mtybilly/Instruct2DS huggingface dataset:
Investigating Human Priors for Playing Video Games
Authors: Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei Efros
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors on human performance. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors. We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, e.g. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play. Videos and the game manipulations are available at https://rach0012.github.io/humanRL_website/
A Distributed Second-Order Algorithm You Can Trust
Authors: Celestine Duenner, Aurelien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate the field, second-order methods are nevertheless attractive as they potentially require fewer communication rounds to converge. However, there are significant drawbacks that impede their wide adoption, such as the computation and the communication of a large Hessian matrix. In this paper we present a new algorithm for distributed training of generalized linear models that only requires the computation of diagonal blocks of the Hessian matrix on the individual workers. To deal with this approximate information we propose an adaptive approach that - akin to trust-region methods - dynamically adapts the auxiliary model to compensate for modeling errors. We provide theoretical rates of convergence for a wide class of problems including $L_1$-regularized objectives. We also demonstrate that our approach achieves state-of-the-art results on multiple large benchmark datasets.
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn’s Algorithm
Authors: Pavel Dvurechensky, Alexander Gasnikov, Alexey Kroshnin
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We analyze two algorithms for approximating the general optimal transport (OT) distance between two discrete distributions of size $n$, up to accuracy $ɛ$. For the first algorithm, which is based on the celebrated Sinkhorn’s algorithm, we prove the complexity bound $\widetildeOłeft(\fracn^2ɛ^2i̊ght)$ arithmetic operations ($\widetildeO$ hides polylogarithmic factors $(łn n)^c$, $c>0$). For the second one, which is based on our novel Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD) algorithm, we prove the complexity bound $\widetildeOłeft(\minłeft\\fracn^9/4ɛ, \fracn^2ɛ^2 \g̊ht\\rh̊t)$ arithmetic operations. Both bounds have better dependence on $ɛ$ than the state-of-the-art result given by $\widetildeOłeft(\fracn^2ɛ^3\rit̊)$. Our second algorithm not only has better dependence on $ɛ$ in the complexity bound, but also is not specific to entropic regularization and can solve the OT problem with different regularizers.
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
Authors: Gintare Karolina Dziugaite, Daniel Roy
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We show that Entropy-SGD (Chaudhari et al., 2017), when viewed as a learning algorithm, optimizes a PAC-Bayes bound on the risk of a Gibbs (posterior) classifier, i.e., a randomized classifier obtained by a risk-sensitive perturbation of the weights of a learned classifier. Entropy-SGD works by optimizing the bound’s prior, violating the hypothesis of the PAC-Bayes theorem that the prior is chosen independently of the data. Indeed, available implementations of Entropy-SGD rapidly obtain zero training error on random labels and the same holds of the Gibbs posterior. In order to obtain a valid generalization bound, we rely on a result showing that data-dependent priors obtained by stochastic gradient Langevin dynamics (SGLD) yield valid PAC-Bayes bounds provided the target distribution of SGLD is eps-differentially private. We observe that test error on MNIST and CIFAR10 falls within the (empirically nonvacuous) risk bounds computed under the assumption that SGLD reaches stationarity. In particular, Entropy-SGLD can be configured to yield relatively tight generalization bounds and still fit real labels, although these same settings do not obtain state-of-the-art performance.
Beyond the One-Step Greedy Approach in Reinforcement Learning
Authors: Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: The famous Policy Iteration algorithm alternates between policy improvement and policy evaluation. Implementations of this algorithm with several variants of the latter evaluation stage, e.g, n-step and trace-based returns, have been analyzed in previous works. However, the case of multiple-step lookahead policy improvement, despite the recent increase in empirical evidence of its strength, has to our knowledge not been carefully analyzed yet. In this work, we introduce the first such analysis. Namely, we formulate variants of multiple-step policy improvement, derive new algorithms using these definitions and prove their convergence. Moreover, we show that recent prominent Reinforcement Learning algorithms are, in fact, instances of our framework. We thus shed light on their empirical success and give a recipe for deriving new algorithms for future study.
