Ideal attribution mechanisms track model interactions for faithful watermarks.
problem Ensuring models provide transparent and fair attribution decisions.
method Introducing ideal attribution mechanisms and a ledger for tracking model interactions.
result A unified framework for evaluating watermarking schemes, clarifying attainable guarantees.
Optimizes budgeted evaluations of LLMs by allocating queries to judges efficiently.
problem Evaluating LLMs with heterogeneous judges and varying costs and reliability.
method Formalizes and analyzes budgeted heteroskedastic multi-judge estimation, proposing EST-IVWE for practical implementation.
result EST-IVWE matches the oracle IVWE rate up to lower-order terms in the budget and is instance-optimal.
Bayesian inference over admissible histories leads to irreversible kinetics.
problem Modeling irreversible processes in systems with uncertain histories.
method A Gibbs-type measure weighted by energy-dissipation action and observation constraints, interpreted as a Bayesian posterior.
result The measure concentrates on maximum-a-posteriori (MAP) histories, recovering classical deterministic evolution.
This work frames reward modelling from preferences as a causal problem.
problem Reward modelling from preference data for AI alignment.
method Causal inference approach to identify challenges and assumptions.
result Causally-inspired approaches improve model robustness.
HS-FNO models non-Markovian PDEs by learning history and future states.
problem Non-Markovian dynamics where future states depend on past history.
method History-Space Fourier Neural Operator (HS-FNO) for delay and memory-driven PDEs.
result HS-FNO achieves lowest aggregate errors across various PDE families.
Overview of affine surface area and its history.
problem None explicitly stated; focuses on overview.
method None explicitly stated; focuses on overview.
result None explicitly stated; focuses on overview.
Survey on DDVV-type inequalities, their history, and recent developments.
problem None explicitly stated; focuses on surveying existing work.
method None explicitly stated; focuses on surveying existing work.
result Survey of DDVV-type inequalities and their history and recent developments.
Social media sources can provide crucial information in crisis situations, but discovering relevant messages is not trivial. Methods have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods). Event-specific models could implement a more focused search area, but …
SRMC framework reduces Monte Carlo variance by history-based sampling in high-dimensional spaces.
problem Efficient sampling in high-dimensional discrete or continuous state spaces.
method Score-Repellent Monte Carlo (SRMC) framework that summarizes history through running average of score evaluations.
result Improves estimator variance and mode coverage with constant memory usage.
Paper analyzes history-based RL methods for MDPs, introduces a theoretical framework and practical algorithm.
problem Improving RL performance in MDPs using history-based features.
method Theoretical framework for history-based RL, practical algorithm design.
result Practical RL algorithm shows effectiveness on continuous control tasks.
Unified framework to bridge human and LLM judgments.
problem Systematic discrepancies between human and LLM evaluations.
method Latent human preference score and linear transformations of covariates.
result Higher agreement with human ratings and exposure of systematic gaps.
Neural networks improve cancer risk prediction from family history data.
problem Improving cancer risk prediction from family history data using machine learning.
method Developed and trained neural network models on large pedigrees to predict hereditary cancers.
result Neural networks can achieve nearly optimal prediction performance and outperform traditional models in misreported data.
The paper explains why estimating a history-dependent policy can reduce MSE in reinforcement learning.
problem Understanding why history-dependent policies can improve MSE in off-policy evaluation.
method The paper derives a bias-variance decomposition of MSE for various OPE estimators, showing how history-dependent policies can decrease variance and increase bias.
result History-dependent policies can decrease the variance of importance sampling estimators, leading to lower MSE.
It is shown how the generating functional method of De Dominicis can be used to solve the dynamics of the original version of the minority game (MG), in which agents observe real as opposed to fake market histories. Here one again finds exact closed equations for correlation and response functions, but now these are de…
This paper suggests claim history will be deprecated in future auto insurance rates.
problem The role of historical claim records in auto insurance rates.
method Proposes a new risk variable elimination method and real-time road risk model design.
result Claim history will be considered a 'noise' factor and deprecated in Pay-How-You-Drive models.
