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.
Optimizes black-box functions with varying costs across multiple sources.
A new method reduces energy consumption in machine learning by using multiple, less costly data sources.
Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical method to solve integrals of expensive-to-evaluate black-box functions, yet so far,active BQ learning schemes focus merely on the integrand itself as information source, and do not allow for information transfer from cheaper, related functions. Here…
We consider Bayesian optimization of an expensive-to-evaluate black-box objective function, where we also have access to cheaper approximations of the objective. In general, such approximations arise in applications such as reinforcement learning, engineering, and the natural sciences, and are subject to an inherent, u…
We derive expressions for the predicitive information rate (PIR) for the class of autoregressive Gaussian processes AR(N), both in terms of the prediction coefficients and in terms of the power spectral density. The latter result suggests a duality between the PIR and the multi-information rate for processes with mutua…
Lookahead, also known as non-myopic, Bayesian optimization (BO) aims to find optimal sampling policies through solving a dynamic program (DP) that maximizes a long-term reward over a rolling horizon. Though promising, lookahead BO faces the risk of error propagation through its increased dependence on a possibly mis-sp…
Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifi…
Study shows changes in information sharing between Bitcoin markets during 2017 crash.
This paper reviews deep learning for multi-modality medical image segmentation.
Most signal processing problems involve the challenging task of multidimensional probability density function (PDF) estimation. In this work, we propose a solution to this problem by using a family of Rotation-based Iterative Gaussianization (RBIG) transforms. The general framework consists of the sequential applicatio…
This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of maximal variance by means of curves, instead of straight lines. Co…
A new sampler for FLMs improves token-level decoding controls.
Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. However, in prac…
StrTransformer recovers sources without labels by optimizing latent matrices and enforcing structural constraints.
This work examines a semi-blind single-channel source separation problem. Our specific aim is to separate one source whose local structure is approximately known, from another a priori unspecified background source, given only a single linear combination of the two sources. We propose a separation technique based on lo…
StrADiff separates sources from mixtures without labels, using structured priors.
Improves neural network performance by dynamically adjusting model weights based on source reliability.
Study evaluates cross-validation methods for clinical ECG classification, finding leave-source-out more reliable.
TASFAR adapts regression models without labeled source data.
Training a source model optimally for its own task is suboptimal for downstream transfer.
This paper proposes an alternative algorithm for multichannel variational autoencoder (MVAE), a recently proposed multichannel source separation approach. While MVAE is notable in its impressive source separation performance, the convergence-guaranteed optimization algorithm and that it allows us to estimate source-cla…
New method disentangles sources of different timescales in planetary seismic data.
Proposes LVGP for multi-source data fusion in science and engineering.
AR-Flow VAE improves blind source separation with flexible autoregressive priors.
Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference methods that take into account the complementarity and redundancy of various information sources. In this paper we formulate a variational autoenc…
Paper proposes a new method to aggregate multiple sources with different label distributions.
SAHMM-VAE separates sources adaptively using hidden Markov priors.
A new method aligns source and target distributions by tuning their weights.
New method restores source features for SFDA without source data.
Paper tackles multi-source domain adaptation for regression.
New framework tackles multi-source domain adaptation with optimism and consistency.
Framework reuses pre-trained models for data-free transfer learning.
Improves understanding of PWS by calculating influence of sources and data.
This paper motivates and develops source traces for temporal difference (TD) learning in the tabular setting. Source traces are like eligibility traces, but model potential histories rather than immediate ones. This allows TD errors to be propagated to potential causal states and leads to faster generalization. Source …
For many years, a combination of principal component analysis (PCA) and independent component analysis (ICA) has been used for blind source separation (BSS). However, it remains unclear why these linear methods work well with real-world data that involve nonlinear source mixtures. This work theoretically validates that…
We extend a recent synchronization analysis of exact finite-state sources to nonexact sources for which synchronization occurs only asymptotically. Although the proof methods are quite different, the primary results remain the same. We find that an observer's average uncertainty in the source state vanishes exponential…
ASKAP observes a region of the Galactic plane, identifying 3963 radio sources.
Novel unsupervised audio source separation using generative priors.
Adaptive kernel approach learns causal effects from diverse data sources.
Study characterizes harmful low-fidelity data sources for surrogate models.
New ICA method for sources with mixed spectra.
Monoaural audio source separation is a challenging research area in machine learning. In this area, a mixture containing multiple audio sources is given, and a model is expected to disentangle the mixture into isolated atomic sources. In this paper, we first introduce a challenging new dataset for monoaural source sepa…
Algorithm identifies sources in product distributions with improved complexity.
Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a two-layer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality i…
Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-…
Paper tackles entity matching over multi-source data, optimizing alignment and mitigating negative transfer.
Develops large-sample theory for non-stationary source separation.
Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of generative adversarial models presented new opportunities in signal regression …