Study stability of operators on warped product manifolds.
problem Stability of operators on warped product manifolds.
method Examined the family of operators La=Δ−aS in a warped product of an infinite interval or real line by a compact manifold. result Stability of the operators La was studied in a specific type of manifold. Book explores infinite translation surfaces, challenging traditional geometry.
problem Understanding infinite-type translation surfaces.
method Detailed classification, construction, and analysis of symmetries.
result Complex dynamics in infinite-type translation flows.
LPCI provides valid prediction intervals for longitudinal data.
problem Current conformal prediction methods for time series data lack cross-sectional coverage when applied to longitudinal datasets.
method Modeling residual data as a quantile fixed-effects regression problem, constructing prediction intervals with a trained quantile regressor.
result LPCI achieves valid cross-sectional coverage and outperforms existing benchmarks in terms of longitudinal coverage rates.
BCI provides calibrated prediction intervals for time series forecasts.
problem Calibration of prediction intervals for time series forecasts.
method BCI wraps around any time series forecasting models and optimizes interval lengths using dynamic programming.
result BCI achieves long-term coverage under arbitrary distribution shifts and temporal dependence.
In [Mas82] and [Vee78] it was proved independently that almost every interval exchange transformation is uniquely ergodic. The Birkhoff ergodic theorem implies that these maps mainly have uniformly distributed orbits. This raises the question under which conditions the orbits yield low-discrepancy sequences. The case o…
The family of translation surfaces (Xg,ωg) constructed by Arnoux and Yoccoz from self-similar interval exchange maps encompasses one example from each genus g greater than or equal to 3. We triangulate these surfaces and deduce general properties they share. The surfaces (Xg,ωg) converge to a surface $(X_\i…
The completeness of a bond market model with infinite number of sources of randomness on a finite time interval in the Heath-Jarrow-Morton framework is studied. It is proved that the market is not complete. A construction of a bounded contingent claim, which can not be replicated, is provided.
The paper develops methods to estimate value functions in reinforcement learning for infinite horizon problems.
problem Constructing confidence intervals for value functions in infinite horizon reinforcement learning.
method Modeling the Q-function using series/sieve method and recursively updating the policy using SAVE method.
result The proposed CI achieves nominal coverage even when the optimal policy is not unique.
New method provides reliable high-confidence prediction intervals for high-impact events.
problem High-impact events require very high confidence prediction intervals, but classical methods provide uninformative intervals.
method Bridge extreme value statistics and conformal prediction to provide reliable and informative prediction intervals.
result Provides reliable and informative prediction intervals with high-confidence coverage.
We show that there are infinitely many nonisomorphic quandle structures on any topogical space X of positive dimension. In particular, we disprove the conjecture, asserting that there are no nontrivial quandle structures on the closed unit interval [0,1].
We construct compactifications for median spaces with compact intervals, generalising Roller boundaries of CAT(0) cube complexes. Examples of median spaces with compact intervals include all finite rank median spaces and all proper median spaces of infinite rank. Our methods also work for general median algebra…
Optimal learning via moderate deviations theory improves statistical accuracy.
problem Statistical estimation of expected loss in various models.
method Develops confidence intervals using moderate deviation principle.
result Proposed confidence intervals are statistically optimal.
Proves left-orderability of certain Dehn fillings on 3-manifolds.
problem Left-orderability of Dehn fillings on 3-manifolds.
method Constructing arcs of representations and using holonomy extension techniques.
result Establishes intervals of orderable Dehn fillings for odd pretzel knots.
Local gluing connects flow lines in finite time intervals.
problem Connecting flow lines in finite time intervals.
method Functional analytic approach to define local gluing map.
result Explicit construction of local gluing map in Euclidean case; intricate construction in non-Euclidean case.
New unbiased variance estimator for random forests using Hoeffding decomposition.
problem Uncertainty quantification in random forests with large kernel sizes and small sample sizes.
method Proposes a new Hoeffding decomposition view for variance estimation, establishing unbiased estimators and ratio consistency.
result Establishes the ratio consistency of the proposed variance estimator, justifying confidence interval coverage rates.