We present in this chapter (Chapter II) the history of ideas which lead up to the development of modern knot theory. We are more detailed when pre-XX century history is reported. With more recent times we are more selective, stressing developments related to Jones type invariants of links. In the Appendix, A.Przybyszew…
Diffusion LLMs can efficiently generate harmful prompts for adversarial testing.
problem Generating harmful prompts for adversarial testing is resource-intensive and costly.
method Transformed adversarial prompt optimization into an efficient inference task using pretrained Diffusion LLMs.
result Only a few conditional samples are required to generate harmful prompts with high reward.
Paper tackles reinforcement learning for STL specifications with state history.
problem Learning optimal policies to satisfy STL specifications often requires too much state history, making the problem computationally intractable.
method Proposes a compact augmented state-space representation to capture state history and an approximation method to solve the objective.
result Shows the performance bound of the approximate solution and compares it with an existing technique.
We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds O(t) i.i.d. pseudo-rewards to its history in round t and then pulls the arm with the highest average reward in its perturbed history. Therefore, we call it perturbed-history exploration (PHE). Th…
We propose a new online algorithm for cumulative regret minimization in a stochastic linear bandit. The algorithm pulls the arm with the highest estimated reward in a linear model trained on its perturbed history. Therefore, we call it perturbed-history exploration in a linear bandit (LinPHE). The perturbed history is …
We give an explicit definition of decentralization and show you that decentralization is almost impossible for the current stage and Bitcoin is the first truly noncentralized currency in the currency history. We propose a new framework of noncentralized cryptocurrency system with an assumption of the existence of a wea…
Cold-start PV forecasting uses synthetic histories to train time-series foundation models.
problem Cold-start PV forecasting
method Zero-shot pipeline with synthetic histories
result TabPFN-TS achieves the lowest error under Real Feedback strategy
Develops methods to correct bias in AI feedback for more accurate alignment.
problem Systematic bias in AI feedback compared to human labels.
method Two debiased alignment methods: DDPO and DIPO.
result Methods improve alignment efficiency and performance close to human-labeled data.
Epsilon-machines are minimal, unifilar presentations of stationary stochastic processes. They were originally defined in the history machine sense, as hidden Markov models whose states are the equivalence classes of infinite pasts with the same probability distribution over futures. In analyzing synchronization, though…
Combining deep learning and ensemble smoothers for better history matching.
problem Dealing with complex facies distributions in history matching.
method Using autoencoders and generative adversarial networks to parameterize facies models, applying distance-based localization.
result Improved history matching performance with deep learning parameterizations.
New framework handles dynamic contexts in reinforcement learning.
problem Learning in environments where contexts change over time.
method Dynamic Contextual Markov Decision Processes (DCMDPs) with logistic aggregation.
result Upper-confidence-bound style algorithm with regret bounds.
A dangerously brief history of the developments of the main ideas in economics, as observed by a physicist, is given. This was published in 'Econophysics of Stock and Other Markets', Eds. A. Chatterjee, B. K. Chakrabarti, New Economic Windows Series, Springer, Milan, 2006, pp~219-224.
Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.
problem Difficult prediction of medium-horizon Alzheimer's disease progression due to tied clinical scores and irregular biomarker observations.
method Developed a residual gap-aware transformer that combines statistical reference with transformer-based residual learning.
result The proposed model reduces mean error and improves prediction-observation correlation compared to baseline models.
Foundation models improve wage gap decomposition by capturing omitted career history factors.
problem Estimating wage disparities using incomplete career history data.
method Fine-tuning foundation models to mitigate omitted variable bias and estimate wage gaps.
result Foundation models can decompose gender wage gaps more accurately than traditional econometric methods.
Marden's Tameness Conjecture predicts that every hyperbolic 3-manifold with finitely generated fundamental group is homeomorphic to the interior of a compact 3-manifold. It was recently established by Agol and Calegari-Gabai. We will survey the history of work on this conjecture and discuss its many applications.
