pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty Based Active Learning Strategy for Interactive Weakly Supervised Learning through Data Programming
Authors: Anonymous
Date: 2020-10-15 00:00:00
Proceeding: NeurIPS Workshop HAMLETS 2020 12
Tasks: Active Learning, BIG-bench Machine Learning, text-classification, Text Classification, Weakly-supervised Learning

Easy to build and reliable machine learning models are what all data analysts want. Although machine learning is advancing daily, the labeling cost for supervised learning and the black-box nature of machine learning are the main obstacles to its further diffusion. As a method of reducing the labeling cost without increasing the black-box nature, weakly supervised learning, especially data programming, is gaining attention. Its advantage is due to labeling functions that domain experts create based on their knowledge instead of labeling each data point manually. However, data programming alone cannot reduce the actual process cost.
This is because domain experts have to carry out a full search in their mind and endlessly implement labeling functions without any insight into what unimplemented labeling functions will be effective. We propose an active learning strategy for interactive weakly supervised learning with labeling functions to solve this problem. The proposed method iteratively presents a small number of highly prioritized data points to be labeled by additional labeling functions considering the uncertainty of predictions. With this method, domain experts need to only implement their knowledge that can be applied to a small number of presented data points as a labeling function. We also verified the effectiveness of this method through a six-class text classification task. The experimental results indicate the effectiveness of the method and its high potential in a machine learning implementation.
A case for new neural networks smoothness constraints
Authors: Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12
Tasks: Adversarial Robustness, Inductive Bias, reinforcement-learning, Reinforcement Learning (RL)

How sensitive should machine learning models be to input changes? We tackle the question of  model smoothness and show that it is a useful inductive bias which aids generalization, adversarial robustness, generative modeling and reinforcement learning. We explore current methods of imposing smoothness constraints and observe they lack the flexibility to adapt to new tasks, they don't account for data modalities, they interact with losses, architectures and optimization in ways not yet fully understood. We conclude that new advances in the field are hinging on finding ways to incorporate data, tasks and learning into our definitions of smoothness.
Oversampling Tabular Data with Deep Generative Models: Is it worth the effort?
Authors: Ramiro Camino, Chris Hammerschmidt, Radu State
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12
Tasks: imbalanced classification

In practice, machine learning experts are often confronted with imbalanced data.
Without accounting for the imbalance, common classifiers perform poorly, and standard evaluation metrics mislead the practitioners on the model's performance.
A standard method to treat imbalanced datasets is under- and oversampling. 
In this process, samples are removed from the majority class, or synthetic samples are added to the minority class.
In this paper, we follow up on recent developments in deep learning.
We take proposals of deep generative models and study these approaches' ability to provide realistic samples that improve performance on imbalanced classification tasks via oversampling.
Across 160K+ experiments, we show that the improvements in terms of performance metric, while shown to be significant when ranking the methods like in the literature, often are minor in absolute terms, especially compared to the required effort.
Furthermore, we notice that a large part of the improvement is due to undersampling, not oversampling.
Graph Conditional Variational Models: Too Complex for Multiagent Trajectories?
Authors: Yannick Rudolph, Ulf Brefeld, Uwe Dick
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12

Recent advances in modeling multiagent trajectories combine graph architectures such as graph neural networks (GNNs) with conditional variational models (CVMs) such as variational RNNs (VRNNs). Originally, CVMs have been proposed to facilitate learning with multi-modal and structured data and thus seem to perfectly match the requirements of multi-modal multiagent trajectories with their structured output spaces. Empirical results of VRNNs on trajectory data support this assumption. In this paper, we revisit experiments and proposed architectures with additional rigour, ablation runs and baselines. In contrast to common belief, we show that prior results with CVMs on trajectory data might be misleading. Given a neural network with a graph architecture and/or structured output function, variational autoencoding does not seem to contribute statistically significantly to empirical performance. Instead, we show that well-known emission functions do contribute, while coming with less complexity, engineering and computation time.
A study of quality and diversity in K+1 GANs
Authors: Ilya Kavalerov, Wojciech Czaja, Rama Chellappa
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12
Tasks: Diversity

We study the K+1 GAN paradigm which generalizes the canonical true/fake GAN by training a generator with a K+1-ary classifier instead of a binary discriminator. We show how the standard formulation of the K+1 GAN does not take advantage of class information fully and show how its learned generative data distribution is no different than the distribution that a traditional binary GAN learns. We then investigate another GAN loss function that dynamically labels its data during training, and show how this leads to learning a generative distribution that emphasizes the target distribution modes. We investigate to what degree our theoretical expectations of these GAN training strategies have impact on the quality and diversity of learned generators on real-world data.
End-to-End Differentiable GANs for Text Generation
Authors: Sachin Kumar, Yulia Tsvetkov
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12
Tasks: Text Generation

