A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Adversarial online nonparametric regression achieves optimal rates with locally adaptive learning.
Study approximates unknown function levels with queries.
In the context of stochastic continuum-armed bandits, we present an algorithm that adapts to the unknown smoothness of the objective function. We exhibit and compute a polynomial cost of adaptation to the H{ö}lder regularity for regret minimization. To do this, we first reconsider the recent lower bound of Locatelli an…
The purpose of these notes is to explain parts of Gromov's survey of Carnot-Carathedory spaces, in the light of subsequent results of M. Rumin. Among the rich material provided by Gromov, most of which pertains to analysis on metric spaces, we choose to concentrate on the H{ö}lder equivalence problem for Carnot manifol…
Efficient algorithms for contextual bandits with smooth regret in continuous action spaces.
We find a local solution to the Ricci flow equation under a negative lower bound for many known curvature conditions. The flow exists for a uniform amount of time, during which the curvature stays bounded below by a controllable negative number. The curvature conditions we consider include 2-non-negative and weakly $\t…
A variant of Gromov's H{ö}lder-equivalence problem, motivated by a pinching problem in Riemannian geometry, is discussed. A partial result is given. The main tool is a general coarea inequality satisfied by packing energies of maps.
Estimates time-varying network connections using multi-stage smoothing.
A new method for embedding temporal relationships in graphs.
Study shows zero-shot super-resolution in neural operators is impossible in many cases.
TATD predicts missing entries in time-evolving tensors by exploiting temporal dependency and sparsity.
This paper selects features in deep neural networks with theoretical guarantees.
We present a principled approach for detecting overlapping temporal community structure in dynamic networks. Our method is based on the following framework: find the overlapping temporal community structure that maximizes a quality function associated with each snapshot of the network subject to a temporal smoothness c…
In a dynamic network, the neighborhood of the vertices evolve across different temporal snapshots of the network. Accurate modeling of this temporal evolution can help solve complex tasks involving real-life social and interaction networks. However, existing models for learning latent representation are inadequate for …
We study finite energy classes of quasiplurisubharmonic (qpsh) functions in the setting of toric compact K{ä}hler manifolds. We characterize toric qpsh functions and give necessary and sufficient conditions for them to have finite (weighted) energy, both in terms of the associated convex function in R n , and through t…
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
Quantile Temporal-Difference learning proved convergent with proof.
Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM)…
Let be a closed oriented surface of genus at least , and denote by its Teichm{ü}ller space. For any isotopy class of closed curves , we compute the first three derivatives of the length function in the shearing coordinates associated to a maxim…
We consider the problem of online nonparametric regression with arbitrary deterministic sequences. Using ideas from the chaining technique, we design an algorithm that achieves a Dudley-type regret bound similar to the one obtained in a non-constructive fashion by Rakhlin and Sridharan (2014). Our regret bound is expre…
In this paper we provide an alternative framework to tackle the first-best Principal-Agent problem under CARA utilities. This framework leads to both a proof of existence and uniqueness of the solution to the Risk-Sharing problem under very general assumptions on the underlying contract space. Our analysis relies on an…
Post-estimation smoothing improves prediction accuracy with structural indices.
Validates economic scenarios using statistical tests on stochastic processes.
Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures. In particular, enforcing smooth output changes while presenting temporally-closed frames from video sequences, proved to be an effective strategy. In this paper we pro…
New algorithms for interactive learning match minimax bounds efficiently.
Unified framework for efficient Gaussian process inference.
EAP clusters evolving data, promoting temporal smoothness and automatic cluster tracking.
Proposes tPARAFAC2 for tracking evolving patterns in time-evolving data.
Efficient spatio-temporal Gaussian process inference method.
Geometric method captures rare topics and temporal alignment in co-author networks.
New analysis shows how temporal variability affects online learning performance.
The Temporal Group LASSO is an example of a multi-task, regularized regression approach for the prediction of response variables that vary over time. The aim of this work is to introduce the reader to the concepts behind the Temporal Group LASSO and its related methods, as well as to the type of potential applications …
Predictive process monitoring is concerned with the analysis of events produced during the execution of a business process in order to predict as early as possible the final outcome of an ongoing case. Traditionally, predictive process monitoring methods are optimized with respect to accuracy. However, in environments …
Several applications of Reinforcement Learning suffer from instability due to high variance. This is especially prevalent in high dimensional domains. Regularization is a commonly used technique in machine learning to reduce variance, at the cost of introducing some bias. Most existing regularization techniques focus o…
PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set of patients with the varying number of medical encounters over time. Despite recent improvements on unconstrained PARAFAC2, its model factors…
Comparing data defined over space and time is notoriously hard, because it involves quantifying both spatial and temporal variability, while at the same time taking into account the chronological structure of data. Dynamic Time Warping (DTW) computes an optimal alignment between time series in agreement with the chrono…
ARM improves multivariate time series forecasting by better capturing series-wise relationships.
New null distance bounds confirm Big Bang singularity in cosmological models.
dCMF models evolving patterns in multiway data with temporal dynamics.
FLUID uses flows to unify filtering and smoothing for complex systems.
The paper proposes a new method for clustering survival data using smoothed log-hazard trajectories.
Solutions to the wave equation on de Sitter-Schwarzschild space with smooth initial data on a Cauchy surface are shown to decay exponentially to a constant at temporal infinity, with corresponding uniform decay on the appropriately compactified space.
Geroch's theorem about the splitting of globally hyperbolic spacetimes is a central result in global Lorentzian Geometry. Nevertheless, this result was obtained at a topological level, and the possibility to obtain a metric (or, at least, smooth) version has been controversial since its publication in 1970. In fact, th…
Proposes DILATE and STRIPE++ for precise time series forecasting.
A new framework for averaging spatio-temporal signals using optimal transport and soft alignments.
Develops state-space deep Gaussian processes for irregular signals.
Unified model forecasts epidemics with spatial and temporal dynamics.
TNC learns time series representations by leveraging temporal neighborhoods.