Parallel and Streaming Algorithms for K-Core Decomposition
Authors: Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: The k-core decomposition is a fundamental primitive in many machine learning and data mining applications. We present the first distributed and the first streaming algorithms to compute and maintain an approximate k-core decomposition with provable guarantees. Our algorithms achieve rigorous bounds on space complexity while bounding the number of passes or number of rounds of computation. We do so by presenting a new powerful sketching technique for k-core decomposition, and then by showing it can be computed efficiently in both streaming and MapReduce models. Finally, we confirm the effectiveness of our sketching technique empirically on a number of publicly available graphs.
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Authors: Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amount of data and extended training time. We have developed a new distributed agent IMPALA (Importance Weighted Actor-Learner Architecture) that not only uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. We demonstrate the effectiveness of IMPALA for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the DeepMind Lab environment (Beattie et al., 2016)) and Atari57 (all available Atari games in Arcade Learning Environment (Bellemare et al., 2013a)). Our results show that IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach.
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
Authors: Trefor Evans, Prasanth Nair
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We introduce a kernel approximation strategy that enables computation of the Gaussian process log marginal likelihood and all hyperparameter derivatives in O(p) time. Our GRIEF kernel consists of p eigenfunctions found using a Nystrom approximation from a dense Cartesian product grid of inducing points. By exploiting algebraic properties of Kronecker and Khatri-Rao tensor products, computational complexity of the training procedure can be practically independent of the number of inducing points. This allows us to use arbitrarily many inducing points to achieve a globally accurate kernel approximation, even in high-dimensional problems. The fast likelihood evaluation enables type-I or II Bayesian inference on large-scale datasets. We benchmark our algorithms on real-world problems with up to two-million training points and 10^33 inducing points.
The Limits of Maxing, Ranking, and Preference Learning
Authors: Moein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We present a comprehensive understanding of three important problems in PAC preference learning: maximum selection (maxing), ranking, and estimating <em>all</em> pairwise preference probabilities, in the adaptive setting. With just Weak Stochastic Transitivity, we show that maxing requires $Ømega(n^2)$ comparisons and with slightly more restrictive Medium Stochastic Transitivity, we present a linear complexity maxing algorithm. With Strong Stochastic Transitivity and Stochastic Triangle Inequality, we derive a ranking algorithm with optimal $\mathcalO(nłog n)$ complexity and an optimal algorithm that estimates all pairwise preference probabilities.
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Authors: Stefan Falkner, Aaron Klein, Frank Hutter
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Modern deep learning methods are very sensitive to many hyperparameters, and, due to the long training times of state-of-the-art models, vanilla Bayesian hyperparameter optimization is typically computationally infeasible. On the other hand, bandit-based configuration evaluation approaches based on random search lack guidance and do not converge to the best configurations as quickly. Here, we propose to combine the benefits of both Bayesian optimization and bandit-based methods, in order to achieve the best of both worlds: strong anytime performance and fast convergence to optimal configurations. We propose a new practical state-of-the-art hyperparameter optimization method, which consistently outperforms both Bayesian optimization and Hyperband on a wide range of problem types, including high-dimensional toy functions, support vector machines, feed-forward neural networks, Bayesian neural networks, deep reinforcement learning, and convolutional neural networks. Our method is robust and versatile, while at the same time being conceptually simple and easy to implement.
More Robust Doubly Robust Off-policy Evaluation
Authors: Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We study the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of a policy from the data generated by another policy(ies). In particular, we focus on the doubly robust (DR) estimators that consist of an importance sampling (IS) component and a performance model, and utilize the low (or zero) bias of IS and low variance of the model at the same time. Although the accuracy of the model has a huge impact on the overall performance of DR, most of the work on using the DR estimators in OPE has been focused on improving the IS part, and not much on how to learn the model. In this paper, we propose alternative DR estimators, called more robust doubly robust (MRDR), that learn the model parameter by minimizing the variance of the DR estimator. We first present a formulation for learning the DR model in RL. We then derive formulas for the variance of the DR estimator in both contextual bandits and RL, such that their gradients w.r.t. the model parameters can be estimated from the samples, and propose methods to efficiently minimize the variance. We prove that the MRDR estimators are strongly consistent and asymptotically optimal. Finally, we evaluate MRDR in bandits and RL benchmark problems, and compare its performance with the existing methods.
