New study shows FTRL mechanism works with correlated events.
problem Forecasting competitions with correlated events.
method Introduces block correlation and uses FTRL mechanism.
result FTRL mechanism retains ε-optimal guarantee with O(b2log(n)/ε2) events for correlated events. Method detects new physics signals without prior knowledge.
problem Selecting signal regions for novel particles.
method Model-agnostic approach using low-pass filtering and density estimation.
result Efficiently identifies data-driven signal regions in high-dimensional feature space.
Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused primarily on preserving pairwise ordering of estimated event times, or relative risk. Model calibration is relatively under explored, despite it…
We develop a probabilistic framework for sequential random projection.
problem Challenges of sequential decision-making under uncertainty.
method Novel construction of a stopped process and method of mixtures.
result Achieved a non-asymptotic probability bound for random projection.
In several real-world applications involving decision making under uncertainty, the traditional expected value objective may not be suitable, as it may be necessary to control losses in the case of a rare but extreme event. Conditional Value-at-Risk (CVaR) is a popular risk measure for modeling the aforementioned objec…
The study identifies and predicts extreme stock price fluctuations using HHT and SVM.
problem Sporadic large stock price fluctuations due to various factors.
method Hilbert-Huang Transformation (HHT) for identifying extreme events (EEs) and Support Vector Regression (SVR) for forecasting.
result High instantaneous energy concentration in stock price during both positive and negative extreme events.
New property ensures neural networks generalize well with limited data.
problem Limited training data limits model generalization in neural networks.
method Introduces NeuRIP, a uniform concentration event for ReLU networks.
result All shallow ReLU networks generalize uniformly if they achieve NeuRIP.
Study finds consumers are more price-sensitive before livestreams than after.
problem Understanding consumer demand during livestreaming lifecycle.
method Examined consumer demand for live events and recorded versions using data from a livestreaming platform.
result Demand is more price-sensitive before livestreams than after.
Unified framework for measuring concentration in weighted networks considering both weight distributions and network structure.
problem Traditional indices neglect the topology of relationships among network elements.
method Develops a family of topology-aware concentration indices that jointly account for weight distributions and network structure.
result The proposed indices preserve key properties and allow concentration to be evaluated across different dimensions of dependence.
We study the behavior of simple models for financial markets with widely spread frequency either in the trading activity of agents or in the occurrence of basic events. The generic picture of a phase transition between information efficient and inefficient markets still persists even when agents trade on widely spread …
Study examines market response to concentrated policy communication using entropy measures.
problem Characterizing market response under concentrated policy communication.
method Jointly examines dispersion and information complexity (entropy) using sliding window cumulative entropy.
result Entropy captures both market volatility and narrative constraints, signaling coherent policy-driven moves.
Method captures shared information across many views robustly.
problem Modeling hundreds of views per event and learning robust embeddings without view knowledge.
method View bootstrapping using multi-view correlation and matrix concentration theory.
result View bootstrapping captures shared information across many views robustly.
A new method estimates rare events using tensor trains.
problem Estimating rare event probabilities in high-dimensional problems.
method Approximating optimal importance distribution via tensor-train decompositions and compositions.
result Better variance reduction and efficient computation of rare event probabilities.
A new definition of events of game-theoretic probability zero in continuous time is proposed and used to prove results suggesting that trading in financial markets results in the emergence of properties usually associated with randomness. This paper concentrates on "qualitative" results, stated in terms of order (or or…
Hydrological storm events are a primary driver for transporting water quality constituents such as turbidity, suspended sediments and nutrients. Analyzing the concentration (C) of these water quality constituents in response to increased streamflow discharge (Q), particularly when monitored at high temporal resolution …
In this paper we consider the task of detecting abnormal communication volume occurring at node-level in communication networks. The signal of the communication activity is modeled by means of a clique stream: each occurring communication event is instantaneous and activates an undirected subgraph spanning over a set o…
This paper treats prediction markets as Bayesian inverse problems to quantify uncertainty and identify event outcomes.
problem Uncertainty and identifiability in prediction market outcomes from price-volume histories.
method Formulates prediction markets as Bayesian inverse problems, introduces a log-odds observation model, and derives posterior uncertainty quantification and identifiability criteria.
result Explicit diagnostics for informative and stable inference regimes, and validation through synthetic data experiments.
