pwc-archive/papers-with-abstracts huggingface dataset:
Learning an Ethical Module for Bias Mitigation of pre-trained Models
Authors: Jean-Rémy Conti, Nathan Noiry, Stephan Clemencon, Vincent Despiegel, Stéphane Gentric
Date: 2021-09-29 00:00:00

In spite of the high performance and reliability of deep learning algorithms in broad range everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against some subgroups of the population. This urges the practitioner to develop fair systems whose performances are uniform among individuals. In this work, we propose a post-processing method designed to mitigate bias of state-of-the-art models. It consists in learning a shallow neural network, called the Ethical Module, which transforms the deep embeddings of a pre-trained model in order to give more representation power to the disadvantaged subgroups. Its training is supervised by the von Mises-Fisher loss, whose hyperparameters allow to control the space allocated to each subgroup in the latent space. Besides being very simple, the resulting methodology is more stable and faster than most current bias mitigation methods. In order to illustrate our idea in a concrete use case, we focus here on gender bias in facial recognition and conduct extensive numerical experiments on standard datasets.
Rethinking Adversarial Transferability from a Data Distribution Perspective
Authors: Yao Zhu, Jiacheng Sun, Zhenguo Li
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Adversarial Attack

Adversarial transferability enables attackers to generate adversarial examples from the source model to attack the target model, which has raised security concerns about the deployment of DNNs in practice. In this paper, we rethink adversarial transferability from a data distribution perspective and further enhance transferability by score matching based optimization. We identify that some samples with injecting small Gaussian noise can fool different target models, and their adversarial examples under different source models have much stronger transferability. We hypothesize that these samples are in the low-density region of the ground truth distribution where models are not well trained. To improve the attack success rate of adversarial examples, we match the adversarial attacks with the directions which effectively decrease the ground truth density. We propose Intrinsic Adversarial Attack (IAA), which smooths the activation function and decreases the impact of the later layers of a given normal model, to increase the alignment of adversarial attack and the gradient of joint data distribution. We conduct comprehensive transferable attacks against multiple DNNs and show that our IAA can boost the transferability of the crafted attacks in all cases and go beyond state-of-the-art methods.
Scalable Robust Federated Learning with Provable Security Guarantees
Authors: Andrew Liu, Jacky Y. Zhang, Nishant Kumar, Dakshita Khurana, Oluwasanmi O Koyejo
Date: 2021-09-29 00:00:00
Tasks: Federated Learning

Federated averaging, the most popular aggregation approach in federated learning, is known to be vulnerable to failures and adversarial updates from clients that wish to disrupt training. While median aggregation remains one of the most popular alternatives to improve training robustness, the naive combination of median and secure multi-party computation (MPC) is unscalable. To this end, we propose an efficient approximate median aggregation with MPC privacy guarantees on the multi-silo setting, e.g., across hospitals, with two semi-honest non-colluding servers. The proposed method protects the confidentiality of client gradient updates against both semi-honest clients and servers. Asymptotically, the cost of our approach scales only linearly with the number of clients, whereas the naive MPC median scales quadratically. Moreover, we prove that the convergence of the proposed federated learning method is robust to a wide range of failures and attacks. Empirically, we show that our method inherits the robustness properties of the median while converging faster than the naive MPC median for even a small number of clients.
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data
Authors: Marine Schimel, Ta-Chu Kao, Kristopher T Jensen, Guillaume Hennequin
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Model Optimization, Variational Inference

Understanding how neural dynamics give rise to behaviour is one of the most fundamental questions in systems neuroscience. To achieve this, a common approach is to record neural populations in behaving animals, and model these data as emanating from a latent dynamical system whose state trajectories can then be related back to behavioural observations via some form of decoding. As recordings are typically performed in localized circuits that form only a part of the wider implicated network, it is important to simultaneously learn the local dynamics and infer any unobserved external input that might drive them. Here, we introduce iLQR-VAE, a novel control-based approach to variational inference in nonlinear dynamical systems, capable of learning both latent dynamics, initial conditions, and ongoing external inputs. As in recent deep learning approaches, our method is based on an input-driven sequential variational autoencoder (VAE). The main novelty lies in the use of the powerful iterative linear quadratic regulator algorithm (iLQR) in the recognition model. Optimization of the standard evidence lower-bound requires differentiating through iLQR solutions, which is made possible by recent advances in differentiable control. Importantly, having the recognition model be implicitly defined by the generative model greatly reduces the number of free parameters and allows for flexible, high-quality inference. This makes it possible for instance to evaluate the model on a single long trial after training on smaller chunks. We demonstrate the effectiveness of iLQR-VAE on a range of synthetic systems, with autonomous as well as input-driven dynamics. We further apply it to neural and behavioural recordings in non-human primates performing two different reaching tasks, and show that iLQR-VAE yields high-quality kinematic reconstructions from the neural data.
AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks
Authors: Duc N.M Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation, Language Modeling, Language Modelling, Neural Architecture Search

