conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Optimization

14,983 papers

Papers per year

18
112
342
1289
1646
'05
'10
'15
'20
'25

Papers

Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy AISTATS 2020
Support recovery and sup-norm convergence rates for sparse pivotal estimation AISTATS 2020
Statistical and Computational Rates in Graph Logistic Regression AISTATS 2020
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization AISTATS 2020
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization AISTATS 2020
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout AISTATS 2020
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games AISTATS 2020
Doubly Sparse Variational Gaussian Processes AISTATS 2020
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments AISTATS 2020
Finite-Time Error Bounds for Biased Stochastic Approximation with Applications to Q-Learning AISTATS 2020
Importance Sampling via Local Sensitivity AISTATS 2020
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling AISTATS 2020
Causal Bayesian Optimization AISTATS 2020
Sample complexity bounds for localized sketching AISTATS 2020
Learning in Gated Neural Networks AISTATS 2020
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training AISTATS 2020
Context Mover’s Distance & Barycenters: Optimal Transport of Contexts for Building Representations AISTATS 2020
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions AISTATS 2020
An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise AISTATS 2020
An Asymptotic Rate for the LASSO Loss AISTATS 2020
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models AISTATS 2020
Orthogonal Gradient Descent for Continual Learning AISTATS 2020
Scalable Gradients for Stochastic Differential Equations AISTATS 2020
One Sample Stochastic Frank-Wolfe AISTATS 2020
Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models AISTATS 2020