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.
Method discovers local independence in systems with continuous variables.
Enhances neural processes for better context handling.
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data ef…
X-Trend quickly adapts to new financial regimes, increasing Sharpe ratio by 18.9%.
Framework allows systems to defer difficult decisions to unknown experts.
We consider the problem of stochastic -armed dueling bandit in the contextual setting, where at each round the learner is presented with a context set of items, each represented by a -dimensional feature vector, and the goal of the learner is to identify the best arm of each context sets. However, unlike the …
Hydra boosts efficiency for long-context reasoning in resource-constrained settings.
Neural Processes (NPs) are a class of models that learn a mapping from a context set of input-output pairs to a distribution over functions. They are traditionally trained using maximum likelihood with a KL divergence regularization term. We show that there are desirable classes of problems where NPs, with this loss, f…
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…
Neural processes approximate Gaussian process inference, revealing three key costs.
New method for contextual bandits with corrupted context.
Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.
Bandit learning algorithms typically involve the balance of exploration and exploitation. However, in many practical applications, worst-case scenarios needing systematic exploration are seldom encountered. In this work, we consider a smoothed setting for structured linear contextual bandits where the adversarial conte…
We introduce a stochastic contextual bandit model where at each time step the environment chooses a distribution over a context set and samples the context from this distribution. The learner observes only the context distribution while the exact context realization remains hidden. This allows for a broad range of appl…
Attention learns PCA on Gaussian data, proving its connection to principal component analysis.
Neural processes (NPs) learn stochastic processes and predict the distribution of target output adaptively conditioned on a context set of observed input-output pairs. Furthermore, Attentive Neural Process (ANP) improved the prediction accuracy of NPs by incorporating attention mechanism among contexts and targets. In …
Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses on a particular approach for this purpose called independence-based learning. Such approach guarantees the learning of the correct structure …
New classifiers account for context-specific independences.
Log-linear models are the popular workhorses of analyzing contingency tables. A log-linear parameterization of an interaction model can be more expressive than a direct parameterization based on probabilities, leading to a powerful way of defining restrictions derived from marginal, conditional and context-specific ind…
IndiSeek learns disentangled representations by balancing independence and completeness.
A new test for conditional independence in discretized data.
VolNP learns IVS from sparse quotes via meta-learning and SABR priors.
TCRI improves domain generalization by enforcing conditional independence constraints.
Circle graph complexes reveal link properties via Khovanov homology.
Introduces CStrees for modeling context-specific causal models from observational and interventional data.
Study uses LLMs to improve financial forecasting by integrating textual and numerical data.
Extracts the finest pattern of mutual independence from data.
We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representations can be learned in a task-independent egocentric space specific to the agent that, when grounded with problem-specific information, are …
New method detects causal relationships from noisy measurements.
The paper explores how semantic independence can be captured in text embeddings using partial orthogonality.
New approach tackles nonidentifiability in nonlinear blind source separation.
We consider the problem of finding model-independent bounds on the price of an Asian option, when the call prices at the maturity date of the option are known. Our methods differ from most approaches to model-independent pricing in that we consider the problem as a dynamic programming problem, where the controlled proc…
This work develops a non-parametric test for relational independence in non-i.i.d. data.
New algorithms for private generalized linear contextual bandits.
This work tackles robust Bayesian optimization under data shift using φ-divergences.
This paper aims at justifying LWF and AMP chain graphs by showing that they do not represent arbitrary independence models. Specifically, we show that every chain graph is inclusion optimal wrt the intersection of the independence models represented by a set of directed and acyclic graphs under conditioning. This impli…
Optimal reinsurance contracts for multiple dependent risks are derived without specific dependency assumptions.
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that…
The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a n…
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…
Parallel score matching accelerates DPM training and improves density estimation.
We propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure. The model is more flexible than traditional graphical models based on exponential family distributions, but i…
A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonp…
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that…
Study shows priors are crucial for accurate causal learning from unlabeled data.
In this paper, we extend Meek's conjecture (Meek 1997) from directed and acyclic graphs to chain graphs, and prove that the extended conjecture is true. Specifically, we prove that if a chain graph H is an independence map of the independence model induced by another chain graph G, then (i) G can be transformed into H …
The n-dimensional torus is uniquely characterized by specific harmonic forms.
Writing the article-Time independent pricing of options in range bound markets; the question in the title came naturally to my mind. It is stated, in the above article, that in certain market conditions the stock price is subjected to an equation that exactly matches a time independent Schrodinger equation. The time in…