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v139/mguni21a | Learning in Nonzero-Sum Stochastic Games with Potentials | https://proceedings.mlr.press/v139/mguni21a.html | [
"David H Mguni",
"Yutong Wu",
"Yali Du",
"Yaodong Yang",
"Ziyi Wang",
"Minne Li",
"Ying Wen",
"Joel Jennings",
"Jun Wang"
] | null | null | Multi-agent reinforcement learning (MARL) has become effective in tackling discrete cooperative game scenarios. However, MARL has yet to penetrate settings beyond those modelled by team and zero-sum games, confining it to a small subset of multi-agent systems. In this paper, we introduce a new generation of MARL learne... | [] | null | 701 | 2103.09284 | title_snapshot |
v139/miao21a | EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture | https://proceedings.mlr.press/v139/miao21a.html | [
"Chenfeng Miao",
"Liang Shuang",
"Zhengchen Liu",
"Chen Minchuan",
"Jun Ma",
"Shaojun Wang",
"Jing Xiao"
] | null | null | In this work, we address the Text-to-Speech (TTS) task by proposing a non-autoregressive architecture called EfficientTTS. Unlike the dominant non-autoregressive TTS models, which are trained with the need of external aligners, EfficientTTS optimizes all its parameters with a stable, end-to-end training procedure, allo... | [] | null | 702 | 2012.03500 | title_snapshot |
v139/miller21a | Outside the Echo Chamber: Optimizing the Performative Risk | https://proceedings.mlr.press/v139/miller21a.html | [
"John P Miller",
"Juan C Perdomo",
"Tijana Zrnic"
] | null | null | In performative prediction, predictions guide decision-making and hence can influence the distribution of future data. To date, work on performative prediction has focused on finding performatively stable models, which are the fixed points of repeated retraining. However, stable solutions can be far from optimal when e... | [] | null | 703 | 2102.08570 | title_snapshot |
v139/miller21b | Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization | https://proceedings.mlr.press/v139/miller21b.html | [
"John P Miller",
"Rohan Taori",
"Aditi Raghunathan",
"Shiori Sagawa",
"Pang Wei Koh",
"Vaishaal Shankar",
"Percy Liang",
"Yair Carmon",
"Ludwig Schmidt"
] | null | null | For machine learning systems to be reliable, we must understand their performance in unseen, out- of-distribution environments. In this paper, we empirically show that out-of-distribution performance is strongly correlated with in-distribution performance for a wide range of models and distribution shifts. Specifically... | [] | null | 704 | 2107.04649 | title_snapshot |
v139/min21a | Signatured Deep Fictitious Play for Mean Field Games with Common Noise | https://proceedings.mlr.press/v139/min21a.html | [
"Ming Min",
"Ruimeng Hu"
] | null | null | Existing deep learning methods for solving mean-field games (MFGs) with common noise fix the sampling common noise paths and then solve the corresponding MFGs. This leads to a nested loop structure with millions of simulations of common noise paths in order to produce accurate solutions, which results in prohibitive co... | [] | null | 705 | 2106.03272 | title_snapshot |
v139/min21b | Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation | https://proceedings.mlr.press/v139/min21b.html | [
"Dongchan Min",
"Dong Bok Lee",
"Eunho Yang",
"Sung Ju Hwang"
] | null | null | With rapid progress in neural text-to-speech (TTS) models, personalized speech generation is now in high demand for many applications. For practical applicability, a TTS model should generate high-quality speech with only a few audio samples from the given speaker, that are also short in length. However, existing metho... | [] | null | 706 | 2106.03153 | title_snapshot |
v139/min21c | On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks | https://proceedings.mlr.press/v139/min21c.html | [
"Hancheng Min",
"Salma Tarmoun",
"Rene Vidal",
"Enrique Mallada"
] | null | null | Neural networks trained via gradient descent with random initialization and without any regularization enjoy good generalization performance in practice despite being highly overparametrized. A promising direction to explain this phenomenon is to study how initialization and overparametrization affect convergence and i... | [] | null | 707 | 2105.06351 | title_judge |
v139/mita21a | An Identifiable Double VAE For Disentangled Representations | https://proceedings.mlr.press/v139/mita21a.html | [
"Graziano Mita",
"Maurizio Filippone",
"Pietro Michiardi"
] | null | null | A large part of the literature on learning disentangled representations focuses on variational autoencoders (VAEs). Recent developments demonstrate that disentanglement cannot be obtained in a fully unsupervised setting without inductive biases on models and data. However, Khemakhem et al., AISTATS, 2020 suggest that e... | [] | null | 708 | 2010.09360 | title_snapshot |
v139/mitchell21a | Offline Meta-Reinforcement Learning with Advantage Weighting | https://proceedings.mlr.press/v139/mitchell21a.html | [
"Eric Mitchell",
"Rafael Rafailov",
"Xue Bin Peng",
"Sergey Levine",
"Chelsea Finn"
] | null | null | This paper introduces the offline meta-reinforcement learning (offline meta-RL) problem setting and proposes an algorithm that performs well in this setting. Offline meta-RL is analogous to the widely successful supervised learning strategy of pre-training a model on a large batch of fixed, pre-collected data (possibly... | [] | null | 709 | 2008.06043 | title_snapshot |
v139/miyagawa21a | The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization | https://proceedings.mlr.press/v139/miyagawa21a.html | [
"Taiki Miyagawa",
"Akinori F Ebihara"
] | null | null | We propose a model for multiclass classification of time series to make a prediction as early and as accurate as possible. The matrix sequential probability ratio test (MSPRT) is known to be asymptotically optimal for this setting, but contains a critical assumption that hinders broad real-world applications; the MSPRT... | [] | null | 710 | 2105.13636 | title_snapshot |
v139/mora21a | PODS: Policy Optimization via Differentiable Simulation | https://proceedings.mlr.press/v139/mora21a.html | [
"Miguel Angel Zamora Mora",
"Momchil Peychev",
"Sehoon Ha",
"Martin Vechev",
"Stelian Coros"
] | null | null | Current reinforcement learning (RL) methods use simulation models as simple black-box oracles. In this paper, with the goal of improving the performance exhibited by RL algorithms, we explore a systematic way of leveraging the additional information provided by an emerging class of differentiable simulators. Building o... | [] | null | 711 | null | null |
v139/morrill21a | Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games | https://proceedings.mlr.press/v139/morrill21a.html | [
"Dustin Morrill",
"Ryan D’Orazio",
"Marc Lanctot",
"James R Wright",
"Michael Bowling",
"Amy R Greenwald"
] | null | null | Hindsight rationality is an approach to playing general-sum games that prescribes no-regret learning dynamics for individual agents with respect to a set of deviations, and further describes jointly rational behavior among multiple agents with mediated equilibria. To develop hindsight rational learning in sequential de... | [] | null | 712 | 2102.06973 | title_snapshot |
