Paper proposes adversarial modifications for link prediction models to improve robustness and interpretability.
problem Improving accuracy is not enough; robustness and interpretability are also crucial for link prediction models.
method Adversarial modifications to identify influential facts and evaluate model sensitivity and interpretability.
result The approach identifies the most influential facts and evaluates the sensitivity of link prediction models to additional facts.
Neural model uses deductive database to predict events from past patterns.
problem Difficulty in predicting future events from past patterns when event types are large.
method Temporal deductive database with rules to prove facts from other facts and past events. Neural nets model fact states and probabilities.
result Neural models derived from concise Datalog programs improve prediction by encoding domain knowledge.
FAKTA automates fact checking across media sources.
problem Automating fact checking across diverse media sources.
method Unified framework integrating document retrieval, stance detection, evidence extraction, and linguistic analysis.
result FAKTA predicts factuality and provides evidence for claims.
RE-NET predicts future interactions in temporal knowledge graphs.
problem Predicting future facts in temporal knowledge graphs.
method Autoregressive architecture with recurrent event encoder and neighborhood aggregator.
result State-of-the-art performance on five public datasets.
TuckER predicts missing facts in knowledge graphs using tensor decomposition.
problem Predicting missing facts in knowledge graphs.
method TuckER uses Tucker decomposition of binary tensor representations.
result TuckER outperforms state-of-the-art models in link prediction.
We create a large dataset for fact checking claims and improve prediction accuracy.
problem Fact checking claims from multiple sources is challenging.
method We created a comprehensive dataset and developed a novel method for automatic veracity prediction.
result Our model achieves a Macro F1 of 49.2%, showing significant performance improvements.
Time-aware fact-checking improves veracity predictions for time-sensitive claims.
problem Fact-checking decisions should consider temporal information of claims and evidence.
method Investigated four temporal ranking methods to optimize evidence ranking for fact-checking models.
result Time-aware evidence ranking surpasses relevance assumptions and improves veracity predictions for time-sensitive claims.
COMPAS recidivism predictions show racial bias against African Americans, study finds.
problem Racial bias in recidivism prediction algorithms.
method Causal analysis using FACT, a fairness measure grounded in causal inference.
result COMPAS shows racial bias against African American defendants, robust to unmeasured confounding.
Tensor-based embeddings improve knowledge graph fact prediction.
problem Predicting new facts in knowledge graphs.
method Knowledge-Enriched Tensor Factorization
result 5% to 50% relative improvement over state-of-the-art techniques.
NePTuNe combines neural and tensor methods for efficient link prediction in knowledge graphs.
problem Incomplete knowledge graphs, especially in link prediction.
method Hybrid model combining neural and tensor factorization methods.
result NePTuNe achieves state-of-the-art performance on FB15K-237 and near state-of-the-art on WN18RR datasets.
Analyzed Indian stock market data to find stylized facts with deviations.
problem Identifying stylized facts in the Indian stock market.
method Historical daily data analysis of NIFTY index stocks over 11 years.
result Significant deviations in leverage, asymmetry, and autocorrelation observed.
Research aims to make fact-checking models more transparent.
problem Making fact-checking models explainable in a complex field.
method Combines fact-checking methods with explainable AI techniques.
result Developed initial solutions for explainable fact-checking.
Publication bias skews asset pricing research findings.
problem Bias in sharing and publishing research findings.
method Meta-studies and empirical Bayes corrections.
result Publication bias effects are minimal and not dominant.
Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of (subject,relation,object). Current KG completion models compel two-thirds of a triple provided (e.g., subject and relation) to predict the remaining one. In this paper, we propose a new…
Neural-symbolic model improves link prediction in knowledge graphs.
problem Effective relational learning and reasoning for AI systems.
method Neural-symbolic graph neural network that learns over all paths in knowledge graphs.
result Neural-symbolic model outperforms path-based approaches in link prediction.
We present and study a Minority Game based model of a financial market where adaptive agents -- the speculators -- interact with deterministic agents -- called producers. Speculators trade only if they detect predictable patterns which grant them a positive gain. Indeed the average number of active speculators grows wi…
Separable losses are inconsistent for structured prediction models.
problem Inconsistency of separable losses in structured prediction models.
method Analysis of separable negative log-likelihood losses for structured prediction.
result Separable losses are not Bayes consistent and may not predict the most probable structure.
