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Michael W. Mahoney

Identifiers

  • name variant Michael W. Mahoney 0.60 · backfill

Papers (92)

  1. AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization cs.LG · 2026 · author #5
  2. Eigen-Spike Emergence and Quadratic Equivalents for Conjugate Kernels on Nonlinearly Separable Data stat.ML · 2026 · author #4
  3. Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization cs.LG · 2026 · author #7
  4. Speculative Interaction Agents: Building Real-Time Agents with Asynchronous I/O and Speculative Tool Calling cs.LG · 2026 · author #7
  5. Free Decompression with Algebraic Spectral Curves stat.ML · 2026 · author #4
  6. Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials cs.LG · 2026 · author #17
  7. Is Flow Matching Just Trajectory Replay for Sequential Data? stat.ML · 2026 · author #3
  8. Foundation Models for Discovery and Exploration in Chemical Space physics.chem-ph · 2025 · author #27
  9. SciML Agents: Write the Solver, Not the Solution cs.LG · 2025 · author #7
  10. Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models stat.ML · 2025 · author #2
  11. Recency Biased Causal Attention for Time-series Forecasting cs.LG · 2025 · author #2
  12. Neural equilibria for long-term prediction of nonlinear conservation laws cs.LG · 2025 · author #5
  13. Chronos: Learning the Language of Time Series cs.LG · 2024 · author #14
  14. Bayesian experimental design using regularized determinantal point processes cs.LG · 2019 · author #3
  15. Residual Networks as Nonlinear Systems: Stability Analysis using Linearization cs.LG · 2019 · author #4
  16. Distributed estimation of the inverse Hessian by determinantal averaging cs.LG · 2019 · author #2
  17. Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction physics.comp-ph · 2019 · author #3
  18. JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks cs.CR · 2019 · author #3
  19. Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression cs.LG · 2019 · author #3
  20. Traditional and Heavy-Tailed Self Regularization in Neural Network Models cs.LG · 2019 · author #2
  21. On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent cs.LG · 2018 · author #7
  22. A Short Introduction to Local Graph Clustering Methods and Software cs.SI · 2018 · author #3
  23. Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning cs.LG · 2018 · author #2
  24. Alchemist: An Apache Spark <=> MPI Interface cs.DC · 2018 · author #4
  25. Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist cs.DC · 2018 · author #4
  26. Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap stat.ML · 2018 · author #3
  27. GPU Accelerated Sub-Sampled Newton's Method cs.LG · 2018 · author #3
  28. Inexact Non-Convex Newton-Type Methods math.OC · 2018 · author #4
  29. Out-of-sample extension of graph adjacency spectral embedding stat.ML · 2018 · author #3
  30. Lectures on Randomized Numerical Linear Algebra cs.DS · 2017 · author #2
  31. Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization cs.DC · 2017 · author #4
  32. A Berkeley View of Systems Challenges for AI cs.AI · 2017 · author #5
  33. Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior cs.LG · 2017 · author #2
  34. LASAGNE: Locality And Structure Aware Graph Node Embedding cs.SI · 2017 · author #4
  35. GIANT: Globally Improved Approximate Newton Method for Distributed Optimization cs.LG · 2017 · author #4
  36. Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study math.OC · 2017 · author #3
  37. Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information math.OC · 2017 · author #3
  38. A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication stat.ML · 2017 · author #3
  39. Capacity Releasing Diffusion for Speed and Locality cs.DS · 2017 · author #4
  40. Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds cs.LG · 2017 · author #3
  41. Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction stat.ML · 2017 · author #9
  42. Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging stat.ML · 2017 · author #3
  43. Avoiding communication in primal and dual block coordinate descent methods cs.DC · 2016 · author #4
  44. Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data astro-ph.IM · 2016 · author #3
  45. Lecture Notes on Spectral Graph Methods cs.DS · 2016 · author #1
  46. Lecture Notes on Randomized Linear Algebra cs.DS · 2016 · author #1