v139/morrill21b | Neural Rough Differential Equations for Long Time Series | https://proceedings.mlr.press/v139/morrill21b.html | [
"James Morrill",
"Cristopher Salvi",
"Patrick Kidger",
"James Foster"
] | null | null | Neural controlled differential equations (CDEs) are the continuous-time analogue of recurrent neural networks, as Neural ODEs are to residual networks, and offer a memory-efficient continuous-time way to model functions of potentially irregular time series. Existing methods for computing the forward pass of a Neural CD... | [] | null | 713 | 2009.08295 | title_snapshot |
v139/moshkovitz21a | Connecting Interpretability and Robustness in Decision Trees through Separation | https://proceedings.mlr.press/v139/moshkovitz21a.html | [
"Michal Moshkovitz",
"Yao-Yuan Yang",
"Kamalika Chaudhuri"
] | null | null | Recent research has recognized interpretability and robustness as essential properties of trustworthy classification. Curiously, a connection between robustness and interpretability was empirically observed, but the theoretical reasoning behind it remained elusive. In this paper, we rigorously investigate this connecti... | [] | null | 714 | 2102.07048 | title_snapshot |
v139/mukherjee21a | Outlier-Robust Optimal Transport | https://proceedings.mlr.press/v139/mukherjee21a.html | [
"Debarghya Mukherjee",
"Aritra Guha",
"Justin M Solomon",
"Yuekai Sun",
"Mikhail Yurochkin"
] | null | null | Optimal transport (OT) measures distances between distributions in a way that depends on the geometry of the sample space. In light of recent advances in computational OT, OT distances are widely used as loss functions in machine learning. Despite their prevalence and advantages, OT loss functions can be extremely sens... | [] | null | 715 | 2012.07363 | title_snapshot |
v139/munteanu21a | Oblivious Sketching for Logistic Regression | https://proceedings.mlr.press/v139/munteanu21a.html | [
"Alexander Munteanu",
"Simon Omlor",
"David Woodruff"
] | null | null | What guarantees are possible for solving logistic regression in one pass over a data stream? To answer this question, we present the first data oblivious sketch for logistic regression. Our sketch can be computed in input sparsity time over a turnstile data stream and reduces the size of a $d$-dimensional data set from... | [] | null | 716 | 2107.06615 | title_snapshot |
v139/murata21a | Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning | https://proceedings.mlr.press/v139/murata21a.html | [
"Tomoya Murata",
"Taiji Suzuki"
] | null | null | Recently, local SGD has got much attention and been extensively studied in the distributed learning community to overcome the communication bottleneck problem. However, the superiority of local SGD to minibatch SGD only holds in quite limited situations. In this paper, we study a new local algorithm called Bias-Varianc... | [] | null | 717 | 2102.03198 | title_snapshot |
v139/murphy21a | Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold | https://proceedings.mlr.press/v139/murphy21a.html | [
"Kieran A Murphy",
"Carlos Esteves",
"Varun Jampani",
"Srikumar Ramalingam",
"Ameesh Makadia"
] | null | null | In the deep learning era, the vast majority of methods to predict pose from a single image are trained to classify or regress to a single given ground truth pose per image. Such methods have two main shortcomings, i) they cannot represent uncertainty about the predictions, and ii) they cannot handle symmetric objects, ... | [] | null | 718 | 2106.05965 | title_snapshot |
v139/mutny21a | No-regret Algorithms for Capturing Events in Poisson Point Processes | https://proceedings.mlr.press/v139/mutny21a.html | [
"Mojmir Mutny",
"Andreas Krause"
] | null | null | Inhomogeneous Poisson point processes are widely used models of event occurrences. We address \emph{adaptive sensing of Poisson Point processes}, namely, maximizing the number of captured events subject to sensing costs. We encode prior assumptions on the rate function by modeling it as a member of a known \emph{reprod... | [] | null | 719 | null | null |
v139/nabati21a | Online Limited Memory Neural-Linear Bandits with Likelihood Matching | https://proceedings.mlr.press/v139/nabati21a.html | [
"Ofir Nabati",
"Tom Zahavy",
"Shie Mannor"
] | null | null | We study neural-linear bandits for solving problems where {\em both} exploration and representation learning play an important role. Neural-linear bandits harnesses the representation power of Deep Neural Networks (DNNs) and combines it with efficient exploration mechanisms by leveraging uncertainty estimation of the m... | [] | null | 720 | 2102.03799 | title_snapshot |
v139/nakagawa21a | Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding | https://proceedings.mlr.press/v139/nakagawa21a.html | [
"Akira Nakagawa",
"Keizo Kato",
"Taiji Suzuki"
] | null | null | Variational autoencoder (VAE) estimates the posterior parameters (mean and variance) of latent variables corresponding to each input data. While it is used for many tasks, the transparency of the model is still an underlying issue. This paper provides a quantitative understanding of VAE property through the differentia... | [] | null | 721 | 2007.15190 | title_snapshot |
v139/nam21a | GMAC: A Distributional Perspective on Actor-Critic Framework | https://proceedings.mlr.press/v139/nam21a.html | [
"Daniel W Nam",
"Younghoon Kim",
"Chan Y Park"
] | null | null | In this paper, we devise a distributional framework on actor-critic as a solution to distributional instability, action type restriction, and conflation between samples and statistics. We propose a new method that minimizes the Cram{é}r distance with the multi-step Bellman target distribution generated from a novel Sam... | [] | null | 722 | 2105.11366 | title_snapshot |
v139/narayanan21a | Memory-Efficient Pipeline-Parallel DNN Training | https://proceedings.mlr.press/v139/narayanan21a.html | [
"Deepak Narayanan",
"Amar Phanishayee",
"Kaiyu Shi",
"Xie Chen",
"Matei Zaharia"
] | null | null | Many state-of-the-art ML results have been obtained by scaling up the number of parameters in existing models. However, parameters and activations for such large models often do not fit in the memory of a single accelerator device; this means that it is necessary to distribute training of large models over multiple acc... | [] | null | 723 | 2006.09503 | title_snapshot |
v139/narayanan21b | Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering | https://proceedings.mlr.press/v139/narayanan21b.html | [
"Shyam Narayanan",
"Sandeep Silwal",
"Piotr Indyk",
"Or Zamir"
] | null | null | Random dimensionality reduction is a versatile tool for speeding up algorithms for high-dimensional problems. We study its application to two clustering problems: the facility location problem, and the single-linkage hierarchical clustering problem, which is equivalent to computing the minimum spanning tree. We show th... | [] | null | 724 | 2107.01804 | title_snapshot |
v139/nash21a | Generating images with sparse representations | https://proceedings.mlr.press/v139/nash21a.html | [
"Charlie Nash",