A method for reasoning on knowledge graphs using debate dynamics.
problem Automatic reasoning on knowledge graphs with interpretability.
method Reinforcement learning agents debate over facts, judge decides truth.
result Method outperforms baselines on triple classification and link prediction tasks.
Task focuses on fact checking in Q&A forums, improving over baseline systems.
problem Fact checking in community Q&A forums to distinguish factual from opinion.
method Two subtasks: distinguishing factual vs. opinion/advice/socializing, predicting answer truthfulness.
result Improved over baseline systems for both subtasks, but not for Subtask B.
Improved volatility forecasting using 1D CNNs with transfer learning.
problem Forecasting stock price volatility using deep learning.
method Used 10 years of daily stock prices, applied transfer learning with CNNs.
result Transfer learning with CNNs outperformed classical ARIMA methods.
Biological data are extremely diverse, complex but also quite sparse. The recent developments in deep learning methods are offering new possibilities for the analysis of complex data. However, it is easy to be get a deep learning model that seems to have good results but is in fact either overfitting the training data …
Price and return predictions are limited by economic complexity, not just volatility.
problem Limited accuracy of price and return probability forecasts by Gaussian distributions.
method Analyzes economic reasons behind limitations in predicting price and return statistical moments.
result Predictions of price and return probabilities by Gaussian distributions are inaccurate due to economic complexity.
Deep LSTM model predicts stock price movements for profitable trading.
problem Developing effective stock prediction models and trading strategies.
method Deep long short-term memory (LSTM) neural network for price predictions, optimized for profitability.
result 340% cumulative returns on S&P 500 over 2010-2018, outperforming benchmarks.
Autoregressive flow models can perform causal discovery and inference tasks.
problem Causal inference tasks such as causal discovery and interventional predictions.
method Using autoregressive flow models to estimate causal directions and make predictions.
result Autoregressive flows can accurately perform causal inference tasks without restrictive assumptions.
The paper reviews exchangeability and its implications for conformal prediction and rank tests.
problem Ensuring distribution-free predictive inference in machine learning and statistics.
method Explains exchangeability and its role in conformal prediction and rank tests.
result Discovers similarities between conformal prediction and rank tests based on exchangeability.
We propose and analyze a regularization approach for structured prediction problems. We characterize a large class of loss functions that allows to naturally embed structured outputs in a linear space. We exploit this fact to design learning algorithms using a surrogate loss approach and regularization techniques. We p…
This paper re-examines conformal e-prediction and its advantages over conformal prediction.
problem The relationship between conformal prediction and conformal e-prediction.
method Systematic re-examination of conformal prediction and conformal e-prediction from a modern perspective.
result Conformal e-prediction has advantages such as ease of designing conditional predictors and guaranteed validity of cross-predictors.
Separating the short jobs from the long is a known technique to improve scheduling performance. In this paper we describe a method we developed for accurately predicting the runtimes classes of the jobs to enable this separation. Our method uses the fact that the runtimes can be represented as a mixture of overlapping …
High-capacity neural network ensembles often benefit more from high-capacity models than from increased diversity.
problem The performance of high-capacity neural network ensembles is often harmed by interventions that promote predictive diversity.
method A large-scale study of nearly 600 neural network classification ensembles, examining various interventions and architectures.
result Discouraging predictive diversity can be benign in large-network ensembles, and higher-capacity models often yield better performance than diverse architectures.
The paper introduces a new σ-LSTM cell for volatility forecasting using stylized facts.
problem Lack of explainability and stylized knowledge in neural network volatility modeling.
method Introduces a new σ-LSTM cell with a stochastic processing layer, designed to incorporate stylized facts about volatility. result Shows good out-of-sample forecasting performance with the σ-LSTM cell. This study compares 16 embedding-based link prediction methods for knowledge graphs.
problem KG incompleteness and missing facts prediction.
method Embedding-based link prediction methods compared.
result Effective and efficient comparison of 16 embedding-based LP methods.
