DADI framework dynamically discovers fair information using reinforcement learning.
problem Discovering fair information from third-party features with unknown objectives.
method Adversarial reinforcement learning agent that balances accuracy and fairness.
result Achieves group fairness by rewarding the agent with the adversary's loss.
Amortized Causal Discovery learns to infer causal graphs from time-series data, improving performance.
problem Inference of causal graphs from time-series data is inefficient due to fitting new models for each sample.
method Proposes Amortized Causal Discovery, a variational model that leverages shared dynamics across samples with different causal graphs.
result Significant improvements in causal discovery performance demonstrated experimentally.
Neural Shadow-Mapping uncovers causal links in dynamic systems.
problem Discovering causal structures in dynamic systems with mirage correlations.
method Neural network based method embedding high-dimensional data into a shadow representation for causal link estimation.
result Demonstrates performance in discovering causal links from video-representations of dynamic systems.
Deep learning improves model discovery from sparse sensor data.
problem Improving physical understanding and predictions from coarse, non-grid sampled data.
method Physics-informed neural networks and automatic differentiation.
result Deep learning can recover underlying equations from sparse, non-grid data.
Media tone around earnings announcements predicts stock returns.
problem Determining if media tone around earnings announcements provides useful information for stock prices.
method Conducted an event study on media tone around earnings announcements for nonfinancial S&P 500 firms.
result Media tone around earnings announcements predicts abnormal stock returns.
A new method uses LLMs to discover causal pathways that affect fairness in machine learning.
problem Discovering fairness-relevant causal pathways in the presence of noise and confounding.
method Hybrid LLM-guided causal discovery framework combining active learning and dynamic scoring.
result LLM-guided methods, including the proposed active, dynamically scored variant, outperform baselines in recovering fairness-relevant structure under noisy conditions.
Bayesian framework for robust model discovery from noisy data.
problem Robust model discovery from noisy, sparse and irregular observations of nonlinear systems.
method Bayesian differential programming using Hamiltonian Monte Carlo and sparsity-promoting priors.
result Efficient inference of posterior distributions over plausible models with quantified uncertainty.
New method discovers causal relationships in complex time series data.
problem Discovering causal relationships in multivariate time series is challenging.
method Temporal Dependency to Causality (TD2C) framework using mutual information.
result TD2C achieves state-of-the-art performance in causal discovery.
Study shows informed traders harm market makers but price discovery benefits outweigh costs.
problem Informed traders' impact on market makers' profitability.
method Agent-based model with heterogeneous learning agents, multi-agent reinforcement learning.
result Informed market order flow is harmful when aggregate informedness is low but beneficial as it increases.
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant infor…
Paper discovers structural dynamics equations from only acceleration data.
problem Discovering equations from only acceleration measurements in structural dynamics.
method Library-based approach with Approximate Bayesian Computation (ABC) prioritizing parsimonious models.
result Efficacy demonstrated in four structural dynamics examples, including linear and nonlinear systems.
b-LOAD extends local causal discovery with prior knowledge, improving causal effect estimation.
problem Local causal discovery struggles in data-scarce settings due to uncertainty and incomplete neighborhoods.
method b-LOAD incorporates prior knowledge directly into local structure learning, using Meek's rules to refine discovery.
result b-LOAD refines the admissible equivalence class and enlarges identifiable causal queries, improving causal effect estimation.
This paper uses LLMs for causal discovery with active learning and dynamic scoring to improve efficiency and fairness.
problem High computational demands and complexities of large-scale data in causal discovery.
method Metadata-based approach, BFS strategy, Active Learning, Dynamic Scoring Mechanism, LLM confidence scores.
result Significantly reduced number of queries and improved efficiency in causal graph construction.
MetaPhysiCa tackles robust physics-informed machine learning for OOD tasks.
problem Designing robust PIML methods for OOD forecasting tasks in physics.
method Meta-learning procedure for causal structure discovery including invariant risk minimization.
result Significantly outperforms existing PIML and deep learning methods in OOD tasks.
BLADE uses Bayesian methods to discover complex systems from scarce data.
problem Efficiently discovering governing equations of complex dynamical systems from limited data.
method Combines replica-exchange stochastic gradient Langevin Monte Carlo with active learning.
result Reduces measurement requirements by 60% for Lotka-Volterra and 40% for Burgers' equation.