Despite being widely used, text generation models trained with maximum likelihood estimation (MLE) suffer from known limitations. Due to  a mismatch between training and inference, they suffer from exposure bias. Generative adversarial networks (GANs), on the other hand, by leveraging a discriminator, can mitigate these limitations. However, discrete nature of text makes the model non-differentiable hindering training. To deal with this issue, the approaches proposed so far, using reinforcement learning or softmax approximations are unstable and have been shown to underperform MLE. In this work, we consider an alternative setup where we represent each word by a pretrained vector. We modify the generator to output a sequence of such word vectors and feed them directly to the discriminator making the training process differentiable. Through experiments on unconditional text generation with Wasserstein GANs, we find that while this approach, without any pretraining is more stable while training and outperforms other GAN based approaches, it still falls behind MLE. We posit that this gap is due to autoregressive nature and architectural requirements for text generation as well as a fundamental difference between the definition of Wasserstein distance in image and text domains.
I’m Sorry for Your Loss: Spectrally-Based Audio Distances Are Bad at Pitch
Authors: Joseph Turian, Max Henry
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12

Growing research demonstrates that synthetic failure modes imply poor generalization. We compare commonly used audio-to-audio losses on a synthetic benchmark, measuring the pitch distance between two stationary sinusoids. The results are surprising: many have poor sense of pitch direction. These shortcomings are exposed using simple rank assumptions. Our task is trivial for humans but difficult for these audio distances, suggesting significant progress can be made in self-supervised audio learning by improving current losses.
It Doesn’t Get Better and Here’s Why: A Fundamental Drawback in Natural Extensions of UCB to Multi-agent Bandits
Authors: Udari Madhushani, Naomi Leonard
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12

We identify a fundamental drawback of natural extensions of Upper Confidence Bound (UCB) algorithms to the multi-agent bandit problem in which multiple agents facing the same explore-exploit problem can share information. We provide theoretical guarantees that when agents use a natural extension of the UCB sampling rule, sharing information about the optimal option degrades their performance. For $K$ the number of agents and $T$ the time horizon, we prove that when agents share information only about the optimal option they suffer an expected group cumulative regret of $O(K\log T+K\log K)$, whereas when they do not share any information they only suffer a group regret of $O(K\log T)$. Further, while information sharing about all options yields much better performance than with no information sharing, we show that including information about the optimal option is not as good as sharing information only about suboptimal options.
Is the Discrete VAE’s Power Stuck in its Prior?
Authors: Haydn Thomas Jones, Juston Moore
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12
Tasks: Decoder

We investigate why probabilistic neural models with discrete latent variables are effective at generating high-quality images. We hypothesize that fitting a more flexible variational posterior distribution and performing joint training of the encoder, decoder, and prior distribution should improve model fit. However, we find that modifying the training procedure for the well-known vector quantized variational autoencoder (VQ-VAE) leads to models with lower marginal likelihood for held-out data and degraded sample quality. These results indicate that current discrete VAEs use their encoder and decoder as a deterministic compression bottleneck. The distribution-matching power of these models lies solely in the prior distribution, which is typically trained after clamping the encoder and decoder.
Power posteriors do not reliably learn the number of components in a finite mixture
Authors: Diana Cai, Trevor Campbell, Tamara Broderick
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12

Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. Data science folk wisdom tells us that a finite mixture model (FMM) with a prior on the number of components will fail to recover the true, data-generating number of components under model misspecification.  But practitioners still widely use FMMs to learn the number of components, and statistical machine learning papers can be found recommending such an approach.  Increasingly, though, data science papers suggest potential alternatives beyond vanilla FMMs, such as power posteriors, coarsening, and related methods.  In this work we start by adding rigor to folk wisdom and proving that, under even the slightest model misspecification, the FMM component-count posterior diverges: the posterior probability of any particular finite number of latent components converges to 0 in the limit of infinite data.  We use the same theoretical techniques to show that power posteriors with fixed power face the same undesirable divergence, and we provide a proof for the case where the power converges to a non-zero constant.  We illustrate the practical consequences of our theory on simulated and real data.  We conjecture how our methods may be applied to lend insight into other component-count robustification techniques.
Decision-Aware Model Learning for Actor-Critic Methods: When Theory Does Not Meet Practice
Authors: Ângelo Gregório Lovatto, Thiago Pereira Bueno, Denis Mauá, Leliane Nunes de Barros
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12

