Enhances persona-based conversation model for multi-turn dialogue.
problem Improving persona-based conversation models for multi-turn dialogue.
method Introduced additional input modality into hredGAN to capture external attributes.
result Persona hredGAN (phredGAN) outperforms existing models in multi-turn dialogue corpora. Performance analysis, from the external point of view of a client who would only have access to returns and holdings of a fund, evolved towards exact attribution made in the context of portfolio optimisation, which is the internal point of view of a manager controlling all the parameters of this optimisation. Attributi…
For many externally driven complex systems neither the noisy driving force, nor the internal dynamics are a priori known. Here we focus on systems for which the time dependent activity of a large number of components can be monitored, allowing us to separate each signal into a component attributed to the external drivi…
TaylorPODA uses Taylor expansions to improve feature attributions for opaque models.
problem Lack of systematic framework for quantifying feature contributions in opaque models.
method Taylor expansion framework with postulates (precision, federation, zero-discrepancy, adaptation).
result TaylorPODA achieves competitive results and provides principled explanations.
We study a dynamical Ising model of agents' opinions (buy or sell) with coupling coefficients reassessed continuously in time according to how past external news (magnetic field) have explained realized market returns. By combining herding, the impact of external news and private information, we test within the same mo…
Robustly detects and attributes climate change impacts under interventions.
problem Detect and attribute climate change impacts from observations robustly.
method Supervised learning with anchor regression for robust predictions under interventions.
result CO2 forcing can be robustly predicted from temperature patterns under strong solar forcing interventions.
Proposes GNAN model for detecting various network structures.
problem Detecting traditional communities in networks.
method GNAN model combining topology and node-attribute information.
result GNAN detects a broader range of network structures.
Improves slot key and value prediction for unseen entities.
problem Dealing with unseen slot keys and values in real-world dialogue systems.
method Leverages external knowledge bases to project slots into an attribute space and generate candidate keys and values.
result Significant improvements in F1 score and accuracy (57.7% and 82.7%, respectively) over a previous approach.
Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors) is sparse. A company that can only observe the interactions between its own customers will generally not be able to accurately estimate its …
In this paper we investigate the behavioural differences between mobile phone customers with prepaid and postpaid subscriptions. Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections betwee…
This paper presents an exclusive classification of the largest crashes in Dow Jones Industrial Average (DJIA), SP500 and NASDAQ in the past century. Crashes are objectively defined as the top-rank filtered drawdowns (loss from the last local maximum to the next local minimum disregarding noise fluctuations), where the …
Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions to measure evidence conflict and stability.
problem Modern data analysis lacks mechanisms to show the clarity, conflict, or stability of evidence behind predictions.
method Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions.
result SEF measures conflict and stability, and shows that conflict can improve loss prediction beyond confidence.
RB-Modulation trains free diffusion models without external adapters.
problem Training-free personalization of diffusion models with style and content control.
method Stochastic optimal control with a style descriptor and cross-attention aggregation.
result Precise content and style extraction and control without external adapters.
New model predicts sales of new products with short life cycles.
problem Forecasting sales of new products with short lead times and life cycles.
method Developed an exponential factorization machine (EFM) to consider attributes and pairwise interactions.
result EFM model outperforms existing models in terms of MAPE and MAE.
An empirical investigation of the interaction of sample size and discretization - in this case the entropy-based method CAIM (Class-Attribute Interdependence Maximization) - was undertaken to evaluate the impact and potential bias introduced into data mining performance metrics due to variation in sample size as it imp…
SureMap estimates model performance across subpopulations efficiently.
problem Estimating model performance across subpopulations with scarce data.
method Simultaneous Gaussian mean estimation with external data.
result High accuracy in both multi-task and single-task disaggregated evaluations.
Bayesian inference reconstructs external potentials in DFT for many-particle systems.
problem Reconstructing external potentials in classical density-functional theory (DFT) for many-particle systems.
method Combines Bayesian inference with classical DFT to probabilistically reconstruct external potentials.
result Accurately infers external potentials and density profiles with uncertainty quantification.
