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)

On Proper Learnability between Average- and Worst-case Robustness NIPS 2023
Generalizing Nonlinear ICA Beyond Structural Sparsity NIPS 2023
Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games NIPS 2023
Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions NIPS 2023
Optimization and Bayes: A Trade-off for Overparameterized Neural Networks NIPS 2023
Balanced Training for Sparse GANs NIPS 2023
Policy Optimization for Continuous Reinforcement Learning NIPS 2023
Investigating how ReLU-networks encode symmetries NIPS 2023
Optimal and Fair Encouragement Policy Evaluation and Learning NIPS 2023
NICE: NoIse-modulated Consistency rEgularization for Data-Efficient GANs NIPS 2023
A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications NIPS 2023
StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation NIPS 2023
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method NIPS 2023
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams NIPS 2023
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks NIPS 2023
ResoNet: Noise-Trained Physics-Informed MRI Off-Resonance Correction NIPS 2023
ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence NIPS 2023
Loss Dynamics of Temporal Difference Reinforcement Learning NIPS 2023
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models NIPS 2023
Agnostically Learning Single-Index Models using Omnipredictors NIPS 2023
On skip connections and normalisation layers in deep optimisation NIPS 2023
Efficient Low-rank Backpropagation for Vision Transformer Adaptation NIPS 2023
Quasi-Monte Carlo Graph Random Features NIPS 2023
Learning Reliable Logical Rules with SATNet NIPS 2023
When are ensembles really effective? NIPS 2023