Awesome Papers

Papers

A fast algorithm for maximal propensity score matching (2022)
Pavel S. Ruzankin
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Bandit-Based Monte Carlo Optimization for Nearest Neighbors (2021)
Vivek Bagaria et al.
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Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: a self-calibrated randomized solution (2026)
Changye Wu (CEREMADE) et al.
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Efficient Greedy Coordinate Descent for Composite Problems (2018)
Sai Praneeth Karimireddy et al.
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A Local Search Framework for Experimental Design (2020)
Lap Chi Lau and Hong Zhou
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A Data-Driven Line Search Rule for Support Recovery in High-dimensional Data Analysis (2021)
Peili Li et al.
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Scalable $k$-d trees for distributed data (2022)
Aritra Chakravorty et al.
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Experimental Design for Any $p$-Norm (2023)
Lap Chi Lau et al.
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Out-of-Sample Embedding with Proximity Data: Projection versus Restricted Reconstruction (2025)
Michael W. Trosset et al.
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An arithmetic method algorithm optimizing k-nearest neighbors compared to regression algorithms and evaluated on real world data sources (2026)
Theodoros Anagnostopoulos et al.
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Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces (2026)
Jie Hu et al.
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Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification (2026)
Julian Rodemann et al.
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SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate (2026)
Lifu Wei et al.
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Variance Reduction for Expectations with Diffusion Teachers (2026)
Jesse Bettencourt et al.
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Detecting Metastable Basins in High Dimensions via Marginal Trajectory Distribution Discrimination (2026)
Taj Jones-McCormick
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On the Epistemic Uncertainty of Overparametrized Neural Networks (2026)
David R\"ugamer
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SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning (2026)
Wenyuan Zhao et al.
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Wasserstein Contraction of Coordinate Ascent Variational Inference (2026)
Rocco Caprio et al.
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