pwc-archive/papers-with-abstracts huggingface dataset:
Variational Label Enhancement
Authors: Ning Xu, Yun-Peng Liu, Jun Shu, Xin Geng
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Multi-Label Learning, Variational Inference

Label distribution  covers a certain number of labels, representing the degree to which each label describes the instance. The learning process on the	instances labeled by label distributions is called label distribution learning (LDL). Unfortunately, many training sets only contain  simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly.  To solve this problem,  we consider the label distributions as the latent vectors and  infer the label distributions from the logical labels in the training datasets by using variational inference. After that, we induce a predictive model to train the label distribution data by employing the multi-output regression technique. The recovery experiment  on thirteen real-world LDL  datasets  and the predictive experiment on ten multi-label learning datasets validate the advantage of our approach  over the state-of-the-art  approaches.
A general recurrent state space framework for modeling neural dynamics during decision-making
Authors: David Zoltowski, Jonathan Pillow, Scott Linderman
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Decision Making, Open-Ended Question Answering, State Space Models

An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit the number of these models that we can fit to neural data. Here we propose a general framework for modeling neural activity during decision-making. The framework includes the canonical drift-diffusion model and enables extensions such as multi-dimensional accumulators, variable and collapsing boundaries, and discrete jumps. Our framework is based on constraining the parameters of recurrent state space models, for which we introduce a scalable variational Laplace-EM inference algorithm. We applied the modeling approach to spiking responses recorded from monkey parietal cortex during two decision-making tasks. We found that a two-dimensional accumulator better captured the trial-averaged responses of a set of parietal neurons than a single accumulator model. Next, we identified a variable lower boundary in the responses of a parietal neuron during a random dot motion task.
Boosting Deep Neural Network Efficiency with Dual-Module Inference
Authors: Liu Liu, Lei Deng, Zhaodong Chen, yuke wang, Shuangchen Li, Jingwei Zhang, Yihua Yang, Zhenyu Gu, Yufei Ding, Yuan Xie
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: CPU

Using Deep Neural Networks (DNNs) in machine learning tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements and energy constraints because of the memory-bound and the compute-bound execution pattern of DNNs. We propose a big-little dual-module inference to dynamically skip unnecessary memory access and computation to speedup DNN inference. Leveraging the error-resilient feature of nonlinear activation functions used in DNNs, we propose to use a lightweight little module that approximates the original DNN layer, which is referred to as the big module, to compute activations of the insensitive region that are more error-resilient. The expensive memory access and computation of the big module can be reduced as the results are only used in the sensitive region. For memory-bound models, our method can reduce the overall memory access by 40% on average and achieve 1.54x to 1.75x speedup on a commodity CPU-based server platform with a negligible impact on model quality. In addition, our method can reduce the operations of the compute-bound ResNet model by 3.02x, with only a 0.5% accuracy drop.
Budgeted Online Influence Maximization
Authors: Pierre Perrault, Zheng Wen, Michal Valko, Jennifer Healey
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: valid

We introduce a new budgeted framework for online  influence  maximization,   considering the total cost of an advertising campaign instead  of  the  common  cardinality  constraint on a chosen influencer set. Our approach models better the  real-world  setting  where  the cost of influencers varies and advertizers want to find the best value for their overall social advertising budget. We propose an algorithm assuming  an  independent  cascade  diffusion model  and  edge- level  semi-bandit  feedback, and provide both theoretical and experimental results.  Our analysis is also valid for the cardinality-constraint  setting  and  improves the state of the art regret bound in this case.
Communication-Efficient Distributed PCA by Riemannian Optimization
Authors: Long-Kai Huang, Jialin Pan
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Riemannian optimization

In this paper, we study the leading eigenvector problem in a statistically distributed setting and propose a communication-efficient algorithm based on Riemannian optimization, which trades local computation for global communication. Theoretical analysis shows that the proposed algorithm linearly converges to the centralized empirical risk minimization solution regarding the number of communication rounds. When the number of data points in local machines is sufficiently large, the proposed algorithm achieves a significant reduction of communication cost over existing distributed PCA algorithms. Superior performance in terms of communication cost of the proposed algorithm is verified on real-world and synthetic datasets.
Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits
Authors: Xi Liu, Ping-Chun Hsieh, Yu Heng Hung, Anirban Bhattacharya, P. Kumar
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Multi-Armed Bandits

