New approach to learning cascaded classifiers for heterogeneous embedded devices.
problem Real-time monitoring and adaptive interventions in mobile health.
method Generalized tree-structured cascades, joint learning of classifiers, novel output combination during training.
result Joint learning of all detectors for tree-structured cascades in resource-constrained environments.
A new framework approximates covariance matrices using tree decompositions.
problem Approximating covariance matrices for Gaussian distributions.
method Cascade of tree decompositions with Cholesky factorization.
result The proposed framework guarantees convergence and outperforms KL divergence.
On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and c…
Method predicts diffusion reach probabilities using node embeddings.
problem Estimating diffusion reach probabilities with limited cascades and network information.
method Representation learning on node embeddings for cascade prediction.
result Proposed method outperforms using available cascade data.
This paper optimizes power grid protection settings to maximize network degradation due to cascading attacks.
problem Cascading attacks on power grids and their undetected nature.
method Constrained Bayesian Optimization applied to transmission line protection settings.
result Even limited misconfiguration of protection settings can cause severe cascading attacks.
Model infers diffusion networks from heterogeneous cascade data.
problem Understanding and predicting diffusion processes in interconnected populations.
method Double mixture directed graph model with layer-specific constraints.
result Convex formulation allows for statistical and computational guarantees.
New insights into cascade feedback linearization of control systems.
problem Obtaining a cascade feedback linearization for invariant control systems.
method Introducing truncated versions of operators from the calculus of variations to prove new theorems.
result Established new geometry and foundational theorems for future work.
The problem of learning tree-structured Gaussian graphical models from independent and identically distributed (i.i.d.) samples is considered. The influence of the tree structure and the parameters of the Gaussian distribution on the learning rate as the number of samples increases is discussed. Specifically, the error…
New tree structure for pseudo-Anosovs from interval maps.
problem Understanding pseudo-Anosovs from interval maps.
method Tree structure on pseudo-Anosovs using rational numbers.
result Deepened dictionary between invariants.
Study a risk model with tree-structured Poisson-Markov random field for rainfall events.
problem Dependence between rainfall frequencies in insurance portfolios.
method Tree-structured Markov random field with Poisson marginals.
result Asymptotic results for portfolio risk and risk allocation.
The paper improves theoretical guarantees for Thompson Sampling in cascading bandits.
problem Optimizing online recommender systems with cascading bandits.
method Develops and analyzes new Thompson Sampling algorithms for cascading bandits.
result Establishes the first theoretical guarantees on Thompson Sampling for cascading bandits.
New research reveals diverse cascade sizes in finite networks, challenging traditional risk assessments.
problem Predicting the size of cascades in finite networks is difficult due to uncertain parameters and missing information.
method Derived explicit closed-form solutions for cascade size distributions in complete and star networks.
result Broad and even bimodal cascade size distributions in finite networks, not centered around the average.
Modeling cascading behavior in complex systems using CTBNs.
problem Understanding which states trigger cascading events in complex systems.
method Continuous-time Bayesian networks (CTBNs) for modeling and identifying likely sentry states.
result Identification of likely sentry states that may lead to cascading behavior.
The inequality of wealth distribution is a universal phenomenon in the civilized nations, and it is often imputed to the Matthew effect, that is, the rich get richer and the poor get poorer. Some philosophers unjustified this phenomenon and tried to put the human civilization upon the evenness of wealth. Noticing the f…
Paper learns tree-structured models from distributed data efficiently.
problem Learning tree-structured Gaussian graphical models from distributed data with limited communication.
method Communication-efficient strategies for central node to learn structure from distributed data.
result Tree structure can be reliably recovered even with limited information (signs of local data samples).
End-to-end learning framework for tree-structured data.
problem Learning models struggle with tree-structured data due to lack of fixed-length vectors.
method Developed a novel framework for generic semantic tree-structured data of arbitrary topology.
result Framework yields comparable performance to standard models with dedicated feature-vectors and outperforms in compositional data.
