pwc-archive/papers-with-abstracts huggingface dataset:
Gradient flows on the feature-Gaussian manifold
Authors: Truyen Nguyen, Xinru Hua, Tam Le, Jose Blanchet, Viet Anh Nguyen
Date: 2021-09-29 00:00:00

The scarcity of labeled data is a long-standing challenge for cross-domain machine learning tasks. This paper leverages the existing dataset (i.e., source) to augment new samples that are close to the dataset of interest (i.e., target). To relieve the need to learn a metric on the feature-label space, we lift both datasets to the space of probability distributions on the feature-Gaussian manifold, and then develop a gradient flow that minimizes the maximum mean discrepancy loss. To perform the gradient flow of distributions on the curved feature-Gaussian space, we unravel the Riemannian structure of the space and compute explicitly the  Riemannian gradient of the loss function induced by the optimal transport metric. For practical purposes, we also propose a discretized flow, and provide conditional results guaranteeing the global convergence  of the flow to the optimum. We illustrate the results of our proposed gradient flow method in several real-world datasets.
Multi-Agent Language Learning: Symbolic Mapping
Authors: Yicheng Feng, Zongqing Lu
Date: 2021-09-29 00:00:00

The study of emergent communication has long been devoted to coax neural network agents to learn a language sharing similar properties with human language. In this paper, we try to find a natural way to help agents learn a compositional and symmetric language in complex settings like dialog games. Inspired by the theory that human language was originated from simple interactions, we hypothesize that language may evolve from simple tasks to difficult tasks. We propose a novel architecture called symbolic mapping as a basic component of the communication system of agent. We find that symbolic mapping learned in simple referential games can notably promote language learning in difficult tasks. Further, we explore vocabulary expansion, and show that with the help of symbolic mapping, agents can easily learn to use new symbols when the environment becomes more complex. All in all, we probe into how symbolic mapping helps language learning and find that a process from simplicity to complexity can serve as a natural way to help multi-agent language learning.
Thinking Deeper With Recurrent Networks: Logical Extrapolation Without Overthinking
Authors: Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
Date: 2021-09-29 00:00:00

Classical machine learning systems perform best when they are trained and tested on the same distribution, and they lack a mechanism to increase model power after training is complete. In contrast, recent work has observed that recurrent networks can exhibit logical extrapolation; models trained only on small/simple problem instances can extend their abilities to solve large/complex instances at test time simply by performing more recurrent iterations.  While preliminary results on these ``thinking systems'' are promising, existing recurrent systems, when iterated many times, often collapse rather than improve their performance.  This ``overthinking'' phenomenon has prevented thinking systems from scaling to particularly large and complex problems. In this paper, we design a recall architecture that keeps an explicit copy of the problem instance in memory so that it cannot be forgotten.  We also propose an incremental training routine that prevents the model from learning behaviors that are specific to iteration number and instead pushes it to learn behaviors that can be repeated indefinitely. Together, these design choices encourage models to converge to a steady state solution rather than deteriorate when many iterations are used. These innovations help to tackle the overthinking problem and boost deep thinking behavior on each of the benchmark tasks proposed by  Schwarzschild et al. (2021a).
Interventional Black-Box Explanations
Authors: Ola Ahmad, Simon Corbeil, Vahid Hashemi, Freddy Lecue
Date: 2021-09-29 00:00:00
Tasks: image-classification, Image Classification

Deep Neural Networks (DNNs) are powerful systems able to freely evolve on their own from training data. However, like any highly parametrized mathematical model, capturing the explanation of any prediction of such models is rather difficult. We believe that there exist relevant mechanisms inside the structure of post-hoc DNNs that supports transparency and interpretability. To capture these mechanisms, we quantify the effects of parameters (pieces of knowledge) on models' predictions using the framework of causality. We introduce a general formalism of the causal diagram to express cause-effect relations inside the DNN's architecture. Then, we develop a novel algorithm to construct explanations of DNN's predictions using the $do$-operator. We call our method, Interventional Black-Box Explanations. On image classification tasks, we explain the behaviour of the model and extract visual explanations from the effects of the causal filters in convolution layers. We qualitatively demonstrate that our method captures more informative concepts compared to traditional attribution-based methods.         
Finally, we believe that our method is orthogonal to logic-based explanation methods and can be leveraged to improve their explanations.
On the Effectiveness of Quasi Character-Level Models for Machine Translation
Authors: Salvador Carrión Ponz, Francisco Casacuberta Nolla
Date: 2021-09-29 00:00:00
Tasks: Low Resource NMT, Machine Translation, NMT, Translation

