nils-herrmann/clean_scirepeval huggingface dataset:
Episodic Memory for Learning Subjective-Timescale Models
Authors: Alexey Zakharov, Matthew Crosby, Zafeirios Fountas

In model-based learning, an agent’s model is commonly defined over transitions between consecutive states of an environment even though planning often requires reasoning over multi-step timescales, with intermediate states either unnecessary, or worse, accumulating prediction error. In contrast, intelligent behaviour in biological organisms is characterised by the ability to plan over varying temporal scales depending on the context. Inspired by the recent works on human time perception, we devise a novel approach to learning a transition dynamics model, based on the sequences of episodic memories that define the agent's subjective timescale – over which it learns world dynamics and over which future planning is performed. We implement this in the framework of active inference and demonstrate that the resulting subjective-timescale model (STM) can systematically vary the temporal extent of its predictions while preserving the same computational efficiency. Additionally, we show that STM predictions are more likely to introduce future salient events (for example new objects coming into view), incentivising exploration of new areas of the environment. As a result, STM produces more informative action-conditioned roll-outs that assist the agent in making better decisions. We validate significant improvement in our STM agent's performance in the Animal-AI environment against a baseline system, trained using the environment's objective-timescale dynamics.
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm
Authors: Ke Sun, Yingnan Zhao, Yi Liu, Enze Shi, Yafei Wang, Aref Sadeghi, Xiaodong Yan, Bei Jiang, Linglong Kong

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive. In this paper, we interpret distributional RL as entropy-regularized maximum likelihood estimation in the \textit{neural Z-fitted iteration} framework and establish the connection of the resulting risk-aware regularization with maximum entropy RL. In addition, We shed light on the stability-promoting distributional loss with desirable smoothness properties in distributional RL, which can yield stable optimization and guaranteed generalization. We also analyze the acceleration behavior while optimizing distributional RL algorithms and show that an appropriate approximation to the true target distribution can speed up the convergence. From the perspective of representation, we find that distributional RL encourages state representation from the same action class classified by the policy in tighter clusters. Finally, we propose a class of \textit{Sinkhorn distributional RL} algorithm that interpolates between the Wasserstein distance and maximum mean discrepancy~(MMD). Experiments on a suite of Atari games reveal the competitive performance of our algorithm relative to existing state-of-the-art distributional RL algorithms.
Deep Probabilistic Video Compression
Authors: Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt

We propose a variational inference approach to deep probabilistic video compression.  Our model uses advances in variational autoencoders (VAEs) for sequential data and combines it with recent work on neural image compression. The approach jointly learns to transform the original video into a  lower-dimensional representation as well as to entropy code this representation according to a temporally-conditioned probabilistic model. We split the latent space into local (per frame) and global (per segment) variables, and show that training the VAE to utilize both representations leads to an improved rate-distortion performance. Evaluation on small videos from public data sets with varying complexity and diversity show that our model yields competitive results when trained on generic video content. Extreme compression performance is achieved for videos with specialized content if the model is trained on similar videos.
Improved Generalization Bound of Permutation Invariant Deep Neural Networks
Authors: Akiyoshi Sannai, Masaaki Imaizumi

We theoretically prove that a permutation invariant property of deep neural networks largely improves its generalization performance. Learning problems with data that are invariant to permutations are frequently observed in various applications, for example, point cloud data and graph neural networks. Numerous methodologies have been developed and they achieve great performances, however, understanding a mechanism of the performance is still a developing problem. In this paper, we derive a theoretical generalization bound for invariant deep neural networks with a ReLU activation to clarify their mechanism. Consequently, our bound shows that the main term of their generalization gap is improved by $\sqrt{n!}$ where $n$ is a number of permuting coordinates of data. Moreover, we prove that an approximation power of invariant deep neural networks can achieve an optimal rate, though the networks are restricted to be invariant. To achieve the results, we develop several new proof techniques such as correspondence with a fundamental domain and a scale-sensitive metric entropy.
Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning
Authors: Xiangkun He, Jianye HAO, Dong Li, Bin Wang, Wulong Liu

