Dean P. Foster

1. A Spectral Algorithm for Latent Dirichlet Allocation
   2012/04/30 by Animashree Anandkumar, Dean P. Foster, Anandkumar, Animashree +7 · 8 citations
   Computer Science · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Text and Document Classification Technologies #Topic Modeling
2. What are the Statistical Limits of Offline RL with Linear Function Approximation?
   2020/10/22 by Ruosong Wang, Wang, Ruosong, Dean P. Foster +3 · 12 citations
   Computer Science · Decision Sciences · #Reinforcement Learning in Robotics #Machine Learning and Algorithms #Advanced Bandit Algorithms Research
3. Variable Selection is Hard
   2014/12/15 by Dean P. Foster, Howard Karloff, Foster, Dean +3 · 6 citations
   Engineering · Computer Science · #Sparse and Compressive Sensing Techniques #Machine Learning and Algorithms #Complexity and Algorithms in Graphs
4. How Does Critical Batch Size Scale in Pre-training?
   2024/10/29 by Hanlin Zhang, Depen Morwani, Zhang, Hanlin +13 · 14 citations
   Psychology · #Human Resource Development and Performance Evaluation
5. Dynamic Local Regret for Non-convex Online Forecasting
   2019/10/16 by Sergül Aydöre, Tianhao Zhu, Aydore, Sergul +3 · 2 citations
   Computer Science · Decision Sciences · Environmental Science · #Advanced Bandit Algorithms Research #Air Quality Monitoring and Forecasting #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
6. Variance Reduced Training with Stratified Sampling for Forecasting Models
   2021/03/02 by Yucheng Lu, Youngsuk Park, Lu, Yucheng +9 · 2 citations
   Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stock Market Forecasting Methods
7. A Study on the Calibration of In-context Learning
   2023/12/07 by Hanlin Zhang, Yifan Zhang, Zhang, Hanlin +13 · 3 citations
   Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Education and Learning Interventions #FOS: Computer and information sciences #Machine Learning (cs.LG)
8. Kernel ridge vs. principal component regression: minimax bounds and adaptability of regularization operators
   2016/05/28 by Lee H. Dicker, Dean P. Foster, Dicker, Lee H. +3 · 1 citation
   Engineering · Mathematics · #FOS: Mathematics #Numerical methods in inverse problems #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #Statistics Theory (math.ST)
9. The Benefits of Implicit Regularization from SGD in Least Squares Problems
   2021/08/10 by Difan Zou, Jingfeng Wu, Zou, Difan +9 · 1 citation
   Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques #Machine Learning and Algorithms
10. Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data
    2021/12/14 by Nilesh Tripuraneni, Dhruv Madeka, Tripuraneni, Nilesh +7 · 1 citation
    Mathematics · #Advanced Causal Inference Techniques #Statistical Methods in Clinical Trials #Statistical Methods and Inference