"Jacob Menick",
"Sander Dieleman",
"Peter Battaglia"
] | null | null | The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models. Previous approaches such as VQ-VAE use deep autoencoders to obtain compact representations, which are more practical as inputs for likelihood-based models. We present an alternative approac... | [] | null | 725 | 2103.03841 | title_snapshot |
v139/natarovskii21a | Geometric convergence of elliptical slice sampling | https://proceedings.mlr.press/v139/natarovskii21a.html | [
"Viacheslav Natarovskii",
"Daniel Rudolf",
"Björn Sprungk"
] | null | null | For Bayesian learning, given likelihood function and Gaussian prior, the elliptical slice sampler, introduced by Murray, Adams and MacKay 2010, provides a tool for the construction of a Markov chain for approximate sampling of the underlying posterior distribution. Besides of its wide applicability and simplicity its m... | [] | null | 726 | 2105.03308 | title_snapshot |
v139/nayman21a | HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search | https://proceedings.mlr.press/v139/nayman21a.html | [
"Niv Nayman",
"Yonathan Aflalo",
"Asaf Noy",
"Lihi Zelnik"
] | null | null | Realistic use of neural networks often requires adhering to multiple constraints on latency, energy and memory among others. A popular approach to find fitting networks is through constrained Neural Architecture Search (NAS), however, previous methods enforce the constraint only softly. Therefore, the resulting network... | [] | null | 727 | 2102.11646 | title_snapshot |
v139/ndousse21a | Emergent Social Learning via Multi-agent Reinforcement Learning | https://proceedings.mlr.press/v139/ndousse21a.html | [
"Kamal K Ndousse",
"Douglas Eck",
"Sergey Levine",
"Natasha Jaques"
] | null | null | Social learning is a key component of human and animal intelligence. By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi-agen... | [] | null | 728 | 2010.00581 | title_snapshot |
v139/neiswanger21a | Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information | https://proceedings.mlr.press/v139/neiswanger21a.html | [
"Willie Neiswanger",
"Ke Alexander Wang",
"Stefano Ermon"
] | null | null | In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function evaluations. One example is budget constrained global optimization of f, for which Bayesian optimization is a popular method. Other properties of interest include local optima, level sets, inte... | [] | null | 729 | 2104.09460 | title_snapshot |
v139/nekoei21a | Continuous Coordination As a Realistic Scenario for Lifelong Learning | https://proceedings.mlr.press/v139/nekoei21a.html | [
"Hadi Nekoei",
"Akilesh Badrinaaraayanan",
"Aaron Courville",
"Sarath Chandar"
] | null | null | Current deep reinforcement learning (RL) algorithms are still highly task-specific and lack the ability to generalize to new environments. Lifelong learning (LLL), however, aims at solving multiple tasks sequentially by efficiently transferring and using knowledge between tasks. Despite a surge of interest in lifelong ... | [] | null | 730 | 2103.03216 | title_snapshot |
v139/nemecek21a | Policy Caches with Successor Features | https://proceedings.mlr.press/v139/nemecek21a.html | [
"Mark Nemecek",
"Ronald Parr"
] | null | null | Transfer in reinforcement learning is based on the idea that it is possible to use what is learned in one task to improve the learning process in another task. For transfer between tasks which share transition dynamics but differ in reward function, successor features have been shown to be a useful representation which... | [] | null | 731 | null | null |
v139/neto21a | Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners | https://proceedings.mlr.press/v139/neto21a.html | [
"Elias Chaibub Neto"
] | null | null | Linear residualization is a common practice for confounding adjustment in machine learning applications. Recently, causality-aware predictive modeling has been proposed as an alternative causality-inspired approach for adjusting for confounders. In this paper, we compare the linear residualization approach against the ... | [] | null | 732 | 2011.04605 | title_snapshot |
v139/ngo21a | Incentivizing Compliance with Algorithmic Instruments | https://proceedings.mlr.press/v139/ngo21a.html | [
"Dung Daniel T Ngo",
"Logan Stapleton",
"Vasilis Syrgkanis",
"Steven Wu"
] | null | null | Randomized experiments can be susceptible to selection bias due to potential non-compliance by the participants. While much of the existing work has studied compliance as a static behavior, we propose a game-theoretic model to study compliance as dynamic behavior that may change over time. In rounds, a social planner i... | [] | null | 733 | 2107.10093 | title_snapshot |
v139/nguyen21a | On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths | https://proceedings.mlr.press/v139/nguyen21a.html | [
"Quynh Nguyen"
] | null | null | We give a simple proof for the global convergence of gradient descent in training deep ReLU networks with the standard square loss, and show some of its improvements over the state-of-the-art. In particular, while prior works require all the hidden layers to be wide with width at least $\Omega(N^8)$ ($N$ being the numb... | [] | null | 734 | 2101.09612 | title_snapshot |
v139/nguyen21b | Value-at-Risk Optimization with Gaussian Processes | https://proceedings.mlr.press/v139/nguyen21b.html | [
"Quoc Phong Nguyen",
"Zhongxiang Dai",
"Bryan Kian Hsiang Low",
"Patrick Jaillet"
] | null | null | Value-at-risk (VaR) is an established measure to assess risks in critical real-world applications with random environmental factors. This paper presents a novel VaR upper confidence bound (V-UCB) algorithm for maximizing the VaR of a black-box objective function with the first no-regret guarantee. To realize this, we f... | [] | null | 735 | 2105.06126 | title_snapshot |
v139/nguyen21c | Cross-model Back-translated Distillation for Unsupervised Machine Translation | https://proceedings.mlr.press/v139/nguyen21c.html | [
"Xuan-Phi Nguyen",
"Shafiq Joty",
"Thanh-Tung Nguyen",
"Kui Wu",
"Ai Ti Aw"
] | null | null | Recent unsupervised machine translation (UMT) systems usually employ three main principles: initialization, language modeling and iterative back-translation, though they may apply them differently. Crucially, iterative back-translation and denoising auto-encoding for language modeling provide data diversity to train th... | [] | null | 736 | 2006.02163 | title_snapshot |
v139/nguyen21d | Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search | https://proceedings.mlr.press/v139/nguyen21d.html | [
"Vu Nguyen",
"Tam Le",
"Makoto Yamada",
"Michael A. Osborne"
] | null | null | Neural architecture search (NAS) automates the design of deep neural networks. One of the main challenges in searching complex and non-continuous architectures is to compare the similarity of networks that the conventional Euclidean metric may fail to capture. Optimal transport (OT) is resilient to such complex structu... | [] | null | 737 | 2006.07593 | title_snapshot |
v139/nguyen21e | Interactive Learning from Activity Description | https://proceedings.mlr.press/v139/nguyen21e.html | [