Efficient and Consistent Adversarial Bipartite Matching
Authors: Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian Ziebart
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Many important structured prediction problems, including learning to rank items, correspondence-based natural language processing, and multi-object tracking, can be formulated as weighted bipartite matching optimizations. Existing structured prediction approaches have significant drawbacks when applied under the constraints of perfect bipartite matchings. Exponential family probabilistic models, such as the conditional random field (CRF), provide statistical consistency guarantees, but suffer computationally from the need to compute the normalization term of its distribution over matchings, which is a #P-hard matrix permanent computation. In contrast, the structured support vector machine (SSVM) provides computational efficiency, but lacks Fisher consistency, meaning that there are distributions of data for which it cannot learn the optimal matching even under ideal learning conditions (i.e., given the true distribution and selecting from all measurable potential functions). We propose adversarial bipartite matching to avoid both of these limitations. We develop this approach algorithmically, establish its computational efficiency and Fisher consistency properties, and apply it to matching problems that demonstrate its empirical benefits.
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Authors: Maryam Fazel, Rong Ge, Sham Kakade, Mehran Mesbahi
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model, 2) they are an “end-to-end” approach, directly optimizing the performance metric of interest, 3) they inherently allow for richly parameterized policies. A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives. In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties. This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.
CRVI: Convex Relaxation for Variational Inference
Authors: Ghazal Fazelnia, John Paisley
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We present a new technique for solving non-convex variational inference optimization problems. Variational inference is a widely used method for posterior approximation in which the inference problem is transformed into an optimization problem. For most models, this optimization is highly non-convex and so hard to solve. In this paper, we introduce a new approach to solving the variational inference optimization based on convex relaxation and semidefinite programming. Our theoretical results guarantee very tight relaxation bounds that get nearer to the global optimal solution than traditional coordinate ascent. We evaluate the performance of our approach on regression and sparse coding.
Fourier Policy Gradients
Authors: Matthew Fellows, Kamil Ciosek, Shimon Whiteson
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We propose a new way of deriving policy gradient updates for reinforcement learning. Our technique, based on Fourier analysis, recasts integrals that arise with expected policy gradients as convolutions and turns them into multiplications. The obtained analytical solutions allow us to capture the low variance benefits of EPG in a broad range of settings. For the critic, we treat trigonometric and radial basis functions, two function families with the universal approximation property. The choice of policy can be almost arbitrary, including mixtures or hybrid continuous-discrete probability distributions. Moreover, we derive a general family of sample-based estimators for stochastic policy gradients, which unifies existing results on sample-based approximation. We believe that this technique has the potential to shape the next generation of policy gradient approaches, powered by analytical results.
Nonparametric variable importance using an augmented neural network with multi-task learning
Authors: Jean Feng, Brian Williamson, Noah Simon, Marco Carone
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In predictive modeling applications, it is often of interest to determine the relative contribution of subsets of features in explaining the variability of an outcome. It is useful to consider this variable importance as a function of the unknown, underlying data-generating mechanism rather than the specific predictive algorithm used to fit the data. In this paper, we connect these ideas in nonparametric variable importance to machine learning, and provide a method for efficient estimation of variable importance when building a predictive model using a neural network. We show how a single augmented neural network with multi-task learning simultaneously estimates the importance of many feature subsets, improving on previous procedures for estimating importance. We demonstrate on simulated data that our method is both accurate and computationally efficient, and apply our method to both a study of heart disease and for predicting mortality in ICU patients.
