conftrace_

Optimization & Theory

36,874 papers (3 classified directly here)

Subtopics

Optimization (14,983) Bayesian Inference (5,566) Learning Theory (5,334) Theory (4,955) Statistical Learning (4,117) Neural Network Optimization (3,649) Stochastic Processes (2,667) Loss Functions (1,162) Distributed Learning (1,135) Stochastic Methods (1,118) Online Algorithms (806) Statistics (684) Information Theory (318) Generalization (220) Sparse Optimization (158) Sample Complexity (99) Game Theory (30) Robust Optimization (17) Optimal Transport (14) Approximation Algorithms (10) Distributed Optimization (8) Convex Optimization (8)

Papers per year

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Papers (including subtopics)

Dual Monolingual Cross-Entropy Delta Filtering of Noisy Parallel Data ACL 2019
ResNet and Batch-normalization Improve Data Separability ACML 2019
Minimax Online Prediction of Varying Bernoulli Process under Variational Approximation ACML 2019
Multivariate Time Series Prediction Based on Optimized Temporal Convolutional Networks with Stacked Auto-encoders ACML 2019
Model-Based Reinforcement Learning Exploiting State-Action Equivalence ACML 2019
Optimal PAC-Bayesian Posteriors for Stochastic Classifiers and their use for Choice of SVM Regularization Parameter ACML 2019
Efficient Diversified Mini-Batch Selection using Variable High-layer Features ACML 2019
Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening ACML 2019
Functional Isolation Forest ACML 2019
Learning to Sample Hard Instances for Graph Algorithms ACML 2019
Exemplar Based Mixture Models with Censored Data ACML 2019
Canonical Soft Time Warping ACML 2019
Variational Inference from Ranked Samples with Features ACML 2019
A Generalization Bound for Online Variational Inference ACML 2019
Regularizing Neural Networks via Stochastic Branch Layers ACML 2019
Focused Anchors Loss: cost-sensitive learning of discriminative features for imbalanced classification ACML 2019
Efficient Learning of Restricted Boltzmann Machines Using Covariance Estimates ACML 2019
Active Change-Point Detection ACML 2019
Trust Region Sequential Variational Inference ACML 2019
Stochastic Gradient Trees ACML 2019
Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension ACML 2019
Forward-Backward Generative Adversarial Networks for Anomaly Detection ACML 2019
Cell-aware Stacked LSTMs for Modeling Sentences ACML 2019
Proximal Splitting Meets Variance Reduction AISTATS 2019
Tossing Coins Under Monotonicity AISTATS 2019