Neural Machine Translation (NMT) models often use subword-level vocabularies to deal with rare or unknown words. Although some studies have shown the effectiveness of purely character-based models, these approaches have resulted in highly expensive models in computational terms. In this work, we explore the advantages of quasi character-level Transformers for low-resource NMT, as well as their ability to mitigate the catastrophic forgetting problem. We first present an empirical study on the effectiveness of these models as a function of the size of the training set. As a result, we found that for data-poor environments, quasi character-level Transformers present a competitive advantage over their large subword-level versions. Similarly, we study the generalization of this phenomenon in different languages, domains, and neural architectures. Finally, we conclude this work by studying the ability of these models to mitigate the effects of catastrophic forgetting in machine translation. Our work suggests that quasi character-level Transformers have a competitive advantage in data-poor environments and, although they do not mitigate the catastrophic forgetting problem, they greatly help to achieve greater consistency between domains.
Learning meta-features for AutoML
Authors: Herilalaina Rakotoarison, Louisot Milijaona, Andry Rasoanaivo, Michele Sebag, Marc Schoenauer
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: AutoML

This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed \mf s with the space of distributions on the hyper-parameter configurations. MetaBu meta-features, learned once and for all, induce a topology on the set of datasets that is exploited to define a distribution of promising hyper-parameter configurations amenable to AutoML. Experiments on the OpenML CC-18 benchmark demonstrate that using MetaBu meta-features boosts the performance of state of the art AutoML systems, AutoSklearn (Feurer et al. 2015) and Probabilistic Matrix Factorization (Fusi et al. 2018). Furthermore, the inspection of MetaBu meta-features gives some hints into when an ML algorithm does well. Finally, the topology based on MetaBu meta-features enables to estimate the intrinsic dimensionality of the OpenML benchmark w.r.t. a given ML algorithm or pipeline.
Pareto Policy Adaptation
Authors: Panagiotis Kyriakis, Jyotirmoy Deshmukh, Paul Bogdan
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a policy gradient method for Multi-Objective Reinforcement Learning under unknown, linear preferences. By enforcing Pareto stationarity, a first-order condition for Pareto optimality, we are able to design a simple policy gradient algorithm that approximates the Pareto front and infers the unknown preferences. Our method relies on a projected gradient descent solver that identifies common ascent directions for all objectives. Leveraging the solution of that solver, we introduce Pareto Policy Adaptation (PPA), a loss function that adapts the policy to be optimal with respect to any distribution over preferences. PPA uses implicit differentiation to back-propagate the loss gradient bypassing the operations of the projected gradient descent solver. Our approach is straightforward, easy to implement and can be used with all existing policy gradient and actor-critic methods. We evaluate our method in a series of reinforcement learning tasks
Extreme normalization: approximating full-data batch normalization with single examples
Authors: Sergey Ioffe
Date: 2021-09-29 00:00:00

While batch normalization has been successful in speeding up the training of neural networks, it is not well understood. We cast batch normalization as an approximation of the limiting case where the entire dataset is normalized jointly, and explore other ways to approximate the gradient from this limiting case. We demonstrate an approximation that removes the need to keep more than one example in memory at any given time, at the cost of a small factor increase in the training step computation, as well as a fully per-example training procedure, which removes the extra computation at the cost of a small drop in the final model accuracy. We further use our insights to improve batch renormalization for very small minibatches. Unlike previously proposed methods, our normalization does not change the function class of the inference model, and performs well in the absence of identity shortcuts.
Guiding Transformers to Process in Steps
Authors: Simas Sakenis, Stuart Shieber
Date: 2021-09-29 00:00:00

