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Erdogdu, Murat A.

  1. High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
    2022/05/03 by Ba, Jimmy, Erdogdu, Murat A., Suzuki, Taiji +3 · 17 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  2. Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
    2022/09/29 by Alireza Mousavi-Hosseini, Sejun Park, Mousavi-Hosseini, Alireza +7 · 11 citations
    Computer Science · #Stochastic Gradient Optimization Techniques #Advanced Neural Network Applications #Machine Learning and ELM
  3. Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks
    2021/06/07 by Melih Barsbey, Milad Sefidgaran, Barsbey, Melih +7 · 8 citations
    Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stochastic Gradient Optimization Techniques
  4. An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
    2020/06/14 by Lu Yu, Yu, Lu, Krishnakumar Balasubramanian +5 · 7 citations
    Computer Science · Decision Sciences · Mathematics · #Stochastic Gradient Optimization Techniques #Advanced Bandit Algorithms Research #Markov Chains and Monte Carlo Methods
  5. Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT
    2019/04/03 by Andreas Anastasiou, Krishnakumar Balasubramanian, Anastasiou, Andreas +3 · 5 citations
    Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Probability (math.PR) #Random Matrices and Applications #Statistics Theory (math.ST) #Stochastic Gradient Optimization Techniques
  6. Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo
    2022/02/10 by Krishnakumar Balasubramanian, Sinho Chewi, Balasubramanian, Krishnakumar +7 · 6 citations
    Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #Statistics Theory (math.ST)
  7. On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness
    2020/05/27 by Murat A. Erdogdu, Erdogdu, Murat A., Rasa Hosseinzadeh +1 · 5 citations
    Mathematics · Physics and Astronomy · #Markov Chains and Monte Carlo Methods #Statistical Mechanics and Entropy #Stochastic processes and statistical mechanics
  8. Convergence of Langevin Monte Carlo in Chi-Squared and Renyi Divergence
    2020/07/22 by Murat A. Erdogdu, Erdogdu, Murat A., Rasa Hosseinzadeh +3 · 4 citations
    Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Point processes and geometric inequalities #Probability (math.PR)
  9. Convergence rates of sub-sampled Newton methods
    2015/08/12 by Erdogdu, Murat A., Montanari, Andrea · 3 citations
    #FOS: Computer and information sciences #Machine Learning (stat.ML)
  10. Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance
    2022/02/23 by N. Mert Vural, Vural, Nuri Mert, Yu Lu +7 · 5 citations
    Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  11. Manipulating SGD with Data Ordering Attacks
    2021/04/19 by Shumailov, Ilia, Shumaylov, Zakhar, Kazhdan, Dmitry +4 · 3 citations
    #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  12. Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality
    2023/03/07 by Mousavi-Hosseini, Alireza, Farghly, Tyler, He, Ye +2 · 4 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Probability (math.PR) #Statistics Theory (math.ST)
  13. Gradient-Based Feature Learning under Structured Data
    2023/09/07 by Alireza Mousavi-Hosseini, Mousavi-Hosseini, Alireza, Denny Wu +5 · 5 citations
    Computer Science · Environmental Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  14. Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance
    2021/02/20 by Hongjian Wang, Wang, Hongjian, Mert Gürbüzbalaban +7 · 3 citations
    Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Random Matrices and Applications #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  15. Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
    2019/06/19 by Li, Xuechen, Wu, Denny, Mackey, Lester +1 · 2 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  16. On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
    2020/11/06 by Ye He, Krishnakumar Balasubramanian, He, Ye +3 · 2 citations
    Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Point processes and geometric inequalities #Statistical Methods and Inference #Statistics Theory (math.ST)
  17. On Empirical Risk Minimization with Dependent and Heavy-Tailed Data
    2021/09/06 by Abhishek Roy, Roy, Abhishek, Krishnakumar Balasubramanian +3 · 2 citations
    Mathematics · Decision Sciences · #Statistical Methods and Inference #Markov Chains and Monte Carlo Methods #Risk and Portfolio Optimization
  18. Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling
    2023/03/01 by Ye He, He, Ye, Tyler Farghly +5 · 2 citations
    Engineering · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Geometric Analysis and Curvature Flows #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Numerical Analysis (math.NA) #Sparse and Compressive Sensing Techniques #Statistics Theory (math.ST)
  19. Flexible results for quadratic forms with applications to variance\n components estimation
    2015/09/14 by Lee H. Dicker, Murat A. Erdogdu, Dicker, Lee H. +1 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Mathematics #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)
  20. Robust Feature Learning for Multi-Index Models in High Dimensions
    2024/10/21 by Mousavi-Hosseini, Alireza, Javanmard, Adel, Erdogdu, Murat A. · 4 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  21. Inference in Graphical Models via Semidefinite Programming Hierarchies
    2017/09/19 by Murat A. Erdogdu, Erdogdu, Murat A., Yash Deshpande +3 · 1 citation
    Computer Science · #Bayesian Modeling and Causal Inference #Data Structures and Algorithms (cs.DS) #Error Correcting Code Techniques #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning and Algorithms
  22. Convergence Rate of Block-Coordinate Maximization Burer-Monteiro Method for Solving Large SDPs
    2018/07/12 by Erdogdu, Murat A., Ozdaglar, Asuman, Parrilo, Pablo A. +1 · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  23. Sampling from the Mean-Field Stationary Distribution
    2024/02/12 by Kook, Yunbum, Zhang, Matthew S., Chewi, Sinho +2 · 2 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  24. A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers
    2024/05/27 by He, Ye, Mousavi-Hosseini, Alireza, Balasubramanian, Krishnakumar +1 · 2 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  25. Riemannian Langevin Algorithm for Solving Semidefinite Programs
    2020/10/21 by Li, Mufan Bill, Erdogdu, Murat A. · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  26. Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics
    2024/08/14 by Alireza Mousavi-Hosseini, Denny Wu, Mousavi-Hosseini, Alireza +3 · 3 citations
    Computer Science · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
  27. Heavy-tailed Sampling via Transformed Unadjusted Langevin Algorithm
    2022/01/20 by He, Ye, Balasubramanian, Krishnakumar, Erdogdu, Murat A. · 1 citation
    #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  28. Improved Discretization Analysis for Underdamped Langevin Monte Carlo
    2023/02/16 by Zhang, Matthew, Chewi, Sinho, Li, Mufan Bill +2 · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  29. Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning
    2023/07/04 by Kastner, Tyler, Erdogdu, Murat A., Farahmand, Amir-massoud · 1 citation
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  30. When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective
    2025/03/14 by Alireza Mousavi-Hosseini, Clayton Sanford, Mousavi-Hosseini, Alireza +5 · 1 voice · 1 citation
    #stat.ML #cs.LG
  31. On the Efficiency of ERM in Feature Learning
    2024/11/18 by Hanchi, Ayoub El, Maddison, Chris J., Erdogdu, Murat A. · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  32. Minimax Linear Regression under the Quantile Risk
    2024/06/17 by Ayoub El Hanchi, Chris J. Maddison, Hanchi, Ayoub El +3 · 1 citation
    Mathematics · #Advanced Statistical Methods and Models #Statistical Methods and Inference