Papers
Computationally Efficient Methods for Invariant Feature Selection with Sparsity
Jane Du, Arindam Banerjee
Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees
Ally Yalei Du, Eric Huang, Dravyansh Sharma
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles
Mathias Drton, Marina Garrote-López, Niko Nikov et al.
Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation
Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz
Nearly Optimal Differentially Private ReLU Regression
Meng Ding, Mingxi Lei, Shaowei Wang et al.
Valid Bootstraps for Network Embeddings with Applications to Network Visualisation
Emerald Dilworth, Ed Davis, Daniel John Lawson
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
Francesco Diana, André Nusser, Chuan Xu et al.
Calibrated Regression Against An Adversary Without Regret
Shachi Deshpande, Charles Marx, Volodymyr Kuleshov
Optimal Submanifold Structure in Log-linear Models
Zhou Derun, Mahito Sugiyama
ELBO, regularized maximum likelihood, and their common one-sample approximation for training stochastic neural networks
Sina Däubener, Simon Damm, Asja Fischer
Online Learning with Stochastically Partitioning Experts
Puranjay Datta, Sharayu Moharir, Jaya Prakash Champati
The Relativity of Causal Knowledge
Gabriele D’Acunto, Claudio Battiloro
Measuring IIA Violations in Similarity Choices with Bayesian Models
Hugo Sales Correa, Suryanarayana Sankagiri, Daniel R. Figueiredo et al.
RL, but don’t do anything I wouldn’t do
Michael K. Cohen, Marcus Hutter, Yoshua Bengio et al.
Building Conformal Prediction Intervals with Approximate Message Passing
Lucas Clarté, Lenka Zdeborová
Optimal Transport for Probabilistic Circuits
Adrian Ciotinga, YooJung Choi
Well-Defined Function-Space Variational Inference in Bayesian Neural Networks via Regularized KL-Divergence
Tristan Cinquin, Robert Bamler
Selective Blocking for Message-Passing Neural Networks on Heterophilic Graphs
Yoonhyuk Choi, Taewook Ko, Jiho Choi et al.
Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization
Yuen Chen, Haozhe Si, Guojun Zhang et al.
NRFlow: Towards Noise-Robust Generative Modeling via High-Order Mechanism
Bo Chen, Chengyue Gong, Xiaoyu Li et al.
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG
Naitong Chen, Jonathan H. Huggins, Trevor Campbell
Adaptive Threshold Sampling for Pure Exploration in Submodular Bandits
Wenjing Chen, Shuo Xing, Victoria G. Crawford
Just Trial Once: Ongoing Causal Validation of Machine Learning Models
Jacob M. Chen, Michael Oberst
Improving Graph Contrastive Learning with Community Structure
Xiang Chen, Kun Yue, Liang Duan et al.
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees
Emma Ceccherini, Ian Gallagher, Andrew Jones et al.