Latent dynamics discovery is challenging in extracting complex dynamics from high-dimensional noisy neural data. Many dimensionality reduction methods have been widely adopted to extract low-dimensional, smooth and time-evolving latent trajectories. However, simple state transition structures, linear embedding assumpti…
New method uses path signatures for causal discovery in time series data.
problem Challenges in understanding causal structure from observational time series data.
method Path signatures and signed areas for model-free causal discovery.
result Confidence sequence regions help identify lag/lead causal relationships.
Algorithm learns stochastic system dynamics from data.
problem Recovering interpretable symbolic expressions for stochastic systems.
method Data-driven, trajectory averaging, drift-informed correction.
result Recover coefficients and densities to within 5% and 0.01 in total variation, respectively.
This review explores the use of machine learning in discovering collective variables for biomolecular dynamics.
problem Understanding the conformational dynamics and molecular recognition in biomolecules.
method Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulations.
result Machine learning algorithms can be used to discover abstract collective variables that describe biomolecular dynamics.
This study compares price discovery in ETH and BTC markets between centralized and decentralized exchanges.
problem Understanding price discovery dynamics in cryptocurrency markets.
method Comparative analysis of centralized and decentralized exchanges, using econometric tools.
result Centralized exchanges lead in ETH price discovery, while futures markets lead in BTC.
Tensor-based method simplifies causal skeleton discovery.
problem Discover causal relationships between variables.
method Express associations as tensors to reduce dimensionality.
result Causal skeleton can be determined using pair-wise tensors.
AC-State discovers minimal latent state for control.
problem Discover minimal latent state from sensory information.
method Multi-step inverse model with information bottleneck.
result Guaranteed discovery of control-endogenous latent states.
A new framework predicts links in time-dependent networks using Bernoulli autoregression.
problem Predicting links in time-dependent networks with additional auxiliary information.
method A Bernoulli autoregressive model with regularization for link discovery.
result The model can discover new links not present in the data.
The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a Bayesian method that allows topic discovery to inform the latent network model and the network structure to facilitate topic identification. We apply this method to the 467 top polit…
DiffATD efficiently discovers targets in partially observable environments using diffusion dynamics.
problem Efficiently discovering targets in partially observable environments with limited sampling.
method DiffATD uses diffusion dynamics to maintain a belief distribution over unobserved states, balancing exploration and exploitation.
result DiffATD outperforms baselines and supervised methods in diverse domains.
This paper investigates the impact of dark pools on price discovery (the efficiency of prices on stock exchanges to aggregate information). Assets are traded in either an exchange or a dark pool, with the dark pool offering better prices but lower execution rates. Informed traders receive noisy and heterogeneous signal…
Framework simplifies vision-based control and goal discovery.
problem Learning proportional control from visual data.
method Introduces NewtonianVAE for proportional control and goal discovery.
result Dramatic simplification and acceleration of vision-based controllers.
We demonstrate the application of an algorithmic trading strategy based upon the recently developed dynamic mode decomposition (DMD) on portfolios of financial data. The method is capable of characterizing complex dynamical systems, in this case financial market dynamics, in an equation-free manner by decomposing the s…
We model continuous-time information flows generated by a number of information sources that switch on and off at random times. By modulating a multi-dimensional Lévy random bridge over a random point field, our framework relates the discovery of relevant new information sources to jumps in conditional expectation mart…
ASD algorithm maximizes model estimates by adaptively labeling points.
problem Maximizing model estimates through adaptive labeling of points in a sequential decision-making problem.
method Formulated a general information-directed sampling (IDS) algorithm with theoretical guarantees for linear, graph, and low-rank models.
result IDS algorithm outperforms in both simulation and real-data experiments for discovering chemical reaction conditions.
Boosts causal discovery by dynamically reweighting samples to learn better DAGs.
problem Overfitting to easier-to-fit samples and violating homogeneity assumptions in causal discovery.
method Adaptive sample reweighting via ReScore function to upweight and downweight samples based on fitting quality.
result Consistent and significant boosts in structure learning performance on synthetic and real-world datasets.
New method for causal discovery in high dimensions with confounder blanket assumption.
problem Inferring causal relationships from observational data in high dimensions.
method Relaxes parametric restrictions and sparsity constraints, focusing on confounder blanket.
result Provable sound and complete structure learning algorithm with finite sample error control.
