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.
RDLI integrates domain logic and context grounding to detect crypto anomalies under scarce labels.
Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the ground truth words as context while at inference it has to generate the entire sequence from scratch. This discrepancy of the fed context l…
Universally valid ground truth is almost impossible to obtain or would come at a very high cost. For supervised learning without universally valid ground truth, a recommended approach is applying crowdsourcing: Gathering a large data set annotated by multiple individuals of varying possibly expertise levels and inferri…
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice and manager interpretation.
Trajectory or behavior prediction of traffic agents is an important component of autonomous driving and robot planning in general. It can be framed as a probabilistic future sequence generation problem and recent literature has studied the applicability of generative models in this context. The variety or Minimum over …
A new learning method for prosthetic arms without explicit rewards.
New method reduces regret in nonparametric bandits with unknown covariate shifts.
FinBloom enhances LLMs for real-time financial queries.
This paper takes a step towards theoretical analysis of the relationship between word embeddings and context embeddings in models such as word2vec. We start from basic probabilistic assumptions on the nature of word vectors, context vectors, and text generation. These assumptions are well supported either empirically o…
New algorithm allows IGL to work with action-inclusive feedback.
Algorithm identifies best arm with biased proxy and selective ground truth audits.
Estimates classifier errors without ground truth using algebraic geometry.
ClauseLens uses reinforcement learning to price reinsurance treaties transparently and auditably.
Transformers converge linearly to optimal models for Gaussian mixtures classification.
We consider a planning problem where the dynamics and rewards of the environment depend on a hidden static parameter referred to as the context. The objective is to learn a strategy that maximizes the accumulated reward across all contexts. The new model, called Contextual Markov Decision Process (CMDP), can model a cu…
A new learning scheme improves model efficiency and performance.
Enhances neural processes to learn from multiple related datasets.
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g.~operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not …
Exposure bias refers to the train-test discrepancy that seemingly arises when an autoregressive generative model uses only ground-truth contexts at training time but generated ones at test time. We separate the contributions of the model and the learning framework to clarify the debate on consequences and review propos…
Unsupervised learning models can be indistinguishable without identifiability, leading to unreliable representations.
We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a vector embedding. In particular, this distribution is supported over the contexts which co-occur with the entity and are embedded in a suitable low…
Satellite imagery helps assess sustainable development with machine learning.
VB-Score evaluates AI systems without ground truth, revealing robustness.
To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physica…
We extend manifold capacity to nonlinear neural representations with contextual information.
This study uses ICL to efficiently generate robust confidence intervals for noisy regression tasks.
New algorithms improve efficiency in learning from personalized rewards.
Interpretation of a machine learning induced models is critical for feature engineering, debugging, and, arguably, compliance. Yet, best of breed machine learning models tend to be very complex. This paper presents a method for model interpretation which has the main benefit that the simple interpretations it provides …
Applying deep learning methods to mammography assessment has remained a challenging topic. Dense noise with sparse expressions, mega-pixel raw data resolution, lack of diverse examples have all been factors affecting performance. The lack of pixel-level ground truths have especially limited segmentation methods in push…
One of the most surprising and exciting discoveries in supervised learning was the benefit of overparameterization (i.e. training a very large model) to improving the optimization landscape of a problem, with minimal effect on statistical performance (i.e. generalization). In contrast, unsupervised settings have been u…
Sparse Blind Source Separation (sparse BSS) is a key method to analyze multichannel data in fields ranging from medical imaging to astrophysics. However, since it relies on seeking the solution of a non-convex penalized matrix factorization problem, its performances largely depend on the optimization strategy. In this …
A two-step approach efficiently selects hyperparameters for FCMs.
PHASE dataset simulates complex social interactions in physical environments.
Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this work, we revisit a classical idea: Stein's Unbiased Risk Estimator (SURE). We show that, in the context of image recovery, SURE and its gener…
Benchmark evaluates financial misinformation detection models, revealing weaknesses without external context.
AV-ASR system improves speech recognition with visual context.
This work studies the class of algorithms for learning with side-information that emerge by extending generative models with embedded context-related variables. Using finite mixture models (FMM) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maxi…
Proposes hinge-Wasserstein to improve uncertainty estimation in regression tasks.
Shapelet transform improves time series classification for earthquake, wind, and wave events.
When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion…
Free Random Projection enhances reinforcement learning by naturally incorporating hierarchical structure.
We construct a solution of the master equation by means of standard tools from homological perturbation theory under just the hypothesis that the ground field be of characteristic zero, thereby avoiding the formality assumption of the relevant Lie algebra. To this end we endow the homology H(g) of any differential grad…
Arriving at the complete probabilistic knowledge of a domain, i.e., learning how all variables interact, is indeed a demanding task. In reality, settings often arise for which an individual merely possesses partial knowledge of the domain, and yet, is expected to give adequate answers to a variety of posed queries. Tha…
Study introduces a new method for multiple parameter regularization in polynomial functional regression.
Study on ground states of semilinear elliptic equations with various potential wells.
Ground-A-Video edits videos without training, preserving intended changes.
The information theoretic quantity known as mutual information finds wide use in classification and community detection analyses to compare two classifications of the same set of objects into groups. In the context of classification algorithms, for instance, it is often used to compare discovered classes to known groun…