arXiv research

This work introduces COLA, a strategy to aggregate conformal prediction sets efficiently.

A conformal procedure improves CoT reasoning by aggregating reasoning paths and calibrating abstention rules.

Improved time series causal discovery with bootstrap aggregation and confidence measures.

Aggregates diverse zero-shot LLM outputs for better corporate disclosure classification.

New method aggregates bootstrapped DAGs for causal discovery.

The paper improves confidence regions for band-limited functions using tighter norm bounds and majority voting.

New method for decentralized learning from diverse clients.

Algorithm aggregates rewards from multiple players to learn related tasks in online bandit learning.

Using Monte Carlo simulation to calculate the Value at Risk (VaR) as a possible risk measure requires adequate techniques. One of these techniques is the application of a compound distribution for the aggregates in a portfolio. In this paper, we consider the aggregated loss of Gamma distributed severities and estimate …

Estimating statistical uncertainties allows autonomous agents to communicate their confidence during task execution and is important for applications in safety-critical domains such as autonomous driving. In this work, we present the uncertainty-aware imitation learning (UAIL) algorithm for improving end-to-end control…

We investigate a multi-household DSGE model in which past aggregate consumption impacts the confidence, and therefore consumption propensity, of individual households. We find that such a minimal setup is extremely rich, and leads to a variety of realistic output dynamics: high output with no crises; high output with i…

Predicting properties of nodes in a graph is an important problem with applications in a variety of domains. Graph-based Semi-Supervised Learning (SSL) methods aim to address this problem by labeling a small subset of the nodes as seeds and then utilizing the graph structure to predict label scores for the rest of the …

Study optimal policy regret in partially observable Markov games with adaptive opponents.

One of the problems on the way to successful implementation of neural networks is the quality of annotation. For instance, different annotators can annotate images in a different way and very often their decisions do not match exactly and in extreme cases are even mutually exclusive which results in noisy annotations a…

Majority Vote is optimal for reliable data labeling under certain conditions.

New method improves LLM judge accuracy by accounting for dependencies in aggregated binary labels.

We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes 2012) in several ways. We show that they represent a distortion between a "score" and an "ordering", thus providing a new view of rank aggregation and order based clustering with interesting connections to web ranking. We show ho…

AI-generated variables bias regression estimates; methods correct for invalid inference.

We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes, 2012) in several ways. We show that they represent a distortion between a 'score' and an 'ordering', thus providing a new view of rank aggregation and order based clustering with interesting connections to web ranking. We show h…

BoC probe assesses neural network confidence coherence, revealing architecture-specific uncertainty.

This study examines representation bias in open-source Qwen models for investment decisions.

Study online learning in MDPs with aggregate bandit feedback, achieving low regret in both stochastic and adversarial settings.

OpenAlpha validates decentralized capital strategies using game theory and market aggregation.

Improves reward bounds for prediction with expert advice using abstention.

Study learns optimal bidding strategy in auctions with dynamic values and aggregated feedback.

New framework handles dynamic contexts in reinforcement learning.

Proposes a method for stable variable selection in high-dimensional data.

Study improves fraud detection in e-commerce with a stacked model combining CNNs, GNNs, and confidence gating.

This paper introduces sample-averaged Q-learning for better RL performance.

New method explains ML performance gaps without causal knowledge.

We develop the setting of sequential prediction based on shifting experts and on a "smooth" version of the method of specialized experts. To aggregate experts predictions, we use the AdaHedge algorithm, which is a version of the Hedge algorithm with adaptive learning rate, and extend it by the meta-algorithm Fixed Shar…

Framework for online resource allocation using social welfare functions.

Contrastive regularization improves semi-supervised learning by better propagating confident pseudo-labels.

DUA-D2C refines D2C for better deep learning generalization.

We introduce a statistical model for operational losses based on heavy-tailed distributions and bipartite graphs, which captures the event type and business line structure of operational risk data. The model explicitly takes into account the Pareto tails of losses and the heterogeneous dependence structures between the…

New method for online learning IC models with node-level feedback.

Cubic predicts stock market indices by fusing stock latent embeddings and converting to binary classification.

Hierarchical framework for model evaluation on leaderboards

This work develops formal statistical inference procedures for machine learning ensemble methods. Ensemble methods based on bootstrapping, such as bagging and random forests, have improved the predictive accuracy of individual trees, but fail to provide a framework in which distributional results can be easily determin…

Unified approach for sample aggregation in transfer learning across various divergence measures.

The paper proposes a method for constructing confidence sets that adapt to the cardinality of the smallest component of a mean vector.

A new framework evaluates LLMs by considering judge reliability.

Unified framework combines dependent microbiome tests.

We propose a novel technique for analyzing adaptive sampling called the {\em Simulator}. Our approach differs from the existing methods by considering not how much information could be gathered by any fixed sampling strategy, but how difficult it is to distinguish a good sampling strategy from a bad one given the limit…

We introduce a simple approach for testing the reliability of homogeneous generators and the Markov property of the stochastic processes underlying empirical time series of credit ratings. We analyze open access data provided by Moody's and show that the validity of these assumptions - existence of a homogeneous genera…

OPERA blends multiple OPE estimators to evaluate new policies offline.

Following a long tradition of physicists who have noticed that the Ising model provides a general background to build realistic models of social interactions, we study a model of financial price dynamics resulting from the collective aggregate decisions of agents. This model incorporates imitation, the impact of extern…

Paper proves impossible for large language models to control hallucinations without sacrificing other properties.