Actor-Critic methods are a prominent class of modern reinforcement learning algorithms based on the classic Policy Iteration procedure. Despite many successful cases, Actor-Critic methods tend to require a gigantic number of experiences and can be very unstable. Recent approaches have advocated learning and using a world model to improve sample efficiency and reduce reliance on the value function estimate. However, learning an accurate dynamics model of the world remains challenging, often requiring computationally costly and data-hungry models. More recent work has shown that learning an everywhere accurate model is unnecessary and often detrimental to the overall task; instead, the agent should improve the world model on task-critical regions. For example, in Iterative Value-Aware Model Learning, the authors extend model-based value iteration by incorporating the value function (estimate) into the model loss function, showing the novel model objective reflects improved performance in the end task. Therefore, it seems natural to expect that model-based Actor-Critic methods can benefit equally from learning value-aware models, improving overall task performance, or reducing the need for large, expensive models. However, we show empirically that combining  Actor-Critic and value-aware model learning can be quite difficult and that naive approaches such as maximum likelihood estimation often achieve superior performance with less computational cost. Our results suggest that, despite theoretical guarantees, learning a value-aware model in continuous domains does not ensure better performance on the overall task.
Less can be more in contrastive learning
Authors: Jovana Mitrovic, Brian McWilliams, Melanie Rey
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12
Tasks: Contrastive Learning, Representation Learning

Unsupervised representation learning provides an attractive alternative to its supervised counterpart   because of the abundance of unlabelled data. Contrastive learning has recently emerged as one of the most successful approaches to unsupervised representation learning. Given a datapoint, contrastive learning involves discriminating between a matching, or positive, datapoint and a number of non-matching, or negative, ones. Usually the other datapoints in the batch serve as the negatives for the given datapoint. It has been shown empirically that large batch sizes are needed to achieve good performance, which led the the belief that a large number of negatives is preferable. In order to understand this phenomenon better, in this work investigate the role of negatives in contrastive learning by decoupling the number of negatives from the batch size. Surprisingly, we discover that for a fixed batch size performance actually degrades as the number of negatives is increased. We also show that using fewer negatives can lead to a better signal-to-noise ratio for the model gradients, which could explain the improved performance.
Why Are Bootstrapped Deep Ensembles Not Better?
Authors: Jeremy Nixon, Balaji Lakshminarayanan, Dustin Tran
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12

Ensemble  methods  have  consistently  reached  state  of  the  art  across  predictive, uncertainty, and out-of-distribution robustness benchmarks. One of the most popular  ways  to  construct  an  ensemble  is  to  independently  train  each  model  on  are sampled (bootstrapped) version of the dataset.  Bootstrapping is popular in the literature on decision trees and frequentist statistics, with strong theoretical guarantees, but it is not used often in practice for deep neural networks. We investigate a common hypothesis for bootstrap’s weak performance—percentage of unique points in the subsampled dataset—and find that even when adjusting for it, boot-strap ensembles of deep neural networks yield no benefit over simpler baselines.This brings to question the role of data randomization as a source of uncertainty in deep learning.
Temperature Scaling for Quantile Calibration
Authors: Saiteja Utpala, Piyush Rai
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12
Tasks: Classification, regression

Deep learning models are often poorly calibrated, i.e., they may produce overconfident predictions that are  wrong, implying that their uncertainty estimates are unreliable. While a  number of approaches have been proposed recently to calibrate classification models, relatively little work exists on calibrating regression models. Temperature Scaling is one of the most popular methods for \emph{classification calibration}. It  performs better than or equal to more sophisticated methods. We investigate the use of Temperature Scaling  for \emph{regression calibration} under notion of quantile calibration.
The Curious Case of Stacking Boosted Relational Dependency Networks
Authors: Siwen Yan, Devendra Singh Dhami, Sriraam Natarajan
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12
Tasks: Relational Reasoning

Reducing bias while learning and inference is an important requirement to achieve generalizable and better performing models. The method of stacking took the first step towards creating such models by reducing inference bias but the question of combining stacking with a model that reduces learning bias is still largely unanswered. In statistical relational learning, ensemble models of relational trees such as boosted relational dependency networks (RDN-Boost) are shown to reduce the learning bias. We combine RDN-Boost and stacking methods with the aim of reducing both learning and inference bias subsequently resulting in better overall performance. However, our evaluation on three relational data sets shows no significant performance improvement over the baseline models.
A bumpy journey: exploring deep Gaussian mixture models
Authors: Margot Selosse, Claire Gormley, Julien Jacques, Christophe Biernacki
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12