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…
Causal autoregressive flows enable accurate causal inference and prediction.
problem Causal discovery and interventional predictions in machine learning.
method Autoregressive normalizing flows with fixed variable orderings.
result Causal models derived from autoregressive flows are identifiable and allow for accurate interventional and counterfactual predictions.
Solves expert prediction problem for 4 experts in finite time horizon.
problem Expert prediction problem in finite horizon with 4 experts.
method Solves nonlinear PDE, shows C2 solution, proves Nash equilibrium and regret conjectures. result Proves Finite vs Geometric regret conjecture for N=4 and shows comb strategies are optimal. Network-assisted regression uses conformal prediction for valid inference.
problem Predicting node attributes using network and conventional covariates with valid statistical inference.
method Network analog of conformal prediction under mild joint exchangeability assumption.
result Achieves finite sample validity and asymptotic conditional validity for various network covariates.
Perceptrons have been known for a long time as a promising tool within the neural networks theory. The analytical treatment for a special class of perceptrons started in seminal work of Gardner \cite{Gar88}. Techniques initially employed to characterize perceptrons relied on a statistical mechanics approach. Many of su…
Prices in financial markets exhibit extreme jumps far more often than can be accounted for by external news. Further, magnitudes of price changes are correlated over long times. These so called stylized facts are quantified by scaling laws similar to, for example, turbulent fluids. They are believed to reflect the comp…
Predictive sampling improves on Thompson sampling for non-stationary bandit environments.
problem Thompson sampling fails in non-stationary bandit environments.
method Proposes predictive sampling, which deprioritizes actions based on information loss rate.
result Predictive sampling outperforms Thompson sampling in all tested non-stationary environments.
We pick up the regime switching model for asset returns introduced by Rogers and Zhang. The calibration involves various markets including implied volatility in order to gain additional predictive power. We focus on the calculation of risk measures by Fourier methods that have successfully been applied to option pricin…
New method estimates uncertainty in knowledge graph embeddings using neural variational inference.
problem Estimating uncertainty in knowledge graph embeddings.
method Constructs an inference network conditioned on symbolic representations of entities and relation types in a Knowledge Graph.
result Improved predictive uncertainty estimates during link prediction.
Study predicts price predictability in ultra-high frequency financial data using entropy tests.
problem Tackles predictability of ultra-high frequency financial data.
method Develops statistical tests based on Shannon entropy and Kullback-Leibler divergence to analyze predictability.
result Degree of randomness increases with aggregation level in transaction time.
Spring-electrical models predict network links based on node proximity.
problem Predicting links in networks.
method Spring-electrical models applied to network layouts.
result The Euclidean distance in network layouts correlates with link probabilities.
Paper tackles stance detection across domains using adversarial domain adaptation.
problem Stance detection in different domains is costly and tedious.
method Adversarial domain adaptation for stance detection.
result Model effectively transfers knowledge for accurate stance detection across domains.
New methods predict links in hypergraphs with multiple entities.
problem Link prediction in knowledge hypergraphs with non-binary relations.
method Introduce HSimplE and HypE embedding-based methods for hypergraphs.
result Proposed methods outperform baselines in hypergraph prediction.
Kernel Bayes' rule has been proposed as a nonparametric kernel-based method to realize Bayesian inference in reproducing kernel Hilbert spaces. However, we demonstrate both theoretically and experimentally that the prediction result by kernel Bayes' rule is in some cases unnatural. We consider that this phenomenon is i…
The paper tackles time series data by applying conformal prediction with nearest neighbors.
problem Time series data violates the exchangeability assumption required for conformal prediction.
method The approach uses the nearest neighbors method with fast parameter tuning and weighted nearest neighbors (FPTO-WNN) to construct reliable prediction intervals.
result Data analysis shows the effectiveness of the proposed approach.
The twisted Connes-Moscovici higher index theorem is generalized to the case of good orbifolds. The higher index is shown to be a rational number, and in fact non-integer in specific examples of 2-orbifolds. This results in a non-commutative geometry model that predicts the occurrence of fractional quantum numbers in t…
The two main tasks in the Recommender Systems domain are the ranking and rating prediction tasks. The rating prediction task aims at predicting to what extent a user would like any given item, which would enable to recommend the items with the highest predicted scores. The ranking task on the other hand directly aims a…