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Optimization

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Papers

Incremental Aggregated Riemannian Gradient Method for Distributed PCA AISTATS 2023
PAC-Bayesian Learning of Optimization Algorithms AISTATS 2023
Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems AISTATS 2023
Reducing Discretization Error in the Frank-Wolfe Method AISTATS 2023
HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent AISTATS 2023
Efficient Second-Order Plane Adjustment CVPR 2023
Neural Dependencies Emerging From Learning Massive Categories CVPR 2023
SLACK: Stable Learning of Augmentations With Cold-Start and KL Regularization CVPR 2023
Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution CVPR 2023
Transformer-Based Learned Optimization CVPR 2023
Lazy-k Decoding: Constrained Decoding for Information Extraction EMNLP 2023
It’s MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk EMNLP 2023
Chordal Averaging on Flag Manifolds and Its Applications ICCV 2023
Iterative Superquadric Recomposition of 3D Objects from Multiple Views ICCV 2023
Toward Large Kernel Models ICML 2023
Constrained Causal Bayesian Optimization ICML 2023
Mixing Predictions for Online Metric Algorithms ICML 2023
Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion ICML 2023
Solving Linear Programs with Fast Online Learning Algorithms ICML 2023
Harmonic Neural Networks ICML 2023
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity ICML 2023
High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors ICML 2023
Group Equivariant Fourier Neural Operators for Partial Differential Equations ICML 2023
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching ICML 2023
Tighter Analysis for ProxSkip ICML 2023