Study on information cascade fragility under mismatched revealing probabilities.
problem Analyzing the fragility of information cascades in decision-making processes with imperfect knowledge of revealing probabilities.
method Examined sequential decision-making models with players having private information and imitating previous decisions. Studied the effect of a mismatch between players' beliefs and actual revealing probabilities.
result Derived closed-form expressions for optimal learning rates and identified phase transitions in the behavior of asymptotic learning rates.
Paper presents a new method for learning hyperbolic representations using tree structures.
problem Learning faithful low-dimensional hyperbolic embeddings of data.
method Metric-first approach to learn tree structure, then embed into hyperbolic manifold.
result Novel fast algorithm TreeRep learns tree approximating original metric.
A search engine usually outputs a list of K web pages. The user examines this list, from the first web page to the last, and chooses the first attractive page. This model of user behavior is known as the cascade model. In this paper, we propose cascading bandits, a learning variant of the cascade model where the obje…
Paper uses RL to mitigate cascading failures in power systems.
problem Mitigating multi-stage cascading failures in power grids.
method Reinforcement Learning applied to DC-OPF for power flow optimization.
result Reduced system collapse rates through RL-based optimization.
Optimal rates for learning hidden tree structures are determined.
problem Learning hidden tree structures from noisy data.
method Study of the (noisy) information threshold and the Chow-Liu algorithm.
result Optimal rates for structure recovery are inversely proportional to the information threshold squared.
Structured prediction tasks pose a fundamental trade-off between the need for model complexity to increase predictive power and the limited computational resources for inference in the exponentially-sized output spaces such models require. We formulate and develop the Structured Prediction Cascade architecture: a seque…
Bayesian optimization tackles expensive cascade processes.
problem Optimizing multistage decision-making processes with expensive costs.
method Formulated as Bayesian optimization framework with two types of acquisition functions.
result Demonstrated effectiveness through numerical experiments and a solar cell simulator application.
Cascades of information-sharing are a primary mechanism by which content reaches its audience on social media, and an active line of research has studied how such cascades, which form as content is reshared from person to person, develop and subside. In this paper, we perform a large-scale analysis of cascades on Faceb…
Model shows financial turbulence similar to turbulence, with wealth cascading from large to small entities.
problem Understanding wealth distribution and dynamics in financial systems.
method Constructed a multiscale model for hierarchical financial structures.
result Found wealth distribution exhibits power law at large scales and Maxwellian at small scales.
Modeling social network activity through user and topic interaction.
problem Understanding complex dynamics of opinion formation in social networks.
method Mixture of Interacting Cascades (MIC) model of marked multidimensional Hawkes processes.
result MIC achieves superior performance in modeling information cascade spread.
The paper addresses the k-tangle enumeration problem. We introduce a notion of cascade diagram for k-tangle projections. An effective enumeration algorithm for projections is proposed based on cascade representation. Tangles projections with up to 12 crossings are tabulated. We provide also pictures of alternating …
Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we …
Paper tackles robust estimation of tree-structured Ising models without side information.
problem Learning tree-structured Ising models with flipped signs of variables.
method Proves unidentifiability, proposes an algorithm with logarithmic sample complexity and polynomial run-time complexity.
result Empirically demonstrates robustness of proposed algorithm in the flipped signs setting.
We consider the problem of finding the graph on which an epidemic cascade spreads, given only the times when each node gets infected. While this is a problem of importance in several contexts -- offline and online social networks, e-commerce, epidemiology, vulnerabilities in infrastructure networks -- there has been ve…
The paper uses tensor decompositions to improve neural network models for tree data.
problem Encoding structural knowledge from tree-structured data efficiently.
method Introduces new aggregation functions using Canonical and Tensor-Train decompositions.
result Proposed models outperform traditional methods on tree classification tasks.
DICE estimates data influence cascade in decentralized learning networks.
problem Lack of fair incentives discourages participation in decentralized learning.
method Designs DICE to estimate influence cascade in decentralized networks.
result Influence cascade is influenced by data, topology, and loss landscape curvature.
