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.
The paper tackles adversarial robustness by maximizing worst-case mutual information.
New method improves graph representations against adversarial attacks.
EIGAN learns private representations without centralized data, outperforming state-of-the-art.
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our framework leverages recent advances in adversarial learning to allow a data holder to learn representations in which a set of sensitive attri…
Adversarial techniques learn invariant representations across multiple domains.
In this paper, we advocate for representation learning as the key to mitigating unfair prediction outcomes downstream. Motivated by a scenario where learned representations are used by third parties with unknown objectives, we propose and explore adversarial representation learning as a natural method of ensuring those…
Representations of data that are invariant to changes in specified factors are useful for a wide range of problems: removing potential biases in prediction problems, controlling the effects of covariates, and disentangling meaningful factors of variation. Unfortunately, learning representations that exhibit invariance …
DBGAN learns graph node representations by balancing distribution consistency.
Deep networks are well-known to be fragile to adversarial attacks. We conduct an empirical analysis of deep representations under the state-of-the-art attack method called PGD, and find that the attack causes the internal representation to shift closer to the "false" class. Motivated by this observation, we propose to …
In traditional generative modeling, good data representation is very often a base for a good machine learning model. It can be linked to good representations encoding more explanatory factors that are hidden in the original data. With the invention of Generative Adversarial Networks (GANs), a subclass of generative mod…
Adversarial transfer learning improves stress assessment across users.
Paper finds exact global optima for adversarial representation learning.
Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in -- and can be recovered from -- the intermediate representations learned by text-based neural classifiers. The …
Neural nets learn robust geometric data representations.
We combine conditional variational autoencoders (VAE) with adversarial censoring in order to learn invariant representations that are disentangled from nuisance/sensitive variations. In this method, an adversarial network attempts to recover the nuisance variable from the representation, which the VAE is trained to pre…
AAT separates robust and non-robust features without supervision.
In practice, there are often explicit constraints on what representations or decisions are acceptable in an application of machine learning. For example it may be a legal requirement that a decision must not favour a particular group. Alternatively it can be that that representation of data must not have identifying in…
Reprogram deep models to resist adversarial attacks without changing parameters.
Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space. More robust r…
Proposes a non-adversarial method for distribution matching.
Study on robustness in linear regression models, focusing on adversarial perturbations.
New method reduces bias in NLI models using ensemble adversarial training.
Proposes an adversarial algorithm to learn unbiased representations via HGR coefficient.
APGE protects graph node representations from inference attacks.
GGAN improves audio representation learning with fewer labels.
Graph Information Bottleneck (GIB) optimizes graph representations for robustness against adversarial attacks.
Proposes adversarial method to estimate Riesz representer.
Theoretical study shows adversarial training improves robustness in deep learning models.
The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the…
New method speeds up training of deep networks robust to adversarial attacks.
The paper proposes a method to learn structured representations from unlabeled data using mutual information maximization.
Deep networks can overfit benignly but still be vulnerable to adversarial attacks.
We provide an approach for learning deep neural net representations of models described via conditional moment restrictions. Conditional moment restrictions are widely used, as they are the language by which social scientists describe the assumptions they make to enable causal inference. We formulate the problem of est…
We investigate the effect of the dimensionality of the representations learned in Deep Neural Networks (DNNs) on their robustness to input perturbations, both adversarial and random. To achieve low dimensionality of learned representations, we propose an easy-to-use, end-to-end trainable, low-rank regularizer (LR) that…
AIB method improves robustness against adversarial perturbations.
Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introdu…
Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation qu…
An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal. In this work, we show that robust optimization can be re-cast as a too…
Proposes GAAE for high-fidelity audio generation and representation learning.
New LFR algorithm ensures fair predictions with theoretical guarantees.
Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG …
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent varia…
Proposes a framework for extracting consistent physiological features across users.
GeoERM learns shared representations on Riemannian manifolds for multi-task learning.
Paper proposes a new method for learning compact representations of sequential data.
Paper improves deep learning models for limit order book data.
Reduces gender classification bias by learning race-invariant face representations.
Adversarial CCA improves representation learning by allowing more flexible priors.