conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Theory

4,950 papers

Papers per year

Papers

The Hessian perspective into the Nature of Convolutional Neural Networks ICML 2023
When do Minimax-fair Learning and Empirical Risk Minimization Coincide? ICML 2023
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning ICML 2023
Topologically Faithful Image Segmentation via Induced Matching of Persistence Barcodes ICML 2023
Vector-Valued Control Variates ICML 2023
Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning ICML 2023
Inflow, Outflow, and Reciprocity in Machine Learning ICML 2023
Defects of Convolutional Decoder Networks in Frequency Representation ICML 2023
Data Feedback Loops: Model-driven Amplification of Dataset Biases ICML 2023
Perturbation Analysis of Neural Collapse ICML 2023
Distribution Free Domain Generalization ICML 2023
Provable Data Subset Selection For Efficient Neural Networks Training ICML 2023
Delayed Feedback in Kernel Bandits ICML 2023
Competitive Gradient Optimization ICML 2023
Tighter Information-Theoretic Generalization Bounds from Supersamples ICML 2023
Sharper Bounds for $\ell_p$ Sensitivity Sampling ICML 2023
A Law of Robustness beyond Isoperimetry ICML 2023
Towards Understanding Generalization of Macro-AUC in Multi-label Learning ICML 2023
Tight Data Access Bounds for Private Top-$k$ Selection ICML 2023
The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent ICML 2023
Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes ICML 2023
Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation ICML 2023
Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations ICML 2023
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels ICML 2023
The Benefits of Model-Based Generalization in Reinforcement Learning ICML 2023