The problem of forecasting conditional probabilities of the next event given the past is considered in a general probabilistic setting. Given an arbitrary (large, uncountable) set C of predictors, we would like to construct a single predictor that performs asymptotically as well as the best predictor in C, on any data.…
New method decomposes local projections to reveal historical drivers of estimates.
problem Uncertainty in interpreting local projections due to black-box nature.
method Decomposes LP estimates into contributions of historical events, interpreting weights as shocks and proximity scores.
result Dominant historical events drive impulse response estimates, revealing underlying mechanisms.
New method corrects seasonal Arctic sea ice predictions with probabilistic models.
problem Systematic biases and errors in climate model forecasts of Arctic sea ice.
method Conditional Variational Autoencoder model to map observation distribution given biased model predictions.
result Probabilistic adjusted forecasts are better calibrated and have smaller errors.
The study analyzes how wartime controls influenced zaibatsu stock prices in Japan.
problem How wartime economic controls affected zaibatsu stock prices in Japan.
method Developed a four-portfolio asset-pricing model and used a CAPM-AR(p)-SV event-study framework.
result Wartime economic controls influenced stock prices through financing wedges and zaibatsu affiliation.
Study tail risk aggregation under dependence uncertainty.
problem Risk aggregation under dependence uncertainty and hidden dependence.
method Introduce hidden dependence, show compatibility with small perturbations, quantify portfolio risk.
result Small deviations in dependence structure can lead to significant risk underestimation.
Survey on risk-aware multi-armed bandits for better decision-making.
problem Risk measures in multi-armed bandits for better decision-making.
method Review of existing research, definition of risk-aware bandit problems, and algorithms for minimizing regret and identifying best arms.
result Consolidation and summarization of existing research on risk measures in multi-armed bandits.
Ethereum transition to PoS reduces energy consumption and decentralizes the network.
problem Transitioning from proof-of-work to proof-of-stake to reduce energy consumption and decentralize the network.
method Analyzed the impact of the Ethereum transition to proof-of-stake on network performance, competing platforms, and transaction fees.
result The transition to PoS has reduced energy consumption by 99.98% and decreased network concentration.
Paper shows minimum observation time for network recovery.
problem Inferring latent networks from event-based observations.
method Two-stage estimator using clipped and binned event data.
result Observation time of order log(d) is sufficient and necessary.
The aim of clinical effectiveness research using repositories of electronic health records is to identify what health interventions 'work best' in real-world settings. Since there are several reasons why the net benefit of intervention may differ across patients, current comparative effectiveness literature focuses on …
Stablecoin liquidity was affected by the SVB collapse, with USDC's transparency leading to market reactions.
problem Impact of stablecoin transparency on liquidity during market turmoil.
method Adapted MCI measure to Uniswap, Difference-in-Differences analysis on MCI and TVL, measured liquidity concentration.
result USDC's transparency led to swift market reactions, while USDT's opacity provided a safety net.
Study quantifies systemic risk in DeFi using network analysis.
problem Systemic risk in decentralized finance (DeFi) ecosystem.
method Network-based fragility analysis of TVL dynamics.
result Developed CFI and RCS to quantify structural fragility and risk contribution.
Some exotic compact objects possess evanescent ergosurfaces: timelike submanifolds on which a Killing vector field, which is timelike everywhere else, becomes null. We show that any manifold possessing an evanescent ergosurface but no event horizon exhibits a linear instability of a peculiar kind: either there are solu…
ASRI index detects crypto market risks with high precision and lead time.
problem Detecting systemic risks in cryptocurrency markets.
method Four weighted sub-indices (Stablecoin, DeFi, Contagion, Regulatory) validated against historical crises.
result ASRI detects significant abnormal signals with high statistical significance and lead time.
Green bond leaks impact equity markets, altering investor reactions.
problem Green bond leaks affect equity market reactions.
method Identified 259 instances of pre-announcement leaks in 2,036 green bond headlines.
result News leaks significantly alter equity trading dynamics and investor reactions.