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
Authors: Louis Filstroff, Alberto Lumbreras, Cédric Févotte
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We present novel understandings of the Gamma-Poisson (GaP) model, a probabilistic matrix factorization model for count data. We show that GaP can be rewritten free of the score/activation matrix. This gives us new insights about the estimation of the topic/dictionary matrix by maximum marginal likelihood estimation. In particular, this explains the robustness of this estimator to over-specified values of the factorization rank, especially its ability to automatically prune irrelevant dictionary columns, as empirically observed in previous work. The marginalization of the activation matrix leads in turn to a new Monte Carlo Expectation-Maximization algorithm with favorable properties.
Automatic Goal Generation for Reinforcement Learning Agents
Authors: Carlos Florensa, David Held, Xinyang Geng, Pieter Abbeel
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Reinforcement learning (RL) is a powerful technique to train an agent to perform a task; however, an agent that is trained using RL is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations. Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing in its environment. We use a generator network to propose tasks for the agent to try to accomplish, each task being specified as reaching a certain parametrized subset of the state-space. The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent, thus automatically producing a curriculum. We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment, even when only sparse rewards are available. Videos and code available at https://sites.google.com/view/goalgeneration4rl.
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Authors: Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric Xing, Shimon Whiteson
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: The score function estimator is widely used for estimating gradients of stochastic objectives in stochastic computation graphs (SCG), eg., in reinforcement learning and meta-learning. While deriving the first-order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and conceptually simple, using the same approach for higher-order derivatives is more challenging. Firstly, analytically deriving and implementing such estimators is laborious and not compliant with automatic differentiation. Secondly, repeatedly applying SL to construct new objectives for each order derivative involves increasingly cumbersome graph manipulations. Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order derivatives. To address all these shortcomings in a unified way, we introduce DiCE, which provides a single objective that can be differentiated repeatedly, generating correct estimators of derivatives of any order in SCGs. Unlike SL, DiCE relies on automatic differentiation for performing the requisite graph manipulations. We verify the correctness of DiCE both through a proof and numerical evaluation of the DiCE derivative estimates. We also use DiCE to propose and evaluate a novel approach for multi-agent learning. Our code is available at https://github.com/alshedivat/lola.
Practical Contextual Bandits with Regression Oracles
Authors: Dylan Foster, Alekh Agarwal, Miroslav Dudik, Haipeng Luo, Robert Schapire
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: A major challenge in contextual bandits is to design general-purpose algorithms that are both practically useful and theoretically well-founded. We present a new technique that has the empirical and computational advantages of realizability-based approaches combined with the flexibility of agnostic methods. Our algorithms leverage the availability of a regression oracle for the value-function class, a more realistic and reasonable oracle than the classification oracles over policies typically assumed by agnostic methods. Our approach generalizes both UCB and LinUCB to far more expressive possible model classes and achieves low regret under certain distributional assumptions. In an extensive empirical evaluation, we find that our approach typically matches or outperforms both realizability-based and agnostic baselines.
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Authors: Marco Fraccaro, Danilo Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent’s representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic environments, due to the difficulty of training such models in larger, potentially partially-observed and 3D environments. In this work we introduce a novel action-conditioned generative model of such challenging environments. The model features a non-parametric spatial memory system in which we store learned, disentangled representations of the environment. Low-dimensional spatial updates are computed using a state-space model that makes use of knowledge on the prior dynamics of the moving agent, and high-dimensional visual observations are modelled with a Variational Auto-Encoder. The result is a scalable architecture capable of performing coherent predictions over hundreds of time steps across a range of partially observed 2D and 3D environments.
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Authors: Guilherme Franca, Daniel Robinson, Rene Vidal
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Recently, there has been an increasing interest in using tools from dynamical systems to analyze the behavior of simple optimization algorithms such as gradient descent and accelerated variants. This paper strengthens such connections by deriving the differential equations that model the continuous limit of the sequence of iterates generated by the alternating direction method of multipliers, as well as an accelerated variant. We employ the direct method of Lyapunov to analyze the stability of critical points of the dynamical systems and to obtain associated convergence rates.
