Statistical model checking for PCTL on MDPs using reinforcement learning.
problem Model checking PCTL specifications on MDPs with statistical methods.
method Reinforcement learning for policy search, statistical model checking with UCB-based Q-learning.
result Provably guaranteed statistical model checking method for PCTL specifications on MDPs.
A machine-checked Itô calculus for Brownian motion on [0,T]
problem Developing an L2 Itô calculus for Brownian motion method Formalized in Lean 4 on top of Mathlib and the BrownianMotion package
result First machine-checked proof of Itô's formula and construction of Itô integral as martingale-valued process
Developed a machine-checked Itô calculus for Brownian motion.
problem Formal verification of Itô calculus for Brownian motion.
method Machine-checked formalization in Lean over Mathlib.
result First machine-checked constructions of the Itô integral and Itô's formula.
This paper enhances privacy in statistical model checking of cyber-physical systems.
problem Privacy concerns in consumer-level applications due to statistical model checking.
method Proposes expected differential privacy and a new exponential mechanism for sequential algorithms.
result Demonstrates a novel mechanism to preserve privacy in statistical model checking.
Paper checks SSC for matrix factorizations using Gurobi.
problem Checking the SSC for various matrix factorizations.
method Formulated as a non-convex quadratic optimization problem over a bounded set, solved with Gurobi.
result SSC can be checked in reasonable time for realistic scenarios.
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.
We study the Yamabe invariants of cylindrical manifolds and compact orbifolds with a finite number of singularities, by means of conformal geometry and the Atiyah-Patodi-Singer L2-index theory. For an n-orbifold M with singularities ΣΓ={(pˇ1,Γ1),...,(pˇs,Γs)} (where each group $Γ_j<O…
Symbolic neural network for analyzing and patching complex systems.
problem Analyzing and verifying complex neural networks.
method Symbolic representation of piecewise-linear neural networks for efficient computation.
result Demonstrated applications in weakest preconditions, strongest postconditions, and patching.
Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' mov…
New functions derived from arrow diagrams for spherical curves, invariant under certain deformations.
problem Defining and analyzing integer-valued functions on spherical curves.
method Introducing new functions and relators to study spherical curves and their isotopy classes.
result Functions derived from arrow diagrams are invariant under specific deformations.
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.
We provide an effective algorithm for determining whether an element of the outer automorphism group of a free group is fully irreducible. Our method produces a finite list which can be checked for periodic proper free factors.
The paper proposes a tool to detect invalid inputs in DL models.
problem Vulnerability of DL models to invalid inputs during runtime.
method Design and implementation of a tool that extracts data flow footprints and conducts assertion-based validation.
result The assertion-based data sanity check mechanism effectively identifies invalid input cases.
DC-Check helps guide ML development by considering data-centric aspects.
problem Lack of standardized framework for data-centric considerations in ML.
method DC-Check is a checklist-style framework for data-centric AI at ML pipeline stages.
result Promotes thoughtfulness and transparency in ML development.
High-performance quantum codes decoded with minimal data.
problem Efficient decoding of linear-rate LDPC quantum codes.
method Tessellations of hyperbolic manifolds, Coxeter groups, and Galois fields.
result Achieved encoding rate of 13/72 with high performance.
This paper deforms complex tori and their mirrors using gerbes.
problem Deforming complex tori and their mirror partners.
method Using flat gerbes to deform complex tori and their mirrors, constructing holomorphic line bundles over deformed objects.
result Deformed complex tori and their mirrors can be studied using flat gerbes.
The choice of model class is fundamental in statistical learning and system identification, no matter whether the class is derived from physical principles or is a generic black-box. We develop a method to evaluate the specified model class by assessing its capability of reproducing data that is similar to the observed…
We show that any two geometric triangulations of a closed hyperbolic, spherical or Euclidean manifold are related by a sequence of Pachner moves and barycentric subdivisions of bounded length. This bound is in terms of the dimension of the manifold, the number of top dimensional simplexes and bound on the lengths of ed…
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.