Observing prices of European put and call options, we calibrate exponential Lévy models nonparametrically. We discuss the efficient implementation of the spectral estimation procedures for Lévy models of finite jump activity as well as for self-decomposable Lévy models. Based on finite sample variances, confidence inte…
We give a singular control approach to the problem of minimizing an energy functional for measures with given total mass on a compact real interval, when energy is defined in terms of a completely monotone kernel. This problem occurs both in potential theory and when looking for optimal financial order execution strate…
Study irrational rotations and construct 2-filling rays on infinite type surfaces.
problem Understanding dense orbits in skew product transformations.
method Skew product transformation with continued fraction analysis.
result Existence of infinite cliques of 2-filling rays on infinite type surfaces.
The paper extends confidence sequences for infinite variance data.
problem Addressing confidence sequences for distributions with infinite variance.
method Establishing lower bounds and deriving tight confidence sequences for relaxed bounded pth-moment distributions. result Derived confidence sequences are tighter than those using Dubins-Savage inequality.
Data-driven method for error estimation without needing class complexity.
problem Constructing confidence intervals for a class of estimates.
method Data-driven approach to derive high-probability upper bounds on maximum error.
result Method naturally adapts to unknown correlation structures and works for finite and infinite classes.
Deep neural nets approximate random dynamical system trajectories uniformly in time.
problem Approximating trajectories of random dynamical systems over infinite time horizons.
method Recurrent neural networks with simple feedback structures.
result Certain random trajectories can be approximated uniformly in time to any desired accuracy.
We derive asymptotic expansions for option data to detect infinite variation volatility.
problem Detecting infinite variation volatility in high-frequency option data.
method Nonparametric higher-order asymptotic expansions for small-time changes of characteristic functions of Itô semimartingales.
result Evidence of infinite variation volatility in high-frequency option data.
This paper deals with applications of coherent risk measures to pricing in incomplete markets. Namely, we study the No Good Deals pricing technique based on coherent risk. Two forms of this technique are presented: one defines a good deal as a trade with negative risk; the other one defines a good deal as a trade with …
Kernel-based function approximation improves reinforcement learning performance.
problem Average reward reinforcement learning in infinite horizon settings.
method Optimistic algorithm based on kernel ridge regression.
result No-regret performance guarantees and confidence intervals for kernel-based predictions.
Bayesian method improves estimation of unseen species.
problem Estimating unseen species in biological and physical sciences.
method Bayesian nonparametric approach with Pitman-Yor prior and Gaussian credible intervals.
result Improves asymptotic credible intervals for unseen species estimation.
This work develops confidence intervals for off-policy evaluation.
problem Estimating expected reward with uncertainty quantification.
method Primal-dual optimization with kernel Bellman loss and martingale concentration inequality.
result Developed practical algorithm for non-asymptotic confidence intervals.
We show that the lamplighter group L has a system of generators for which the spectrum of the discrete Laplacian on the Cayley graph is a union of an interval and a countable set of isolated points accumulating to a point outside this interval. This is the first example of a group with infinitely many gaps in the spect…
The paper develops a theory for random forests, separating variance components and providing methods for estimating prediction intervals.
problem Understanding the variance and uncertainty in random forest predictions.
method Design-based theory, Monte Carlo averaging, PASR resampling.
result The floor of prediction uncertainty is positive and persists even without observation overlap, providing conservative prediction intervals.
A framework for quantifying uncertainty in feature importance values.
problem Stable interpretation of feature importance values in machine learning models.
method A novel method based on pairwise comparisons of feature importance values to produce confidence intervals for feature ranks.
result The method produces simultaneous confidence intervals for feature ranks, enabling selection of top-k important features.
In [13], it is proved that any subgroup of Diff+ω(I) (the group of orientation preserving analytic diffeomorphisms of the interval) is either metaabelian or does not satisfy a law. A stronger question is asked whether or not the Girth Alternative holds for subgroups of Diff+ω(I). In th…
Unified framework for error quantification in off-policy evaluation with distributional shift.
problem Establishing high-confidence CI for target policy value from offline data.
method Unified error analysis quantifying misspecification and sampling errors.
result Achieves tightest possible CI and robustness against distributional shifts.
Root-finding methods improve efficiency of conformal prediction sets.
problem Efficiently computing conformal prediction sets is difficult.
method Exploiting root-finding algorithms to approximate boundaries of conformal prediction intervals.
result The approach can overcome limitations of previous strategies.