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Authors: Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimiliano Pontil
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning. We show that an approximate version of the bilevel problem can be solved by taking into explicit account the optimization dynamics for the inner objective. Depending on the specific setting, the outer variables take either the meaning of hyperparameters in a supervised learning problem or parameters of a meta-learner. We provide sufficient conditions under which solutions of the approximate problem converge to those of the exact problem. We instantiate our approach for meta-learning in the case of deep learning where representation layers are treated as hyperparameters shared across a set of training episodes. In experiments, we confirm our theoretical findings, present encouraging results for few-shot learning and contrast the bilevel approach against classical approaches for learning-to-learn.
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Authors: Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We introduce SCAL, an algorithm designed to perform efficient exploration-exploration in any unknown weakly-communicating Markov Decision Process (MDP) for which an upper bound c on the span of the optimal bias function is known. For an MDP with $S$ states, $A$ actions and $Γ łeq S$ possible next states, we prove a regret bound of $O(c\sqrtΓ SAT)$, which significantly improves over existing algorithms (e.g., UCRL and PSRL), whose regret scales linearly with the MDP diameter $D$. In fact, the optimal bias span is finite and often much smaller than $D$ (e.g., $D=+ınfty$ in non-communicating MDPs). A similar result was originally derived by Bartlett and Tewari (2009) for REGAL.C, for which no tractable algorithm is available. In this paper, we relax the optimization problem at the core of REGAL.C, we carefully analyze its properties, and we provide the first computationally efficient algorithm to solve it. Finally, we report numerical simulations supporting our theoretical findings and showing how SCAL significantly outperforms UCRL in MDPs with large diameter and small span.
Addressing Function Approximation Error in Actor-Critic Methods
Authors: Scott Fujimoto, Herke van Hoof, David Meger
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.
Clipped Action Policy Gradient
Authors: Yasuhiro Fujita, Shin-ichi Maeda
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Many continuous control tasks have bounded action spaces. When policy gradient methods are applied to such tasks, out-of-bound actions need to be clipped before execution, while policies are usually optimized as if the actions are not clipped. We propose a policy gradient estimator that exploits the knowledge of actions being clipped to reduce the variance in estimation. We prove that our estimator, named clipped action policy gradient (CAPG), is unbiased and achieves lower variance than the conventional estimator that ignores action bounds. Experimental results demonstrate that CAPG generally outperforms the conventional estimator, indicating that it is a better policy gradient estimator for continuous control tasks. The source code is available at https://github.com/pfnet-research/capg.
Born Again Neural Networks
Authors: Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Knowledge Distillation (KD) consists of transferring “knowledge” from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student’s compactness, without sacrificing too much performance. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating the effect of the teacher outputs on both predicted and non-predicted classes.
The Generalization Error of Dictionary Learning with Moreau Envelopes
Authors: Alexandros Georgogiannis
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: This is a theoretical study on the sample complexity of dictionary learning with a general type of reconstruction loss. The goal is to estimate a $m \times d$ matrix $D$ of unit-norm columns when the only available information is a set of training samples. Points $x$ in $ℝ^m$ are subsequently approximated by the linear combination $Da$ after solving the problem $\min_a ın ℝ^d  Φ(x - Da) + g(a)$; function $g:ℝ^d \to [0,+ınfty)$ is either an indicator function or a sparsity promoting regularizer. Here is considered the case where $ Φ(x) = ınf_z ın ℝ^m  ||x-z||_2^2 + h(||z||_2)$ and $h$ is an even and univariate function on the real line. Connections are drawn between $Φ$ and the Moreau envelope of $h$. A new sample complexity result concerning the $k$-sparse dictionary problem removes the spurious condition on the coherence of $D$ appearing in previous works. Finally, comments are made on the approximation error of certain families of losses. The derived generalization bounds are of order $\mathcalO(\sqrtłog n /n)$ and valid without any further restrictions on the set of dictionaries with unit-norm columns.