We propose a new family of bandit algorithms, that are formulated in a general way based on the Biased Maximum Likelihood Estimation (BMLE) method originally appearing in the adaptive control literature. We design the reward-bias term to tackle the exploration and exploitation tradeoff for stochastic bandit problems. We provide a general recipe for the BMLE algorithm and derive a simple explicit closed-form expression for the index of an arm for exponential family reward distributions. We prove that the derived BMLE indices achieve a logarithmic finite-time regret bound and hence attain order-optimality, for both exponential families and the cases beyond parametric distributions. Through extensive simulations, we demonstrate that the proposed algorithms achieve regret performance comparable to the best of several state-of-the-art baseline methods, while being computationally efficient in comparison to other best-performing methods. The generality of the proposed approach makes it possible to address more complex models, including general adaptive control of Markovian systems.
Identifying the Reward Function by Anchor Actions
Authors: Sinong Geng, Houssam Nassif, Carlos Manzanares, Max Reppen, Ronnie Sircar
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1

We propose a reward function estimation framework for inverse reinforcement learning with deep energy-based policies. Our method sequentially estimates the policy, the $Q$-function, and the reward. We refer to it as the PQR method. This method does not require the assumption that the reward depends on the state only, but instead allows also for dependency on the choice of action. Moreover, the method allows for the state transitions to be stochastic. To accomplish this, we assume the existence of one anchor action whose reward is known, typically the action of doing nothing, yielding no reward. We present both estimators and algorithms for the PQR method. When the environment transition is known, we prove that the reward estimator of PQR uniquely recovers the true reward.
With unknown transitions, convergence analysis is presented for the PQR method.
Finally, we apply PQR to both synthetic and real-world datasets, demonstrating superior performance in terms of reward estimation compared to competing methods.
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers
Authors: Zinan Lin, Kiran Thekumparampil, Giulia Fanti, Sewoong Oh
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Disentanglement, Model Selection

Standard deep generative models have latent codes that can be arbitrarily rotated, and a specific coordinate has no meaning. For manipulation and exploration of the samples, we seek a disentangled latent code where each coordinate is associated with a distinct property of the target distribution. Recent advances has been dominated by Variational Autoencoder (VAE)-based methods, while training disentangled generative adversarial networks (GANs) remains challenging. To this end, we make two contributions: a novel approach for training disentangled GANs and a novel approach for selecting the best disentangled model. First, we propose a regularizer that achieves higher disentanglement scores than state-of-the-art VAE- and GAN-based approaches. This contrastive regularizer is inspired by a natural notion of disentanglement: latent traversal. Latent traversal refers to generating images by varying one latent code while fixing the rest. We turn this intuition into a regularizer by adding a discriminator that detects how the latent codes are coupled together, in paired examples. Next, one major weakness of all disentanglement benchmark tests is that all reported scores are  based on hyperparameters  tuned with a predefined disentangled representations on synthetic datasets. This is neither fair nor realistic, as one can arbitrarily improve the performance with more hyperparameter tuning and real datasets do not come with such supervision. We propose an unsupervised model selection scheme based on medoids. Numerical experiments confirm that  thus selected models improve upon the state-of-the-art models selected with supervised hyperparameter tuning.
Outsourced Bayesian Optimization
Authors: Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Bayesian Optimization, Privacy Preserving

This paper presents the outsourced-Gaussian process-upper confidence bound (O-GP-UCB) algorithm, which is the first algorithm for privacy-preserving Bayesian optimization (BO) in the outsourced setting with a provable performance guarantee. We consider the outsourced setting where the entity holding the dataset and the entity performing BO are represented by different parties, and the dataset cannot be released non-privately. For example, a hospital holds a dataset of sensitive medical records and outsources the BO task on this dataset to an industrial AI company. The key idea of our approach is to make the BO performance of our algorithm similar to that of non-private GP-UCB run using the original dataset, which is achieved by using a random projection-based transformation that preserves both privacy and the pairwise distances between inputs. Our main theoretical contribution is to show that a regret bound similar to that of the standard GP-UCB algorithm can be established for our O-GP-UCB algorithm. We empirically evaluate the performance of our O-GP-UCB algorithm with synthetic and real-world datasets.
Planning to Explore via Latent Disagreement
Authors: Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1

