nils-herrmann/clean_scirepeval huggingface dataset:
Fast Machine Learning with Byzantine Workers and Servers
Authors: El-Mahdi El-Mhamdi, Rachid Guerraoui, Arsany Guirguis

Machine Learning (ML) solutions are nowadays distributed and are prone to various types of component failures, which can be encompassed in so-called Byzantine behavior. This paper introduces LiuBei, a Byzantine-resilient ML algorithm that does not trust any individual component in the network (neither workers nor servers), nor does it induce additional communication rounds (on average), compared to standard non-Byzantine resilient algorithms. LiuBei builds upon gradient aggregation rules (GARs) to tolerate a minority of Byzantine workers. Besides, LiuBei replicates the parameter server on multiple machines instead of trusting it. We introduce a novel filtering mechanism that enables workers to filter out replies from Byzantine server replicas without requiring communication with all servers. Such a filtering mechanism is based on network synchrony, Lipschitz continuity of the loss function, and the GAR used to aggregate workers’ gradients. We also introduce a protocol, scatter/gather, to bound drifts between models on correct servers with a small number of communication messages. We theoretically prove that LiuBei achieves Byzantine resilience to both servers and workers and guarantees convergence. We build LiuBei using TensorFlow, and we show that LiuBei tolerates Byzantine behavior with an accuracy loss of around 5% and around 24% convergence overhead compared to vanilla TensorFlow. We moreover show that the throughput gain of LiuBei compared to another state–of–the–art Byzantine–resilient ML algorithm (that assumes network asynchrony) is 70%.
Dynamic Graph Representation Learning via Self-Attention Networks
Authors: Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, Hao Yang

Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization. Previous methods on graph representation learning mainly focus on static graphs, however, many real-world graphs are dynamic and evolve over time. In this paper, we present Dynamic Self-Attention Network (DySAT), a novel neural architecture that operates on dynamic graphs and learns node representations that capture both structural properties and temporal evolutionary patterns. Specifically, DySAT computes node representations by jointly employing self-attention layers along two dimensions: structural neighborhood and temporal dynamics. We conduct link prediction experiments on two classes of graphs: communication networks and bipartite rating networks. Our experimental results show that DySAT has a significant performance gain over several different state-of-the-art graph embedding baselines.
A new accelerated gradient method inspired by continuous-time perspective
Authors: Yasong Feng, Weiguo Gao

Nesterov's accelerated method are widely used in problems with machine learning background including deep learning. To give more insight about the acceleration phenomenon, an ordinary differential equation was obtained from Nesterov's accelerated method by taking step sizes approaching zero, and the relationship between Nesterov's method and the differential equation is still of research interest. In this work, we give the precise order of the iterations of Nesterov's accelerated method converging to the solution of derived differential equation as step sizes go to zero. We then present a new accelerated method with higher order. The new method is more stable than ordinary method for large step size and converges faster. We further apply the new method to matrix completion problem and show its better performance through numerical experiments.
Deep amortized clustering
Authors: Juho Lee, Yoonho Lee, Yee Whye Teh

We propose a \textit{deep amortized clustering} (DAC), a neural architecture which learns to cluster datasets efficiently using a few forward passes. DAC implicitly learns what makes a cluster, how to group data points into clusters, and how to count the number of clusters in datasets. DAC is meta-learned using labelled datasets for training, a process distinct from traditional clustering algorithms which usually require hand-specified prior knowledge about cluster shapes/structures. We empirically show, on both synthetic and image data, that DAC can efficiently and accurately cluster new datasets coming from the same distribution used to generate training datasets.
An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks
Authors: Giannis Nikolentzos, George Panagopoulos, Michalis Vazirgiannis

Graph neural networks and graph kernels have achieved great success in solving machine learning problems on graphs. Recently, there has been considerable interest in determining the expressive power mainly of graph neural networks and of graph kernels, to a lesser extent.  Most studies have focused on the ability of these approaches to distinguish non-isomorphic graphs or to identify specific graph properties. However, there is often a need for algorithms whose produced graph representations can accurately capture similarity/distance of graphs. This paper studies the expressive power of graph neural networks and graph kernels from an empirical perspective. Specifically, we compare the graph representations and similarities produced by these algorithms against those generated by a well-accepted, but intractable graph similarity function. We also investigate the impact of node attributes on the performance of the different models and kernels. Our results reveal interesting findings. For instance, we find that theoretically more powerful models do not necessarily yield higher-quality representations, while graph kernels are shown to be very competitive with graph neural networks.
Provable More Data Hurt in High Dimensional Least Squares Estimator
Authors: Zeng Li, Chuanlong Xie, QINWEN WANG

