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AI Blogs
Reinforcement Learning for Joint Optimization of Multiple Rewards Mridul Agarwal, Vaneet Aggarwal; 24(49):1−41, 2023. Abstract To find optimal policies that maximize the long-term rewards of Markov Decision Processes, dynamic programming and backward induction are typically used to solve the Bellman…
Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process Cheng Zeng, Jeffrey W Miller, Leo L Duan; 24(153):1−32, 2023. Abstract In applications of mixture modeling and clustering, it is often unknown how many components and clusters exist. One approach is to…
Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks Lingjun Li, Jun Li; 24(51):1−44, 2023. Abstract This paper presents a novel approach for online change-point detection in high-dimensional data, specifically focusing on the covariance structure. The proposed…
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd; 24(154):1−48, 2023. Abstract Analyzing the influence of changes to the training data on model predictions can provide valuable insights into the predictions, the models themselves,…
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I Jordan, Ion Stoica; 24(53):1−45, 2023. Abstract This study focuses on a multi-round mechanism design problem that aims to maximize welfare in situations…
The aim of this paper is to enhance oil production from gas-lifted oil wells by solving Mixed-Integer Linear Programs (MILPs). These programs need to be repeatedly solved as the parameters of the wells change. Instead of using expensive exact methods…
Adaptive Data Depth via Multi-Armed Bandits Tavor Baharav, Tze Leung Lai; 24(155):1−29, 2023. Abstract Data depth is an important tool in data science, robust statistics, and computational geometry. However, many common measures of depth are computationally intensive, making them less…
The rapid growth of large language models (LLMs) has highlighted the importance of discrete speech tokenization in injecting speech into these models. However, this process of discretization results in a loss of information, leading to a decrease in overall performance.…
Minimax Rates and Randomized Sketches for Kernel-based Estimation in Partially Functional Linear Models Authors: Shaogao Lv, Xin He, Junhui Wang; Journal of Machine Learning Research, 24(55):1−38, 2023. Abstract This study focuses on the partially functional linear model (PFLM), where predictive…
We present a unique approach to defining assistance systems that utilize information fusion to combine various sources of information and provide an assessment. The main contribution of this study is the development of a comprehensive framework for fusing multiple information…