Many real-word decision or control problems involve multiple conflicting objectives and uncertainties, which requires learned policies are not only Pareto optimal but also robust. In this paper, we proposed a novel algorithm to approximate a representation for robust Pareto frontier through Bayesian-optimization-directed robust multi-objective reinforcement learning (BRMORL). Firstly, environmental uncertainty is modeled as an adversarial agent over the entire space of preferences by incorporating zero-sum game into multi-objective reinforcement learning (MORL). Secondly, a comprehensive metric based on hypervolume and information entropy is presented to evaluate convergence, diversity and evenness of the distribution for Pareto solutions. Thirdly, the agent’s learning process is regarded as a black-box, and the comprehensive metric we proposed is computed after each episode of training, then a Bayesian optimization (BO) algorithm is adopted to guide the agent to evolve towards improving the quality of the approximated Pareto frontier. Finally, we demonstrate the effectiveness of proposed approach on challenging multi-objective tasks across four environments, and show our scheme can produce robust policies under environmental uncertainty.
Data augmentation as stochastic optimization
Authors: Boris Hanin, Yi Sun

We present a theoretical framework recasting data augmentation as stochastic optimization for a sequence of time-varying proxy losses. This provides a unified language for understanding techniques commonly thought of as data augmentation, including synthetic noise and label-preserving transformations, as well as more traditional ideas in stochastic optimization such as learning rate and batch size scheduling. We then specialize our framework to study arbitrary augmentations in the context of a simple model (overparameterized linear regression). We extend in this setting the classical Monro-Robbins theorem to include augmentation and obtain rates of convergence, giving conditions on the learning rate and augmentation schedule under which augmented gradient descent converges. Special cases give provably good schedules for augmentation with additive noise, minibatch SGD, and minibatch SGD with noise.
A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem
Authors: Zhihao Xing, Shikui Tu

We present a graph neural network assisted Monte Carlo Tree Search approach for the classical traveling salesman problem (TSP). We adopt a greedy algorithm framework to construct the optimal solution to TSP by adding the nodes successively. A graph neural network (GNN) is trained to capture the local and global graph structure and give the prior probability of selecting each vertex every step. The prior probability provides a heuristics for MCTS, and the MCTS output is an improved probability for selecting the successive vertex, as it is the feedback information by fusing the prior with the scouting procedure. Experimental results on TSP up to 100 nodes demonstrate that the proposed method obtains shorter tours than other learning-based methods.
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
Authors: Qingfeng Lan, Yangchen Pan, Alona Fyshe, Martha White

Q-learning suffers from overestimation bias, because it approximates the maximum action value using the maximum estimated action value. Algorithms have been proposed to reduce overestimation bias, but we lack an understanding of how bias interacts with performance, and the extent to which existing algorithms mitigate bias. In this paper, we 1) highlight that the effect of overestimation bias on learning efficiency is environment-dependent; 2) propose a generalization of Q-learning, called \emph{Maxmin Q-learning}, which provides a parameter to flexibly control bias; 3) show theoretically that there exists a parameter choice for Maxmin Q-learning that leads to unbiased estimation with a lower approximation variance than Q-learning; and 4) prove the convergence of our algorithm in the tabular case, as well as convergence of several previous Q-learning variants, using a novel Generalized Q-learning framework. We empirically verify that our algorithm better controls estimation bias in toy environments, and that it achieves superior performance on several benchmark problems.
Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective
Authors: Hanze Dong, Yanwei Fu, Leonid Sigal, SungJu Hwang, Yu-Gang Jiang, Xiangyang Xue

This paper studies the problem of domain division which aims to segment instances drawn from different probabilistic distributions. This problem exists in many previous recognition tasks, such as Open Set Learning (OSL) and Generalized Zero-Shot Learning (G-ZSL), where the testing instances come from either seen or unseen/novel classes with different probabilistic distributions. Previous works only calibrate the conﬁdent prediction of classiﬁers of seen classes (WSVM Scheirer et al. (2014)) or taking unseen classes as outliers Socher et al. (2013). In contrast, this paper proposes a probabilistic way of directly estimating and ﬁne-tuning the decision boundary between seen and unseen classes. In particular, we propose a domain division algorithm to split the testing instances into known, unknown and uncertain domains, and then conduct recognition tasks in each domain. Two statistical tools, namely, bootstrapping and KolmogorovSmirnov (K-S) Test, for the ﬁrst time, are introduced to uncover and ﬁne-tune the decision boundary of each domain. Critically, the uncertain domain is newly introduced in our framework to adopt those instances whose domain labels cannot be predicted conﬁdently. Extensive experiments demonstrate that our approach achieved the state-of-the-art performance on OSL and G-ZSL benchmarks.
Refining the variational posterior through iterative optimization
Authors: Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato

Variational inference (VI) is a popular approach for approximate Bayesian inference that is particularly promising for highly parameterized models such as deep neural networks.  A key challenge of variational inference is to approximate the posterior over model parameters with a distribution that is simpler and tractable yet sufficiently expressive. In this work, we propose a method for training highly flexible variational distributions by starting with a coarse approximation and iteratively refining it. Each refinement step makes cheap, local adjustments and only requires optimization of simple variational families. We demonstrate theoretically that our method always improves a bound on the approximation (the Evidence Lower BOund) and observe this empirically across a variety of benchmark tasks.  In experiments, our method consistently outperforms recent variational inference methods for deep learning in terms of log-likelihood and the ELBO.  We see that the gains are further amplified on larger scale models, significantly outperforming standard VI and deep ensembles on residual networks on CIFAR10.
Unsupervised Cipher Cracking Using Discrete GANs
Authors: Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser

This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext. We demonstrate that CipherGAN is capable of cracking language data enciphered using shift and Vigenere ciphers to a high degree of fidelity and for vocabularies much larger than previously achieved. We present how CycleGAN can be made compatible with discrete data and train in a stable way. We then prove that the technique used in CipherGAN avoids the common problem of uninformative discrimination associated with GANs applied to discrete data.
The Differentiable Cross-Entropy Method
Authors: Brandon Amos, Denis Yarats

We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant (DCEM) that enables us to differentiate the output of CEM with respect to the objective function's parameters. In the machine learning setting this brings CEM inside of the end-to-end learning pipeline in cases this has otherwise been impossible. We show applications in a synthetic energy-based structured prediction task and in non-convex continuous control. In the control setting we show on the simulated cheetah and walker tasks that we can embed their optimal action sequences with DCEM and then use policy optimization to fine-tune components of the controller as a step towards combining model-based and model-free RL.
Motion Planning Transformers: One Model to Plan them All
Authors: Jacob John Johnson, Linjun Li, Ahmed Qureshi, Michael C. Yip

Transformers have become the powerhouse of natural language processing and recently found use in computer vision tasks. Their effective use of attention can be used in other contexts as well, and in this paper, we propose a transformer-based approach for efficiently solving complex motion planning problems. Traditional neural network-based motion planning uses convolutional networks to encode the planning space, but these methods are limited to fixed map sizes, which is often not realistic in the real world. Our approach first identifies regions on the map using transformers to provide attention to map areas likely to include the best path and then applies traditional planners to generate the final collision-free path. We validate our method on a variety of randomly generated environments with different map sizes, demonstrating reduction in planning complexity and achieving comparable accuracy to traditional planners.
How can we generalise learning distributed representations of graphs?
Authors: Paul M Scherer, Pietro Lio

We propose a general framework to construct unsupervised models capable of learning distributed representations of discrete structures such as graphs based on R-Convolution kernels and distributed semantics research. Our framework combines the insights and observations of Deep Graph Kernels and Graph2Vec towards a unified methodology for performing similarity learning on graphs of arbitrary size. This is exemplified by our own instance G2DR which extends Graph2Vec from labelled graphs towards unlabelled graphs and tackles issues of diagonal dominance through pruning of the subgraph vocabulary composing graphs. These changes produce new state of the art results in the downstream application of G2DR embeddings in graph classification tasks over datasets with small labelled graphs in binary classification to multi-class classification on large unlabelled graphs using an off-the-shelf support vector machine.
Neural Speed Reading via Skim-RNN
Authors: Minjoon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi

Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens. Skim-RNN gives a significant computational advantage over an RNN that always updates the entire hidden state. Skim-RNN uses the same input and output interfaces as a standard RNN and can be easily used instead of RNNs in existing models.  In our experiments, we show that Skim-RNN can achieve significantly reduced computational cost without losing accuracy compared to standard RNNs across five different natural language tasks. In addition, we demonstrate that the trade-off between accuracy and speed of Skim-RNN can be dynamically controlled during inference time in a stable manner. Our analysis also shows that Skim-RNN running on a single CPU offers lower latency compared to standard RNNs on GPUs.
Redefining The Self-Normalization Property
Authors: Zhaodong Chen, Zhao WeiQin, Lei Deng, Guoqi Li, Yuan Xie

