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.
Extends positive and almost positive links to successively almost positive ones.
Success conditioning optimizes policies by imitating successful trajectories, solving a trust-region optimization problem.
New metrics solve machine learning limitations.
Machine learning predicts TV show success based on factors like characters and direction.
We review recent numerical results on the role of talent and luck in getting success by means of a schematic agent-based model. In general the role of luck is found to be very relevant in order to get success, while talent is necessary but not sufficient. Funding strategies to improve the success of the most talented p…
Predicting startup success using Crunchbase data and deep learning.
Enhances VC startup success predictions using graph augmented time series models.
Proposes a new framework to manage venture capital portfolio risk by focusing on deal-level correlations.
A new method predicts student skill success rates in real-time.
Study predicts success of crypto-tokens on Pump.fun platform.
New method forecasts workforce reintegration success rates.
New bounds on AE success probability in GP models.
We study the problem of identifying the top arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that w…
Vanguard uses AI to create personalized financial plans.
Paper proposes a new method for training nonconvex models.
The muti-layer information bottleneck (IB) problem, where information is propagated (or successively refined) from layer to layer, is considered. Based on information forwarded by the preceding layer, each stage of the network is required to preserve a certain level of relevance with regards to a specific hidden variab…
We consider the problem of learning from sparse and underspecified rewards, where an agent receives a complex input, such as a natural language instruction, and needs to generate a complex response, such as an action sequence, while only receiving binary success-failure feedback. Such success-failure rewards are often …
In this paper, we propose a new fast and robust recursive algorithm for near-separable nonnegative matrix factorization, a particular nonnegative blind source separation problem. This algorithm, which we refer to as the successive nonnegative projection algorithm (SNPA), is closely related to the popular successive pro…
Study of new link types and their invariants, extending previous results.
System recommends workouts and predicts success rates using RNNs.
The paper introduces SuccessProbaMax to optimize policy success probability in online advertising.
To a compact Riemann surface of genus g can be assigned a principally polarized abelian variety (PPAV) of dimension g, the Jacobian of the Riemann surface. The Schottky problem is to discern the Jacobians among the PPAVs. Buser and Sarnak showed, that the square of the first successive minimum, the squared norm of the …
PixelHop uses SSL for image classification, outperforming CNN.
Using the trends of estimated abilities in terms of item response theory for online testing, we can predict the success/failure status for the final examination to each student at early stages in courses. In prediction, we applied the newly developed nearest neighbor method for determining the similarity of learning sk…
GRPO optimizes LLMs with verifiable rewards, amplifying policy success.
We address the issue of the factors driving startup success in raising funds. Using the popular and public startup database Crunchbase, we explicitly take into account two extrinsic characteristics of startups: the competition that the companies face, using similarity measures derived from the Word2Vec algorithm, as we…
A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent …
Bias is essential for machine learning success, quantifiable and conserved.
Develops a fair post-processing method for student success predictions.
We present a simple and general result that the sign of the variations or increments of uncorrelated times series are predictable with a remarkably high success probability of 75% for symmetric sign distributions. The origin of this paradoxical result is explained in details. We also present some tests on synthetic, fi…
Gradient ascent method successfully removes specific data points from neural networks without retraining.
A new algorithm selects independent coordinates for complex manifolds.
AdaBoost's success explained through noise influence measure.
Study shows adversarial attacks can fool speech-to-text models, and PCA is ineffective as a defense.
PixelHop++ improves image classification with a smaller model size.
This study analyzes counterfactual explanations for student success models.
Recently, deep models have had considerable success in several tasks, especially with low-level representations. However, effective learning from sparse noisy samples is a major challenge in most deep models, especially in domains with structured representations. Inspired by the proven success of human guided machine l…
Dan Lovallo and Daniel Kahneman must be commended for their clear identification of causes and cures to the planning fallacy in "Delusions of Success: How Optimism Undermines Executives' Decisions" (HBR July 2003). Their look at overoptimism, anchoring, competitor neglect, and the outside view in forecasting is highly …
Active management is a term that has many meanings and we have found the defining characteristics needed for success as an "active manager" elusive within the literature. In this paper we offer a set of criteria that defines an active manager and his success. In order to facilitate this, we introduce several definition…
DG separates successes and failures by gating updates with advantage and surprisal.
Conformal prediction uses past experience to determine precise levels of confidence in new predictions. Given an error probability , together with a method that makes a prediction of a label , it produces a set of labels, typically containing , that also contains with probability . Con…
Study on information evolution in interactive decision making using multi-armed bandits.
Transforming sparse outcomes into dense process rewards for efficient reinforcement learning.
Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range shift in organism, range of invasive species influenced by climate change are important parameters in understanding the i…
The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a worldwide discussion of the future of our human society with a mixed mood of hope, anxiousness, excitement and fear. We try to dymystify AlphaGo Zero by a qualitative analysis to indicate that AlphaGo Zero can be understood as a specially structured…
Domain randomization (DR) is a successful technique for learning robust policies for robot systems, when the dynamics of the target robot system are unknown. The success of policies trained with domain randomization however, is highly dependent on the correct selection of the randomization distribution. The majority of…
Paper tackles non-convex optimization for higher moments in portfolio management.
Proposes a deep latent factor model for better recommendation systems.