Optimality

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Period ending 2026-09-21

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Period ending 2026-09-14

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Period ending 2026-09-07

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73 papers

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Intervention problems in the Linear Threshold Model: A general formulation and new results
Sep 15, 2026Giacomo Como, Fabio Fagnani, Stephane DurandDecision Thresholds Graph Theory
Per-Matrix Optimality Is Not Enough: Three-Level Optimization for Low-Rank LLM Compression
Sep 14, 2026Huicheng Zhang, Xiyao Feng, Ze-Tong Li +6Large Language Model Compression Singular Value Decomposition
An explicit solution of the five-expert prediction PDE and the exact optimality set of COMB
Sep 14, 2026Jeff Calder, Nadejda DrenskaInequalities Optimality
When Can One Obtain Certificates of Optimality Using Positivstellensaetze?
Sep 8, 2026Nayoon Kim, Allen Gehret, Shenyuan Ma +1Finite-Sample Certificates Optimality
Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approach
Sep 7, 2026Nguyen Thach, Hau Chan, David Parkes +1Crowdsourced Framework Optimality
Graph4BiLO: Graph Neural Network Approximation for Bilevel Mixed-Integer Linear Optimization
Aug 31, 2026Jessica D. Elrefaei, Kaixun Hua, Seungbae Kim +2Bilevel Optimization Graph Neural Networks
When Local Variance Optimality Is Not Enough: RoPE-Aligned Q/K Rotations for Dynamic 4-Bit Quantisation
Aug 13, 2026Shuhan Wang, Yilin Luo, Nan Xu +1Weight-Only Quantization Rotary Position Embedding
Training Under Challenge: Executable Certificates and Challenge-Closed Optimality for Neural Networks
Aug 12, 2026Farhang Yeganegi, Arian Eamaz, Mojtaba SoltanalianFinite-Sample Certificates Certificates
Towards a Formal Definition of Agent Memory: Basis, Span, Optimality, and the Sequential Memory Problem
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Distributed Team Orchestration via Supervisor Networks: Convergence, Optimality, and Resilience
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A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning
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When Does Trace-Driven Evaluation Mislead MoE Expert Caching? Replay Semantics, Workload Contamination, and Operating Regimes
Aug 8, 2026Yu ZhangMixture-Of-Expert Inference Experts
Multiscale Reward Hedging from Correct Demonstrations
Aug 7, 2026Pahan DewasurendraO(T^Β)$ Simultaneous Regret External Reward Models
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Aug 2, 2026Mohammadreza Doostmohammadian, Amir Ahmad Ghods, Alireza Aghasi +2Lipschitz Constant Distributed Optimization
Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexity
Jul 30, 2026Naman Saxena, Mudit Gaur, Vaneet AggarwalEntropy Regularized Reinforcement Learning Optimal Sample Complexity
Finding Optimal Cost-Bounded Plan Reductions: Refined Model
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On the Order-Conditional Optimality of Gaffke's Bound
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New Complexity-Theoretic Frontiers of Tractability for Neural Network Training
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ExplAIner: A Declarative Query Language for Explaining Classification Models
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A Function-Space Dichotomy for Compositional Learning: Exponential Sub-Optimality of the Neural Tangent Kernel
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Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction
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Behavior Cloning is Not All You Need: The Optimality of On-Policy Distillation for Noisy Expert Feedback
Jun 29, 2026Ved Sriraman, Peihan Liu, Daniel Hsu +1On-Policy Distillation Imitation Learning
The More the Merrier: Combining Properties for ABox Abduction under Repair Semantics in ELbot
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Jun 12, 2026Bernhard Paus Graesdal, Alexandre Amice, Pablo A. Parrilo +1Collision-Free Trajectories Semidefinite Programming
Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides
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Minimum Distortion Quantization with Specified Output Distribution
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May 31, 2026Peng Chen, Hailiang Zhao, Xueyan Tang +2Learning-Augmented Algorithms Optimality
A Tight Theory of Error Feedback Algorithms in Distributed Optimization
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May 25, 2026Yunlong Hou, Zixin Zhong, Vincent Y. F. TanMulti-Armed Bandits Linear Bandits
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May 23, 2026Ke Sun, Yizhou Zhao, Jiayi Xin +2Reinforcement Learning With Verifiable Reward Reweighting
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Approaching I/O-optimality for Approximate Attention
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May 21, 2026Stefan Huber, Hannes Unger, Georg Schäfer +1Optimality
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Adaptive Estimation and Optimal Control in Offline Contextual MDPs without Stationarity
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Distributed Learning with Adversarial Gradient Perturbations
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On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization
May 4, 2026Kaixuan Ji, Qiwei Di, Heyang Zhao +2Kullback-Leibler Regularization Linear Bandits
Statistical Consistency and Generalization of Contrastive Representation Learning
May 4, 2026Yuanfan Li, Xiyuan Wei, Tianbao Yang +1Contrastive Learning Generalization Bounds
Bringing Order to Asynchronous SGD: Towards Optimality under Data-Dependent Delays with Momentum
May 3, 2026Tehila Dahan, Roie Reshef, Sharon Goldstein +1Stochastic Gradient Descent Learning Rates
Value Functions for Temporal Logic: Optimal Policies and Safety Filters
May 1, 2026Oswin So, William Sharpless, Sylvia Herbert +1Value Functions Signal Temporal Logic
Global Optimality for Constrained Exploration via Penalty Regularization
Apr 30, 2026Florian Wolf, Ilyas Fatkhullin, Niao HePolicy Gradient Efficient Exploration
Scalable Algorithms with Provable Optimality Bounds for the Multiple Watchman Route Problem
Apr 17, 2026Srikar Gouru, Ariel Felner, Jiaoyang LiOptimality

Sep 15, 2026 · math.OCJ/K move · Enter open · S save

Intervention problems in the Linear Threshold Model: A general formulation and new results

Giacomo Como, Fabio Fagnani, Stephane Durand

We study an optimal intervention problem for linear threshold models. This is a popular class of dynamical network systems whereby a number of agents, identified with the nodes of a graph, strategically change their binary action (0 or 1) according to a threshold rule. Specifically, an agent adopts action 1 if and only if the fraction of its neighbors in the interaction graph that do so is greater than or equal to a prescribed threshold. Assuming that a planner can modify the agents' thresholds at a cost equal to the aggregate threshold increase, we study the minimum intervention cost needed to ensure global convergence to the all-1 configuration. Our main contribution is the introduction of a new graph-theoretic quantity, called oriented path number, that is the minimum number of disjoint paths needed to cover the graph that can be oriented to form a directed acyclic graph. When thresholds are all equal to 1/2, the optimal cost is shown to coincide with the oriented path number, whereas, in the general case, it turns out to be the main ingredient of a bound on the optimal intervention cost.

Decision Thresholds Graph Theory Optimality

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