Papers
Signature Isolation Forest
Marta Campi, Guillaume Staerman, Gareth W. Peters et al.
Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings
Nikolaos Nakis, Chrysoula Kosma, Giannis Nikolentzos et al.
SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings
Shivvrat Arya, Tahrima Rahman, Vibhav Giridhar Gogate
SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph
Mátyás Schubert, Tom Claassen, Sara Magliacane
Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage
Achraf Azize, Debabrota Basu
Sparse Activations as Conformal Predictors
Margarida M Campos, João Cálem, Sophia Sklaviadis et al.
Sparse Causal Effect Estimation using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding
Shimeng Huang, Niklas Pfister, Jack Bowden
Spectral Differential Network Analysis for High-Dimensional Time Series
Michael Hellstern, Byol Kim, Zaid Harchaoui et al.
Spectral Representation for Causal Estimation with Hidden Confounders
Haotian Sun, Antoine Moulin, Tongzheng Ren et al.
StableMDS: A Novel Gradient Descent-Based Method for Stabilizing and Accelerating Weighted Multidimensional Scaling
Zhongxi Fang, Xun Su, Tomohisa Tabuchi et al.
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory
Zhi Zhang, Chris Chow, Yasi Zhang et al.
Statistical Guarantees for Unpaired Image-to-Image Cross-Domain Analysis using GANs
Saptarshi Chakraborty, Peter Bartlett
Statistical Inference for Feature Selection after Optimal Transport-based Domain Adaptation
Nguyen Thang Loi, Duong Tan Loc, Vo Nguyen Le Duy
Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces
Shengbo Wang, Nian Si, Jose Blanchet et al.
Statistical Test for Auto Feature Engineering by Selective Inference
Tatsuya Matsukawa, Tomohiro Shiraishi, Shuichi Nishino et al.
Steering No-Regret Agents in MFGs under Model Uncertainty
Leo Widmer, Jiawei Huang, Niao He
Stein Boltzmann Sampling: A Variational Approach for Global Optimization
Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis
SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity
Peihao Wang, Zhiwen Fan, Dejia Xu et al.
Steinmetz Neural Networks for Complex-Valued Data
Shyam Venkatasubramanian, Ali Pezeshki, Vahid Tarokh
Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
Shaan Ul Haque, Siva Theja Maguluri
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
Yuyang Deng, Fuli Qiao, Mehrdad Mahdavi
Stochastic Gradient Descent for Bézier Simplex Representation of Pareto Set in Multi-Objective Optimization
Yasunari Hikima, Ken Kobayashi, Akinori Tanaka et al.
Stochastic Rounding for LLM Training: Theory and Practice
Kaan Ozkara, Tao Yu, Youngsuk Park
Stochastic Weight Sharing for Bayesian Neural Networks
Moule Lin, Shuhao Guan, Weipeng Jing et al.