This paper investigates the finite-sample prediction risk of the high-dimensional least squares estimator. We derive the central limit theorem for the prediction risk when both the sample size and the number of features tend to infinity. Furthermore, the finite-sample distribution and the confidence interval of the prediction risk are provided. Our theoretical results demonstrate the sample-wise non-monotonicity of the prediction risk and confirm ''more data hurt'' phenomenon.
Provable Robust Learning for Deep Neural Networks under Agnostic Corrupted Supervision
Authors: Boyang Liu, Mengying Sun, Ding Wang, Pang-Ning Tan, Jiayu Zhou

Training deep neural models in the presence of corrupted supervisions is challenging as the corrupted data points may significantly impact the generalization performance.  To alleviate this problem, we present an efficient robust algorithm that achieves strong guarantees without any assumption on the type of corruption and provides a unified framework for both classification and regression problems. Different from many existing approaches that quantify the quality of individual data points (e.g., loss values) and filter out data points accordingly, the proposed algorithm focuses on controlling the collective impact of data points on the averaged gradient.  Even when a corrupted data point failed to be excluded by the proposed algorithm, the data point will have very limited impacts on the overall loss, as compared with state-of-the-art filtering data points based on loss values. Extensive empirical results on multiple benchmark datasets have demonstrated the robustness of the proposed method under different types of corruption.
Fortuitous Forgetting in Connectionist Networks
Authors: Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron Courville

Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact be favorable to learning. We introduce forget-and-relearn as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting step selectively removes undesirable information from the model, and the relearning step reinforces features that are consistently useful under different conditions. The forget-and-relearn framework unifies many existing iterative training algorithms in the image classification and language emergence literature, and allows us to understand the success of these algorithms in terms of the disproportionate forgetting of undesirable information. We leverage this understanding to improve upon existing algorithms by designing more targeted forgetting operations. Insights from our analysis provide a coherent view on the dynamics of iterative training in neural networks and offer a clear path towards performance improvements.
Adaptive Single-Pass Stochastic Gradient Descent in Input Sparsity Time
Authors: Sepideh Mahabadi, David Woodruff, Samson Zhou

We study sampling algorithms for variance reduction methods for stochastic optimization. Although stochastic gradient descent (SGD) is widely used for large scale machine learning, it sometimes experiences slow convergence rates due to the high variance from uniform sampling. In this paper, we introduce an algorithm that approximately samples a gradient from the optimal distribution for a common finite-sum form with $n$ terms, while just making a single pass over the data, using input sparsity time, and $\tO{Td}$ space. Our algorithm can be implemented in big data models such as the streaming and distributed models. Moreover, we show that our algorithm can be generalized to approximately sample Hessians and thus provides variance reduction for second-order methods as well. We demonstrate the efficiency of our algorithm on large-scale datasets.
Off-policy Multi-step Q-learning
Authors: Gabriel Kalweit, Maria Huegle, Joschka Boedecker

In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control. Deep Q-learning, however, still suffers from poor data-efficiency which is limiting with regard to real-world applications. We follow the idea of multi-step TD-learning to enhance data-efficiency while remaining off-policy by proposing two novel Temporal-Difference formulations: (1) Truncated Q-functions which represent the return for the first n steps of a policy rollout and (2) Shifted Q-functions, acting as the farsighted return after this truncated rollout. We prove that the combination of these short- and long-term predictions is a representation of the full return, leading to the Composite Q-learning algorithm. We show the efficacy of Composite Q-learning in the tabular case and compare our approach in the function-approximation setting with TD3, Model-based Value Expansion and TD3(Delta), which we introduce as an off-policy variant of TD(Delta). We show on three simulated robot tasks that Composite TD3 outperforms TD3 as well as state-of-the-art off-policy multi-step approaches in terms of data-efficiency.
Deep Kernel Processes
Authors: Laurence Aitchison, Adam X. Yang, Sebastian W. Ober

We define deep kernel processes in which positive definite Gram matrices are progressively transformed by nonlinear kernel functions and by sampling from (inverse) Wishart distributions. Remarkably, we find that deep Gaussian processes (DGPs), Bayesian neural networks (BNNs), infinite BNNs, and infinite BNNs with bottlenecks can all be written as deep kernel processes. For DGPs the equivalence arises because the Gram matrix formed by the inner product of features is Wishart distributed, and as we show, standard isotropic kernels can be written entirely in terms of this Gram matrix (we do not need knowledge of the underlying features). We define a tractable deep kernel process, the deep inverse Wishart process and give a doubly-stochastic inducing-point variational inference scheme that operates on the Gram matrices, not on the features (as in DGPs). We show that the deep inverse Wishart process gives superior performance to DGPs and infinite BNN on standard fully-connected baselines.
Communicate Then Adapt: An Effective Decentralized Adaptive Method for Deep Training
Authors: Bicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, Yingya Zhang, Pan Pan, Wotao Yin