"Khanh X Nguyen",
"Dipendra Misra",
"Robert Schapire",
"Miroslav Dudik",
"Patrick Shafto"
] | null | null | We present a novel interactive learning protocol that enables training request-fulfilling agents by verbally describing their activities. Unlike imitation learning (IL), our protocol allows the teaching agent to provide feedback in a language that is most appropriate for them. Compared with reward in reinforcement lear... | [] | null | 738 | 2102.07024 | title_snapshot |
v139/nguyen21f | Nonmyopic Multifidelity Acitve Search | https://proceedings.mlr.press/v139/nguyen21f.html | [
"Quan Nguyen",
"Arghavan Modiri",
"Roman Garnett"
] | null | null | Active search is a learning paradigm where we seek to identify as many members of a rare, valuable class as possible given a labeling budget. Previous work on active search has assumed access to a faithful (and expensive) oracle reporting experimental results. However, some settings offer access to cheaper surrogates s... | [] | null | 739 | null | null |
v139/nguyen21g | Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks | https://proceedings.mlr.press/v139/nguyen21g.html | [
"Quynh Nguyen",
"Marco Mondelli",
"Guido F Montufar"
] | null | null | A recent line of work has analyzed the theoretical properties of deep neural networks via the Neural Tangent Kernel (NTK). In particular, the smallest eigenvalue of the NTK has been related to the memorization capacity, the global convergence of gradient descent algorithms and the generalization of deep nets. However, ... | [] | null | 740 | 2012.11654 | title_snapshot |
v139/nguyen21h | Temporal Predictive Coding For Model-Based Planning In Latent Space | https://proceedings.mlr.press/v139/nguyen21h.html | [
"Tung D Nguyen",
"Rui Shu",
"Tuan Pham",
"Hung Bui",
"Stefano Ermon"
] | null | null | High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments. To handle high-dimensional sensory inputs, existing approaches use representation learning to map high-dimensional observations into a lower-dimensional latent space that is mo... | [] | null | 741 | 2106.07156 | title_snapshot |
v139/nguyen21i | Differentially Private Densest Subgraph Detection | https://proceedings.mlr.press/v139/nguyen21i.html | [
"Dung Nguyen",
"Anil Vullikanti"
] | null | null | Densest subgraph detection is a fundamental graph mining problem, with a large number of applications. There has been a lot of work on efficient algorithms for finding the densest subgraph in massive networks. However, in many domains, the network is private, and returning a densest subgraph can reveal information abou... | [] | null | 742 | 2105.13287 | title_snapshot |
v139/ni21a | Data Augmentation for Meta-Learning | https://proceedings.mlr.press/v139/ni21a.html | [
"Renkun Ni",
"Micah Goldblum",
"Amr Sharaf",
"Kezhi Kong",
"Tom Goldstein"
] | null | null | Conventional image classifiers are trained by randomly sampling mini-batches of images. To achieve state-of-the-art performance, practitioners use sophisticated data augmentation schemes to expand the amount of training data available for sampling. In contrast, meta-learning algorithms sample support data, query data, ... | [] | null | 743 | 2010.07092 | title_snapshot |
v139/nichol21a | Improved Denoising Diffusion Probabilistic Models | https://proceedings.mlr.press/v139/nichol21a.html | [
"Alexander Quinn Nichol",
"Prafulla Dhariwal"
] | null | null | Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning varian... | [] | null | 744 | 2102.09672 | title_snapshot |
v139/nietert21a | Smooth $p$-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications | https://proceedings.mlr.press/v139/nietert21a.html | [
"Sloan Nietert",
"Ziv Goldfeld",
"Kengo Kato"
] | null | null | Discrepancy measures between probability distributions, often termed statistical distances, are ubiquitous in probability theory, statistics and machine learning. To combat the curse of dimensionality when estimating these distances from data, recent work has proposed smoothing out local irregularities in the measured ... | [] | null | 745 | 2101.04039 | title_snapshot |
v139/niu21a | AdaXpert: Adapting Neural Architecture for Growing Data | https://proceedings.mlr.press/v139/niu21a.html | [
"Shuaicheng Niu",
"Jiaxiang Wu",
"Guanghui Xu",
"Yifan Zhang",
"Yong Guo",
"Peilin Zhao",
"Peng Wang",
"Mingkui Tan"
] | null | null | In real-world applications, data often come in a growing manner, where the data volume and the number of classes may increase dynamically. This will bring a critical challenge for learning: given the increasing data volume or the number of classes, one has to instantaneously adjust the neural model capacity to obtain p... | [] | null | 746 | 2107.00254 | title_snapshot |
v139/niwa21a | Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction | https://proceedings.mlr.press/v139/niwa21a.html | [
"Kenta Niwa",
"Guoqiang Zhang",
"W. Bastiaan Kleijn",
"Noboru Harada",
"Hiroshi Sawada",
"Akinori Fujino"
] | null | null | A novel asynchronous decentralized optimization method that follows Stochastic Variance Reduction (SVR) is proposed. Average consensus algorithms, such as Decentralized Stochastic Gradient Descent (DSGD), facilitate distributed training of machine learning models. However, the gradient will drift within the local nodes... | [] | null | 747 | null | null |
v139/no21a | WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points | https://proceedings.mlr.press/v139/no21a.html | [
"Albert No",
"Taeho Yoon",
"Kwon Sehyun",
"Ernest K Ryu"
] | null | null | Generative adversarial networks (GAN) are a widely used class of deep generative models, but their minimax training dynamics are not understood very well. In this work, we show that GANs with a 2-layer infinite-width generator and a 2-layer finite-width discriminator trained with stochastic gradient ascent-descent have... | [] | null | 748 | 2102.07541 | title_snapshot |
v139/nock21a | The Impact of Record Linkage on Learning from Feature Partitioned Data | https://proceedings.mlr.press/v139/nock21a.html | [
"Richard Nock",
"Stephen Hardy",
"Wilko Henecka",
"Hamish Ivey-Law",
"Jakub Nabaglo",
"Giorgio Patrini",
"Guillaume Smith",
"Brian Thorne"
] | null | null | There has been recently a significant boost to machine learning with distributed data, in particular with the success of federated learning. A common and very challenging setting is that of vertical or feature partitioned data, when multiple data providers hold different features about common entities. In general, trai... | [] | null | 749 | null | null |
v139/nori21a | Accuracy, Interpretability, and Differential Privacy via Explainable Boosting | https://proceedings.mlr.press/v139/nori21a.html | [
"Harsha Nori",
"Rich Caruana",
"Zhiqi Bu",
"Judy Hanwen Shen",
"Janardhan Kulkarni"
] | null | null | We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent method for training interpretable ML models, yields state-of-the-art accuracy while protecting privacy. Our experiments on multiple classification and regression datasets show that DP-EBM models suffer surprisingly little accurac... | [] | null | 750 | 2106.09680 | title_snapshot |
