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Optimization & Theory

43,292 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Optimization (17,731) Learning Theory (6,809) Bayesian Inference (6,267) Theory (5,579) Statistical Learning (4,564) Neural Network Optimization (4,155) Stochastic Processes (2,948) Stochastic Methods (1,283) Loss Functions (1,220) Distributed Learning (1,202) Online Algorithms (1,013) Statistics (822) Generalization (376) Information Theory (370) Sample Complexity (282) Sparse Optimization (241) Game Theory (151) Convex Optimization (132) Optimal Transport (104) Robust Optimization (84) Distributed Optimization (73) Approximation Algorithms (33)

Papers per year

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52
352
828
3,646
5,854
2000
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Papers (including subtopics)

When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method ICLR 2024 When Semantic Segmentation Meets Frequency Aliasing ICLR 2024 When the Misidentified Adverbial Phrase Functions as a Complement EMNLP 2024 When to Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks AAAI 2024 When Will Gradient Regularization Be Harmful? ICML 2024 When will my ML Job finish? Toward providing Completion Time Estimates through Predictability-Centric Scheduling OSDI 2024 Where Do Large Learning Rates Lead Us? NeurIPS 2024 Where is the signal in tokenization space? EMNLP 2024 Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in DNNs ICLR 2024 Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning ICML 2024 Which Is More Effective in Label Noise Cleaning, Correction or Filtering? AAAI 2024 Who Finds This Voice Attractive? A Large-Scale Experiment Using In-the-Wild Data Interspeech 2024 Who Needs Decoders? Efficient Estimation of Sequence-Level Attributes with Proxies EACL 2024 Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots RSS 2024 Why are Sensitive Functions Hard for Transformers? ACL 2024 Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning ICML 2024 Why do Variational Autoencoders Really Promote Disentanglement? ICML 2024 Why Do We Need Weight Decay in Modern Deep Learning? NeurIPS 2024 Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition ICML 2024 Why is parameter averaging beneficial in SGD? An objective smoothing perspective AISTATS 2024 Why is SAM Robust to Label Noise? ICLR 2024 Why Transformers Need Adam: A Hessian Perspective NeurIPS 2024 Why Warmup the Learning Rate? Underlying Mechanisms and Improvements NeurIPS 2024 Wide Two-Layer Networks can Learn from Adversarial Perturbations NeurIPS 2024 Winner-takes-all learners are geometry-aware conditional density estimators ICML 2024