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.
A new linear contextual bandit algorithm with improved regret bound.
Responds to comments on Bayesian Logic Regression algorithm, provides extensions and tutorial.
Novel bounds for deep MDA algorithms improve performance and efficiency.
Novel framework for Bayesian reinforcement learning infers value function distributions.
New algorithm trains latent diffusion models using interacting particles.
We develop a technique for deriving data-dependent error bounds for transductive learning algorithms based on transductive Rademacher complexity. Our technique is based on a novel general error bound for transduction in terms of transductive Rademacher complexity, together with a novel bounding technique for Rademacher…
Novel DCD-based algorithms improve RLS performance in noisy channels.
Paper introduces a novel error measure for neural networks integrating statistical and information theory.
We present the Procrustes measure, a novel measure based on Procrustes rotation that enables quantitative comparison of the output of manifold-based embedding algorithms (such as LLE (Roweis and Saul, 2000) and Isomap (Tenenbaum et al, 2000)). The measure also serves as a natural tool when choosing dimension-reduction …
Novel neural network solves PDEs with multi-scale resolution.
Paper introduces algorithms for explaining monotonic classifiers.
Novel algorithm identifies nonlinear Granger causal relationships using kernel ridge regression.
The EM algorithm is one of many important tools in the field of statistics. While often used for imputing missing data, its widespread applications include other common statistical tasks, such as clustering. In clustering, the EM algorithm assumes a parametric distribution for the clusters, whose parameters are estimat…
New algorithms minimize regret in multi-task and lifelong linear bandits with shared representation.
In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky MCMC algorithms, for efficient sampling from a generic target probability density function (pdf). The new class of algorithms employs adaptive non-parametric proposal densities which become closer and closer to …
We investigate contextual online learning with nonparametric (Lipschitz) comparison classes under different assumptions on losses and feedback information. For full information feedback and Lipschitz losses, we design the first explicit algorithm achieving the minimax regret rate (up to log factors). In a partial feedb…
Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical contributions to the learning to rank problem both in the online and batch settings. First, we propose a perceptron-like algorithm for learnin…
We present novel, computationally efficient, and differentially private algorithms for two fundamental high-dimensional learning problems: learning a multivariate Gaussian and learning a product distribution over the Boolean hypercube in total variation distance. The sample complexity of our algorithms nearly matches t…
This article proposes a novel density estimation based algorithm for carrying out supervised machine learning. The proposed algorithm features O(n) time complexity for generating a classifier, where n is the number of sampling instances in the training dataset. This feature is highly desirable in contemporary applicati…
We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in …
Recently, a novel family of biologically plausible online algorithms for reducing the dimensionality of streaming data has been derived from the similarity matching principle. In these algorithms, the number of output dimensions can be determined adaptively by thresholding the singular values of the input data matrix. …
Novel neural computer learns algorithmic solutions for symbolic tasks.
Paper introduces a new gradient statistic to improve deep learning convergence.
New model for online ranking with feature analysis.
The hidden Markov model (HMM) is a generative model that treats sequential data under the assumption that each observation is conditioned on the state of a discrete hidden variable that evolves in time as a Markov chain. In this paper, we derive a novel algorithm to cluster HMMs through their probability distributions.…
Novel framework combines tree-based discretization and ILP matching for causal inference.
We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommende…
Proposes a Siamese NN for algorithm selection focusing on alike performing instances.
Novel algorithm for Markov decision processes using rank-one approximation.
Regularisation improves ML classifier stability against poisoning attacks.
Kernel-based function approximation improves reinforcement learning performance.
New algorithm improves on static methods in Active Simple Hypothesis Testing.
Markov chain (MC) algorithms are ubiquitous in machine learning and statistics and many other disciplines. Typically, these algorithms can be formulated as acceptance rejection methods. In this work we present a novel estimator applicable to these methods, dubbed Markov chain importance sampling (MCIS), which efficient…
Novel PO algorithms improve LLM alignment tasks.
Novel algorithms improve warfarin dose prediction accuracy.
Novel topology optimization using CWGANs reduces computational cost.
Novel algorithms for online learning with uncertain feedback graphs reduce regret.
New algorithm finds mixed Nash equilibria in GANs.
We propose a novel Riemannian manifold preconditioning approach for the tensor completion problem with rank constraint. A novel Riemannian metric or inner product is proposed that exploits the least-squares structure of the cost function and takes into account the structured symmetry that exists in Tucker decomposition…
The simplicial condition and other stronger conditions that imply it have recently played a central role in developing polynomial time algorithms with provable asymptotic consistency and sample complexity guarantees for topic estimation in separable topic models. Of these algorithms, those that rely solely on the simpl…
Paper introduces novel Bandit algorithms for non-stationary environments in finance.
Improved neural network verification using Lagrangian decomposition and parallel algorithms.
We present a generic framework for parallel coordinate descent (CD) algorithms that includes, as special cases, the original sequential algorithms Cyclic CD and Stochastic CD, as well as the recent parallel Shotgun algorithm. We introduce two novel parallel algorithms that are also special cases---Thread-Greedy CD and …
A new algorithm reduces suboptimal arm selection in correlated bandits.
This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decomposition step, and achieve…
Paper tackles non-stationary kernelized bandits with near-optimal algorithm.
A new decision tree method avoids overfitting without hyperparameters.
A novel SVR parameter optimization method using GSA outperforms other meta-heuristics in stock market forecasting.