v139/nota21a | Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods | https://proceedings.mlr.press/v139/nota21a.html | [
"Chris Nota",
"Philip Thomas",
"Bruno C. Da Silva"
] | null | null | Hindsight allows reinforcement learning agents to leverage new observations to make inferences about earlier states and transitions. In this paper, we exploit the idea of hindsight and introduce posterior value functions. Posterior value functions are computed by inferring the posterior distribution over hidden compone... | [] | null | 751 | null | null |
v139/ober21a | Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes | https://proceedings.mlr.press/v139/ober21a.html | [
"Sebastian W Ober",
"Laurence Aitchison"
] | null | null | We consider the optimal approximate posterior over the top-layer weights in a Bayesian neural network for regression, and show that it exhibits strong dependencies on the lower-layer weights. We adapt this result to develop a correlated approximate posterior over the weights at all layers in a Bayesian neural network. ... | [] | null | 752 | 2005.08140 | title_snapshot |
v139/oberst21a | Regularizing towards Causal Invariance: Linear Models with Proxies | https://proceedings.mlr.press/v139/oberst21a.html | [
"Michael Oberst",
"Nikolaj Thams",
"Jonas Peters",
"David Sontag"
] | null | null | We propose a method for learning linear models whose predictive performance is robust to causal interventions on unobserved variables, when noisy proxies of those variables are available. Our approach takes the form of a regularization term that trades off between in-distribution performance and robustness to intervent... | [] | null | 753 | 2103.02477 | title_snapshot |
v139/oh21a | Sparsity-Agnostic Lasso Bandit | https://proceedings.mlr.press/v139/oh21a.html | [
"Min-Hwan Oh",
"Garud Iyengar",
"Assaf Zeevi"
] | null | null | We consider a stochastic contextual bandit problem where the dimension $d$ of the feature vectors is potentially large, however, only a sparse subset of features of cardinality $s_0 \ll d$ affect the reward function. Essentially all existing algorithms for sparse bandits require a priori knowledge of the value of the s... | [] | null | 754 | 2007.08477 | title_snapshot |
v139/oring21a | Autoencoder Image Interpolation by Shaping the Latent Space | https://proceedings.mlr.press/v139/oring21a.html | [
"Alon Oring",
"Zohar Yakhini",
"Yacov Hel-Or"
] | null | null | One of the fascinating properties of deep learning is the ability of the network to reveal the underlying factors characterizing elements in datasets of different types. Autoencoders represent an effective approach for computing these factors. Autoencoders have been studied in the context of enabling interpolation betw... | [] | null | 755 | 2008.01487 | title_snapshot |
v139/oymak21a | Generalization Guarantees for Neural Architecture Search with Train-Validation Split | https://proceedings.mlr.press/v139/oymak21a.html | [
"Samet Oymak",
"Mingchen Li",
"Mahdi Soltanolkotabi"
] | null | null | Neural Architecture Search (NAS) is a popular method for automatically designing optimized deep-learning architectures. NAS methods commonly use bilevel optimization where one optimizes the weights over the training data (lower-level problem) and hyperparameters - such as the architecture - over the validation data (up... | [] | null | 756 | 2104.14132 | title_snapshot |
v139/ozair21a | Vector Quantized Models for Planning | https://proceedings.mlr.press/v139/ozair21a.html | [
"Sherjil Ozair",
"Yazhe Li",
"Ali Razavi",
"Ioannis Antonoglou",
"Aaron Van Den Oord",
"Oriol Vinyals"
] | null | null | Recent developments in the field of model-based RL have proven successful in a range of environments, especially ones where planning is essential. However, such successes have been limited to deterministic fully-observed environments. We present a new approach that handles stochastic and partially-observable environmen... | [] | null | 757 | 2106.04615 | title_snapshot |
v139/ozdenizci21a | Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling | https://proceedings.mlr.press/v139/ozdenizci21a.html | [
"Ozan Özdenizci",
"Robert Legenstein"
] | null | null | Deep neural networks have been shown to be susceptible to adversarial attacks. This lack of adversarial robustness is even more pronounced when models are compressed in order to meet hardware limitations. Hence, if adversarial robustness is an issue, training of sparsely connected networks necessitates considering adve... | [] | null | 758 | null | null |
v139/pal21a | Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics | https://proceedings.mlr.press/v139/pal21a.html | [
"Avik Pal",
"Yingbo Ma",
"Viral Shah",
"Christopher V Rackauckas"
] | null | null | Democratization of machine learning requires architectures that automatically adapt to new problems. Neural Differential Equations (NDEs) have emerged as a popular modeling framework by removing the need for ML practitioners to choose the number of layers in a recurrent model. While we can control the computational cos... | [] | null | 759 | 2105.03918 | title_snapshot |
v139/pal21b | RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting | https://proceedings.mlr.press/v139/pal21b.html | [
"Soumyasundar Pal",
"Liheng Ma",
"Yingxue Zhang",
"Mark Coates"
] | null | null | Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical models often fall short in handling the complexity and high non-linearity present in time-series data. Recent advances in deep learning allow for better modelling of spatial and tempo... | [] | null | 760 | 2106.06064 | title_snapshot |
v139/pan21a | Inference for Network Regression Models with Community Structure | https://proceedings.mlr.press/v139/pan21a.html | [
"Mengjie Pan",
"Tyler Mccormick",
"Bailey Fosdick"
] | null | null | Network regression models, where the outcome comprises the valued edge in a network and the predictors are actor or dyad-level covariates, are used extensively in the social and biological sciences. Valid inference relies on accurately modeling the residual dependencies among the relations. Frequently homogeneity assum... | [] | null | 761 | 2106.04271 | title_snapshot |
v139/pang21a | Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification | https://proceedings.mlr.press/v139/pang21a.html | [
"Bo Pang",
"Ying Nian Wu"
] | null | null | We propose a latent space energy-based prior model for text generation and classification. The model stands on a generator network that generates the text sequence based on a continuous latent vector. The energy term of the prior model couples a continuous latent vector and a symbolic one-hot vector, so that discrete c... | [] | null | 762 | 2108.11556 | title_snapshot |
v139/papini21a | Leveraging Good Representations in Linear Contextual Bandits | https://proceedings.mlr.press/v139/papini21a.html | [
"Matteo Papini",
"Andrea Tirinzoni",
"Marcello Restelli",
"Alessandro Lazaric",
"Matteo Pirotta"
] | null | null | The linear contextual bandit literature is mostly focused on the design of efficient learning algorithms for a given representation. However, a contextual bandit problem may admit multiple linear representations, each one with different characteristics that directly impact the regret of the learning algorithm. In parti... | [] | null | 763 | 2104.03781 | title_snapshot |
