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Rachel Ward

  1. Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
    2024/04/22 by Marah Abdin, Jyoti Aneja, Abdin, Marah +262 · 8 voices · 349 citations
    Computer Science · #Computational Physics and Python Applications
  2. First Proof
    2026/02/05 by Mohammed Abouzaid, Andrew J. Blumberg, Martin Hairer +8 · 23 voices · 3 citations
    #cs.AI #math.AG #math.CO #math.GT #math.HO #math.RA
  3. Phi-4 Technical Report
    2024/12/12 by Marah Abdin, Abdin, Marah, Jyoti Aneja +51 · 4 voices · 194 citations
    Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #cs.AI #cs.CL
  4. Stochastic Gradient Descent, Weighted Sampling, and the Randomized\n Kaczmarz algorithm
    2013/10/21 by Deanna Needell, Nathan Srebro, Needell, Deanna +3 · 15 citations
    Computer Science · Engineering · Mathematics · #52A99 #60G99 #62L20 #65B99 #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Point processes and geometric inequalities #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  5. TinyGSM: achieving >80% on GSM8k with small language models
    2023/12/14 by Bingbin Liu, Sebastien Bubeck, Liu, Bingbin +14 · 1 voice · 6 citations
    Computer Science · Social Sciences · #Computation and Language (cs.CL) #Educational Assessment and Pedagogy #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Text Readability and Simplification #cs.CL #cs.LG
  6. The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance
    2022/02/11 by Matthew Faw, Isidoros Tziotis, Faw, Matthew +9 · 7 citations
    Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques
  7. Matrix Concentration for Products
    2020/03/11 by De Huang, Jonathan Niles‐Weed, Huang, De +5 · 8 citations
    Mathematics · #FOS: Mathematics #Limits and Structures in Graph Theory #Probability (math.PR) #Random Matrices and Applications #Stochastic processes and statistical mechanics
  8. Completing Any Low-rank Matrix, Provably
    2013/06/12 by Yudong Chen, Chen, Yudong, Srinadh Bhojanapalli +5 · 3 citations
    Computer Science · Engineering · #Blind Source Separation Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Microwave Imaging and Scattering Analysis #Sparse and Compressive Sensing Techniques
  9. Implicit Regularization and Convergence for Weight Normalization
    2019/11/18 by Xiaoxia Wu, Edgar Dobriban, Wu, Xiaoxia +13 · 4 citations
    Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Mathematics #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  10. Low-rank matrix recovery via iteratively reweighted least squares\n minimization
    2010/10/12 by Massimo Fornasier, Fornasier, Massimo, Holger Rauhut +3 · 2 citations
    Computer Science · Engineering · Medicine · #49M30 #52A41 #65J22 #65K10 #Advanced MRI Techniques and Applications #Blind Source Separation Techniques #Electrical and Bioimpedance Tomography #FOS: Mathematics #Numerical Analysis (math.NA) #Sparse and Compressive Sensing Techniques
  11. Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network
    2019/02/19 by Xiaoxia Wu, Simon S. Du, Wu, Xiaoxia +3 · 5 citations
    Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  12. One-bit compressive sensing with norm estimation
    2014/04/28 by Karin Knudson, Rayan Saab, Knudson, Karin +3 · 2 citations
    Computer Science · Engineering · #90C05 #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Microwave Imaging and Scattering Analysis #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Probability (math.PR) #Sparse and Compressive Sensing Techniques
  13. AdaOja: Adaptive Learning Rates for Streaming PCA
    2019/05/28 by Amelia Henriksen, Henriksen, Amelia, Rachel Ward +1 · 2 citations
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Computation (stat.CO) #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stochastic Gradient Optimization Techniques
  14. Generating synthetic data for neural operators
    2024/01/04 by Erisa Hasani, Rachel Ward, Hasani, Erisa +1 · 3 citations
    Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Numerical Analysis (math.NA)
  15. Concentration Inequalities for Sums of Markov Dependent Random Matrices
    2023/03/03 by Joe Neeman, Bobby Shi, Neeman, Joe +3 · 2 citations
    Mathematics · #FOS: Mathematics #Markov Chains and Monte Carlo Methods #Probability (math.PR) #Random Matrices and Applications #Spectral Theory in Mathematical Physics
  16. Generalization Bounds for Sparse Random Feature Expansions
    2021/03/04 by Abolfazl Hashemi, Hayden Schaeffer, Hashemi, Abolfazl +9 · 2 citations
    Computer Science · Engineering · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Probability (math.PR) #Sparse and Compressive Sensing Techniques
  17. Johnson-Lindenstrauss Embeddings with Kronecker Structure
    2021/06/24 by Stefan Bamberger, Bamberger, Stefan, Felix Krahmer +3 · 1 citation
    Computer Science · Engineering · Mathematics · #15A69 #68Q87 #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #Tensor decomposition and applications
  18. Concentration of Random Feature Matrices in High-Dimensions
    2022/04/14 by Zhijun Chen, Hayden Schaeffer, Chen, Zhijun +3 · 1 citation
    Computer Science · #FOS: Computer and information sciences #FOS: Mathematics #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA) #Probability (math.PR) #Topological and Geometric Data Analysis
  19. A polynomial-time relaxation of the Gromov-Hausdorff distance
    2016/10/17 by Soledad Villar, Villar, Soledad, Afonso S. Bandeira +5 · 1 citation
    Computer Science · Mathematics · #Computational Geometry (cs.CG) #Digital Image Processing Techniques #FOS: Computer and information sciences #FOS: Mathematics #Geometric Topology (math.GT) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Point processes and geometric inequalities #Topological and Geometric Data Analysis
  20. Sparse Legendre expansions via ℓ1 minimization
    2010/03/01 by Holger Rauhut, Rauhut, Holger, Rachel Ward +1 · 1 citation
    Decision Sciences · Engineering · Mathematics · #15A12 #15B52 #41A10 #42A61 #42C05 #60B20 #65F35 #94A12 #94A20 #Advanced Optimization Algorithms Research #Classical Analysis and ODEs (math.CA) #FOS: Mathematics #Functional Analysis (math.FA) #Numerical Analysis (math.NA) #Probabilistic and Robust Engineering Design #Probability (math.PR) #Sparse and Compressive Sensing Techniques