This paper extends a Kyle model to include price-responsive traders, revealing new dynamics and equilibria.
problem Real-world market dynamics involve price-responsive traders, affecting market equilibrium and insider profits.
method Developed a continuous-time Kyle model with two types of price-responsive traders (momentum and contrarian), leading to a forward-backward Riccati system for equilibrium.
result The model shows that feedback effects can lead to multiple equilibria and amplify price informativeness.
EDL discovers state-covering skills without relying on task rewards.
problem Discovering skills in reinforcement learning without a task-oriented reward function.
method EDL optimizes information-theoretic objective using different machinery to address coverage problem.
result EDL discovers state-covering skills more effectively than existing methods.
New method integrates sparse parametric and nonparametric techniques for complex system modeling.
problem Lack of accurate modeling for complex biological systems due to nonlinearities.
method Sparse nonparametric estimation framework combining parametric and nonparametric techniques.
result Accurately captures nonlinearities in complex systems without prior information.
Proposes DCNAR for dynamic causal inference from neural time series.
problem Uncertainty and evolution of causal structure in real-world domains.
method Two-stage neural causal modeling integrating discovery and inference.
result Dynamic causal inferences are more stable and meaningful than alternatives.
This paper explores conformal prediction in the learning under privileged information (LUPI) paradigm. We use the SVM+ realization of LUPI in an inductive conformal predictor, and apply it to the MNIST benchmark dataset and three datasets in drug discovery. The results show that using privileged information produces va…
Automates discovery of interpretable Lagrangians from data.
problem Lack of interpretable Lagrangian discovery methods.
method Data-driven machine learning algorithm to derive interpretable Lagrangians.
result Automated discovery of interpretable Lagrangians and conservation laws.
Temporal observations such as videos contain essential information about the dynamics of the underlying scene, but they are often interleaved with inessential, predictable details. One way of dealing with this problem is by focusing on the most informative moments in a sequence. We propose a model that learns to discov…
GP-NODE combines Gaussian processes and NeuralODEs for Bayesian system identification.
problem Bayesian systems identification from partial, noisy and irregular observations.
method Differentiable programming, Hamiltonian Monte Carlo, Gaussian Process priors, sparsity-promoting priors.
result Efficient inference of posterior distributions over plausible models with quantified uncertainty.
Bayesian nonparametrics enables dynamic skill discovery from expert data.
problem Fixed K for offline skill discovery in reinforcement learning.
method Variational inference, continuous relaxations, Bayesian nonparametrics.
result Nonparametric model with dynamically-changing number of options.
Paper proposes DTW-SOM for visual exploration of time-series motifs.
problem Exploring motifs extracted by time-series motif discovery algorithms.
method Adapted Self-Organizing Map (DTW-SOM) using Dynamic Time Warping distance.
result DTW-SOM effectively visualizes motifs from time-series data.
New method converts video of dye plumes into PDEs for better understanding.
problem Inferring continuum models from uncalibrated video data.
method Develops a pipeline to convert grayscale recordings into scalar fields, isolates drift, and identifies transport laws.
result Selected reduced model outperforms advection-diffusion baselines and retains structural interpretability.
Information theoretic measures (e.g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in high dimension. If these dependencies are assumed to follow a Markov factor graph model, this exploration process is called structure discover…
New method uses information theory to uncover causal relationships in complex systems.
problem Discovering causal relationships in multivariate systems, especially in Bayesian networks and hypergraphs.
method Partial Information Decomposition (PID) to explicitly model higher-order interactions.
result PID components reveal direct causal neighbors and collider relationships in Bayesian networks and multi-tail hyperedges in causal hypergraphs.
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors and solve diverse tasks. However, learning an accurate model for complex dynamical systems is diffi…
This paper reviews information theory in open-world machine learning.
problem Lack of a unified theoretical foundation for open-world machine learning.
method Synthesis of information theoretic approaches.
result Established a pathway toward provable and trustworthy open world intelligence.
Paper discovers differential equations from data using neural networks and Bayesian methods.
problem Discovering differential equations from datasets using machine learning.
method Integrates neural network-based surrogates with Sparse Bayesian Learning (SBL).
result Proposes a robust model discovery algorithm and a Physics Informed Normalizing Flow (PINF).