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.
NIPS 2018 Adversarial Vision Challenge aims to improve machine vision models.
Local explanations of DNNs are insensitive to parameter values.
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning, held in Long Beach, California, USA on December 7, 2017
This is the Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems, held in Barcelona, Spain on December 9, 2016
This is the Proceedings of NIPS 2017 Workshop on Machine Learning for the Developing World, held in Long Beach, California, USA on December 8, 2017
Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning. The 2016 edition of the conference comprised more than 2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and over 100%…
This paper presents details of our winning solutions to the task IV of NIPS 2017 Competition Track entitled Classifying Clinically Actionable Genetic Mutations. The machine learning task aims to classify genetic mutations based on text evidence from clinical literature with promising performance. We develop a novel mul…
The problem of Hybrid Linear Modeling (HLM) is to model and segment data using a mixture of affine subspaces. Different strategies have been proposed to solve this problem, however, rigorous analysis justifying their performance is missing. This paper suggests the Theoretical Spectral Curvature Clustering (TSCC) algori…
Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning labels to every unlabeled node using information from the node as well as its neighbors. It is often the case that a node is not only influenc…
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the struct…
ICML workshop on making machine learning models more understandable.
Improved guarantees for sparse random embeddings with explicit bounds and empirical superiority.
Unified dynamic approach for sparse model selection improves efficiency and accuracy.
ML4H workshop at NeurIPS 2018 focuses on health applications of machine learning.
Though CNNs have achieved the state-of-the-art performance on various vision tasks, they are vulnerable to adversarial examples --- crafted by adding human-imperceptible perturbations to clean images. However, most of the existing adversarial attacks only achieve relatively low success rates under the challenging black…
Notions of "fair classification" that have arisen in computer science generally revolve around equalizing certain statistics across protected groups. This approach has been criticized as ignoring societal issues, including how errors can hurt certain groups disproportionately. We pose a modification of one of the fairn…
Workshop on ML for developing countries to enhance sustainability.
Study no-arbitrage conditions in 1D diffusion markets with interest rates.
Neural Index Policy for multi-action bandits with heterogeneous budgets.
New research shows many recent defenses against adversarial examples are ineffective against black-box attacks.
Mask-RCNN applied to ISIC 2018 lesion tasks.
New algorithm finds optimal policy with polynomial trajectories in deterministic systems.
Paper improves ATN for generating adversarial examples.
Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullback-Leibler divergence between the target distribution and its approximation by implementing a form of functional gradient descent on a reprod…
A new model CDTM improves text classification by concentrating document topics.
New method improves fairness in machine learning models.
We study an interacting particle system in motivated by Stein variational gradient descent [Q. Liu and D. Wang, NIPS 2016], a deterministic algorithm for sampling from a given probability density with unknown normalization. We prove that in the large particle limit the empirical measure of the particle s…
New algorithm approximates optimal transport cost with additive error in near-linear time.
This volume is a collection of contributions from the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at the Neural Information Processing Systems (NIPS 2015) conference. Modern multivariate statistical methods developed in the rapidly growing field of machine learning are being increasingly…
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement lea…
Neural networks are known to be vulnerable to adversarial examples. In this note, we evaluate the two white-box defenses that appeared at CVPR 2018 and find they are ineffective: when applying existing techniques, we can reduce the accuracy of the defended models to 0%.
ARIMA and LSTM models predict stock prices with varying accuracy.
Amobee wins WASSA 2018 emotion prediction with language models and LSTM.
In this paper, we propose a novel approach to automatically determine the batch size in stochastic gradient descent methods. The choice of the batch size induces a trade-off between the accuracy of the gradient estimate and the cost in terms of samples of each update. We propose to determine the batch size by optimizin…
FOLKLORE algorithm speeds up online multiclass logistic regression.
We study Principal Component Analysis (PCA) in a setting where a part of the corrupting noise is data-dependent and, as a result, the noise and the true data are correlated. Under a bounded-ness assumption on the true data and the noise, and a simple assumption on data-noise correlation, we obtain a nearly optimal samp…
Improved performance in classifying domestic activities.
MedAusbild team won ISIC challenge by classifying seven skin diseases.
99% of papers use real-world data, but only 3% provide formal comparisons.
Bayesian approach models policy distribution for faster exploration and transfer learning.
Deep reinforcement learning improves forex trading by handling complex, random processes.
Study tackles discretization problem in crafting adversarial examples for discrete integer domains.
ETM identifies field-specific keywords in text classification.
Paper derives a fast learning rate for deep neural networks without scale invariant activation functions.
Improved path-length regret bounds for adaptive and oblivious adversaries.
New interpolation methods outperform Gaussian smoothing in derivative-free optimization.
Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning because it automatically extracts meaningful features through a sparse and part-based representation. However, NMF has the drawback of being highly ill-posed, that is, there typically exist many different but equivalent facto…
Study analyzes Airbnb booking lead times during global crises using a new metric.