Local Private Hypothesis Testing: Chi-Square Tests
Authors: Marco Gaboardi, Ryan Rogers
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: The local model for differential privacy is emerging as the reference model for practical applications of collecting and sharing sensitive information while satisfying strong privacy guarantees. In the local model, there is no trusted entity which is allowed to have each individual’s raw data as is assumed in the traditional curator model. Individuals’ data are usually perturbed before sharing them. We explore the design of private hypothesis tests in the local model, where each data entry is perturbed to ensure the privacy of each participant. Specifically, we analyze locally private chi-square tests for goodness of fit and independence testing.
Inductive Two-Layer Modeling with Parametric Bregman Transfer
Authors: Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Latent prediction models, exemplified by multi-layer networks, employ hidden variables that automate abstract feature discovery. They typically pose nonconvex optimization problems and effective semi-definite programming (SDP) relaxations have been developed to enable global solutions (Aslan et al., 2014).However, these models rely on nonparametric training of layer-wise kernel representations, and are therefore restricted to transductive learning which slows down test prediction. In this paper, we develop a new inductive learning framework for parametric transfer functions using matching losses. The result for ReLU utilizes completely positive matrices, and the inductive learner not only delivers superior accuracy but also offers an order of magnitude speedup over SDP with constant approximation guarantees.
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Authors: Octavian Ganea, Gary Becigneul, Thomas Hofmann
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning. We here present a novel method to embed directed acyclic graphs. Following prior work, we first advocate for using hyperbolic spaces which provably model tree-like structures better than Euclidean geometry. Second, we view hierarchical relations as partial orders defined using a family of nested geodesically convex cones. We prove that these entailment cones admit an optimal shape with a closed form expression both in the Euclidean and hyperbolic spaces, and they canonically define the embedding learning process. Experiments show significant improvements of our method over strong recent baselines both in terms of representational capacity and generalization.
Parameterized Algorithms for the Matrix Completion Problem
Authors: Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We consider two matrix completion problems, in which we are given a matrix with missing entries and the task is to complete the matrix in a way that (1) minimizes the rank, or (2) minimizes the number of distinct rows. We study the parameterized complexity of the two aforementioned problems with respect to several parameters of interest, including the minimum number of matrix rows, columns, and rows plus columns needed to cover all missing entries. We obtain new algorithmic results showing that, for the bounded domain case, both problems are fixed-parameter tractable with respect to all aforementioned parameters. We complement these results with a lower-bound result for the unbounded domain case that rules out fixed-parameter tractability w.r.t. some of the parameters under consideration.
Synthesizing Programs for Images using Reinforced Adversarial Learning
Authors: Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S. M. Ali Eslami, Oriol Vinyals
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders. This is where graphics engines may come in handy since they abstract away low-level details and represent images as high-level programs. Current methods that combine deep learning and renderers are limited by hand-crafted likelihood or distance functions, a need for large amounts of supervision, or difficulties in scaling their inference algorithms to richer datasets. To mitigate these issues, we present SPIRAL, an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images. The goal of this agent is to fool a discriminator network that distinguishes between real and rendered data, trained with a distributed reinforcement learning setup without any supervision. A surprising finding is that using the discriminator’s output as a reward signal is the key to allow the agent to make meaningful progress at matching the desired output rendering. To the best of our knowledge, this is the first demonstration of an end-to-end, unsupervised and adversarial inverse graphics agent on challenging real world (MNIST, Omniglot, CelebA) and synthetic 3D datasets. A video of the agent can be found at https://youtu.be/iSyvwAwa7vk.
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Authors: Yuanxiang Gao, Li Chen, Baochun Li
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Training deep neural networks (DNNs) requires an increasing amount of computation resources, and it becomes typical to use a mixture of GPU and CPU devices. Due to the heterogeneity of these devices, a recent challenge is how each operation in a neural network can be optimally placed on these devices, so that the training process can take the shortest amount of time possible. The current state-of-the-art solution uses reinforcement learning based on the policy gradient method, and it suffers from suboptimal training times. In this paper, we propose Spotlight, a new reinforcement learning algorithm based on proximal policy optimization, designed specifically for finding an optimal device placement for training DNNs. The design of our new algorithm relies upon a new model of the device placement problem: by modeling it as a Markov decision process with multiple stages, we are able to prove that Spotlight achieves a theoretical guarantee on performance improvements. We have implemented Spotlight in the CIFAR-10 benchmark and deployed it on the Google Cloud platform. Extensive experiments have demonstrated that the training time with placements recommended by Spotlight is 60.9% of that recommended by the policy gradient method.