To solve complex tasks, intelligent agents first need to explore their environments. However, providing manual feedback to agents during exploration can be challenging. This work focuses on task-agnostic exploration, where an agent explores a visual environment without yet knowing the tasks it will later be asked to solve. While current methods often learn reactive exploration behaviors to maximize retrospective novelty, we learn a world model trained from images to plan for expected surprise. Novelty is estimated as ensemble disagreement in the latent space of the world model. Exploring and learning the world model without rewards, our approach, latent disagreement (LD), efficiently adapts to a range of control tasks with high-dimensional image inputs.
Predicting deliberative outcomes
Authors: Vikas Garg, Tommi Jaakkola
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Structured Prediction

We extend structured prediction to deliberative outcomes. Specifically, we learn parameterized games that can map any inputs to equilibria as the outcomes. Standard structured prediction models rely heavily on global scoring functions and are therefore unable to model individual player preferences or how they respond to others asymmetrically. Our games take as input, e.g., UN resolution to be voted on, and map such contexts to initial strategies, player utilities, and interactions. Players are then thought to repeatedly update their strategies in response to weighted aggregates of other players' choices towards maximizing their individual utilities. The output from the game is a sample from the resulting (near) equilibrium mixed strategy profile. We characterize conditions under which players converge to an equilibrium in such games and when the game parameters can be provably recovered from observations. Empirically, we demonstrate on two real voting datasets that our games can recover interpretable strategic interactions, and predict strategies for players in new settings.
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification
Authors: Hui Ye, Zhiyu Chen, Da-Han Wang, Brian Davison
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set. We propose a novel deep learning method called APLC-XLNet.   Our approach fine-tunes  the  recently  released  generalized  autoregressive  pretraining model (XLNet) to learn the dense representation for the input text.   We propose the Adaptive Probabilistic Label Cluster (APLC) to approximate the cross entropy loss by exploiting the  unbalanced  label  distribution  to  form  clusters that explicitly reduce the computational time. Our experiments, carried out on five benchmark datasets, show that our approach significantly outperforms existing state-of-the-art methods. The code of our method will be released publicly at GitHub.
Representing Unordered Data Using Multiset Automata and Complex Numbers
Authors: Justin DeBenedetto, David Chiang
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1

Unordered, variable-sized inputs arise in many settings across multiple fields.  The ability for set- and multiset- oriented neural networks to handle this type of input has been the focus of much work in recent years.  We propose to represent multisets using complex-weighted multiset automata and show how the multiset representations of certain existing neural architectures can be viewed as special cases of ours.  Namely, (1) we provide a new theoretical and intuitive justification for the Transformer model's representation of positions using sinusoidal functions, and (2) we extend the DeepSets model to use complex numbers, enabling it to outperform the existing model on an extension of one of their tasks.
Safe screening rules for L0-regression
Authors: Alper Atamturk, Andres Gomez
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: regression

We give safe screening rules to eliminate variables from regression with L0 regularization or cardinality constraint. These rules are based on guarantees that a feature may or may not be selected in an optimal solution. 
The screening rules can be computed from a convex relaxation solution in linear time, without solving the L0 optimization problem. Thus, they can be used in a preprocessing step to safely remove variables from consideration apriori. 
Numerical experiments on real and synthetic data indicate that, 
on average, 76\% of the variables can be fixed to their optimal values, hence, reducing the computational burden for optimization substantially. Therefore, the proposed fast and effective screening rules extend the scope of
algorithms for L0 regression to larger data sets.
Self-Modulating Nonparametric Event-Tensor Factorization
Authors: Zheng Wang, Xinqi Chu, Shandian Zhe
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Point Processes, Stochastic Optimization

