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.
Paper introduces a new power-dominance axis in estimator design.
Algorithm finds safe zones in policy Markov Decision Processes to limit trajectory escape.
PredictaBoard benchmarks LLM score predictors to assess their ability to anticipate errors.
Proposes a multilingual email segmentation benchmark and model.
Brillouin zones were introduced by Brillouin in the thirties to describe quantum mechanical properties of crystals, that is, in a lattice in . They play an important role in solid-state physics. It was shown by Bieberbach that Brillouin zones tile the underlying space and that each zone has the same area. We gene…
Study option pricing in sideways markets and target zones.
Machine learning predicts CO2 emissions in power grids, reducing uncertainty.
Paper presents a WiFi-based indoor sensor localization technique.
Study shows critical width for rigidity of equatorial zones on spheres.
For a given lattice, we establish an equivalence involving a closed zone of the corresponding Voronoi polytope, a lamina hyperplane of the corresponding Delaunay partition and a quadratic form of rank 1 being an extreme ray of the corresponding L-type domain.
We consider the effects of the 2008 global financial crisis on the global stock market before, during, and after the crisis. We generate complex networks from a cross-correlation matrix such as the threshold network (TN) and the minimal spanning tree (MST). In the threshold network, we assign a threshold value by using…
The Zeeman-Hamilton operators of free charged particles are identified with the Laplacians of certain Riemannian manifolds, called Zeeman manifolds. The quantum Hilbert space decomposes into subspaces (Zeeman zones) which are invariant under the actions both of the Zeeman operator and the natural Heisenberg group repre…
The Zone of Avoidance makes it difficult for astronomers to catalogue galaxies at low latitudes to our galactic plane due to high star densities and extinction. However, having a complete sky map of galaxies is important in a number of fields of research in astronomy. There are many unclassified sources of light in the…
Model predicts and optimizes trading of electricity price spreads across multiple zones.
Optimizes portfolio with two controls to minimize trades and maintain signal integrity.
CoDeQ simplifies joint model compression by integrating pruning and quantization.
In the present paper which a sequel to dg-ga/9511005 and dg-ga//9610013 a global Weierstrass representation of an arbitrary closed oriented surface of genus in the the three-space is constructed. The Weierstrass spectrum of a torus immersed into is introduced and finite-zone planes as well as finite-zone…
We study the problem of optimal trading using general alpha predictors with linear costs and temporary impact. We do this within the framework of stochastic optimization with finite horizon using both limit and market orders. Consistently with other studies, we find that the presence of linear costs induces a no-tradin…
Study reveals different drivers of electricity price volatility across Europe.
We study optimal trading in an Almgren-Chriss model with running and terminal inventory costs and general predictive signals about price changes. As a special case, this allows to treat optimal liquidation in "target zone models": asset prices with a reflecting boundary enforced by regulatory interventions. In this cas…
We explore the loss landscape of fully-connected and convolutional neural networks using random, low-dimensional hyperplanes and hyperspheres. Evaluating the Hessian, , of the loss function on these hypersurfaces, we observe 1) an unusual excess of the number of positive eigenvalues of , and 2) a large value of $…
Hierarchical GANs reduce anomaly detection costs.
We propose a novel defense against all existing gradient based adversarial attacks on deep neural networks for image classification problems. Our defense is based on a combination of deep neural networks and simple image transformations. While straightforward in implementation, this defense yields a unique security pro…
Unified principle LZN unifies generative modeling, representation learning, and classification.
We introduce the safe linear stochastic bandit framework---a generalization of linear stochastic bandits---where, in each stage, the learner is required to select an arm with an expected reward that is no less than a predetermined (safe) threshold with high probability. We assume that the learner initially has knowledg…
CoCoRL learns safe constraints from demonstrations with unknown rewards.
Adopting a zonal structure of electricity market requires specification of zones' borders. In this paper we use social welfare as the measure to assess quality of various zonal divisions. The social welfare is calculated by Market Coupling algorithm. The analyzed divisions are found by the usage of extended Locational …
SafePILCO is a Python tool for safe reinforcement learning.
Safe learning of stochastic dynamics with safety constraints.
High dimensional regression benefits from sparsity promoting regularizations. Screening rules leverage the known sparsity of the solution by ignoring some variables in the optimization, hence speeding up solvers. When the procedure is proven not to discard features wrongly the rules are said to be \emph{safe}. In this …
We show that when a third party, the adversary, steps into the two-party setting (agent and operator) of safely interruptible reinforcement learning, a trade-off has to be made between the probability of following the optimal policy in the limit, and the probability of escaping a dangerous situation created by the adve…
Study on neural networks to identify redundancy issues in safe machine learning.
We study safe screening for metric learning. Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances and an instance in a different class. However, the number of possible triplets is quite huge even for a small dataset. Our safe triplet scree…
In this note we discuss - in what is intended to be a pedagogical fashion - FX option pricing in target zones with attainable boundaries. The boundaries must be reflecting. The no-arbitrage requirement implies that the differential (foreign minus domestic) short-rate is not deterministic. When the band is narrow, we ca…
Bitcoin fails to prove safe haven status during pandemic.
Proposes a method to accelerate safe sequential learning using offline data.
Meta-learning priors improves safe Bayesian optimization.
Revel tackles safe exploration in RL with verified symbolic policies.
ASE safely explores unknown MDPs with unknown dynamics, improving sample efficiency.
Safe-House secures DeFi by limiting losses and enhancing security.
OSIL learns safe policies from unsafe demonstrations.
We study the problem of safe learning and exploration in sequential control problems. The goal is to safely collect data samples from operating in an environment, in order to learn to achieve a challenging control goal (e.g., an agile maneuver close to a boundary). A central challenge in this setting is how to quantify…
The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduced safe screening allows us to identify a part of non-active features/samples. So far, safe screening has been individually studied either fo…
A new screening rule 'dynamic Sasvi' improves sparse optimization speed.
Central bank strategy to maintain currency exchange rate within limits.
Our analysis of financial data, in terms of super-exponential growth, suggests that the seed of the 2002/03 crisis of the Dutch supermarket giant AHOLD was planted in 1996. It became quite visible in 1999 when the post-bubble destabilization regime was well-developed and acted as the precursor of an inevitable collapse…
Crypto-assets perform better than gold as safe-havens during market crashes.
Safe autonomous decisions made with machine learning predictions using Conformal Decision Theory.