Neural architecture search  (NAS)  has demonstrated success in discovering promising architectures for vision or language modeling tasks, and it has recently been introduced to searching for graph neural networks  (GNNs)  as well. Despite the preliminary success, we argue that for GNNs, NAS has to be customized further, due to the topological complicacy of GNN input data (graph) as well as the notorious training instability. Besides optimizing the GNN model architecture, we propose to simultaneously optimize the input graph topology, via a set of parameterized data augmentation operators. That yields AutoCoG, the first unified data-model co-search NAS framework for GNNs.  By defining a highly flexible data-model co-search space, AutoCoG is gracefully formulated as a principled bi-level optimization, that can be end-to-end solved by the differential search methods.  Experiments demonstrate that AutoCoG produces state-of-the-art performance at standard benchmarks including Cora, PubMed, and Citeseer, outperforming both state-of-the-art hand-crafted  GNNs as well as recent  GNN-NAS methods. AutoCoG can also scale to searching deeper  GCNs in larger-scale datasets.  Our method consistently achieves state-of-the-art (SOTA) results on Cora, Citeseer, Pubmed, and ogbn-arxiv.  Specifically, we achieve gains of up to 2.04% for Cora, 2.54% for Citeseer, 2.08% for Pubmed, and finally 0.83% for ogbn-arxiv on our benchmarks.
Long Document Summarization with Top-Down and Bottom-Up Representation Inference
Authors: Bo Pang, Erik Nijkamp, Wojciech Maciej Kryscinski, Silvio Savarese, Yingbo Zhou, Caiming Xiong
Date: 2021-09-29 00:00:00
Tasks: Document Summarization, Text Summarization

Text summarization aims to condense long documents and retain key information. Critical to the success of a summarization model is the faithful inference of latent representations of words or tokens in the source documents. Most recent models infer the latent representations with a transformer encoder, which is purely bottom-up. Also, self-attention-based inference models face the challenge of quadratic complexity with respect to sequence length. We propose a principled inference framework to improve summarization models on these two aspects. Our framework assumes a hierarchical latent structure of a document where the top-level captures the long range dependency at a coarser time scale and the bottom token level preserves the details. Critically, this hierarchical structure enables token representations to be updated in both a bottom-up and top-down manner. In the bottom-up pass, token representations are inferred with local self-attention to leverage its efficiency. Top-down correction is then applied to allow tokens to capture long-range dependency. We demonstrate the effectiveness of the proposed framework on a diverse set of summarization datasets, including narrative, conversational, scientific documents and news. Our model achieves (1) competitive or better performance on short documents with higher memory and compute efficiency, compared to full attention transformers, and (2) state-of--the-art performance on a wide range of long document summarization benchmarks, compared to recent efficient transformers. We also show that our model can summarize an entire book and achieve competitive performance using $0.27\%$ parameters (464M vs. 175B) and much less training data, compared to a recent GPT-3-based model. These results indicate the general applicability and benefits of the proposed framework.
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm
Authors: Boyang Liu, Zhuangdi Zhu, Pang-Ning Tan, Jiayu Zhou
Date: 2021-09-29 00:00:00
Tasks: Backdoor Attack, Data Poisoning

Training deep neural networks with data corruption is a challenging problem. One example of such corruption is the backdoor data poisoning attack, in which an adversary strategically injects a backdoor trigger to a small fraction of the training data to subtly compromise the training process. Consequently, the trained deep neural network would misclassify testing examples that have been corrupted by the same trigger. While the label of the data could be changed to arbitrary values by an adversary, the extent of corruption injected to the feature values are strictly limited in order to keep the backdoor attack in disguise, which leads to a resemblance between the backdoor attack and a milder attack that involves only noisy labels. In this paper, we investigate an intriguing question: Can we leverage algorithms that defend against noisy labels corruptions to defend against general backdoor attacks? We first discuss the limitations of directly using the noisy-label defense algorithms to defend against backdoor attacks. Next, we propose a meta-algorithm that transforms an existing noisy label defense algorithm to one that protects against backdoor attacks. Extensive experiments on different types of backdoor attacks show that, by introducing a lightweight alteration for minimax optimization to the existing noisy-label defense algorithms, the robustness against backdoor attacks can be substantially improved, while the intial form of those algorithms would fail in presence of a backdoor attacks.
Generating Antimicrobial Peptides from Latent Secondary Structure Space
Authors: Danqing Wang, Zeyu Wen, Lei LI, Hao Zhou
Date: 2021-09-29 00:00:00
Tasks: Drug Discovery