Decentralized adaptive gradient methods, in which each node averages only with its neighbors, are critical to save communication and wall-clock training time in deep learning tasks. While different in concrete recursions, existing decentralized adaptive methods share the same algorithm structure: each node scales its gradient with information of the past squared gradients (which is referred to as the adaptive step) before or while it communicates with neighbors. In this paper, we identify the limitation of such adapt-then/while-communicate structure: it will make the developed algorithms highly sensitive to heterogeneous data distributions, and hence deviate their limiting points from the stationary solution. To overcome this limitation, we propose an effective decentralized adaptive method with a communicate-then-adapt structure, in which each node conducts the adaptive step after finishing the neighborhood communications. The new method is theoretically guaranteed to approach to the stationary solution in the non-convex scenario. Experimental results on a variety of CV/NLP  tasks show that our method has a clear superiority to other existing decentralized adaptive methods.
The Gambler's Problem and Beyond
Authors: Baoxiang Wang, Shuai Li, Jiajin Li, Siu On Chan

We analyze the Gambler's problem, a simple reinforcement learning problem where the gambler has the chance to double or lose their bets until the target is reached. This is an early example introduced in the reinforcement learning textbook by Sutton and Barto (2018), where they mention an interesting pattern of the optimal value function with high-frequency components and repeating non-smooth points. It is however without further investigation. We provide the exact formula for the optimal value function for both the discrete and the continuous cases. Though simple as it might seem, the value function is pathological: fractal, self-similar, derivative taking either zero or infinity, not smooth on any interval, and not written as elementary functions. It is in fact one of the generalized Cantor functions, where it holds a complexity that has been uncharted thus far. Our analyses could lead insights into improving value function approximation, gradient-based algorithms, and Q-learning, in real applications and implementations.
Log representation as an interface for log processing applications
Authors: Mohammad Amin Sadeghi, Shameem Parambath, Ji Lucas, Youssef Meguebli, Maguette Toure, Fawaz Al Qahtani, Ting Yu, Sanjay Chawla

Log files are files that record events, messages, or transactions. Logs are rich containers of data because they can store a sequence of structured textual and numerical data. Many sequential forms of data including natural languages and temporal signals can be represented as logs.

We propose to represent logs at a few levels of abstraction including field level, log level, and log sequence level. The representation for each level can be computed from the previous level. These representations are in vector format and serve as interfaces to downstream applications. We use a version of transformer networks to encode numerical information as well as textual information that is suitable for log embedding. We show how a number of log processing applications can be readily solved with our representation.
Using latent space regression to analyze and leverage compositionality in GANs
Authors: Lucy Chai, Jonas Wulff, Phillip Isola

In recent years, Generative Adversarial Networks have become ubiquitous in both research and public perception, but how GANs convert an unstructured latent code to a high quality output is still an open question. In this work, we investigate regression into the latent space as a probe to understand the compositional properties of GANs.  We find that combining the regressor and a pretrained generator provides a strong image prior, allowing us to create composite images from a collage of random image parts at inference time while maintaining global consistency. To compare compositional properties across different generators, we measure the trade-offs between reconstruction of the unrealistic input and image quality of the regenerated samples. We find that the regression approach enables more localized editing of individual image parts compared to direct editing in the latent space, and we conduct experiments to quantify this independence effect. Our method is agnostic to the semantics of edits, and does not require labels or predefined concepts during training. Beyond image composition, our method extends to a number of related applications, such as image inpainting or example-based image editing, which we demonstrate on several GANs and datasets, and
because it uses only a single forward pass, it can operate in real-time. Code is available on our project page: https://chail.github.io/latent-composition/.
Depth Without the Magic: Inductive Bias of Natural Gradient Descent
Authors: Anna Kerekes, Anna Mészáros, Ferenc Huszár

