Stat.ML Papers
@StatMLPapers
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Unofficial updates of statistical machine learning papers on arXiv
Joined June 2012
BayesSum: Bayesian Quadrature in Discrete Spaces
arxiv.org
This paper addresses the challenging computational problem of estimating intractable expectations over discrete domains. Existing approaches, including Monte Carlo and Russian Roulette estimators,...
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DAG Learning from Zero-Inflated Count Data Using Continuous Optimization
arxiv.org
We address network structure learning from zero-inflated count data by casting each node as a zero-inflated generalized linear model and optimizing a smooth, score-based objective under a directed...
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Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learning
arxiv.org
Generalizing causal knowledge across diverse environments is challenging, especially when estimates from large-scale datasets must be applied to smaller or systematically different contexts, where...
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Riemannian Stochastic Interpolants for Amorphous Particle Systems
arxiv.org
Modern generative models hold great promise for accelerating diverse tasks involving the simulation of physical systems, but they must be adapted to the specific constraints of each domain....
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On The Hidden Biases of Flow Matching Samplers
arxiv.org
We study the implicit bias of flow matching (FM) samplers via the lens of empirical flow matching. Although population FM may produce gradient-field velocities resembling optimal transport (OT),...
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Decision-Focused Bias Correction for Fluid Approximation
arxiv.org
Fluid approximation is a widely used approach for solving two-stage stochastic optimization problems, with broad applications in service system design such as call centers and healthcare...
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Data Valuation for LLM Fine-Tuning: Efficient Shapley Value Approximation via Language Model Arithmetic
arxiv.org
Data is a critical asset for training large language models (LLMs), alongside compute resources and skilled workers. While some training data is publicly available, substantial investment is...
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TENG++: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets under General Boundary Conditions
arxiv.org
Partial Differential Equations (PDEs) are central to modeling complex systems across physical, biological, and engineering domains, yet traditional numerical methods often struggle with...
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Consensus dimension reduction via multi-view learning
arxiv.org
A plethora of dimension reduction methods have been developed to visualize high-dimensional data in low dimensions. However, different dimension reduction methods often output different and...
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xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R
arxiv.org
The double machine learning (DML) method combines the predictive power of machine learning with statistical estimation to conduct inference about the structural parameter of interest. This paper...
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Provably Extracting the Features from a General Superposition
arxiv.org
It is widely believed that complex machine learning models generally encode features through linear representations, but these features exist in superposition, making them challenging to recover....
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CauSTream: Causal Spatio-Temporal Representation Learning for Streamflow Forecasting
arxiv.org
Streamflow forecasting is crucial for water resource management and risk mitigation. While deep learning models have achieved strong predictive performance, they often overlook underlying physical...
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Multivariate Uncertainty Quantification with Tomographic Quantile Forests
arxiv.org
Quantifying predictive uncertainty is essential for safe and trustworthy real-world AI deployment. Yet, fully nonparametric estimation of conditional distributions remains challenging for...
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Efficient and scalable clustering of survival curves
arxiv.org
Survival analysis encompasses a broad range of methods for analyzing time-to-event data, with one key objective being the comparison of survival curves across groups. Traditional approaches for...
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Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game
arxiv.org
We introduce Stackelberg Learning from Human Feedback (SLHF), a new framework for preference optimization. SLHF frames the alignment problem as a sequential-move game between two policies: a...
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On the Universal Representation Property of Spiking Neural Networks
arxiv.org
Inspired by biology, spiking neural networks (SNNs) process information via discrete spikes over time, offering an energy-efficient alternative to the classical computing paradigm and classical...
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Learning Confidence Ellipsoids and Applications to Robust Subspace Recovery
arxiv.org
We study the problem of finding confidence ellipsoids for an arbitrary distribution in high dimensions. Given samples from a distribution $D$ and a confidence parameter $α$, the goal is to...
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Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods
arxiv.org
Discrete diffusion models have recently gained significant attention due to their ability to process complex discrete structures for language modeling. However, fine-tuning these models with...
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