Neural networks have matched or surpassed human abilities in many tasks that humans solve quickly and unconsciously, i.e., via Kahneman's “System 1”, but have not been as successful when applied to “System 2” tasks that involve conscious multi-step reasoning. In this work, we argue that the kind of training that works for System 1 tasks is not sufficient for System 2 tasks, propose an alternative, and empirically demonstrate its effectiveness. Specifically, while learning a direct mapping from inputs to outputs is feasible for System 1 tasks, we argue that algorithmic System 2 tasks can only be solved by learning a mapping from inputs to outputs through a series of intermediate steps. We first show that by using enough intermediate steps a 1-layer 1-head Transformer can in principle compute any finite function, proving the generality of the approach. We then show empirically that a 1-layer 1-head Transformer cannot learn to compute the sum of binary numbers directly from the inputs, but is able to compute the sum when trained to first generate a series of intermediate results. This demonstrates, at a small scale, how a fixed-size neural network can lack the expressivity to encode the direct input-output mapping for an algorithmic task and yet be fully capable of computing the outputs through intermediate steps. Finally, we show that a Frozen Pretrained Transformer is able to learn binary addition when trained to compute the carry bits before the sum, while it fails to learn the task without using intermediates. These results indicate that explicitly guiding the neural networks through the intermediate computations can be an effective approach for tackling algorithmic tasks.
A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease
Authors: Sayan Ghosal, Qiang Chen, Giulio Pergola, Aaron L Goldman, William Ulrich, Daniel R Weinberger, Archana Venkataraman
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: feature selection

We propose a novel end-to-end framework for whole-brain and whole-genome imaging-genetics. Our genetics network uses hierarchical graph convolution and pooling operations to embed subject-level data onto a low-dimensional latent space. The hierarchical network implicitly tracks the convergence of genetic risk across well-established biological pathways, while an attention mechanism automatically identifies the salient edges of this network at the subject level. In parallel, our imaging network projects multimodal data onto a set of latent embeddings. For interpretability, we implement a Bayesian feature selection strategy to extract the discriminative imaging biomarkers; these feature weights are optimized alongside the other model parameters. We couple the imaging and genetic embeddings with a predictor network, to ensure that the learned representations are linked to phenotype. We evaluate our framework on a schizophrenia dataset that includes two functional MRI paradigms and gene scores derived from Single Nucleotide Polymorphism data. Using repeated 10-fold cross-validation, we show that our imaging-genetics fusion achieves the better classification performance than state-of-the-art baselines. In an exploratory analysis, we further show that the biomarkers identified by our model are reproducible and closely associated with deficits in schizophrenia.
DEEP GRAPH TREE NETWORKS
Authors: Nan Wu, Chaofan Wang
Date: 2021-09-29 00:00:00
Tasks: Graph Learning, Graph Neural Network

We propose Graph Tree Networks (GTree), a self-interpretive deep graph neural network architecture which originates from the tree representation of the graphs. In the tree representation, each node forms its own tree where the node itself is the root node and all its neighbors up to hop-k are the subnodes. Under the tree representation, the message propagates upward from the leaf nodes to the root node naturally and straightforwardly to update the root node's hidden features. This message passing (or neighborhood aggregation) scheme is essentially different from that in the vanilla GCN, GAT and many of their derivatives, and is demonstrated experimentally a superior message passing scheme. Models adopting this scheme has the capability of going deep. Two scalable graph learning models are proposed within this GTree network architecture - Graph Tree Convolution Network (GTCN) and Graph Tree Attention Network (GTAN), with demonstrated state-of-the-art performances on several benchmark datasets. The deep capability is also demonstrated for both models.
Partially Relaxed Masks for Lightweight Knowledge Transfer without Forgetting in Continual Learning
Authors: Tatsuya Konishi, Mori Kurokawa, Roberto Legaspi, Chihiro Ono, Zixuan Ke, Gyuhak Kim, Bing Liu
Date: 2021-09-29 00:00:00
Tasks: Continual Learning, Incremental Learning, Transfer Learning

The existing research on continual learning (CL) has focused mainly on preventing catastrophic forgetting. In the task-incremental learning setting of CL, several approaches have achieved excellent results, with almost no forgetting. The goal of this work is to endow such systems with the additional ability to transfer knowledge among tasks when the tasks are similar and have shared knowledge to achieve higher accuracy. Since the existing system HAT is one of most effective task-incremental learning algorithms, this paper extends HAT with the aim of both objectives, i.e., overcoming catastrophic forgetting and transferring knowledge among tasks without introducing additional mechanisms into the architecture of HAT. The current study finds that task similarity, which indicates knowledge sharing and transfer, can be computed via the clustering of task embeddings optimized by HAT. Thus, we propose a new approach, named “partially relaxed masks” (PRM), to exploit HAT’s masks to not only keep some parameters from being modified in learning subsequent tasks as much as possible to prevent forgetting but also enable remaining parameters to be updated to facilitate knowledge transfer. Extensive experiments demonstrate that PRM performs competitively compared with the latest baselines while also requiring much less computation time.
Generating High-Fidelity Privacy-Conscious Synthetic Patient Data for Causal Effect Estimation with Multiple Treatments
Authors: Jingpu Shi, Dong Wang, Gino Tesei, Beau Norgeot
Date: 2021-09-29 00:00:00
Tasks: Causal Inference

