Stat.ML Papers @StatMLPapers
Unofficial updates of statistical machine learning papers on arXiv arxiv.org/list/stat.ML/r… Joined June 2012-
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Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling ift.tt/vmJxzEA
Testing for correlation between network structure and high-dimensional node covariates ift.tt/4NbKowU
Diffusion Generative Models Meet Compressed Sensing, with Applications to Image Data and Financial Time Series ift.tt/7K6O9U5
An invertible generative model for forward and inverse problems ift.tt/bDZ8tCz
Batched Stochastic Matching Bandits ift.tt/3ZoCbFO
Connections between reinforcement learning with feedback,test-time scaling, and diffusion guidance: An anthology ift.tt/bgaynTv
SharedRep-RLHF: A Shared Representation Approach to RLHF with Diverse Preferences ift.tt/6M3OoKt
The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs ift.tt/qlNtiFk
Sparse Autoencoder Neural Operators: Model Recovery in Function Spaces ift.tt/5JmDEA7
RAGuard: A Novel Approach for in-context Safe Retrieval Augmented Generation for LLMs ift.tt/MeR1c6D
Simulation-based Inference via Langevin Dynamics with Score Matching ift.tt/wxkrg4a
Prob-GParareal: A Probabilistic Numerical Parallel-in-Time Solver for Differential Equations ift.tt/QbzfWFs
Towards understanding Accelerated Stein Variational Gradient Flow -- Analysis of Generalized Bilinear Kernels for Gaussian target distributions ift.tt/aP9QX4e
Sharp Convergence Rates of Empirical Unbalanced Optimal Transport for Spatio-Temporal Point Processes ift.tt/xZ40Kwd
A Primer on Causal and Statistical Dataset Biases for Fair and Robust Image Analysis ift.tt/2hMVruE
We Have It Covered: A Resampling-based Method for Uplift Model Comparison ift.tt/GX6gc4E
Parking Availability Prediction via Fusing Multi-Source Data with A Self-Supervised Learning Enhanced Spatio-Temporal Inverted Transformer ift.tt/7nqGDb4
Convergence of Unadjusted Langevin in High Dimensions: Delocalization of Bias ift.tt/Nbcy5mA
The Strong, Weak and Benign Goodhart's law. An independence-free and paradigm-agnostic formalisation ift.tt/dGM4QpJ
Asymptotic convexity of wide and shallow neural networks ift.tt/n3LxET9
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