Unified approach for influence maximization using diffusion cascade representations.
problem Influence maximization on networks with diffusion cascades.
method Multi-task neural network learning influencer and susceptible vectors; greedy algorithm for influence maximization.
result IMINFECTOR outperforms other methods in efficiency and seed set quality.
Tree-structured boosting connects gradient boosted stumps and full decision trees.
problem Connecting gradient boosted stumps and full decision trees.
method Introducing tree-structured boosting to create a single decision tree.
result Tree-structured boosting produces models equivalent to CART or gradient boosted stumps at the extremes.
Framework predicts contagion in assortative banking networks.
problem Predicting contagion in assortative banking networks.
method Probabilistic framework with explicit edge probabilities, analyzing default cascades.
result Cascade condition characterizes systemic risk in banking networks.
New method solves tree-structured Schrödinger Bridge problems.
problem Computing Schrödinger Bridge between tree-structured distributions.
method Iterative Markovian Fitting (IMF) procedure for tree-structured costs.
result Extends IMF to tree-structured Schrödinger Bridge problems.
Improves information cascade models using contrastive training and DSTs.
problem Improving models of information cascades using limited labeled data.
method Proposes a contrastive training procedure for models of information cascades as directed spanning trees (DSTs).
result Unsupervised training with additional content features achieves significantly better results, reaching half the accuracy of a fully supervised model.
A cascaded CNN reduces artifacts in low-dose CT image denoising.
problem Reduce artifacts in denoised low-dose CT images.
method Cascaded training of CNNs on a dataset to iteratively improve denoising performance.
result The cascaded CNN approach significantly reduces artifacts in denoised CT images.
Paper presents a probabilistic model to improve LLM cascade performance.
problem Complexity of LLM cascades and their interaction error rates.
method Probabilistic model for joint performance distribution of LLMs.
result Improves area under the error-cost curve by 4.3% on average for cascades with k≥3 models.
We define the beta diffusion tree, a random tree structure with a set of leaves that defines a collection of overlapping subsets of objects, known as a feature allocation. A generative process for the tree structure is defined in terms of particles (representing the objects) diffusing in some continuous space, analogou…
Model explains financial market intermittency with negative correlation.
problem Verifying multifractality in financial markets.
method Extended multiplicative random cascade model with an additional stochastic term.
result Model accurately reproduces empirical financial data.
Extends neural network approximation to probability measures and tree-structured data.
problem Universal approximation of functions on probability measures and tree-structured domains.
method Proof of neural network density in probability measure spaces and Cartesian products.
result Universal approximation theorem for tree-structured domains, including JSON.
Exact learning of tree-structured models with side info and noise.
problem Learning tree-structured graphical models with side information and noise.
method Probabilistic tools from strong large deviations theory.
result Exact asymptotics of structure learning from samples.
Model financial default cascades on sparse graphs via hitting times.
problem Capturing systemic risk in large, sparsely-connected financial networks.
method Dynamic particle systems with hitting times and convergence theory.
result Characterization of default time distribution in tree-like networks.
Improved Bayesian optimization for conditional parameter spaces.
problem Efficient global optimization of expensive-to-evaluate functions in conditional parameter spaces.
method Additive tree-structured covariance function for conditional parameter optimization.
result Significantly improved sample-efficiency and wider applicability compared to existing methods.
Estimating tree structured Gaussian Graphical Model from noisy data.
problem Recover the original independence structure from noisy observations.
method Address the unidentifiability of tree structured graphical models and provide an algorithm to find the equivalence class of trees.
result An O(n^3) algorithm to find the equivalence class of trees.
A model infers dynamic networks from partial observation of cascading processes.
problem Inferring evolving networks from partial observation of node and edge data.
method A novel framework based on a mixture of coupled hierarchical Dirichlet processes.
result Explicit predictive distribution over edges of the underlying network, including future edges.
Develops a novel framework for activity shaping in social networks.
problem Controlling user activity spread in social networks.
method Optimizes the spectral radius of the Hazard matrix for local control actions.
result Minimizes the influence of information cascades through targeted resource allocation.