A causal effect can be defined as the comparison of outcomes from two or more alternative treatments. Knowing this treatment effect is critically important in healthcare because it makes it possible to identify the best treatment for a person when more than one option exists. In the past decade, there has been exponentially growing interest in the use of observational data collected as a part of routine healthcare practice to determine the effect of a treatment with causal inference models. Validation of these models, however, has been a challenge because the ground truth is unknown: only one treatment-outcome pair for each person can be observed. There have been multiple efforts to fill this void using synthetic data where the ground truth can be generated. However, to date, these datasets have been severely limited in their utility either by being modeled after small non-representative patient populations, being dissimilar to real target populations, or only providing known effects for two cohorts (treated vs control). In this work, we produced a large-scale and realistic synthetic dataset that supports multiple hypertension treatments, by modeling after a nationwide cohort of more than 250,000 hypertension patients' multi-year history of diagnoses, medications, and laboratory values. We designed a data generation process by combining an adapted ADS-GAN model for fictitious patient information generation and a neural network for treatment outcome generation. Wasserstein distance of 0.35 demonstrates that our synthetic data follows a nearly identical joint distribution to the patient cohort used to generate the data. Our dataset provides ground truth effects for about 30 hypertension treatments on blood pressure outcomes. Patient privacy was a primary concern for this study; the $\epsilon$-identifiability metric, which estimates the probability of actual patients being identified, is 0.008%, ensuring that our synthetic data cannot be used to identify any actual patients. Using our dataset, we tested the bias in causal effect estimation of three well-established models: propensity sore stratification, doubly robust approach (DR) with logistic regression, DR with random forest (RF) classification. Interestingly, we found that while the RF DR outperformed the logistic DR as expected, the best performance actually came from  propensity score stratification, despite the theoretical strength of statistical properties of the DR family of models. We believe this dataset will facilitate the additional development, evaluation, and comparison of real-world causal models. The approach we used can be readily extended to other types of diseases in the clinical domain, and to datasets in other domains as well.
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
Authors: Carl Hvarfner, Danny Stoll, Artur Souza, Luigi Nardi, Marius Lindauer, Frank Hutter
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Bayesian Optimization, Hyperparameter Optimization

Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sample-efficiency, vanilla BO can not utilize readily available prior beliefs the practitioner has on the potential location of the optimum.  Thus, BO disregards a valuable source of information, reducing its appeal to ML practitioners. To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user. In contrast to previous approaches, $\pi$BO is conceptually simple and can easily be integrated with existing libraries and many acquisition functions. We provide regret bounds when $\pi$BO is applied to the common Expected Improvement acquisition function and prove convergence at regular rates independently of the prior. Further, our experiments show that $\pi$BO outperforms competing approaches across a wide suite of benchmarks and prior characteristics. We also demonstrate that $\pi$BO improves on the state-of-the-art performance for a popular deep learning task, with a $12.5\times$ time-to-accuracy speedup over prominent BO approaches.
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning
Authors: Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha
Date: 2021-09-29 00:00:00
Tasks: Inductive Bias, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Multi-agent reinforcement learning (MARL) becomes more challenging in the presence of more agents, as the capacity of the joint state and action spaces grows exponentially in the number of agents. To address such a challenge of scale, we identify a class of cooperative MARL problems with permutation invariance, and formulate it as mean-field Markov decision processes (MDP). To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation- invariant actor-critic neural architecture. We prove that MF-PPO attains the globally optimal policy at a sublinear rate of convergence. Moreover, its sample complexity is independent of the number of agents. We validate the theoretical advantages of MF-PPO with numerical experiments in the multi-agent particle environment (MPE). In particular, we show that the inductive bias introduced by the permutation-invariant neural architecture enables MF-PPO to outperform existing competitors with a smaller number of model parameters, which is the key to its generalization performance.