Develops a framework for inferring causal relationships in networked data with uncertainty quantification.
problem Extracting reliable inference from complex Hawkes network data with uncertainty.
method Statistical inference framework based on maximum likelihood estimation and concentration inequalities of continuous-time martingales.
result Provides a non-asymptotic confidence set for uncertainty quantification.
Theory for algebraic data on categories via concentration structures.
problem Defining algebraic structures on categories.
method Introducing concentration structures and concentration monoids.
result Every group can be represented as a concentration monoid of a trivial category.
Study on liquidity providers' performance in decentralized exchanges.
problem Unclear profitability of liquidity providers in decentralized exchanges.
method Reconstructing LP PnL dynamics from on-chain events, introducing a new metric.
result Only about one out of six LPs avoids losses, suggesting open questions about LP participation motives.
Paper addresses concentration of distances for fractional quasi p-norms, identifying conditions for concentration and anti-concentration.
problem Understanding concentration of distances for fractional quasi p-norms in high dimensions.
method Analyzes conditions for concentration and anti-concentration of distances for fractional quasi p-norms.
result Identifies conditions for concentration and anti-concentration of fractional quasi p-norms, ruling out some approaches and specifying conditions for control.
Study Finsler metric measure manifolds' concentration properties.
problem Understanding concentration properties in Finsler metric measure manifolds.
method Established relationships with observable diameter, isoperimetric inequalities, and first eigenvalue.
result Derived a Cheng type upper bound estimate for the first closed eigenvalue.
Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal r…
New method improves missing mass concentration bounds.
problem Missing mass concentration problem
method New method of estimating concentration of heterogenic sums
result Slightly improved state-of-the-art bounds
We apply an asymmetric version of Kirman's herding model to volatile financial markets. In the relation between returns and agent concentration we use the square root law proposed by Zhang. This can be derived by extending the idea of a critical mean field theory suggested by Plerou et al. We show that this model is eq…
Surfaces in 3-manifolds concentrate at curvature critical points.
problem Understanding concentration of surfaces in 3-manifolds.
method Proving surfaces concentrate at critical points of scalar curvature.
result Simply connected H-surfaces concentrate at curvature critical points.
Sharp concentration bounds for i.i.d. variables.
problem Controlling the tail probabilities of independent variables.
method Extension of Sanov's theorem using large deviations and information theory.
result Matching concentration and anti-concentration bounds for i.i.d. samples of any size.
Federated Q-Learning achieves linear regret speedup with low communication cost.
problem Achieving linear regret speedup in federated reinforcement learning without high communication costs.
method Proposed two federated Q-Learning algorithms: FedQ-Hoeffding and FedQ-Bernstein, using event-triggered synchronization, novel step size selection, and concentration inequalities.
result Total regrets achieve linear speedup compared to single-agent counterparts with logarithmic communication cost.
We survey recent results related to the concentration of eigenfunctions. We also prove some new results concerning ball-concentration, as well as showing that eigenfunctions saturating lower bounds for L1-norms must also, in a measure theoretical sense, have extreme concentration near a geodesic.
A new model CDTM improves text classification by concentrating document topics.
problem Unsupervised text classification with diverse topic distributions.
method Imposes an exponential entropy penalty on document topic distribution to encourage concentration.
result More coherent topics and concentrated, sparse document-topic distributions.
Simplified proof of Gaussian concentration inequality using covariance.
problem Gaussian concentration inequality proof
method Covariance representation based on characteristic functions
result Elementary proof of Gaussian concentration inequality
Study on inequalities for multinomial variables.
problem Understanding concentration inequalities for multinomial variables.
method Investigation of Dirichlet and Multinomial random variables.
result Results on concentration inequalities for multinomial variables.
Study provides bounds for estimating intrinsic dimension using Gaussian kernels.
problem Estimating intrinsic dimension from data.
method Finite-sample concentration and anti-concentration bounds for Gaussian kernel sums.
result Explicit dependence on sample size, bandwidth, and geometric parameters.
Developed concentrated liquidity in n-dimensional AMM with polar coordinates in Rust.
problem Risk of stacking too many stablecoin pools.
method Building concentrated liquidity positions with ticks in polar coordinates in Rust.
result Hedging risk of stacking stablecoin pools.