Value prediction
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Benchmarks
Py150
Most implemented
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
Value Prediction Network
Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation
Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
Papers
Pseudorandom Streams within Diffusion Models Act as Learnable Inputs That Affect Generation Quality
Digital learning systems consume concrete pseudorandom values rather than abstract random variables. These values enter the realized loss and its gradient during training. If a pseudorandom stream contains structure that…
Value predictionAuto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models
Predicting missing cell values in tabular data is a fundamental problem in data cleaning. While state-of-the-art reasoning models show great promise in predicting missing values in tables, by reasoning holistically acros…
Value predictionNASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning
Augmenting model-free reinforcement learning (RL) with representations learned through observation dynamics prediction (observation-predictive RL) can improve sample efficiency and performance, with minor modifications a…
Reinforcement LearningValue predictionHow Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position
World models have become a central abstraction in modern AI. The term now refers to several different objects: action-conditioned environment models, latent imagination models, future-video predictors, interactive neural…
Instruction FollowingValue predictionFlowBank: Query-Adaptive Agentic Workflows Optimization through Precompute-and-Reuse
Large Language Model (LLM)-based multi-agent systems are increasingly powerful, but current agentic workflow optimization paradigms make an unsatisfying trade-off. Task-level methods spend substantial offline compute yet…
Value predictionQ-Delta: Beyond Key-Value Associative State Evolution
Linear attention reformulates sequence modeling as recurrent state evolution, enabling efficient linear-time inference. Under the key-value associative paradigm, existing approaches restrict the role of the query to the …
Value prediction