Papers
Select and Optimize: Learning to solve large-scale TSP instances
Hanni Cheng, Haosi Zheng, Ya Cong et al.
Semantic Strengthening of Neuro-Symbolic Learning
Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck
Semi-Verified PAC Learning from the Crowd
Shiwei Zeng, Jie Shen
Sequential Gradient Descent and Quasi-Newton’s Method for Change-Point Analysis
Xianyang Zhang, Trisha Dawn
Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
Luca Masserano, Tommaso Dorigo, Rafael Izbicki et al.
Singular Value Representation: A New Graph Perspective On Neural Networks
Dan Meller, Nicolas Berkouk
SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals
Alexander K. Lew, George Matheos, Tan Zhi-Xuan et al.
Smoothly Giving up: Robustness for Simple Models
Tyler Sypherd, Nathaniel Stromberg, Richard Nock et al.
SoundSynp: Sound Source Detection from Raw Waveforms with Multi-Scale Synperiodic Filterbanks
Yuhang He, Andrew Markham
Sparse Bayesian optimization
Sulin Liu, Qing Feng, David Eriksson et al.
Sparse Spectral Bayesian Permanental Process with Generalized Kernel
Jeremy Sellier, Petros Dellaportas
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash et al.
Spectral Augmentations for Graph Contrastive Learning
Amur Ghose, Yingxue Zhang, Jianye Hao et al.
Spread Flows for Manifold Modelling
Mingtian Zhang, Yitong Sun, Chen Zhang et al.
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits
Wonyoung Kim, Myunghee Cho Paik, Min-Hwan Oh
Statistical Analysis of Karcher Means for Random Restricted PSD Matrices
Hengchao Chen, Xiang Li, Qiang Sun
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard et al.
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints
Yao Yao, Qihang Lin, Tianbao Yang
Stochastic Mirror Descent for Large-Scale Sparse Recovery
Sasila Ilandarideva, Yannis Bekri, Anatoli Iouditski et al.
Stochastic Optimization for Spectral Risk Measures
Ronak Mehta, Vincent Roulet, Krishna Pillutla et al.
Stochastic Tree Ensembles for Estimating Heterogeneous Effects
Nikolay Krantsevich, Jingyu He, P. Richard Hahn
Strong Lottery Ticket Hypothesis with $\varepsilon$–perturbation
Zheyang Xiong, Fangshuo Liao, Anastasios Kyrillidis
Structure of Nonlinear Node Embeddings in Stochastic Block Models
Christopher Harker, Aditya Bhaskara
Subset verification and search algorithms for causal DAGs
Davin Choo, Kirankumar Shiragur
Surveillance Evasion Through Bayesian Reinforcement Learning
Dongping Qi, David Bindel, Alexander Vladimirsky