Paper tackles variable-length, incomplete wearable sensor data to improve personalized insights.
problem Variable-length and incomplete time series data from wearable sensors.
method HeartSpace integrates a time series encoding module and pattern aggregation network, along with a Siamese-triplet network for representation learning.
result Empirical evaluation shows significant performance gains in personality prediction, demographics inference, and user identification.
Proposes a method to use external machine-learning predictions in multinomial logistic regression.
problem Improving statistical inference using summary-level external machine-learning predictions.
method Empirical-likelihood framework incorporating moment constraints from external nonparametric machine-learning predictions.
result Fused estimator achieves strict efficiency gain over primary-only estimator under mild conditions.
Paper proposes AI for stock market forecasting using external knowledge.
problem Forecasting stock prices influenced by external factors.
method Learning from historical data and external temporal knowledge graphs modeled as Hawkes processes.
result Dynamic representations effectively rank stocks based on returns.
New estimator improves ATT estimation efficiency with external controls.
problem Reduced efficiency when incorporating external controls into ATT estimation.
method Proposes a novel doubly robust estimator for ATT that maintains higher efficiency than standard approaches.
result Demonstrates improved efficiency of the new estimator compared to standard approaches, even under model misspecification.
Method estimates model performance on external samples from limited statistical characteristics.
problem Limited access to multiple datasets due to privacy and commercial restrictions.
method Search for weights that match external statistics and are closest to uniform, using model performance on weighted internal sample as an estimation.
result Estimated external performance is closer to actual performance than internal performance.
The study assesses external validity by evaluating worst-case treatment effects across subpopulations.
problem Underrepresentation of marginalized groups and limited study populations.
method Develops a semiparametrically efficient estimator for worst-case treatment effects (WTE) and uses cross-fitting to guard against brittle findings.
result The proposed framework guards against invalid findings due to unanticipated population shifts.
A method for logistic regression inference using both internal and external data.
problem Inability to estimate intercept and marginal case proportion in case-control logistic regression.
method Empirical likelihood approach integrating internal and external data.
result Intercept parameter becomes identifiable with external information, and all parameters are estimable consistently.
Study long-term asset liquidation behavior with external flows.
problem Investigate optimal liquidation in presence of external flows.
method Convergence analysis of BSDEs for value function and strategy.
result Long-term liquidation may not occur due to external flows.
Study examines remittances in Nepal, linking external demand and domestic monetary conditions.
problem Understanding the dynamics of remittances in Nepal's economy.
method Constructed composite indices via PCA for external demand and domestic monetary conditions. Used ARDL, cointegration, DOLS, ECM, and machine learning for analysis.
result Strong positive long-run effect of external demand on remittances, significant negative impact of tighter domestic monetary conditions.
Framework for estimating treatment effects using external control data.
problem Improving efficiency in estimating average treatment effects (ATE) in hybrid trials.
method Developed a formal causal inference framework based on exchangeability assumptions and graphical criteria. Proposed estimators and efficient doubly-robust methods.
result Established finite-sample performance and demonstrated application to spinal muscular atrophy trial.
Algorithm learns RBMs with arbitrary external fields, improving on previous constraints.
problem Learning RBMs with arbitrary external fields, improving on previous constraints.
method Greedy algorithm that maximizes covariance between observed nodes sharing latent neighbors.
result Algorithm can learn RBMs with arbitrary external fields, improving on previous constraints.
Study identifies negative data externalities affecting model performance on specific groups.
problem Negative data externalities on group performance in machine learning models.
method Characterized and detected data-model inefficiencies, focusing on specific types of externalities.
result Negative data externalities can lower model performance on specific sub-groups, even with larger datasets.
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
Estimates non-parametric logistic model using case-control data and external summary info.
problem Imbalanced binary data in case-control studies.
method Two-step estimation procedure with deep neural network for functional approximation.
result Proposed estimator achieves optimal convergence rate in non-parametric regression.
