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Tamara Broderick

  1. Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
    2022/06/08 by Brian L. Trippe, Trippe, Brian L., Jason Yim +11 · 29 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomolecules (q-bio.BM) #Cancer-related gene regulation #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Protein Structure and Dynamics #Software Engineering Research
  2. Covariances, Robustness, and Variational Bayes
    2017/09/08 by Ryan Giordano, Tamara Broderick, Giordano, Ryan +3 · 8 citations
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Statistical Methods and Bayesian Inference
  3. An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?
    2020/11/30 by Tamara Broderick, Broderick, Tamara, Ryan Giordano +3 · 9 citations
    Decision Sciences · Economics, Econometrics and Finance · #Climate Change Policy and Economics #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Forecasting Techniques and Applications #Market Dynamics and Volatility #Methodology (stat.ME) #Monetary Policy and Economic Impact #Risk and Portfolio Optimization
  4. Streaming Variational Bayes
    2013/07/25 by Tamara Broderick, Nicholas Boyd, Broderick, Tamara +7 · 6 citations
    Computer Science · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms
  5. Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
    2018/02/05 by Trevor Campbell, Campbell, Trevor, Tamara Broderick +1 · 4 citations
    Computer Science · Engineering · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference
  6. Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
    2018/09/25 by Jonathan H. Huggins, Trevor Campbell, Huggins, Jonathan H. +5 · 4 citations
    Computer Science · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Statistical Methods and Inference #Statistics Theory (math.ST)
  7. Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
    2018/03/15 by Raj Agrawal, Tamara Broderick, Agrawal, Raj +3 · 2 citations
    Computer Science · Decision Sciences · #Bayesian Modeling and Causal Inference #Computation (stat.CO) #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME)
  8. How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences
    2022/09/29 by Mikołaj J. Kasprzak, Ryan Giordano, Kasprzak, Mikołaj J. +3 · 3 citations
    Computer Science · Mathematics · #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Probability (math.PR) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)
  9. Local Exchangeability
    2019/06/22 by Trevor Campbell, Campbell, Trevor, Saifuddin Syed +7 · 2 citations
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Statistical Methods and Inference #Statistical Methods and Bayesian Inference
  10. Can we globally optimize cross-validation loss? Quasiconvexity in ridge\n regression
    2021/07/19 by William Stephenson, Zachary Frangella, Stephenson, William T. +5 · 2 citations
    Engineering · Mathematics · Computer Science · #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #Face and Expression Recognition
  11. Multi-marginal Schrödinger Bridges with Iterative Reference Refinement
    2024/08/12 by Yunyi Shen, Shen, Yunyi, Renato Berlinghieri +3 · 4 citations
    Engineering · Mathematics · Physics and Astronomy · #Electromagnetic Scattering and Analysis #Electromagnetic Simulation and Numerical Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Numerical methods in inverse problems
  12. Posteriors, conjugacy, and exponential families for completely random\n measures
    2014/10/24 by Tamara Broderick, Ashia Wilson, Broderick, Tamara +3 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)
  13. A translation of "The characteristic function of a random phenomenon" by Bruno de Finetti
    2015/12/03 by David Alvarez-Melis, Alvarez-Melis, David, Tamara Broderick +1 · 1 citation
    Arts and Humanities · Economics, Econometrics and Finance · Mathematics · #60E10 #Complex Systems and Time Series Analysis #FOS: Mathematics #Mathematical Dynamics and Fractals #Philosophy and History of Science #Statistics Theory (math.ST)
  14. Edge-exchangeable graphs and sparsity
    2016/03/22 by Tamara Broderick, Broderick, Tamara, Diana Cai +1 · 1 citation
    Computer Science · Mathematics · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Mathematics #Limits and Structures in Graph Theory #Machine Learning (stat.ML) #Methodology (stat.ME) #Statistics Theory (math.ST) #Topological and Geometric Data Analysis
  15. PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
    2017/09/26 by Jonathan H. Huggins, Ryan P. Adams, Huggins, Jonathan H. +3 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Statistical Methods and Inference
  16. The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise\n Interactions in High Dimensions
    2019/05/15 by Raj Agrawal, Agrawal, Raj, Jonathan H. Huggins +5 · 1 citation
    Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Metabolomics and Mass Spectrometry Studies #Methodology (stat.ME)
  17. The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear\n Interaction Discovery in Linear Time
    2021/06/23 by Raj Agrawal, Tamara Broderick, Agrawal, Raj +1 · 1 citation
    Computer Science · Biochemistry, Genetics and Molecular Biology · Materials Science · #Computational Drug Discovery Methods #Metabolomics and Mass Spectrometry Studies #Machine Learning in Materials Science
  18. For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
    2021/07/13 by Brian L. Trippe, Trippe, Brian L., Hilary K. Finucane +3 · 1 citation
    Mathematics · Computer Science · #Statistical Methods and Inference #Statistical Methods and Bayesian Inference #Bayesian Methods and Mixture Models
  19. Linear Response Methods for Accurate Covariance Estimates from Mean\n Field Variational Bayes
    2015/06/12 by Ryan Giordano, Tamara Broderick, Giordano, Ryan +3 · 2 citations
    Computer Science · Decision Sciences · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Probabilistic and Robust Engineering Design #Statistical Methods and Bayesian Inference
  20. Do LLMs Benefit From Their Own Words?
    2026/02/27 by Jenny Y. Huang, Leshem Choshen, Ramon Astudillo +2 · 1 voice · 3 citations
    #cs.CL #cs.AI
  21. Developing a Series of AI Challenges for the United States Department of the Air Force
    2022/07/14 by Vijay Gadepally, Gregory Angelides, Gadepally, Vijay +81 · 1 citation
    Business, Management and Accounting · Decision Sciences · #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #Computers and Society (cs.CY) #Data Quality and Management #FOS: Computer and information sciences #Scientific Computing and Data Management
  22. The Bayesian Infinitesimal Jackknife for Variance
    2023/05/10 by Ryan Giordano, Tamara Broderick, Giordano, Ryan +1 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)
  23. Sensitivity of MCMC-based analyses to small-data removal
    2024/08/14 by Tin D. Nguyen, Ryan Giordano, Nguyen, Tin D. +5 · 1 citation
    Computer Science · Engineering · #Computation (stat.CO) #FOS: Computer and information sciences #Ion-surface interactions and analysis #Machine Learning and Algorithms #Methodology (stat.ME)
  24. Wild posteriors in the wild
    2025/02/28 by Yunyi Shen, Shen, Yunyi, Tamara Broderick +1 · 1 voice · 1 citation
    #stat.CO #stat.ME
  25. Data-dependent compression of random features for large-scale kernel\n approximation
    2018/10/09 by Raj Agrawal, Trevor Campbell, Agrawal, Raj +5 · 1 citation
    Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Stochastic Gradient Optimization Techniques
  26. Could dropping a few cells change the takeaways from differential expression?
    2023/12/11 by Miriam Shiffman, Shiffman, Miriam, Ryan Giordano +3 · 1 citation
    Biochemistry, Genetics and Molecular Biology · #Computation (stat.CO) #FOS: Biological sciences #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Gene expression and cancer classification #Methodology (stat.ME) #Quantitative Methods (q-bio.QM) #Single-cell and spatial transcriptomics