Network growth processes can be understood as generative models of the structure and history of complex networks. This point of view naturally leads to the problem of network archaeology: reconstructing all the past states of a network from its structure---a difficult permutation inference problem. In this paper, we in…
Deep learning calibrates CO2 storage formations from seismic and well data.
problem Uncertainty in CO2 storage formation properties.
method Two deep learning models for well and seismic data, integrated into MCMC history matching.
result Significant uncertainty reduction in key parameters and accurate CO2 plume predictions.
A new method uses a frozen language model to improve sample efficiency in reinforcement learning.
problem Improving sample efficiency in reinforcement learning with partially observable environments.
method FROZEN Hopfield network and HELM (History Embedding Language Model) method.
result HELM achieves new state-of-the-art results on Minigrid and Procgen environments.
HDT improves MCMC on graphs with history-dependent sampling.
problem Efficient sampling from target distributions on general graphs with low computational overhead.
method History-driven target (HDT) framework that replaces the original target distribution with a history-dependent one.
result Near-zero variance performance and scalability to large graphs with memory-efficient implementation.
Rhino learns causal relationships from time series data with history-dependent noise.
problem Discovering causal relationships from time series data with non-linear relations, instantaneous effects, and history-dependent noise.
method Combines vector auto-regression, deep learning, and variational inference.
result Demonstrates better causal relationship discovery performance compared to baselines.
Proposes using frequent sequences to improve sequential recommendation models.
problem Combining user history and recent actions for personalized recommendations.
method Uses frequent sequences to identify relevant parts of user history, embedding items based on preferences and dynamics in a unified metric model.
result Outperforms state-of-the-art methods, especially on sparse datasets.
Explains the history and challenges of minimal surfaces.
problem Understanding the regularity of minimal surfaces.
method Historical overview and technical analysis.
result Outlines the evolution and current state of minimal surfaces.
In the light of contemporary discussions of inter and transdisciplinarity, this paper approaches econophysics and sociophysics to seek a response to the question -- whether these interdisciplinary fields could contribute to physics and economics. Drawing upon the literature on history and philosophy of science, the pap…
Auto-regressive sequence-to-sequence models with attention mechanism have achieved state-of-the-art performance in many tasks such as machine translation and speech synthesis. These models can be difficult to train. The standard approach, teacher forcing, guides a model with reference output history during training. Th…
A new algorithm adapts to changing user behaviors in finance.
problem Adapting to changing user behaviors in financial recommendations.
method History-Augmented Collaborative Filtering using a custom neural network.
result The algorithm provides dynamic financial recommendations.
Machine-assisted treatment recommendations hold a promise to reduce physician time and decision errors. We formulate the task as a sequence-to-sequence prediction model that takes the entire time-ordered medical history as input, and predicts a sequence of future clinical procedures and medications. It is built on the …
This survey analyzes knowledge discovery in cryptocurrency transactions.
problem Understanding user behaviors and collective actions in cryptocurrency transactions.
method Data mining techniques and literature review.
result Classified existing research into three aspects and discussed major findings.
Many households in developing countries lack formal financial histories, making it difficult for firms to extend credit, and for potential borrowers to receive it. However, many of these households have mobile phones, which generate rich data about behavior. This article shows that behavioral signatures in mobile phone…
A new method detects and removes false trailing balances in credit data.
problem False trailing balances in credit data corrupt risk event timing.
method TruEnd-procedure defines and removes false trailing balances.
result Improved accuracy in predicting risk events and reducing credit losses.
Variance-reduced algorithms, although achieve great theoretical performance, can run slowly in practice due to the periodic gradient estimation with a large batch of data. Batch-size adaptation thus arises as a promising approach to accelerate such algorithms. However, existing schemes either apply prescribed batch-siz…
This paper examines challenges in analyzing NFT transaction data.
problem Challenges in analyzing NFT transaction data due to non-fungible nature and blockchain.
method Analysis of transaction history of eight NFT collections.
result Illustrates challenges such as price differentiation, lateral swaps, and volatility.
Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model)…
Predicts clinical events using a landmark approach with machine learning for large biomarker histories.
problem Dynamic prediction of clinical events from large biomarker histories.
method Landmark approach extended to endogenous markers history combined with machine learning methods for survival data.
result Superlearner combining regularized regressions and random survival forests outperforms standard survival models.