In gradient descent, changing how we parametrize the model can lead to drastically different optimization trajectories, giving rise a surprising range of meaningful inductive biases: identifying sparse classifiers or reconstructing low-rank matrices without explicit regularization. This implicit regularization has been hypothesised to be a contributing factor to good generalization in deep learning. However, natural gradient descent is approximately invariant to reparameterization, it always follows the same trajectory and finds the same optimum. The question naturally arises: What happens if we eliminate the role of parameterization, which solution will be found, what new properties occur?  We characterize the behaviour of natural gradient flow in deep linear networks for separable classification under logistic loss and deep matrix factorization. Some of our findings extend to nonlinear neural networks with sufficient but finite over-parametrization. We demonstrate that there exist learning problems where natural gradient descent fails to generalize, while gradient descent with the right architecture peforms well.
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
Authors: Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas Griffiths

Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks. Here, we reformulate the model-agnostic meta-learning algorithm (MAML) of Finn et al. (2017) as a method for probabilistic inference in a hierarchical Bayesian model. In contrast to prior methods for meta-learning via hierarchical Bayes, MAML is naturally applicable to complex function approximators through its use of a scalable gradient descent procedure for posterior inference. Furthermore, the identification of MAML as hierarchical Bayes provides a way to understand the algorithm’s operation as a meta-learning procedure, as well as an opportunity to make use of computational strategies for efficient inference. We use this opportunity to propose an improvement to the MAML algorithm that makes use of techniques from approximate inference and curvature estimation.
Multi-Objective Value Iteration with Parameterized Threshold-Based Safety Constraints
Authors: Hussein Sibai, Sayan Mitra

We consider an environment with multiple reward functions. One of them represents goal achievement and the others represent instantaneous safety conditions. We consider a scenario where the safety rewards should always be above some thresholds. The thresholds are parameters with values that differ between users.
%The thresholds are not known at the time the policy is being designed.
We efficiently compute a family of policies that cover all threshold-based constraints and maximize the goal achievement reward. We introduce a new parameterized threshold-based scalarization method of the reward vector that encodes our objective. We present novel data structures to store the value functions of the Bellman equation that allow their efficient computation using the value iteration algorithm. We present results for both discrete and continuous state spaces.
Unsupervised Disentanglement with Tensor Product Representations on the Torus
Authors: Michael Rotman, Amit Dekel, Shir Gur, Yaron Oz, Lior Wolf

The current methods for learning representations with auto-encoders almost exclusively employ vectors as the latent representations.  In this work, we propose to employ a tensor product structure for this purpose. This way, the obtained representations are naturally disentangled. In contrast to the conventional variations methods, which are targeted toward normally distributed features, the latent space in our representation is distributed uniformly over a set of unit circles. We argue that the torus structure of the latent space captures the generative factors effectively. We employ recent tools for measuring unsupervised disentanglement, and in an extensive set of experiments demonstrate the advantage of our method in terms of disentanglement, completeness, and informativeness. The code for our proposed method is available at https://github.com/rotmanmi/Unsupervised-Disentanglement-Torus.
Communicating via Markov Decision Processes
Authors: Samuel Sokota, Christian Schroeder de Witt, Maximilian Igl, Luisa M Zintgraf, Philip Torr, J Zico Kolter, Shimon Whiteson, Jakob Nicolaus Foerster

We consider the problem of communicating exogenous information by means of Markov decision process trajectories. This setting, which we call a Markov coding game (MCG), generalizes both source coding and a large class of referential games. MCGs also isolate a problem that is important in decentralized control settings in which cheap-talk is not available---namely, they require balancing communication with the associated cost of communicating. We contribute a theoretically grounded approach to MCGs based on maximum entropy reinforcement learning and minimum entropy coupling that we call greedy minimum entropy coupling (GME). We show both that GME is able to outperform a relevant baseline on small MCGs and that GME is able to scale efficiently to extremely large MCGs. To the latter point, we demonstrate that GME is able to losslessly communicate binary images via trajectories of Cartpole and Pong, while simultaneously achieving the maximal or near maximal expected returns, and that it is even capable of performing well in the presence of actuator noise.
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
Authors: Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Qing Deng, Keith Ross

The field of Deep Reinforcement Learning (DRL) has recently seen a surge in research in batch reinforcement learning, which aims for sample-efficient learning from a given data set without additional interactions with the environment. In the batch DRL setting, commonly employed off-policy DRL algorithms can perform poorly and sometimes even fail to learn altogether.  In this paper we propose anew algorithm, Best-Action Imitation Learning (BAIL), which unlike many off-policy DRL algorithms does not involve maximizing Q functions over the action space. Striving for simplicity as well as performance, BAIL first selects from the batch the actions it believes to be high-performing actions for their corresponding states; it then uses those state-action pairs to train a policy network using imitation learning.  Although BAIL is simple, we demonstrate that BAIL achieves state of the art performance on the Mujoco benchmark, typically outperforming BatchConstrained deep Q-Learning (BCQ) by a wide margin.