v139/park21a | Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data | https://proceedings.mlr.press/v139/park21a.html | [
"Sung Woo Park",
"Junseok Kwon"
] | null | null | We propose a novel Wasserstein distributional normalization method that can classify noisy labeled data accurately. Recently, noisy labels have been successfully handled based on small-loss criteria, but have not been clearly understood from the theoretical point of view. In this paper, we address this problem by adopt... | [] | null | 764 | null | null |
v139/park21b | Unsupervised Representation Learning via Neural Activation Coding | https://proceedings.mlr.press/v139/park21b.html | [
"Yookoon Park",
"Sangho Lee",
"Gunhee Kim",
"David Blei"
] | null | null | We present neural activation coding (NAC) as a novel approach for learning deep representations from unlabeled data for downstream applications. We argue that the deep encoder should maximize its nonlinear expressivity on the data for downstream predictors to take full advantage of its representation power. To this end... | [] | null | 765 | 2112.04014 | title_snapshot |
v139/park21c | Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression | https://proceedings.mlr.press/v139/park21c.html | [
"Junhyung Park",
"Uri Shalit",
"Bernhard Schölkopf",
"Krikamol Muandet"
] | null | null | We propose to analyse the conditional distributional treatment effect (CoDiTE), which, in contrast to the more common conditional average treatment effect (CATE), is designed to encode a treatment’s distributional aspects beyond the mean. We first introduce a formal definition of the CoDiTE associated with a distance f... | [] | null | 766 | 2102.08208 | title_snapshot |
v139/park21d | Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation | https://proceedings.mlr.press/v139/park21d.html | [
"Sung Woo Park",
"Dong Wook Shu",
"Junseok Kwon"
] | null | null | In this paper, we present a novel generative adversarial network (GAN) that can describe Markovian temporal dynamics. To generate stochastic sequential data, we introduce a novel stochastic differential equation-based conditional generator and spatial-temporal constrained discriminator networks. To stabilize the learni... | [] | null | 767 | null | null |
v139/parmentier21a | Optimal Counterfactual Explanations in Tree Ensembles | https://proceedings.mlr.press/v139/parmentier21a.html | [
"Axel Parmentier",
"Thibaut Vidal"
] | null | null | Counterfactual explanations are usually generated through heuristics that are sensitive to the search’s initial conditions. The absence of guarantees of performance and robustness hinders trustworthiness. In this paper, we take a disciplined approach towards counterfactual explanations for tree ensembles. We advocate f... | [] | null | 768 | 2106.06631 | title_snapshot |
v139/patil21a | PHEW : Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data | https://proceedings.mlr.press/v139/patil21a.html | [
"Shreyas Malakarjun Patil",
"Constantine Dovrolis"
] | null | null | Methods that sparsify a network at initialization are important in practice because they greatly improve the efficiency of both learning and inference. Our work is based on a recently proposed decomposition of the Neural Tangent Kernel (NTK) that has decoupled the dynamics of the training process into a data-dependent ... | [] | null | 769 | 2010.11354 | title_snapshot |
v139/paulus21a | CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints | https://proceedings.mlr.press/v139/paulus21a.html | [
"Anselm Paulus",
"Michal Rolinek",
"Vit Musil",
"Brandon Amos",
"Georg Martius"
] | null | null | Bridging logical and algorithmic reasoning with modern machine learning techniques is a fundamental challenge with potentially transformative impact. On the algorithmic side, many NP-hard problems can be expressed as integer programs, in which the constraints play the role of their ’combinatorial specification’. In thi... | [] | null | 770 | 2105.02343 | title_snapshot |
v139/peer21a | Ensemble Bootstrapping for Q-Learning | https://proceedings.mlr.press/v139/peer21a.html | [
"Oren Peer",
"Chen Tessler",
"Nadav Merlis",
"Ron Meir"
] | null | null | Q-learning (QL), a common reinforcement learning algorithm, suffers from over-estimation bias due to the maximization term in the optimal Bellman operator. This bias may lead to sub-optimal behavior. Double-Q-learning tackles this issue by utilizing two estimators, yet results in an under-estimation bias. Similar to ov... | [] | null | 771 | 2103.00445 | title_snapshot |
v139/peng21a | Homomorphic Sensing: Sparsity and Noise | https://proceedings.mlr.press/v139/peng21a.html | [
"Liangzu Peng",
"Boshi Wang",
"Manolis Tsakiris"
] | null | null | \emph{Unlabeled sensing} is a recent problem encompassing many data science and engineering applications and typically formulated as solving linear equations whose right-hand side vector has undergone an unknown permutation. It was generalized to the \emph{homomorphic sensing} problem by replacing the unknown permutati... | [] | null | 772 | null | null |
v139/peng21b | How could Neural Networks understand Programs? | https://proceedings.mlr.press/v139/peng21b.html | [
"Dinglan Peng",
"Shuxin Zheng",
"Yatao Li",
"Guolin Ke",
"Di He",
"Tie-Yan Liu"
] | null | null | Semantic understanding of programs is a fundamental problem for programming language processing (PLP). Recent works that learn representations of code based on pre-training techniques in NLP have pushed the frontiers in this direction. However, the semantics of PL and NL have essential differences. These being ignored,... | [] | null | 773 | 2105.04297 | title_snapshot |
v139/pentyala21a | Privacy-Preserving Video Classification with Convolutional Neural Networks | https://proceedings.mlr.press/v139/pentyala21a.html | [
"Sikha Pentyala",
"Rafael Dowsley",
"Martine De Cock"
] | null | null | Many video classification applications require access to personal data, thereby posing an invasive security risk to the users’ privacy. We propose a privacy-preserving implementation of single-frame method based video classification with convolutional neural networks that allows a party to infer a label from a video wi... | [] | null | 774 | 2102.03513 | title_snapshot |
v139/perez21a | Rissanen Data Analysis: Examining Dataset Characteristics via Description Length | https://proceedings.mlr.press/v139/perez21a.html | [
"Ethan Perez",
"Douwe Kiela",
"Kyunghyun Cho"
] | null | null | We introduce a method to determine if a certain capability helps to achieve an accurate model of given data. We view labels as being generated from the inputs by a program composed of subroutines with different capabilities, and we posit that a subroutine is useful if and only if the minimal program that invokes it is ... | [] | null | 775 | 2103.03872 | title_snapshot |
v139/perez-nieves21a | Modelling Behavioural Diversity for Learning in Open-Ended Games | https://proceedings.mlr.press/v139/perez-nieves21a.html | [
