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

1
52
337
806
3400
3289
'00
'05
'10
'15
'20
'25

Papers (including subtopics)

A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm NIPS 2023
Softmax Output Approximation for Activation Memory-Efficient Training of Attention-based Networks NIPS 2023
Memory Efficient Optimizers with 4-bit States NIPS 2023
Replicable Reinforcement Learning NIPS 2023
Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing NIPS 2023
Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness NIPS 2023
DynaDojo: An Extensible Platform for Benchmarking Scaling in Dynamical System Identification NIPS 2023
Online Constrained Meta-Learning: Provable Guarantees for Generalization NIPS 2023
Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction NIPS 2023
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning NIPS 2023
Physics-Driven ML-Based Modelling for Correcting Inverse Estimation NIPS 2023
Kernel-Based Tests for Likelihood-Free Hypothesis Testing NIPS 2023
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning NIPS 2023
Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference NIPS 2023
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework NIPS 2023
Large-Scale Distributed Learning via Private On-Device LSH NIPS 2023
Koopman Kernel Regression NIPS 2023
Neural (Tangent Kernel) Collapse NIPS 2023
Expert load matters: operating networks at high accuracy and low manual effort NIPS 2023
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design NIPS 2023
Strategic Data Sharing between Competitors NIPS 2023
Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model NIPS 2023
Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation NIPS 2023
Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization NIPS 2023
Best Arm Identification with Fixed Budget: A Large Deviation Perspective NIPS 2023