Paper verifies RNNs using automata learning and model checking.
problem Verifying the correctness of RNNs is challenging.
method Learn a deterministic finite automaton from RNN, use model checking for verification.
result Can discover and generalize counterexamples to faulty flows.
New method evaluates language model forecasters by checking consistency of predictions.
problem Evaluating the performance of language model forecasters is difficult due to lack of ground truth.
method Developed a consistency check framework based on arbitrage to evaluate forecasters.
result Consistency metrics correlate with ground truth performance of LLM forecasters.
Let Sm be the set of all m×m density matrices (Hermitian positively semi-definite matrices of unit trace). Consider a problem of estimation of an unknown density matrix ρ∈Sm based on outcomes of n measurements of observables X1,…,Xn∈Hm (Hm bei…
SANST uses self-attentive networks with spatial and temporal embeddings for better POI recommendations.
problem Next point-of-interest (POI) recommendation for users based on their history.
method SANST incorporates spatio-temporal patterns into self-attentive networks.
result SANST outperforms state-of-the-art models by up to 13.65% in nDCG@10.
The paper proposes a method to evaluate superhuman models by checking for logical inconsistencies.
problem Evaluating superhuman models when ground truth is hard to obtain.
method A framework using consistency checks to identify logical inconsistencies in model decisions.
result Logical inconsistencies can be discovered in superhuman model decisions across various tasks.
New research shows calibration error is flawed when dealing with model uncertainty.
problem Current model evaluation techniques conflate model uncertainty with aleatoric uncertainty.
method Posterior predictive checks to evaluate deep learning models.
result Calibration error and variants are incorrect when model uncertainty is present.
The chapter improves deep learning models by interpreting and improving their performance.
problem Deep learning models often lack interpretability, leading to poor understanding of their predictions.
method The approach involves attributing importance to features and feature groups, including interactions, to improve model performance.
result The proposed attributions provide insights across various domains and can be used to improve model generalization.
Scalable model checking for stochastic systems using Gaussian Processes and Bayesian Neural Networks.
problem Efficiently verifying properties of stochastic systems with high-dimensional parameter spaces.
method Stochastic Variational Smoothed Model Checking (SV-smMC) using Gaussian Processes and Bayesian Neural Networks.
result SV-smMC scales to larger datasets and enables application to high-dimensional parameter spaces.
In this paper we extend the notion of Futaki invariant to big and nef classes in such a way that it defines a continuous function on the \K\ cone up to the boundary. We apply this concept to prove that reduced normal crossing singularities are sufficient to check K-semistability. A similar improvement on Donaldson's …
Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on private data reduces the privacy budget, and ad-hoc privacy attacks are often required to test model privacy. We introduce three tools to ma…
This paper formalizes Uniswap v3 using PTA and FST for rigorous analysis.
problem Formal modeling of Uniswap v3's concentrated liquidity for rigorous analysis.
method Formal state machine models using PTA and FST, proving rounding bounds.
result Formal justification of Uniswap v3's ε-slack and rounding safety. Proves SYZ mirror symmetry for del Pezzo and rational elliptic surfaces.
problem Proving mirror symmetry for specific Calabi-Yau surfaces.
method Adapting Hein's work, constructing asymptotically semi-flat Calabi-Yau metrics, and defining a mirror map.
result Existence and uniqueness of Calabi-Yau metrics on Y∖D. The paper bounds Pachner moves and systoles in hyperbolic 3-manifolds.
problem Bounding Pachner moves and systoles in cusped hyperbolic 3-manifolds.
method Using geometric ideal triangulations and dihedral angles, the paper gives bounds on Pachner moves and systoles.
result Lower bounds on systole length and Pachner move sequence length.