Parallel Bayesian Network Structure Learning
Authors: Tian Gao, Dennis Wei
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Recent advances in Bayesian Network (BN) structure learning have focused on local-to-global learning, where the graph structure is learned via one local subgraph at a time. As a natural progression, we investigate parallel learning of BN structures via multiple learning agents simultaneously, where each agent learns one local subgraph at a time. We find that parallel learning can reduce the number of subgraphs requiring structure learning by storing previously queried results and communicating (even partial) results among agents. More specifically, by using novel rules on query subset and superset inference, many subgraph structures can be inferred without learning. We provide a sound and complete parallel structure learning (PSL) algorithm, and demonstrate its improved efficiency over state-of-the-art single-thread learning algorithms.
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
Authors: Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d'Alche-Buc
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA). The structure prediction model is able to abstain from predicting some labels in the structured output at a cost chosen by the user in a flexible way. For that purpose, we decompose the problem into the learning of a pair of predictors, one devoted to structured abstention and the other, to structured output prediction. To compare fully labeled training data with predictions potentially containing abstentions, we define a wide class of asymmetric abstention-aware losses. Learning is achieved by surrogate regression in an appropriate feature space while prediction with abstention is performed by solving a new pre-image problem. Thus, SOLA extends recent ideas about Structured Output Prediction via surrogate problems and calibration theory and enjoys statistical guarantees on the resulting excess risk. Instantiated on a hierarchical abstention-aware loss, SOLA is shown to be relevant for fine-grained opinion mining and gives state-of-the-art results on this task. Moreover, the abstention-aware representations can be used to competitively predict user-review ratings based on a sentence-level opinion predictor.
Conditional Neural Processes
Authors: Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, S. M. Ali Eslami
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet, GPs are computationally expensive, and it can be hard to design appropriate priors. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets. We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion.
Temporal Poisson Square Root Graphical Models
Authors: Sinong Geng, Zhaobin Kuang, Peggy Peissig, David Page
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal event data. By estimating the temporal relationships for all possible pairs of event types, TPSQRs can offer a holistic perspective about whether the occurrences of any given event type could excite or inhibit any other type. A TPSQR is learned by estimating a collection of interrelated PSQRs that share the same template parameterization. These PSQRs are estimated jointly in a pseudo-likelihood fashion, where Poisson pseudo-likelihood is used to approximate the original more computationally intensive pseudo-likelihood problem stemming from PSQRs. Theoretically, we demonstrate that under mild assumptions, the Poisson pseudolikelihood approximation is sparsistent for recovering the underlying PSQR. Empirically, we learn TPSQRs from a real-world large-scale electronic health record (EHR) with millions of drug prescription and condition diagnosis events, for adverse drug reaction (ADR) detection. Experimental results demonstrate that the learned TPSQRs can recover ADR signals from the EHR effectively and efficiently.
Budgeted Experiment Design for Causal Structure Learning
Authors: AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We study the problem of causal structure learning when the experimenter is limited to perform at most $k$ non-adaptive experiments of size $1$. We formulate the problem of finding the best intervention target set as an optimization problem, which aims to maximize the average number of edges whose directions are resolved. We prove that the corresponding objective function is submodular and a greedy algorithm suffices to achieve $(1-\frac1e)$-approximation of the optimal value. We further present an accelerated variant of the greedy algorithm, which can lead to orders of magnitude performance speedup. We validate our proposed approach on synthetic and real graphs. The results show that compared to the purely observational setting, our algorithm orients the majority of the edges through a considerably small number of interventions.