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Methodology
← Optimization & Theory
Machine Learning
›
Optimization & Theory
›
Theory
4950 directly classified papers
Papers per year
2000: 1
2001: 2
2002: 3
2003: 3
2004: 9
2005: 4
2006: 32
2007: 25
2008: 31
2009: 25
2010: 37
2011: 37
2012: 45
2013: 76
2014: 66
2015: 72
2016: 102
2017: 156
2018: 246
2019: 353
2020: 447
2021: 567
2022: 646
2023: 741
2024: 670
2025: 426
2026: 128
Papers
Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games
NIPS 2023
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
NIPS 2023
TD Convergence: An Optimization Perspective
NIPS 2023
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
NIPS 2023
Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
NIPS 2023
Survival Instinct in Offline Reinforcement Learning
NIPS 2023
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes
NIPS 2023
Replicability in Reinforcement Learning
NIPS 2023
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning
NIPS 2023
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
NIPS 2023
Trade-off Between Efficiency and Consistency for Removal-based Explanations
NIPS 2023
Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars
NIPS 2023
Characterizing Out-of-Distribution Error via Optimal Transport
NIPS 2023
Stability Guarantees for Feature Attributions with Multiplicative Smoothing
NIPS 2023
Abide by the law and follow the flow: conservation laws for gradient flows
NIPS 2023
On the spectral bias of two-layer linear networks
NIPS 2023
When Does Optimizing a Proper Loss Yield Calibration?
NIPS 2023
Evaluating Neuron Interpretation Methods of NLP Models
NIPS 2023
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent
NIPS 2023
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks
NIPS 2023
Low Tensor Rank Learning of Neural Dynamics
NIPS 2023
What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models
NIPS 2023
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model
NIPS 2023
Leveraging the two-timescale regime to demonstrate convergence of neural networks
NIPS 2023
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality
NIPS 2023
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