"Nicolas Perez-Nieves",
"Yaodong Yang",
"Oliver Slumbers",
"David H Mguni",
"Ying Wen",
"Jun Wang"
] | null | null | Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e.g., Rock-Paper-Scissors). Yet, there is a lack of rigorous treatment for defining diversity and constructing diversity-aware learning dynamics. In this work, we o... | [] | null | 776 | 2103.07927 | title_snapshot |
v139/perolat21a | From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization | https://proceedings.mlr.press/v139/perolat21a.html | [
"Julien Perolat",
"Remi Munos",
"Jean-Baptiste Lespiau",
"Shayegan Omidshafiei",
"Mark Rowland",
"Pedro Ortega",
"Neil Burch",
"Thomas Anthony",
"David Balduzzi",
"Bart De Vylder",
"Georgios Piliouras",
"Marc Lanctot",
"Karl Tuyls"
] | null | null | In this paper we investigate the Follow the Regularized Leader dynamics in sequential imperfect information games (IIG). We generalize existing results of Poincar{é} recurrence from normal-form games to zero-sum two-player imperfect information games and other sequential game settings. We then investigate how adapting ... | [] | null | 777 | 2002.08456 | title_snapshot |
v139/pervez21a | Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders | https://proceedings.mlr.press/v139/pervez21a.html | [
"Adeel Pervez",
"Efstratios Gavves"
] | null | null | Variational autoencoders with deep hierarchies of stochastic layers have been known to suffer from the problem of posterior collapse, where the top layers fall back to the prior and become independent of input. We suggest that the hierarchical VAE objective explicitly includes the variance of the function parameterizin... | [] | null | 778 | null | null |
v139/petersen21a | Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision | https://proceedings.mlr.press/v139/petersen21a.html | [
"Felix Petersen",
"Christian Borgelt",
"Hilde Kuehne",
"Oliver Deussen"
] | null | null | Sorting and ranking supervision is a method for training neural networks end-to-end based on ordering constraints. That is, the ground truth order of sets of samples is known, while their absolute values remain unsupervised. For that, we propose differentiable sorting networks by relaxing their pairwise conditional swa... | [] | null | 779 | 2105.04019 | title_snapshot |
v139/petrenko21a | Megaverse: Simulating Embodied Agents at One Million Experiences per Second | https://proceedings.mlr.press/v139/petrenko21a.html | [
"Aleksei Petrenko",
"Erik Wijmans",
"Brennan Shacklett",
"Vladlen Koltun"
] | null | null | We present Megaverse, a new 3D simulation platform for reinforcement learning and embodied AI research. The efficient design of our engine enables physics-based simulation with high-dimensional egocentric observations at more than 1,000,000 actions per second on a single 8-GPU node. Megaverse is up to 70x faster than D... | [] | null | 780 | 2107.08170 | title_snapshot |
v139/phan21a | Towards Practical Mean Bounds for Small Samples | https://proceedings.mlr.press/v139/phan21a.html | [
"My Phan",
"Philip Thomas",
"Erik Learned-Miller"
] | null | null | Historically, to bound the mean for small sample sizes, practitioners have had to choose between using methods with unrealistic assumptions about the unknown distribution (e.g., Gaussianity) and methods like Hoeffding’s inequality that use weaker assumptions but produce much looser (wider) intervals. In 1969, \citet{An... | [] | null | 781 | 2106.03163 | title_snapshot |
v139/plassier21a | DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs | https://proceedings.mlr.press/v139/plassier21a.html | [
"Vincent Plassier",
"Maxime Vono",
"Alain Durmus",
"Eric Moulines"
] | null | null | Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning. A workhorse class of methods to achieve this task are Markov chain Monte Carlo (MCMC) algorithms and their design to handle distributed datasets has been the subject of many works. However, existing ... | [] | null | 782 | 2106.06300 | title_snapshot |
v139/poklukar21a | GeomCA: Geometric Evaluation of Data Representations | https://proceedings.mlr.press/v139/poklukar21a.html | [
"Petra Poklukar",
"Anastasiia Varava",
"Danica Kragic"
] | null | null | Evaluating the quality of learned representations without relying on a downstream task remains one of the challenges in representation learning. In this work, we present Geometric Component Analysis (GeomCA) algorithm that evaluates representation spaces based on their geometric and topological properties. GeomCA can b... | [] | null | 783 | 2105.12486 | title_snapshot |
v139/popov21a | Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech | https://proceedings.mlr.press/v139/popov21a.html | [
"Vadim Popov",
"Ivan Vovk",
"Vladimir Gogoryan",
"Tasnima Sadekova",
"Mikhail Kudinov"
] | null | null | Recently, denoising diffusion probabilistic models and generative score matching have shown high potential in modelling complex data distributions while stochastic calculus has provided a unified point of view on these techniques allowing for flexible inference schemes. In this paper we introduce Grad-TTS, a novel text... | [] | null | 784 | 2105.06337 | title_snapshot |
v139/potapczynski21a | Bias-Free Scalable Gaussian Processes via Randomized Truncations | https://proceedings.mlr.press/v139/potapczynski21a.html | [
"Andres Potapczynski",
"Luhuan Wu",
"Dan Biderman",
"Geoff Pleiss",
"John P Cunningham"
] | null | null | Scalable Gaussian Process methods are computationally attractive, yet introduce modeling biases that require rigorous study. This paper analyzes two common techniques: early truncated conjugate gradients (CG) and random Fourier features (RFF). We find that both methods introduce a systematic bias on the learned hyperpa... | [] | null | 785 | 2102.06695 | title_snapshot |
v139/price21a | Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset | https://proceedings.mlr.press/v139/price21a.html | [
"Ilan Price",
"Jared Tanner"
] | null | null | That neural networks may be pruned to high sparsities and retain high accuracy is well established. Recent research efforts focus on pruning immediately after initialization so as to allow the computational savings afforded by sparsity to extend to the training process. In this work, we introduce a new ‘DCT plus Sparse... | [] | null | 786 | 2102.07655 | title_snapshot |
v139/qi21a | BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining | https://proceedings.mlr.press/v139/qi21a.html | [
"Weizhen Qi",
"Yeyun Gong",
"Jian Jiao",
"Yu Yan",
"Weizhu Chen",
"Dayiheng Liu",
"Kewen Tang",
"Houqiang Li",
"Jiusheng Chen",
"Ruofei Zhang",
"Ming Zhou",
"Nan Duan"
] | null | null | In this paper, we propose BANG, a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation. AR and NAR generation can be uniformly regarded as to what extent previous tokens can be attended, and BANG bridges AR and NAR generation through designing a novel model structu... | [] | null | 787 | 2012.15525 | title_snapshot |
v139/qian21a | A Probabilistic Approach to Neural Network Pruning | https://proceedings.mlr.press/v139/qian21a.html | [
"Xin Qian",
"Diego Klabjan"