The deep Gaussian mixture model (DGMM) is a framework directly inspired by the finite mixture of factor analysers model (MFA) and the deep learning architecture composed of multiple layers. The MFA is a generative model that considers a data point as arising from a latent variable (termed the score) which is sampled from a standard multivariate Gaussian distribution and then transformed linearly. The linear transformation matrix (termed the loading matrix) is specific to a component in the finite mixture. The DGMM consists of stacking MFA layers, in the sense that the latent scores are no longer assumed to be drawn from a standard Gaussian, but rather are drawn from a mixture of factor analysers model. Thus the latent scores are at one point considered to be the input of an MFA and also to have latent scores themselves. The latent scores of the DGMM's last layer only are considered to be drawn from a standard multivariate Gaussian distribution. In recent years, the DGMM gained prominence in the literature: intuitively, this model should be able to capture distributions more precisely than a simple Gaussian mixture model. We show in this work that while the DGMM is an original and novel idea, in certain cases it is challenging to infer its parameters. In addition, we give some insights to the probable reasons of this difficulty. Experimental results are provided on github: https://github.com/ansubmissions/ICBINB, alongside an R package that implements the algorithm and a number of ready-to-run R scripts.
Rethinking Deep Policy Gradients via State-Wise Policy Improvement
Authors: Kai-Chun Hu, Ping-Chun Hsieh, Ting Han Wei, I-Chen Wu
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12
Tasks: Policy Gradient Methods, Value prediction

Deep policy gradient is one of the major frameworks in reinforcement learning, and it has been shown to improve parameterized policies across various tasks and environments. However, recent studies show that the key components of the deep policy gradient methods, such as gradient estimation, value prediction, and optimization landscapes, fail to reflect the conceptual framework.
This paper aims to investigate the mechanism behind the deep policy gradient methods through the lens of state-wise policy improvement. Based on the fundamental properties of policy improvement, we propose an alternative theoretical framework to reinterpret the deep policy gradient update as training a binary classifier, with labels provided by the advantage function. This framework obviates the statistical difficulties in the gradient estimates and predicted values of the deep policy gradient update. Experimental results are included to corroborate the proposed framework.
Adversarial training for predictive tasks: theoretical analysis and limitations in the deterministic case.
Authors: Thibault Lesieur, Jérémie Messud, Issa Hammoud, Hanyuan Peng, Céline Lacombe, Paulien Jeunesse
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12

To train a deep neural network to mimic the outcomes of processing sequences, a version of Conditional Generalized Adversarial Network (CGAN) can be used. It has been observed by others that CGAN can help to improve the results even for deterministic sequences, where only one output is associated with the processing of a given input. Surprisingly, our CGAN-based tests on deterministic geophysical processing sequences did not produce a real improvement compared to the use of an $L_p$ loss; we here propose a first theoretical explanation why. Our analysis goes from the non-deterministic case to the deterministic one. It led us to develop an adversarial way to train a content loss that gave better results on our data.
Independent versus truncated finite approximations for Bayesian nonparametric inference
Authors: Tin D. Nguyen, Jonathan H. Huggins, Lorenzo Masoero, Lester Mackey, Tamara Broderick
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2020 12
Tasks: Denoising, Image Denoising

Bayesian nonparametric models based on completely random measures (CRMs) offers flexibility when the number of clusters or latent components in a data set is unknown. However, managing the infinite dimensionality of CRMs often leads to slow computation during inference. Practical inference typically relies on either integrating out the infinite-dimensional parameter or using a finite approximation: a truncated finite approximation (TFA) or an independent finite approximation (IFA). The atom weights of TFAs are constructed sequentially, while the atoms of IFAs are independent, which facilitates more convenient inference schemes. While the approximation error of TFA has been systematically addressed,  there has not yet been a similar study of IFA.  We quantify the approximation error between IFAs and two common target nonparametric priors (beta-Bernoulli process and Dirichlet process mixture model) and prove that, in the worst-case, TFAs provide more component-efficient approximations than IFAs. However, in experiments on image denoising and topic modeling tasks with real data, we find that the error of Bayesian approximation methods overwhelms any finite approximation error, and IFAs perform very similarly to TFAs.