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.
Generalizes uniformization to algebraic correspondences.
We provide comments on the article "High-dimensional simultaneous inference with the bootstrap" by Ruben Dezeure, Peter Buhlmann and Cun-Hui Zhang.
Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instance, high-dimensional settings might arise in economic studies due to very rich data sets with many potential covariates or in the analysis o…
New algorithm achieves both static and dynamic regret optimally against an oblivious adversary for deterministic losses.
Simultaneous Latent Budget Trees for stratified classification
Proposes MELODIC family for simultaneous binary logistic regression.
Stochastic optimization is key to efficient inversion in PDE-constrained optimization. Using 'simultaneous shots', or random superposition of source terms, works very well in simple acquisition geometries where all sources see all receivers, but this rarely occurs in practice. We develop an approach that interpolates d…
A new knot invariant measures crossings in three orthogonal directions.
This work finds mixed equilibria in machine learning problems using measures and simultaneous gradient ascent-descent.
Unified method for simultaneous denoising and clustering.
Proposes a method to optimize budget allocation for collecting and analyzing streaming data.
Proposes sparsified intervals for high-dimensional regression coefficients.
We investigate a type of distance between triangulations on finite type surfaces where one moves between triangulations by performing simultaneous flips. We consider triangulations up to homeomorphism and our main results are upper bounds on distance between triangulations that only depend on the topology of the surfac…
We present an efficient algorithm for simultaneously training sparse generalized linear models across many related problems, which may arise from bootstrapping, cross-validation and nonparametric permutation testing. Our approach leverages the redundancies across problems to obtain significant computational improvement…
Simultaneous inference after model selection is of critical importance to address scientific hypotheses involving a set of parameters. In this paper, we consider high-dimensional linear regression model in which a regularization procedure such as LASSO is applied to yield a sparse model. To establish a simultaneous pos…
Motivated by a problem in local differential geometry of Cauchy--Riemann (CR) structures of hypersurface type, we find a canonical form for pairs consisting of a nondegenerate Hermitian form and a self-adjoint antilinear operator, or, equivalently, consisting of a nondegenerate Hermitian form and a symmetric bilinear f…
The paper constructs hyperbolic elements in multiple spaces.
Support selection and eventwise decoupling for simultaneous bets proven.
There are recent cryptographic protocols that are based on Multiple Simultaneous Conjugacy Problems in braid groups. We improve an algorithm, due to Sang Jin Lee and Eonkyung Lee, to solve these problems, by applying a method developed by the author and Nuno Franco, originally intended to solve the Conjugacy Search Pro…
Tensor CANDECOMP/PARAFAC (CP) decomposition is an important tool that solves a wide class of machine learning problems. Existing popular approaches recover components one by one, not necessarily in the order of larger components first. Recently developed simultaneous power method obtains only a high probability recover…
This paper studies an environment of simultaneous, separate, first-price auctions for complementary goods. Agents observe private values of each good before making bids, and the complementarity between goods is explicitly incorporated in their utility. For simplicity, a model is presented with two first-price auctions …
We develop a coherent framework for integrative simultaneous analysis of the exploration-exploitation and model order selection trade-offs. We improve over our preceding results on the same subject (Seldin et al., 2011) by combining PAC-Bayesian analysis with Bernstein-type inequality for martingales. Such a combinatio…
COPT optimizes graph distances via simultaneous optimal transport.
Alt-GDA outperforms Sim-GDA in minimax games with near-optimal local convergence.
Geodesic flows with diagonalisable integrals are orthogonal.
We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneousl…
The paper improves nonparametric confidence bands for band-limited functions.
Study flip graphs for surfaces of infinite type, finding uncountably many connected components.
RAF model explains neural networks' dual rule learning and fact memorization.
New findings on hypersurfaces in Euclidean space that are both maximal and minimal.
A method for constructing tight prediction intervals for multiple numerical outputs.
The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduced safe screening allows us to identify a part of non-active features/samples. So far, safe screening has been individually studied either fo…
We give necessary and sufficient local conditions for the simultaneous unitarizability of a set of analytic matrix maps from an analytic 1-manifold into SL_n(C) under conjugation by a single analytic matrix map. We apply this result to the monodromy arising from an integrable partial differential equation to construct …
BSFP method reveals latent patterns in multi-omic data for predicting lung function in HIV-associated OLD.
Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of models - known as individual survival distributions (ISDs) - produces patient-specific survival functions that offer greater descriptiv…
Given two observers, we define the "relative velocity" of one observer with respect to the other in four different ways. All four definitions are given intrinsically, i.e. independently of any coordinate system. Two of them are given in the framework of spacelike simultaneity and, analogously, the other two are given i…
Characterizes metrics on Lie groups, proving non-simultaneous existence of balanced and pluriclosed metrics.
Simultaneously estimates travel times and route choice model parameters.
Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is easy to implement while often outperforming more traditional clustering algorithms. However, there are…
Efficient reinforcement learning for simultaneous-move zero-sum games using optimistic value iteration.
This paper aims at achieving a "good" estimator for the gradient of a function on a high-dimensional space. Often such functions are not sensitive in all coordinates and the gradient of the function is almost sparse. We propose a method for gradient estimation that combines ideas from Spall's Simultaneous Perturbation …
IANN visualizes all input variables effects simultaneously.
We present a deep-learning network that detects multiple small objects (hundreds to thousands) in a scene while simultaneously estimating their x,y pixel locations together with a characteristic feature-set (for instance, target orientation and color). All estimations are performed in a single, forward pass which makes…
A distributed bootstrap method for high-dimensional data reduces communication rounds efficiently.
Paper presents a novel gradient-based method for training models and hyperparameters simultaneously.
Paper proposes GPM for simultaneous community detection and group synchronization.
The paper improves confidence regions for band-limited functions using tighter norm bounds and majority voting.
Paper proposes algorithms to minimize both dynamic and adaptive regret simultaneously.