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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)

Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing AISTATS 2024
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference AISTATS 2024
Provable local learning rule by expert aggregation for a Hawkes network AISTATS 2024
Structural perspective on constraint-based learning of Markov networks AISTATS 2024
DAGnosis: Localized Identification of Data Inconsistencies using Structures AISTATS 2024
Variational Gaussian Process Diffusion Processes AISTATS 2024
Causal Modeling with Stationary Diffusions AISTATS 2024
Scalable Meta-Learning with Gaussian Processes AISTATS 2024
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization AISTATS 2024
Near-Optimal Convex Simple Bilevel Optimization with a Bisection Method AISTATS 2024
Training Implicit Generative Models via an Invariant Statistical Loss AISTATS 2024
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games AISTATS 2024
On Convergence in Wasserstein Distance and f-divergence Minimization Problems AISTATS 2024
Extragradient Type Methods for Riemannian Variational Inequality Problems AISTATS 2024
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses AISTATS 2024
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics AISTATS 2024
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint AISTATS 2024
Safe and Interpretable Estimation of Optimal Treatment Regimes AISTATS 2024
Learning Extensive-Form Perfect Equilibria in Two-Player Zero-Sum Sequential Games AISTATS 2024
Understanding Progressive Training Through the Framework of Randomized Coordinate Descent AISTATS 2024
Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures AISTATS 2024
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers AISTATS 2024
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent AISTATS 2024
Online learning in bandits with predicted context AISTATS 2024
Optimising Distributions with Natural Gradient Surrogates AISTATS 2024