The importance of nodes in a network constantly fluctuates based on changes in the network structure as well as changes in external interest. We propose an evolving teleportation adaptation of the PageRank method to capture how changes in external interest influence the importance of a node. This framework seamlessly g…
UAMM uses external market prices to improve AMM efficiency and reduce liquidity provider risk.
problem Traditional AMMs lack consideration of external markets and risk management.
method UAMM calculates prices by incorporating external market prices and impermanent loss, maintaining constant product curve properties.
result UAMM eliminates arbitrage opportunities when external market prices are efficient, reducing liquidity provider risk.
This article prices OTC derivatives with either an exogenously determined initial margin profile or endogenously approximated initial margin. In the former case, margin valuation adjustment (MVA) is defined as the liability-side discounted expected margin profile, while in the latter, an extended partial differential e…
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible a…
CAPM interpretation is flawed; beta reflects proxy for underlying driver, not causal transmission.
problem Inconsistent interpretation of CAPM regression as contemporaneous causation.
method Formalized CAPM as a structural causal model and analyzed admissible three-node graphs.
result Contemporaneous betas act like proxies rather than mechanisms; genuine market-to-stock channel appears only at a lag.
New framework for fairness in machine learning models using SHAP values and adversarial learning.
problem Fairness of model predictions, especially for unprivileged groups.
method Develops a new fairness definition and a framework using SHAP values and adversarial learning to mitigate bias.
result Models produced are fairer and performant, demonstrating the approach on various datasets.
Model shows PoS networks can be captured by external finance, leading to centralization.
problem Long-term centralization of PoS networks under external finance pressures.
method Heterogeneous macroeconomic model with two actor classes: investors and consumers.
result External finance forces PoS networks to centralize, leading to zero internal staking yield.
Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks of such systems have previously been studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may be…
In this paper, we provide an integrated systems modeling approach to analyzing global externalities from a microeconomic perspective. Various forms of policy (fiscal, monetary, etc.) have addressed flaws and market failures in models, but few have been able to successfully eliminate modern externalities that remain an …
The paper simplifies complex mechanical systems with external forces.
problem Analyzing symmetric discrete mechanical systems with external forces.
method Lagrangian reduction and reconstruction for principal bundles.
result Evolution of momentum maps and Poisson structures under different conditions.
Estimates personalized policies robust to shifts in target populations.
problem Estimating policies that perform well in diverse target populations.
method Develops methods for estimating robust policies considering shifts in outcomes and characteristics.
result Welfare-maximizing policies are robust to certain shifts in potential outcomes.
This paper addresses external validity bias in causal inference.
problem Estimating causal effects in a target population.
method Synthesis of approaches for generalizability and transportability, including tests for heterogeneity of treatment effects and differences between study and target populations.
result Framework for addressing external validity bias in causal inference.
Online learning algorithms, widely used to power search and content optimization on the web, must balance exploration and exploitation, potentially sacrificing the experience of current users for information that will lead to better decisions in the future. Recently, concerns have been raised about whether the process …
Study finds multifractal cross-correlations between agricultural markets and external uncertainties.
problem Investigating relationships between agricultural spot markets and external uncertainties.
method Multifractal detrending moving-average cross-correlation analysis (MF-X-DMA).
result Maize exhibits intrinsic joint multifractality with all uncertainty proxies.
Proposes a Taylor framework to unify and analyze attribution methods.
problem Lack of a unified guideline for feature contribution assignment in machine learning models.
method Introduces a Taylor attribution framework to model the attribution problem and reformulates fourteen mainstream methods.
result Empirically validates the Taylor reformulations and reveals a positive correlation between performance and principles followed.
The notion of \emph{policy regret} in online learning is a well defined? performance measure for the common scenario of adaptive adversaries, which more traditional quantities such as external regret do not take into account. We revisit the notion of policy regret and first show that there are online learning settings …
In this paper, we study the evolution of submannifold moving by mean curvature minus a external force field. We prove that the flow has a long-time smooth solution for all time under almost optimal conditions. Those conditions are that the second fundamental form on the initial submanifolds is not too large, the extern…