no code implementations • 24 Apr 2024 • Linyu Liu, Yu Pan, Xiaocheng Li, Guanting Chen
Large language models (LLMs) are highly capable of many tasks but they can sometimes generate unreliable or inaccurate outputs.
1 code implementation • 19 Mar 2024 • Zhongze Cai, Shang Liu, Hanzhao Wang, Huaiyang Zhong, Xiaocheng Li
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).
no code implementations • 12 Oct 2023 • Hanzhao Wang, Xiaocheng Li, Kalyan Talluri
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.
no code implementations • 1 Oct 2023 • Guanting Chen, Xiaocheng Li, Chunlin Sun, Hanzhao Wang
As artificial intelligence (AI) systems play an increasingly prominent role in human decision-making, challenges surface in the realm of human-AI interactions.
no code implementations • 10 Aug 2023 • Hanzhao Wang, Zhongze Cai, Xiaocheng Li, Kalyan Talluri
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.
1 code implementation • 6 Jul 2023 • Xiaocheng Li, Shang Liu, Chunlin Sun, Hanzhao Wang
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.
1 code implementation • 6 Jul 2023 • Shang Liu, Xiaocheng Li
Uncertainty sampling is a prevalent active learning algorithm that queries sequentially the annotations of data samples which the current prediction model is uncertain about.
1 code implementation • 26 Jan 2023 • Chunlin Sun, Shang Liu, Xiaocheng Li
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.
no code implementations • 19 Aug 2022 • Zhongze Cai, Hanzhao Wang, Kalyan Talluri, Xiaocheng Li
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.
no code implementations • 15 Aug 2022 • Moqi Zhang, Weihui Deng, Xiaocheng Li
With the development of streaming media technology, increasing communication relies on sound and visual information, which puts a massive burden on online media.
no code implementations • 23 Jul 2022 • Hanzhao Wang, Xiaocheng Li, Kalyan Talluri
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.
no code implementations • 25 May 2022 • Shang Liu, Jiashuo Jiang, Xiaocheng Li
Finally, we also extend the non-stationarity measure to the problem of online convex optimization with constraints and obtain new regret bounds accordingly.
no code implementations • 25 Dec 2021 • Hanzhao Wang, Kalyan Talluri, Xiaocheng Li
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 $x_t$.
no code implementations • 27 Oct 2021 • Guanting Chen, Xiaocheng Li, Yinyu Ye
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.
no code implementations • 12 Feb 2021 • Xiaocheng Li, Chunlin Sun, Yinyu Ye
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.
no code implementations • 13 Dec 2020 • Jiashuo Jiang, Xiaocheng Li, Jiawei Zhang
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).
1 code implementation • NeurIPS 2020 • Xiaocheng Li, Chunlin Sun, Yinyu Ye
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
no code implementations • 12 Sep 2019 • Xiaocheng Li, Yinyu Ye
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.
no code implementations • 15 Nov 2017 • Xiaocheng Li, Huaiyang Zhong, Margaret L. Brandeau
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).
no code implementations • 15 Nov 2017 • Huaiyang Zhong, Xiaocheng Li, David Lobell, Stefano Ermon, Margaret L. Brandeau
Eradicating hunger and malnutrition is a key development goal of the 21st century.
no code implementations • 7 Nov 2017 • Kuan Fang, Yu Xiang, Xiaocheng Li, Silvio Savarese
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.
no code implementations • 29 Jun 2017 • Zhenpeng Zhou, Xiaocheng Li
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.
1 code implementation • AAAI 2017 2017 • Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon
Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts.