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

Building Rome with Convex Optimization RSS 2025 BUINUS at IWSLT: Evaluating the Impact of Data Augmentation and QLoRA-based Fine-Tuning for Maltese to English Speech Translation ACL 2025 Bundle Neural Network for message diffusion on graphs ICLR 2025 Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient ACL 2025 Bypassing LLM Guardrails: An Empirical Analysis of Evasion Attacks against Prompt Injection and Jailbreak Detection Systems ACL 2025 Byte Latent Transformer: Patches Scale Better Than Tokens ACL 2025 Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning ICML 2025 C2IQL: Constraint-Conditioned Implicit Q-learning for Safe Offline Reinforcement Learning ICML 2025 Cacheback: Speculative Decoding With Nothing But Cache EMNLP 2025 Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains EMNLP 2025 CacheQuant: Comprehensively Accelerated Diffusion Models CVPR 2025 CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention ICML 2025 CAKE: Cascading and Adaptive KV Cache Eviction with Layer Preferences ICLR 2025 CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges ACL 2025 Calibrated Computation-Aware Gaussian Processes AISTATS 2025 Calibrated Disambiguation for Partial Multi-label Learning AAAI 2025 Calibrated Inference: Statistical Inference that Accounts for Both Sampling Uncertainty and Distributional Uncertainty JMLR 2025 Calibrated Language Models and How to Find Them with Label Smoothing ICML 2025 Calibrated Multi-Preference Optimization for Aligning Diffusion Models CVPR 2025 Calibrated Value-Aware Model Learning with Probabilistic Environment Models ICML 2025 Calibrating Language Models for Neural Ranking under Noisy Supervision with Relaxed Labels EMNLP 2025 Calibrating Large Language Models with Sample Consistency AAAI 2025 Calibrating LLM Confidence by Probing Perturbed Representation Stability EMNLP 2025 Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies EMNLP 2025 Calibrating LLMs with Information-Theoretic Evidential Deep Learning ICLR 2025