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.
IBP-R improves verified adversarial robustness with simple, effective interval bound propagation.
Proves the existence of accurate, certifiably robust neural networks.
Training neural networks with verifiable robustness guarantees is challenging. Several existing approaches utilize linear relaxation based neural network output bounds under perturbation, but they can slow down training by a factor of hundreds depending on the underlying network architectures. Meanwhile, interval bound…
Neural networks are part of many contemporary NLP systems, yet their empirical successes come at the price of vulnerability to adversarial attacks. Previous work has used adversarial training and data augmentation to partially mitigate such brittleness, but these are unlikely to find worst-case adversaries due to the c…
Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minimizing an upper bound on the worst-case loss over all possible adversarial perturbations. While these techniques show promise, they often res…
Paper aims to ensure reliable detection of out-of-distribution data with certifiable worst-case guarantees.
We present an efficient technique, which allows to train classification networks which are verifiably robust against norm-bounded adversarial attacks. This framework is built upon the work of Gowal et al., who applies the interval arithmetic to bound the activations at each layer and keeps the prediction invariant to t…
Paper proposes faster certified robust training methods with short warmup.
Training Deep Neural Networks that are robust to norm bounded adversarial attacks remains an elusive problem. While exact and inexact verification-based methods are generally too expensive to train large networks, it was demonstrated that bounded input intervals can be inexpensively propagated from a layer to another t…
Develops first robustness verification for complex Transformers.
SAWAR improves SA models by making them more robust to data uncertainties.
Proposes a method for obtaining interval bounds in off-policy evaluation.
Paper provides efficient robustness certificates for neural networks.
New method defends against patch attacks with high-certainty guarantees.
Paper proves neural networks can be approximated using interval bounds.
The paper analyzes the expected size of conformal prediction sets.
The paper studies sub and super-replication price bounds for contingent claims defined on general trajectory based market models. No prior probabilistic or topological assumptions are placed on the trajectory space, trading is assumed to take place at a finite number of occasions but not bounded in number nor necessari…
Generalized belief propagation converges to optimal solutions on graphs with motifs.
Belief propagation recovers backpropagation results.
A susceptibility propagation that is constructed by combining a belief propagation and a linear response method is used for approximate computation for Markov random fields. Herein, we formulate a new, improved susceptibility propagation by using the concept of a diagonal matching method that is based on mean-field app…
Variational inference is a powerful concept that underlies many iterative approximation algorithms; expectation propagation, mean-field methods and belief propagations were all central themes at the school that can be perceived from this unifying framework. The lectures of Manfred Opper introduce the archetypal example…
Guaranteed bounds for posterior inference in probabilistic programs.
This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, the mapping from the continuous latent factors to the observed signal is parametrized by a convolutional neural network. The alternating bac…
Study reveals decurve flows in graph propagation models.
Unified model combines feature and label propagation for semi-supervised classification.
Improved error correction using neural networks and belief propagation.
A new approach estimates propagators for trading risky assets.
This thesis investigates belief propagation's performance in graphical models with loops.
LNPE enhances local connections in embeddings using extended neighbor propagation.
Decoupled GCN is shown to be equivalent to label propagation.
The back-propagation algorithm is the cornerstone of deep learning. Despite its importance, few variations of the algorithm have been attempted. This work presents an approach to discover new variations of the back-propagation equation. We use a domain specific lan- guage to describe update equations as a list of primi…
A new method for target propagation using iterative approximations converges fast and is more biologically plausible.
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems. In the general case of a loopy Graphical Model, Belief Propagation is a heuristic which is quite successful in practice, even though its empirical success, typically, lacks theoretical gu…
Global propagator for massless Dirac operator defined and analyzed.
This paper proposes methods to compute differentially private confidence intervals for the median.
The article constructs Feynman propagators for normally hyperbolic operators on curved spacetimes.
The paper proposes a scalable framework for uncertainty quantification and propagation in surrogate-based Bayesian inference.
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capt…
Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propagating "soft labels" (e.g. probability distributions, class membership scores) over hypergraphs, by means of optimal transportation. Borrowin…
SOLBP extends efficient inference to uncertain Bayesian networks.
We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature. The mechanics of di…
New -BP algorithm improves belief propagation for graphs with loops.
New group testing method uses Belief Propagation for accurate screening.
Belief propagation (BP) can do exact inference in loop-free graphs, but its performance could be poor in graphs with loops, and the understanding of its solution is limited. This work gives an interpretable belief propagation rule that is actually minimization of a localized -divergence. We term this algorithm as $α…
We discuss positivity properties of `distinguished propagators', i.e. distinguished inverses of operators that frequently occur in scattering theory and wave propagation. We relate this to the work of Duistermaat and Hörmander on distinguished parametrices (approximate inverses), which has played a major role in quantu…
Study shows uniform-time chaos propagation in mean field Langevin dynamics.
As one of the most important types of (weaker) supervised information in machine learning and pattern recognition, pairwise constraint, which specifies whether a pair of data points occur together, has recently received significant attention, especially the problem of pairwise constraint propagation. At least two reaso…
Wave propagation framework using cone structures and observers' vector fields.