The following pages link to (Q4762624):
Displaying 50 items.
- A stochastic successive minimization method for nonsmooth nonconvex optimization with applications to transceiver design in wireless communication networks (Q301668) (← links)
- An empirical analysis of scenario generation methods for stochastic optimization (Q323497) (← links)
- Interpretable domain adaptation via optimization over the Stiefel manifold (Q331683) (← links)
- An online AUC formulation for binary classification (Q408053) (← links)
- Online variance minimization (Q420931) (← links)
- The dropout learning algorithm (Q490652) (← links)
- Improved local learning rule for information maximization and related applications (Q557628) (← links)
- Pegasos: primal estimated sub-gradient solver for SVM (Q633112) (← links)
- A stochastic version of Stein variational gradient descent for efficient sampling (Q782348) (← links)
- Regularized margin-based conditional log-likelihood loss for prototype learning (Q969081) (← links)
- An online core vector machine with adaptive MEB adjustment (Q991962) (← links)
- Online learning via congregational gradient descent (Q1377562) (← links)
- Dyad ranking using Plackett-Luce models based on joint feature representations (Q1640577) (← links)
- Uncertainty-safe large scale support vector machines (Q1658451) (← links)
- Online learning for changing environments using coin betting (Q1688998) (← links)
- DC programming and DCA: thirty years of developments (Q1749443) (← links)
- Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks (Q1783942) (← links)
- Adaptive soft \(k\)-nearest-neighbour classifiers (Q1860089) (← links)
- A loss bound model for on-line stochastic prediction algorithms (Q1893728) (← links)
- Online optimization for max-norm regularization (Q2014582) (← links)
- Stochastic approximation on Riemannian manifolds (Q2020323) (← links)
- Stochastic proximal gradient methods for nonconvex problems in Hilbert spaces (Q2028468) (← links)
- A selective overview of deep learning (Q2038303) (← links)
- Stochastic DCA for minimizing a large sum of DC functions with application to multi-class logistic regression (Q2057761) (← links)
- Analysis of stochastic gradient descent in continuous time (Q2058762) (← links)
- Consistent online Gaussian process regression without the sample complexity bottleneck (Q2058904) (← links)
- On the mean field limit of the random batch method for interacting particle systems (Q2070421) (← links)
- Tackling algorithmic bias in neural-network classifiers using Wasserstein-2 regularization (Q2103876) (← links)
- A hybrid stochastic optimization framework for composite nonconvex optimization (Q2118109) (← links)
- Genuinely distributed Byzantine machine learning (Q2166362) (← links)
- Physics-informed distribution transformers via molecular dynamics and deep neural networks (Q2168329) (← links)
- A stochastic variational framework for recurrent Gaussian processes models (Q2188216) (← links)
- Properties of the sign gradient descent algorithms (Q2214992) (← links)
- Optimization for deep learning: an overview (Q2218095) (← links)
- Random batch methods (RBM) for interacting particle systems (Q2222655) (← links)
- Convergence of online mirror descent (Q2278461) (← links)
- Robust learning in SpikeProp (Q2281710) (← links)
- Cauchy noise loss for stochastic optimization of random matrix models via free deterministic equivalents (Q2287214) (← links)
- Gradient methods for solving Stackelberg games (Q2290375) (← links)
- Accelerating deep neural network training with inconsistent stochastic gradient descent (Q2292210) (← links)
- Fast and strong convergence of online learning algorithms (Q2305549) (← links)
- An empirical study of on-line models for relational data streams (Q2361576) (← links)
- Tutorial on brain-inspired computing. II: Multilayer perceptron and natural gradient learning (Q2493279) (← links)
- Survival analysis on data streams: analyzing temporal events in dynamically changing environments (Q2509460) (← links)
- A randomized stochastic approximation algorithm for self-learning (Q2577206) (← links)
- Online algorithm for variance components estimation (Q2656798) (← links)
- Randomized Kaczmarz with averaging (Q2660609) (← links)
- Efficient and sparse neural networks by pruning weights in a multiobjective learning approach (Q2669736) (← links)
- A new on-line learning model (Q2731457) (← links)
- Online learning and game theory. A quick overview with recent results and applications (Q2786537) (← links)