Tensor factorization is a fundamental framework to analyze high-order interactions in data. Despite the success of the existing methods, the valuable temporal information are severely underused. The timestamps of the interactions are either ignored or discretized into crude steps. The recent work although formulates event-tensors to keep the timestamps in factorization and can capture mutual excitation effects among the interaction events, it overlooks another important type of temporal influence, inhibition. In addition, it uses a local window to exclude all the long-term dependencies. To overcome these limitations, we propose a self-modulating nonparametric Bayesian factorization model. We use the latent factors to construct mutually governed, general random point processes, which can capture various short-term/long-term, excitation/inhibition effects, so as to encode the complex temporal dependencies into factor representations.  In addition, our model couples with a latent Gaussian process to estimate and fuse nonlinear yet static relationships between the entities. For efficient inference, we derive a fully decomposed model evidence lower bound to dispense with the huge kernel matrix and costly summations inside the rate and log rate functions. We then develop an efficient stochastic optimization algorithm. We show the advantage of our method in four real-world applications.
Sparse Shrunk Additive Models
Authors: Hong Chen, Guodong Liu, Heng Huang
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Additive models, feature selection, regression

Most existing feature selection methods in literature are linear models, so that the nonlinear relations between features and response variables are not considered. Meanwhile, in these feature selection models, the interactions between features are often ignored or just discussed under prior structure information. To address these challenging issues, we consider the problem of sparse additive models for high-dimensional nonparametric regression with the allowance of the flexible interactions between features. A new method, called as sparse shrunk additive models (SSAM), is proposed to explore the structure information among features. This method bridges sparse kernel regression and sparse feature selection. Theoretical results on the convergence rate and sparsity characteristics of SSAM are established by the novel analysis techniques with integral operator and concentration estimate. In particular, our algorithm and theoretical analysis only require the component functions to be continuous and bounded, which are not necessary to be in reproducing kernel Hilbert spaces. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed approach.
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory
Authors: Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Computational Efficiency, Graph Matching

Graph matching, also known as network alignment, aims at recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs. To tackle this task, we propose a spectral method, GRAph Matching by Pairwise eigen-Alignments (GRAMPA), which first constructs a similarity matrix as a weighted sum of outer products between all pairs of eigenvectors of the two graphs, and then outputs a matching by a simple rounding procedure. For a universality class of correlated Wigner models, GRAMPA achieves exact recovery of the latent matching between two graphs with edge correlation $1 - 1/\mathrm{polylog}(n)$ and average degree at least $\mathrm{polylog}(n)$. This matches the state-of-the-art guarantees for polynomial-time algorithms established for correlated Erd\H{o}s-R\'{e}nyi graphs, and significantly improves over existing spectral methods. The superiority of GRAMPA is also demonstrated on a variety of synthetic and real datasets, in terms of both statistical accuracy and computational efficiency.
Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics
Authors: Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Off-policy evaluation

Off-policy evaluation plays an important role in modern reinforcement learning.
However, most of the existing off-policy evaluation only focus on the value estimation, without providing an accountable confidence interval, that can reflect the uncertainty caused by limited observed data and algorithmic errors. Recently,  Feng  et  al.  (2019) proposed a novel kernel loss for learning value functions, which can also be used to test whether the learned value function satisfies the Bellman equation. In this work, we investigate the statistical properties of the kernel loss, which allows us to find a feasible set that contains the true value function with high probability. We further utilize this set to construct an accountable confidence interval for off-policy value estimation, and a post-hoc diagnosis for existing estimators. Empirical results show that our methods yield a tight yet accountable confidence interval in different settings, which demonstrate the effectiveness of our method.
Adversarial Robustness Against the Union of Multiple Threat Models
Authors: Pratyush Maini, Eric Wong, Zico Kolter
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Adversarial Robustness

Owing to the susceptibility of deep learning systems to adversarial attacks, there has been a great deal of work in developing (both empirically and certifiably) robust classifiers.  While most work has defended against a single type of attack, recent work has looked at defending against multiple threat models using simple aggregations of multiple attacks. However, these methods can be difficult to tune, and can easily result in imbalanced degrees of robustness to individual threat models, resulting in a sub-optimal worst-case loss over the combined threat model.  In this work, we develop a natural generalization of the standard PGD-based procedure to incorporate multiple threat models into a single attack, by taking the worst-case over all steepest descent directions. This approach has the advantage of directly converging upon a trade-off between different threat models which minimizes the worst-case performance over the union. With this approach, we are able to train standard architectures which are simultaneously robust against l_∞, l_2, and l_1 attacks, outperforming past approaches on the MNIST and CIFAR10 datasets and achieving adversarial accuracy of 46.1% against the union of (l_∞,l_2,l_1) perturbations with radius= (0.03, 0.5, 12) on the latter, improving upon previous approaches which achieve 40.6% accuracy.