Mathematics & Optimization

Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data JMLR 2023 Dimensionless machine learning: Imposing exact units equivariance JMLR 2023 Directed Acyclic Graph Structure Learning from Dynamic Graphs AAAI 2023 Directed Graphical Models and Causal Discovery for Zero-Inflated Data CLeaR 2023 Disaggregating Stateful Network Functions NSDI 2023 Discovering Many Diverse Solutions with Bayesian Optimization AISTATS 2023 Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models ICML 2023 Discrete Distribution Estimation under User-level Local Differential Privacy AISTATS 2023 Discrete Langevin Samplers via Wasserstein Gradient Flow AISTATS 2023 Discrete-Smoothness in Online Algorithms with Predictions NeurIPS 2023 Discrete Variational Calculus for Accelerated Optimization JMLR 2023 DISCS: A Benchmark for Discrete Sampling NeurIPS 2023 Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning ICML 2023 Distributed Algorithms for U-statistics-based Empirical Risk Minimization JMLR 2023 Distributed Bundle Adjustment with Block-Based Sparse Matrix Compression for Super Large Scale Datasets ICCV 2023 Distributed Community Detection in Large Networks JMLR 2023 Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost ICML 2023 Distributed Linear Bandits under Communication Constraints ICML 2023 Distributed Projection-Free Online Learning for Smooth and Convex Losses AAAI 2023 Distributed Sparse Regression via Penalization JMLR 2023 Distributed Stochastic Nested Optimization for Emerging Machine Learning Models: Algorithm and Theory AAAI 2023 Distributionally Robust Bayesian Optimization with φ-divergences NeurIPS 2023 Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction ICML 2023 Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint IJCAI 2023 Diversity Maximization in the Presence of Outliers AAAI 2023