Antimicrobial peptides (AMPs) have shown promising results in broad-spectrum antibiotics and resistant infection treatments, which makes it attract plenty of attention in drug discovery. Recently, many researchers bring deep generative models to AMP design. However, few studies consider structure information during the generation, though it has shown crucial influence on antimicrobial activity in all AMP mechanism theories. In this paper, we propose LSSAMP that uses the multi-scale VQ-VAE to learn the positional latent spaces modeling the secondary structure. By sampling in the latent secondary structure space, we can generate peptides with ideal amino acids and secondary structures at the same time. Experimental results show that our LSSAMP can generate peptides with multiply ideal physical attributes and a high probability of being predicted as AMPs by public AMP prediction models.
Hybrid Local SGD for Federated Learning with Heterogeneous Communications
Authors: Yuanxiong Guo, Ying Sun, Rui Hu, Yanmin Gong
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Federated Learning

Communication is a key bottleneck in federated learning where a large number of edge devices collaboratively learn a model under the orchestration of a central server without sharing their own training data. While local SGD has been proposed to reduce the number of communication rounds and become the algorithm of choice for FL, its total communication cost is still prohibitive when each device needs to communicate with the remote server repeatedly for many times over bandwidth-limited networks. In light of both device-to-device (D2D) and device-to-server (D2E) cooperation opportunities in modern communication networks, this paper proposes a new federated optimization algorithm dubbed hybrid local SGD (HL-SGD) in FL settings where devices are grouped into a set of disjoint clusters with high D2D communication bandwidth. HL-SGD subsumes previous proposed algorithms such ad local SGD and gossip SGD and enables us to strike the best balance between reducing communication cost and improving model accuracy. We analyze the convergence of HL-SGD in the presence of heterogeneous data for general nonconvex settings. We also perform extensive experiments and show that the use of hybrid model aggregation via D2D and D2E communications in HL-SGD can largely improve the communication efficiency of federated learning.
Generalizing Successor Features to continuous domains for Multi-task Learning
Authors: Melissa Mozifian, Dieter Fox, David Meger, Fabio Ramos, Animesh Garg
Date: 2021-09-29 00:00:00
Tasks: continuous-control, Continuous Control, Decision Making, Deep Reinforcement Learning, Multi-Task Learning, Reinforcement Learning (RL), Robot Manipulation, Sequential Decision Making

The deep reinforcement learning (RL) framework has shown great promise to tackle sequential decision-making problems, where the agent learns to behave optimally through interactions with the environment and receiving rewards.
The ability of an RL agent to learn different reward functions concurrently has many benefits, such as the decomposition of task rewards and skill reuse. One obstacle for achieving this, is the amount of data required as well as the capacity of the model for solving multiple tasks. In this paper, we consider the problem of continuous control for various robot manipulation tasks with an explicit representation that promotes skill reuse while learning multiple tasks, related through the reward function. Our approach relies on two key concepts: successor features (SF), a value function representation that decouples the dynamics of the environment from the rewards, and an actor-critic framework that incorporates the learned SF representations.
We propose a practical implementation of successor features in continuous action spaces. We first show how to learn a decomposable representation required by SF. Our proposed methods, is able to learn decoupled state and reward  features representations. We study this approach on a non-trivial continuous control problems with compositional structure built into the reward functions of various tasks.
High Probability Generalization Bounds for Minimax Problems with Fast Rates
Authors: Shaojie Li, Yong liu
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Distributed Computing, Generalization Bounds, Vocal Bursts Intensity Prediction

Minimax problems are receiving an increasing amount of attention in a wide range of applications in machine learning (ML), for instance, reinforcement learning, robust optimization, adversarial learning, and distributed computing, to mention but a few. Current studies focus on the fundamental understanding of general minimax problems with an emphasis on convergence behavior. As a comparison, there is far less work to study the generalization performance. Additionally, existing generalization bounds are almost all derived in expectation, and the high probability bounds are all presented in the slow order $\mathcal{O}\left( 1/\sqrt{n}\right)$, where $n$ is the sample size. In this paper, we provide improved generalization analyses for almost all existing generalization measures of minimax problems, which enables the minimax problems to establish sharper bounds of order $\mathcal{O}\left( 1/n \right)$, significantly, with high probability. We then use the improved learning bounds to establish $\mathcal{O}\left(1/n \right)$ high probability generalization bounds for classical empirical saddle point (ESP) solution and several popular gradient-based optimization algorithms, including gradient descent ascent (GDA), stochastic gradient descent ascent (SGDA), proximal point method (PPM), extra-gradient (EG), and optimistic gradient descent ascent (OGDA). Overall, we provide a comprehensive understanding of sharper generalization bounds of minimax problems.
Go with the Flow: the distribution of information processing in multi-path networks
Authors: Mats Leon Richter, Krupal Shah, Anna Wiedenroth, Saketh Bachu, Ulf Krumnack
Date: 2021-09-29 00:00:00