  47. DCAR: A Discriminative and Compact Audio Representation to Improve Event Detection cs.SD · 2016 · author #6
  48. Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies cs.DC · 2016 · author #16
  49. Sub-sampled Newton Methods with Non-uniform Sampling math.OC · 2016 · author #5
  50. A Simple and Strongly-Local Flow-Based Method for Cut Improvement cs.SI · 2016 · author #3
  51. FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods math.OC · 2016 · author #5
  52. Parallel Local Graph Clustering cs.DC · 2016 · author #4
  53. Variational Perspective on Local Graph Clustering math.OC · 2016 · author #5
  54. Sub-Sampled Newton Methods II: Local Convergence Rates math.OC · 2016 · author #2
  55. Sub-Sampled Newton Methods I: Globally Convergent Algorithms math.OC · 2016 · author #2
  56. A Local Perspective on Community Structure in Multilayer Networks cs.SI · 2015 · author #2
  57. Optimal Subsampling Approaches for Large Sample Linear Regression stat.ME · 2015 · author #3
  58. Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction cs.DS · 2015 · author #3
  59. Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning math.OC · 2015 · author #4
  60. Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments cs.DC · 2015 · author #3
  61. Tree decompositions and social graphs cs.DS · 2014 · author #3
  62. Fast Randomized Kernel Methods With Statistical Guarantees stat.ML · 2014 · author #2
  63. Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks cs.SI · 2014 · author #5
  64. A Statistical Perspective on Algorithmic Leveraging stat.ME · 2013 · author #2
  65. Quantile Regression for Large-scale Applications cs.DS · 2013 · author #3
  66. Semi-supervised Eigenvectors for Large-scale Locally-biased Learning cs.LG · 2013 · author #2
  67. Revisiting the Nystrom Method for Improved Large-Scale Machine Learning cs.LG · 2013 · author #2
  68. Low-distortion Subspace Embeddings in Input-sparsity Time and Applications to Robust Linear Regression cs.DS · 2012 · author #2
  69. The Fast Cauchy Transform and Faster Robust Linear Regression cs.DS · 2012 · author #4
  70. Approximating Higher-Order Distances Using Random Projections cs.LG · 2012 · author #2
  71. Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis cs.DS · 2012 · author #1
  72. On the Hyperbolicity of Small-World and Tree-Like Random Graphs cs.SI · 2012 · author #4
  73. Randomized Dimensionality Reduction for k-means Clustering cs.DS · 2011 · author #3
  74. Regularized Laplacian Estimation and Fast Eigenvector Approximation cs.DS · 2011 · author #2
  75. LSRN: A Parallel Iterative Solver for Strongly Over- or Under-Determined Systems cs.DS · 2011 · author #3
  76. Fast approximation of matrix coherence and statistical leverage cs.DS · 2011 · author #3
  77. Localization on low-order eigenvectors of data matrices cs.DM · 2011 · author #2
  78. Randomized algorithms for matrices and data cs.DS · 2011 · author #1
  79. Computation in Large-Scale Scientific and Internet Data Applications is a Focus of MMDS 2010 cs.DS · 2010 · author #1
  80. CUR from a Sparse Optimization Viewpoint cs.DS · 2010 · author #3
  81. Algorithmic and Statistical Perspectives on Large-Scale Data Analysis cs.DS · 2010 · author #1
  82. Implementing regularization implicitly via approximate eigenvector computation cs.DS · 2010 · author #1
  83. Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving cs.NA · 2010 · author #2
  84. Empirical Comparison of Algorithms for Network Community Detection cs.DS · 2010 · author #3
  85. A Local Spectral Method for Graphs: with Applications to Improving Graph Partitions and Exploring Data Graphs Locally cs.DS · 2009 · author #1
  86. Learning with Spectral Kernels and Heavy-Tailed Data cs.LG · 2009 · author #1
  87. An Improved Approximation Algorithm for the Column Subset Selection Problem cs.DS · 2008 · author #2
  88. Algorithmic and Statistical Challenges in Modern Large-Scale Data Analysis are the Focus of MMDS 2008 cs.DS · 2008 · author #1
  89. Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters cs.DS · 2008 · author #4
  90. Faster Least Squares Approximation cs.DS · 2007 · author #2
  91. Relative-Error CUR Matrix Decompositions cs.DS · 2007 · author #2
  92. Sampling Algorithms and Coresets for Lp Regression cs.DS · 2007 · author #5

Mentions

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Frequent Coauthors