of the system in setting (ii).
Simple and Fast Algorithm for Binary Integer and Online Linear Programming
In this paper, we develop a simple and fast online algorithm for solving a class of binary integer linear programs (LPs) arisen in general resource allocation problem.
Data Structures and Algorithms Optimization and Control
Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds
We study an online linear programming (OLP) problem under a random input model in which the columns of the constraint matrix along with the corresponding coefficients in the objective function are generated i. i. d.
Quantile Markov Decision Process
In this paperwe consider the problem of optimizing the quantiles of the cumulative rewards of a Markov decision process(MDP), which we refer to as a quantile Markov decision process (QMDP).
Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans
Eradicating hunger and malnutrition is a key development goal of the 21st century.
Recurrent Autoregressive Networks for Online Multi-Object Tracking
The external memory explicitly stores previous inputs of each trajectory in a time window, while the internal memory learns to summarize long-term tracking history and associate detections by processing the external memory.
Graph Convolution: A High-Order and Adaptive Approach
In this paper, we presented a novel convolutional neural network framework for graph modeling, with the introduction of two new modules specially designed for graph-structured data: the $k$-th order convolution operator and the adaptive filtering module.
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts.
