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)

Adversarial De-confounding in Individualised Treatment Effects Estimation AISTATS 2023
Optimal and Private Learning from Human Response Data AISTATS 2023
Bayesian Optimization with Conformal Prediction Sets AISTATS 2023
Alternating Projected SGD for Equality-constrained Bilevel Optimization AISTATS 2023
Improved Robust Algorithms for Learning with Discriminative Feature Feedback AISTATS 2023
Can 5th Generation Local Training Methods Support Client Sampling? Yes! AISTATS 2023
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization AISTATS 2023
Sequential Gradient Descent and Quasi-Newton’s Method for Change-Point Analysis AISTATS 2023
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework AISTATS 2023
Compress Then Test: Powerful Kernel Testing in Near-linear Time AISTATS 2023
Select and Optimize: Learning to solve large-scale TSP instances AISTATS 2023
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity AISTATS 2023
Testing of Horn Samplers AISTATS 2023
Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces AISTATS 2023
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes AISTATS 2023
On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks AISTATS 2023
Flexible risk design using bi-directional dispersion AISTATS 2023
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles AISTATS 2023
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data AISTATS 2023
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces AISTATS 2023
Exact Gradient Computation for Spiking Neural Networks via Forward Propagation AISTATS 2023
The Ordered Matrix Dirichlet for State-Space Models AISTATS 2023
Energy-Based Models for Functional Data using Path Measure Tilting AISTATS 2023
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning AISTATS 2023
Variational Inference for Neyman-Scott Processes AISTATS 2023