We describe the infinitesimal moduli space of pairs (Y,V) where Y is a manifold with G2 holonomy, and V is a vector bundle on Y with an instanton connection. These structures arise in connection to the moduli space of heterotic string compactifications on compact and non-compact seven dimensional spaces, e.…
By the SYZ construction, a mirror pair (X,Xˇ) of a complex torus X and a mirror partner Xˇ of the complex torus X is described as the special Lagrangian torus fibrations X→B and Xˇ→B on the same base space B. Then, by the SYZ transform, we can construct a simpl…
This paper characterizes mu-cscK metrics using Perelman's W-entropy.
problem Characterizing mu-cscK metrics and understanding their properties.
method Using Perelman's W-entropy as a functional on the tangent bundle of Kähler metrics, the paper characterizes mu-cscK metrics as critical points of this functional.
result The W-entropy is monotonic along geodesics and provides a lower bound for mu-entropy.
We prove the following result announced in Todorov and Valov: Any homogeneous, metric ANR-continuum is a VGn-continuum provided dimGX=n≥1 and Hˇn(X;G)=0, where G is a principal ideal domain. This implies that any homogeneous n-dimensional metric ANR-continuum with $\check{H}^n(X;G)\neq…
We specify a result of Yokoi \cite{yo} by proving that if G is an abelian group and X is a homogeneous metric ANR compactum with dimGX=n and Hˇn(X;G)=0, then X is an (n,G)-bubble. This implies that any such space X has the following properties: Hˇn−1(A;G)=0 for every closed…
Develops a method to estimate policy values robustly in the presence of confounding variables.
problem Infinite-horizon reinforcement learning with unobserved confounding variables makes policy evaluation unidentifiable.
method Robust approach estimating sharp bounds on policy value using optimization over state-occupancy ratios and sensitivity model.
result Proves convergence to sharp bounds as more confounded data is collected.
New method finds suboptimal actions with fewer checks in complex learning scenarios.
problem Finding optimal actions in complex learning environments with limited feature information.
method Applying Kiefer-Wolfowitz theorem to find suboptimal actions with minimal checks.
result Proves a bound on the regret for linear bandits and optimal policy learning in RL.
Homotopy equivalence shown between complex and thickened versions of manifolds.
problem Homotopy equivalence between manifold complexes and thickened versions.
method Natural bijections and homotopy equivalences of Vietoris-Rips and Čech complexes and thickened versions.
result Natural bijections between complexes and thickened versions are homotopy equivalences.
The current article stems from our study on the asymptotic behavior of holomorphic isometric embeddings of the Poincaré disk into bounded symmetric domains. As a first result we prove that any holomorphic curve exiting the boundary of a bounded symmetric domain Ω must necessarily be asymptotically totally geodesic. A…
Study how past radiation determines present matter in Penrose's cyclic cosmology.
problem Determining matter content in the present eon from past radiation in Penrose's cyclic cosmology.
method Solve Einstein's equations for a spherical wave in the past eon, then apply reciprocity to find the present eon's matter content.
result The present eon is filled with three types of radiation: a damped wave, an in-going wave, and randomly scattered waves.
Proposes a general method to derive regret bounds for multi-armed bandit algorithms.
problem Deriving regret bounds for randomized multi-armed bandit algorithms.
method Checking sufficient conditions on sampling probabilities and distributions.
result Proves logarithmic regret bounds for various bandit algorithms and new models.
Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race. To avoid disparate treatment, sensitive attributes should not be considered. On the other hand, in order to avoid disparate impact,…
Study combines SEM, OLS, and DML for robustness checks in survey-based research.
problem Stability of SEM findings under alternative estimation frameworks.
method Staged robustness analysis framework connecting SEM, OLS, and DML.
result Identifies stable and unstable relationships across SEM, OLS, and DML checks.
Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection. However, these networks have been shown to often mispredict on inputs with minor adversarial or even accidental perturbations. Consequences of such errors can …
Constructing brane quantization for An-resolutions using SYZ mirror symmetry.
problem Quantizing branes on singular fibers using SYZ mirror symmetry.
method Constructing coisotropic A-branes and their mirrors via fiberwise geometric quantization.
result Establishing a mirror isomorphism between endomorphism algebras.
We present FAKTA which is a unified framework that integrates various components of a fact checking process: document retrieval from media sources with various types of reliability, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis. FAKTA predicts the factuality of…