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Introduces an efficient Bayesian deep ensemble method for predictive regression that combines low-dimensional ensemble representation, closed-form Bayesian aggregation, and independent ensemble training to achieve calibrated uncertainty estimates with computational efficiency.
提出一种基于AdaBoost的文本提示提升（TPB）框架，通过顺序集成弱文本提示分类器来改进视觉-语言模型的少样本分类性能，并在跨模型迁移中保持增益。
This paper investigates how different inference-time deployment rules (rollout strategies) impact multi-horizon volatility forecasting. It shows that non-default rollout rules often improve performance and that validation-based deployment policies provide a low-cost improvement over standard MIMO deployment, emphasizing the importance of deployment policy alongside model architecture.
This paper identifies a fundamental constraint on multi-model LLM systems: accuracy is capped by the rate at which all models fail on the same query. Across 67 frontier models, the all-wrong rate is significantly underestimated by common metrics, limiting gains from voting, routing, and ensemble strategies.
AgentCodec is a source-available library unifying 28 LLM reliability techniques (retries, ensembling, generator/critic refinement, etc.) under a single OpenAI-compatible API, with adaptive routers that can reduce inference costs by ~56% at matched quality. It adopts a communication-theory framing and supports drop-in replacement for OpenAI, Anthropic, and Ollama clients.
This paper introduces CAGE, a counterfactual graph-based method for calibrating multi-agent LLM systems, evaluating on benchmarks like TriviaQA and MMLU-Pro across various communication topologies. The method outperforms existing post-hoc and LLM-elicited calibration approaches.
This paper reveals a fundamental vulnerability in LLM watermarking: when users have access to multiple models, averaging their output distributions cancels watermark perturbations, enabling detection evasion. The authors propose WASH and demonstrate empirically that averaging 3-5 models suppresses detection z-scores below thresholds while improving text quality.
This paper presents a comparative evaluation of classical, ensemble, and neural machine learning approaches for predicting financial distress under severe class imbalance, using SMOTE for oversampling and SHAP for interpretability.
This paper investigates disagreement-based drift detection in ensembles of incremental decision trees, finding that while effective in neural networks, the method underperforms loss-based detectors for tree ensembles due to limited model plasticity.
OpenAI presents a novel exploration strategy for deep reinforcement learning using ensembles of Q-functions with upper-confidence bounds (UCB), demonstrating significant performance improvements on the Atari benchmark.
OpenAI presents PATE (Private Aggregation of Teacher Ensembles), a privacy-preserving approach that trains a student model on noisy outputs from multiple teacher models trained on disjoint datasets, providing strong differential privacy guarantees without exposing sensitive training data.