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)

Window-Based Distribution Shift Detection for Deep Neural Networks NIPS 2023
Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency NIPS 2023
Explainable and Efficient Randomized Voting Rules NIPS 2023
A Guide Through the Zoo of Biased SGD NIPS 2023
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars NIPS 2023
Prioritizing Samples in Reinforcement Learning with Reducible Loss NIPS 2023
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks NIPS 2023
Understanding Contrastive Learning via Distributionally Robust Optimization NIPS 2023
K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing NIPS 2023
Online Nonstochastic Model-Free Reinforcement Learning NIPS 2023
Time-Reversed Dissipation Induces Duality Between Minimizing Gradient Norm and Function Value NIPS 2023
Cascading Contextual Assortment Bandits NIPS 2023
Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes NIPS 2023
Sample based Explanations via Generalized Representers NIPS 2023
Continuous Parametric Optical Flow NIPS 2023
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning NIPS 2023
Bottleneck Structure in Learned Features: Low-Dimension vs Regularity Tradeoff NIPS 2023
Noise-Adaptive Thompson Sampling for Linear Contextual Bandits NIPS 2023
Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent NIPS 2023
Learning a Neuron by a Shallow ReLU Network: Dynamics and Implicit Bias for Correlated Inputs NIPS 2023
Separable Physics-Informed Neural Networks NIPS 2023
Gradient-Free Kernel Stein Discrepancy NIPS 2023
ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding NIPS 2023
ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints NIPS 2023
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning NIPS 2023