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Stanford University
Title: Data science and policy: Addressing inequity in health |
MIT
Title: Targeting climate science and data science at policies |
Harvard University
Title: Identifying Prediction Mistakes in Observational Data |
Columbia University
Title: Bridging the Gap Between Deep Learning and Probabilistic Modeling |
University of Oxford
Title: What can statisticians learn from the analysis of C.elegans data? |
University of Washington
Title: Spectral Independence: A New Tool to Analyze Markov Chains |
Stanford University
Title: Learning to Generate Data by Estimating Gradients of the Data Distribution |
Harvard University
Title: Incentive-Aware Machine Learning for Decision Making |
Stanford University
Title: What Can Conformal Inference Offer To Statistics? |
Stanford University
Title: Algorithmic Thresholds in Mean-Field Spin Glasses |
Stanford University
Title: Reliable machine learning in the wild |
Berkeley
Title: Understanding Statistical-vs-Computational Tradeoffs via Low-Degree Polynomials |
Princeton University
Title: Theoretical Foundations of Pre-trained Models |
Princeton University
Title: Latent Confounding Adjustment with Text: Opportunities and Limitations |
Princeton University
Title: “Partisans, Racialists, and Neutrals: Investigating the Interdependence of Attitudes towards Social Groups” |
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Cornell University
Title: Efficient Rich-observation Reinforcement Learning: A Representation Learning Approach |
Cornell University
Title: Sampling from the SK measure via algorithmic stochastic localization |
NYU
Title: Privacy of Noisy SGD |
UCLA
Title: Spectral and post-spectral estimators for grouped panel data models |
University of Pennsylvania
Title: Matching and integration of datasets with low-rank signals and applications in single-cell data analysis |
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Princeton University
Title: Thompson Sampling-Guided Directed Evolution for Sequence Optimization |
University of Wisconsin-Madison
Title: Markovian Linear Stochastic Approximation: Bias and Extrapolation |
Princeton University
Title: Machine learning for determining protein structure and dynamics from cryo-EM images |
ETH Zürich
Sharp Matrix Concentration |
Columbia University
Title: Confidence Intervals for Nonparametric Empirical Bayes Analysis |
Stanford University
Title: Testing thresholds in high-dimensional random geometric graphs |
University of Chicago
Title: Testing the stability of a black-box algorithm |
New York University
Title: On Symmetries and Feature Learning in Simple Neural Networks |
University of Pennsylvania
Title: Efficient derivative-free Bayesian inference for large-scale inverse problems |