The approaches that prevent gradient explosion and vanishing have boosted the performance of deep neural networks in recent years. A unique one among them is the self-normalizing neural network (SNN), which is generally more stable than initialization techniques without explicit normalization. The self-normalization property of SNN in previous studies comes from the Scaled Exponential Linear Unit (SELU) activation function. %, which has achieved competitive accuracy on moderate-scale benchmarks.  However, it has been shown that in deeper neural networks, SELU either leads to gradient explosion or loses its self-normalization property. Besides, its accuracy on large-scale benchmarks like ImageNet is less satisfying.  In this paper, we analyze the forward and backward passes of SNN with mean-field theory and block dynamical isometry. A new definition for self-normalization property is proposed that is easier to use both analytically and numerically. A proposition is also proposed which enables us compare the strength of the self-normalization property between different activation functions. We further develop two new activation functions, leaky SELU (lSELU) and scaled SELU (sSELU), that have stronger self-normalization property. The optimal parameters in them can be easily solved with a constrained optimization program. Besides, analysis on the activation's mean in the forward pass reveals that the self-normalization property on mean gets weaker with larger fan-in, which explains the performance degradation on ImageNet. This can be solved with weight centralization, mixup data augmentation, and centralized activation function. On moderate-scale datasets CIFAR-10, CIFAR-100, and Tiny ImageNet, the direct application of lSELU and sSELU achieves up to 2.13% higher accuracy. On Conv MobileNet V1 - ImageNet, sSELU with Mixup, trainable $\lambda$, and centralized activation function reaches 71.95% accuracy that is even better than Batch Normalization.(code in Supplementary Material)
Regret Minimization for Partially Observable Deep Reinforcement Learning
Authors: Peter H. Jin, Sergey Levine, Kurt Keutzer

Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully observed state and must compensate for partial or non-Markovian observations by using finite-length frame-history observations or recurrent networks. In this work, we propose a new deep reinforcement learning algorithm based on counterfactual regret minimization that iteratively updates an approximation to a cumulative clipped advantage function and is robust to partially observed state. We demonstrate that on several partially observed reinforcement learning tasks, this new class of algorithms can substantially outperform strong baseline methods: on Pong with single-frame observations, and on the challenging Doom (ViZDoom) and Minecraft (Malmö) first-person navigation benchmarks.
Comparing Distributions by Measuring Differences that Affect Decision Making
Authors: Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon

Measuring the discrepancy between two probability distributions is a fundamental problem in machine learning and statistics. We propose a new class of discrepancies based on the optimal loss for a decision task -- two distributions are different if the optimal decision loss is higher on their mixture than on each individual distribution. By suitably choosing the decision task, this generalizes the Jensen-Shannon divergence and the maximum mean discrepancy family. We apply our approach to two-sample tests, and on various benchmarks, we achieve superior test power compared to competing methods. In addition, a modeler can directly specify their preferences when comparing distributions through the decision loss. We apply this property to understanding the effects of climate change on different social and economic activities, evaluating sample quality, and selecting features targeting different decision tasks.
Simulating Action Dynamics with Neural Process Networks
Authors: Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, Yejin Choi

Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated. In this work, we introduce Neural Process Networks to understand procedural text through (neural) simulation of action dynamics.   Our model complements existing memory architectures with dynamic entity tracking by explicitly modeling actions as state transformers. The model updates the states of the entities by executing learned action operators. Empirical results demonstrate that our proposed model can reason about the unstated causal effects of actions, allowing it to provide more accurate contextual information for understanding and generating procedural text, all while offering more interpretable internal representations than existing alternatives.
Reward Shifting for Optimistic Exploration and Conservative Exploitation
Authors: Hao Sun, Lei Han, Jian Guo, Bolei Zhou

In this work, we study the simple yet universally applicable case of reward shaping, the linear transformation, in value-based Deep Reinforcement Learning. We show that reward shifting, as the simplest linear reward transformation, is equivalent to changing initialization of the $Q$-function in function approximation. Based on such an equivalence, we bring the key insight that a positive reward shifting leads to conservative exploitation, while a negative reward shifting leads to curiosity-driven exploration. In this case, a conservative exploitation improves offline RL value estimation, and the optimistic value estimation benefits the exploration of online RL. We verify our insight on a range of tasks: (1) In offline RL, the conservative exploitation leads to improved learning performance based on off-the-shelf algorithms; (2) In online continuous control, multiple value functions with different shifting constants can be used to trade-off between exploration and exploitation thus improving learning efficiency; (3) In online RL with discrete action space, a negative reward shifting brings an improvement over the previous curiosity-based exploration method.