Develops adiabatic theory for ACW flow on surfaces.
problem Evolution of large closed surfaces under area-constrained Willmore flow.
method Constructs a map on a four-dimensional manifold of barycenters to characterize ACW flow dynamics.
result Explicit four-dimensional effective dynamics of barycenters serves as an asymptotic approximation for ACW flow.
New bounds for causal effect identification in time series graphs with latent confounders.
problem Identifying causal effects in time series graphs with latent confounders over unbounded time intervals.
method Applying the Causal Identification algorithm to a constant-size segment of the time series graph.
result A bound on the number of past time steps needed for causal effect identification.
Paper develops a method to estimate value of a policy in confounded MDPs.
problem Estimating value of a policy in the presence of unmeasured confounders.
method Uses auxiliary variables to identify target policy's value in a confounded MDP.
result Develops an off-policy value estimator robust to model misspecification.
New hyperbolic knots with convex Upsilon invariants constructed.
problem Constructing knots with convex Upsilon invariants.
method Combinatorial method for (1,1)-knots and connected sum operation. result Infinitely many mutually non-concordant hyperbolic knots with convex Upsilon invariants.
The paper shows examples of geodesics switching infinitely often on certain manifolds.
problem Understanding geodesics with infinitely many switches on Finsler and sub-Finsler manifolds.
method Provided examples and explicit structures on Carnot groups, presented a sufficient condition for chattering.
result Geodesics on certain manifolds can exhibit a countable number of switches in arbitrarily small time intervals.
We develop a second-order model for limit order books in a single scaling regime.
problem Modeling price and volume dynamics in a limit order book with market and limit orders at a common time scale.
method Established a first- and second-order approximation for an infinite dimensional limit order book model.
result Proved the existence and uniqueness of a solution for the second-order approximation.
Given a Kaehler group G and a primitive class φ∈H1(G;Z), we show that the rank gradient of (G;φ) is zero if and only if Ker φ is finitely generated. Using this approach, we give a quick proof of the fact (originally due to Napier and Ramachandran) that Kaehler groups are not properly ascending or descending…
Having a regression model, we are interested in finding two-sided intervals that are guaranteed to contain at least a desired proportion of the conditional distribution of the response variable given a specific combination of predictors. We name such intervals predictive intervals. This work presents a new method to fi…
New method uses LSTM and signature theory to solve complex financial PDEs.
problem Solving path-dependent PDEs for financial derivatives pricing.
method Combining LSTM networks and rough paths theory.
result Efficient algorithms for pricing and hedging path-dependent derivatives.
Proves irreducible SU(2) representations for 3-surgery knots.
problem Irreducible SU(2) representations for 3-surgery knots.
method Instanton Floer homology and symplectic Floer homology.
result Proves irreducible SU(2) representations for 3-surgery knots.
We investigate the statistical complexity of estimating the parameters of a discrete-state Markov chain kernel from a single long sequence of state observations. In the finite case, we characterize (modulo logarithmic factors) the minimax sample complexity of estimation with respect to the operator infinity norm, while…
The main purpose of this work is to generalize the $S^3_\bfw$ Sasaki join construction $M\star_\bfl S^3_\bfw$ described in \cite{BoTo14a} when the Sasakian structure on M is regular, to the general case where the Sasakian structure is only quasi-regular. This gives one of the main results, Theorem 3.2, which describe…
SCOTCH learns system structure from irregular time series using neural SDEs.
problem Learning system structure from irregular time series data.
method SCOTCH uses neural stochastic differential equations (SDE) with variational inference.
result SCOTCH improves structure learning performance on synthetic and real-world datasets.
These notes outline recent developments in classical minimal surface theory that are essential in classifying the properly embedded minimal planar domains M in R^3 with infinite topology (equivalently, with an infinite number of ends). This final classification result by Meeks, Perez, and Ros states that such an M must…
Prediction intervals are a valuable way of quantifying uncertainty in regression problems. Good prediction intervals should be both correct, containing the actual value between the lower and upper bound at least a target percentage of the time; and tight, having a small mean width of the bounds. Many prior techniques f…
New methods for ordinal classification of interval-valued data and functional data.
problem Ordinal classification of interval-valued data and functional data.
method Six ordinal classifiers are proposed, including parametric, binary decomposition, logistic regression, distance-based, k-nearest-neighbor, kernel PCA, and random forest methods.
result Considering ordering and interval-valued information improves the accuracy of ordinal classification.