The architectures of convolution neural networks (CNN) have a great impact on the predictive performance and efficiency of the model.
Yet, the development of these architectures is still driven by trial and error, making the design of novel models a costly endeavor. 
To move towards a more guided process, the impact of design decisions on information processing must be understood better. 
This work contributes by analyzing the processing of the information in neural architectures with parallel pathways.
Using logistic regression probes and similarity indices, we characterize the role of different pathways in the network during the inference process.
In detail, we find that similar sized pathways advance the solution quality at a similar pace, with high redundancy.
On the other hand, shorter pathways dominate longer ones by majorly transporting (and improving) the main signal, while longer pathways do not advance the solution quality directly. 
Additionally, we explore the situation in which networks start to ``skip'' layers and how the skipping of layers is expressed.
To Smooth or not to Smooth? On Compatibility between Label Smoothing and Knowledge Distillation
Authors: Keshigeyan Chandrasegaran, Ngoc-Trung Tran, Yunqing Zhao, Ngai-Man Cheung
Date: 2021-09-29 00:00:00
Tasks: image-classification, Image Classification, Knowledge Distillation, Machine Translation

This work investigates the compatibility between label smoothing (LS) and knowledge distillation (KD). Contemporary findings addressing this thesis statement take dichotomous standpoints. Specifically, Muller et al. [1] claim that LS erases relative information in the logits; therefore a LS-trained teacher can hurt KD. On the contrary, Shen et al. [2] claim that LS enlarges the distance between semantically similar classes; therefore a LS-trained teacher is compatible with KD. Critically, there is no effort to understand and resolve these contradictory findings, leaving the primal question $-$ to smooth or not to smooth a teacher network? $-$ unanswered. 

In this work, we establish a foundational understanding on the compatibility between LS and KD. We begin by meticulously scrutinizing these contradictory findings under a unified empirical consistency. Through our profound investigation, we discover that in the presence of a LS-trained teacher, KD at higher temperatures systematically diffuses penultimate layer representations learnt by the student towards semantically similar classes. This systematic diffusion essentially curtails the benefits of distilling from a LS-trained teacher, thereby rendering KD at increased temperatures ineffective. We show this systematic diffusion qualitatively by visualizing penultimate layer representations, and quantitatively using our proposed relative distance metric called diffusion index ($\eta$). 

Importantly, our discovered systematic diffusion was the missing concept which is instrumental in understanding and resolving these contradictory findings. Our discovery is comprehensively supported by large-scale experiments and analyses including image classification (standard, fine-grained), neural machine translation and compact student network distillation tasks spanning across multiple datasets and teacher-student architectures. Finally, we shed light on the question $-$ to smooth or not to smooth a teacher network? $-$ in order to help practitioners make informed decisions.
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning
Authors: Dongping Liao, Xitong Gao, Yiren Zhao, Hao Dai, Li Li, Kafeng Wang, Kejiang Ye, Yang Wang, Cheng-Zhong Xu
Date: 2021-09-29 00:00:00
Tasks: Federated Learning

Federated learning (FL) enables edge clients to train collaboratively while preserving individual's data privacy.  As clients do not inherently share identical data distributions, they may disagree in the direction of parameter updates, resulting in high compute and communication costs in comparison to centralized learning.  Recent advances in FL focus on reducing data transmission during training; yet they neglected the increase of computational cost that dwarfs the merit of reduced communication.  To this end, we propose FedDrop, which introduces channel-wise weighted dropout layers between convolutions to accelerate training while minimizing their impact on convergence.  Empirical results show that FedDrop can drastically reduce the amount of FLOPs required for training with a small increase in communication, and push the Pareto frontier of communication/computation trade-off further than competing FL algorithms.
Deep Inverse Reinforcement Learning via Adversarial One-Class Classification
Authors: Daiko Kishikawa, Sachiyo Arai
Date: 2021-09-29 00:00:00
Tasks: Classification, One-Class Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Traditional inverse reinforcement learning (IRL) methods require a loop to find the optimal policy for each reward update (called an inner loop), resulting in very time-consuming reward estimation. In contrast, classification-based IRL methods, which have been studied recently, do not require an inner loop and estimate rewards quickly, although it is difficult to prepare an appropriate baseline corresponding to the expert trajectory. In this study, we introduced adversarial one-class classification into the classification-based IRL framework, and consequently developed a novel IRL method that requires only expert trajectories. We experimentally verified that the developed method can achieve the same performance as existing methods.