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Continuous Optimization

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Wasserstein Logistic Regression with Mixed Features NIPS 2022
A Variant of Anderson Mixing with Minimal Memory Size NIPS 2022
The Hessian Screening Rule NIPS 2022
Approximate Secular Equations for the Cubic Regularization Subproblem NIPS 2022
Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees NIPS 2022
Local Bayesian optimization via maximizing probability of descent NIPS 2022
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization NIPS 2022
Modular Flows: Differential Molecular Generation NIPS 2022
Wasserstein $K$-means for clustering probability distributions NIPS 2022
Improved Imaging by Invex Regularizers with Global Optima Guarantees NIPS 2022
Joint Entropy Search for Multi-Objective Bayesian Optimization NIPS 2022
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions NIPS 2022
Learning Mixed Multinomial Logits with Provable Guarantees NIPS 2022
Giga-scale Kernel Matrix-Vector Multiplication on GPU NIPS 2022
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization NIPS 2022
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels NIPS 2022
High-dimensional Additive Gaussian Processes under Monotonicity Constraints NIPS 2022
Fused Orthogonal Alternating Least Squares for Tensor Clustering NIPS 2022
A heuristic for statistical seriation UAI 2021
Fast and Smooth Interpolation on Wasserstein Space AISTATS 2021
Differentiable Greedy Algorithm for Monotone Submodular Maximization: Guarantees, Gradient Estimators, and Applications AISTATS 2021
Direct-Search for a Class of Stochastic Min-Max Problems AISTATS 2021
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances AISTATS 2021
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings AISTATS 2021
Wasserstein barycenters can be computed in polynomial time in fixed dimension JMLR 2021