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.
New bounds on AE success probability in GP models.
Previous studies have used a specific success metric within an algorithmic search framework to prove machine learning impossibility results. However, this specific success metric prevents us from applying these results on other forms of machine learning, e.g. transfer learning. We define decomposable metrics as a categ…
Proposes a new framework to manage venture capital portfolio risk by focusing on deal-level correlations.
The paper introduces SuccessProbaMax to optimize policy success probability in online advertising.
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…
Study predicts success of crypto-tokens on Pump.fun platform.
DG separates successes and failures by gating updates with advantage and surprisal.
Study on information evolution in interactive decision making using multi-armed bandits.
Maximizing withdrawal success in a pooled annuity fund with multiple annuitants.
Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search. This paper considers a resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed bandit for achieving best performance in grid search by selecting an appropriate batch s…
Quantum circuits are hard to learn on average.
Fewer degrees of freedom can train deep networks, showing a sharp phase transition.
GRPO optimizes LLMs with verifiable rewards, amplifying policy success.
Paper predicts embryo implantation probability from IVF time-lapse imaging.
Random braids that are formed by multiplying randomly chosen permutation braids are studied by analyzing their behavior under Garside's weighted decomposition and cycling. Using this analysis, we propose a polynomial-time algorithm to the conjugacy problem that is successful for random braids in overwhelming probabilit…
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…
We consider the problem of evaluating the quality of startup companies. This can be quite challenging due to the rarity of successful startup companies and the complexity of factors which impact such success. In this work we collect data on tens of thousands of startup companies, their performance, the backgrounds of t…
A new machine learning model uses score matching to estimate probability densities efficiently.
We apply the formalism of the continuous time random walk to the study of financial data. The entire distribution of prices can be obtained once two auxiliary densities are known. These are the probability densities for the pausing time between successive jumps and the corresponding probability density for the magnitud…
SEEDA optimizes dose allocation in clinical trials to balance efficacy and safety.
System recommends workouts and predicts success rates using RNNs.
We analyse derivative securities whose value is NOT a deterministic function of an underlying which means presence of a basis risk at any time. The key object of our analysis is conditional probability distribution at a given underlying value and moment of time. We consider time evolution of this probability distributi…
Paper establishes lower bounds for Gaussian process bandit optimization under various perturbation models.
Two-parameter models can learn high-dimensional targets via gradient flow.
The paper develops a theory for identifying the best arm in non-parametric multi-armed bandits with a fixed budget.
Building on the view of machine learning as search, we demonstrate the necessity of bias in learning, quantifying the role of bias (measured relative to a collection of possible datasets, or more generally, information resources) in increasing the probability of success. For a given degree of bias towards a fixed targe…
Maximum mean discrepancy (MMD), also called energy distance or N-distance in statistics and Hilbert-Schmidt independence criterion (HSIC), specifically distance covariance in statistics, are among the most popular and successful approaches to quantify the difference and independence of random variables, respectively. T…
Paper analyzes high probability convergence of adaptive SGD with momentum.
Maximizes probability of completing investment schedules with optimal portfolio weights.
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This class of optimization problems, known as rank minimization, is NP-HARD, and for most practical problems there are no efficient algorithms that…
Proposes IPT for modeling complex joint distributions.
In this paper, a simple, general method of adding auxiliary stochastic neurons to a multi-layer perceptron is proposed. It is shown that the proposed method is a generalization of recently successful methods of dropout (Hinton et al., 2012), explicit noise injection (Vincent et al., 2010; Bishop, 1995) and semantic has…
Privacy improves robustness in statistical estimation.
We present two different approaches for parameter learning in several mixture models in one dimension. Our first approach uses complex-analytic methods and applies to Gaussian mixtures with shared variance, binomial mixtures with shared success probability, and Poisson mixtures, among others. An example result is that …
Deep learning is the mainstream technique for many machine learning tasks, including image recognition, machine translation, speech recognition, and so on. It has outperformed conventional methods in various fields and achieved great successes. Unfortunately, the understanding on how it works remains unclear. It has th…
Sequence probability predicts correctness in LLMs, but not for repeated prompts
Calibrated PRMs improve inference efficiency for LLMs by dynamically adjusting compute budgets.
Paper analyzes convergence of ODE samplers in Wasserstein distances.
Active seriation recovers item order from noisy pairwise similarity measurements.
The paper explores statistical and topological properties of sliced probability divergences.
Paper tackles best mixed arm identification with cost constraints in bandit models.
New method for generating images with conditional probability models.
Bayesian probability theory is one of the most successful frameworks to model reasoning under uncertainty. Its defining property is the interpretation of probabilities as degrees of belief in propositions about the state of the world relative to an inquiring subject. This essay examines the notion of subjectivity by dr…
Probabilistic models can be defined by an energy function, where the probability of each state is proportional to the exponential of the state's negative energy. This paper considers a generalization of energy-based models in which the probability of a state is proportional to an arbitrary positive, strictly decreasing…
Random non-linear Fourier features have recently shown remarkable performance in a wide-range of regression and classification applications. Motivated by this success, this article focuses on a sparse non-linear Fourier feature (NFF) model. We provide a characterization of the sufficient number of data points that guar…
By specifying model free preferences towards simple nested classes of lottery pairs, we develop the dual story to stand on equal footing with that of (primal) risk apportionment. The dual story provides an intuitive interpretation, and full characterization, of dual counterparts of such concepts as prudence and tempera…
Improved adaptive algorithms for identifying the best arm in MABs with fixed budget.
The paper explores theoretical insights into WGANs for better understanding and stability.