] | null | null | Neural network pruning techniques reduce the number of parameters without compromising predicting ability of a network. Many algorithms have been developed for pruning both over-parameterized fully-connected networks (FCN) and convolutional neural networks (CNN), but analytical studies of capabilities and compression r... | [] | null | 788 | 2105.10065 | title_snapshot |
v139/qian21b | Global Prosody Style Transfer Without Text Transcriptions | https://proceedings.mlr.press/v139/qian21b.html | [
"Kaizhi Qian",
"Yang Zhang",
"Shiyu Chang",
"Jinjun Xiong",
"Chuang Gan",
"David Cox",
"Mark Hasegawa-Johnson"
] | null | null | Prosody plays an important role in characterizing the style of a speaker or an emotion, but most non-parallel voice or emotion style transfer algorithms do not convert any prosody information. Two major components of prosody are pitch and rhythm. Disentangling the prosody information, particularly the rhythm component,... | [] | null | 789 | 2106.08519 | title_judge |
v139/qiao21a | Efficient Differentiable Simulation of Articulated Bodies | https://proceedings.mlr.press/v139/qiao21a.html | [
"Yi-Ling Qiao",
"Junbang Liang",
"Vladlen Koltun",
"Ming C Lin"
] | null | null | We present a method for efficient differentiable simulation of articulated bodies. This enables integration of articulated body dynamics into deep learning frameworks, and gradient-based optimization of neural networks that operate on articulated bodies. We derive the gradients of the contact solver using spatial algeb... | [] | null | 790 | 2109.07719 | title_snapshot |
v139/qiao21b | Oneshot Differentially Private Top-k Selection | https://proceedings.mlr.press/v139/qiao21b.html | [
"Gang Qiao",
"Weijie Su",
"Li Zhang"
] | null | null | Being able to efficiently and accurately select the top-$k$ elements with differential privacy is an integral component of various private data analysis tasks. In this paper, we present the oneshot Laplace mechanism, which generalizes the well-known Report Noisy Max \cite{dwork2014algorithmic} mechanism to reporting no... | [] | null | 791 | 2105.08233 | title_snapshot |
v139/qin21a | Density Constrained Reinforcement Learning | https://proceedings.mlr.press/v139/qin21a.html | [
"Zengyi Qin",
"Yuxiao Chen",
"Chuchu Fan"
] | null | null | We study constrained reinforcement learning (CRL) from a novel perspective by setting constraints directly on state density functions, rather than the value functions considered by previous works. State density has a clear physical and mathematical interpretation, and is able to express a wide variety of constraints su... | [] | null | 792 | 2106.12764 | title_snapshot |
v139/qin21b | Budgeted Heterogeneous Treatment Effect Estimation | https://proceedings.mlr.press/v139/qin21b.html | [
"Tian Qin",
"Tian-Zuo Wang",
"Zhi-Hua Zhou"
] | null | null | Heterogeneous treatment effect (HTE) estimation is receiving increasing interest due to its important applications in fields such as healthcare, economics, and education. Current HTE estimation methods generally assume the existence of abundant observational data, though the acquisition of such data can be costly. In s... | [] | null | 793 | null | null |
v139/qiu21a | Neural Transformation Learning for Deep Anomaly Detection Beyond Images | https://proceedings.mlr.press/v139/qiu21a.html | [
"Chen Qiu",
"Timo Pfrommer",
"Marius Kloft",
"Stephan Mandt",
"Maja Rudolph"
] | null | null | Data transformations (e.g. rotations, reflections, and cropping) play an important role in self-supervised learning. Typically, images are transformed into different views, and neural networks trained on tasks involving these views produce useful feature representations for downstream tasks, including anomaly detection... | [] | null | 794 | 2103.16440 | title_snapshot |
v139/qiu21b | Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions | https://proceedings.mlr.press/v139/qiu21b.html | [
"Shuang Qiu",
"Xiaohan Wei",
"Jieping Ye",
"Zhaoran Wang",
"Zhuoran Yang"
] | null | null | While single-agent policy optimization in a fixed environment has attracted a lot of research attention recently in the reinforcement learning community, much less is known theoretically when there are multiple agents playing in a potentially competitive environment. We take steps forward by proposing and analyzing new... | [] | null | 795 | 2207.12463 | title_snapshot |
v139/qiu21c | Optimization Planning for 3D ConvNets | https://proceedings.mlr.press/v139/qiu21c.html | [
"Zhaofan Qiu",
"Ting Yao",
"Chong-Wah Ngo",
"Tao Mei"
] | null | null | It is not trivial to optimally learn a 3D Convolutional Neural Networks (3D ConvNets) due to high complexity and various options of the training scheme. The most common hand-tuning process starts from learning 3D ConvNets using short video clips and then is followed by learning long-term temporal dependency using lengt... | [] | null | 796 | 2201.04021 | title_snapshot |
v139/qiu21d | On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game | https://proceedings.mlr.press/v139/qiu21d.html | [
"Shuang Qiu",
"Jieping Ye",
"Zhaoran Wang",
"Zhuoran Yang"
] | null | null | To achieve sample efficiency in reinforcement learning (RL), it necessitates to efficiently explore the underlying environment. Under the offline setting, addressing the exploration challenge lies in collecting an offline dataset with sufficient coverage. Motivated by such a challenge, we study the reward-free RL probl... | [] | null | 797 | 2110.09771 | title_snapshot |
v139/radford21a | Learning Transferable Visual Models From Natural Language Supervision | https://proceedings.mlr.press/v139/radford21a.html | [
"Alec Radford",
"Jong Wook Kim",
"Chris Hallacy",
"Aditya Ramesh",
"Gabriel Goh",
"Sandhini Agarwal",
"Girish Sastry",
"Amanda Askell",
"Pamela Mishkin",
"Jack Clark",
"Gretchen Krueger",
"Ilya Sutskever"
] | null | null | State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promisi... | [] | null | 798 | 2103.00020 | title_snapshot |
v139/raghuram21a | A General Framework For Detecting Anomalous Inputs to DNN Classifiers | https://proceedings.mlr.press/v139/raghuram21a.html | [
"Jayaram Raghuram",
"Varun Chandrasekaran",
"Somesh Jha",
"Suman Banerjee"
] | null | null | Detecting anomalous inputs, such as adversarial and out-of-distribution (OOD) inputs, is critical for classifiers (including deep neural networks or DNNs) deployed in real-world applications. While prior works have proposed various methods to detect such anomalous samples using information from the internal layer repre... | [] | null | 799 | 2007.15147 | title_snapshot |
v139/rahman21a | Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning | https://proceedings.mlr.press/v139/rahman21a.html | [
"Muhammad A Rahman",
"Niklas Hopner",
"Filippos Christianos",
"Stefano V Albrecht"
] | null | null | Ad hoc teamwork is the challenging problem of designing an autonomous agent which can adapt quickly to collaborate with teammates without prior coordination mechanisms, including joint training. Prior work in this area has focused on closed teams in which the number of agents is fixed. In this work, we consider open te... | [] | null | 800 | 2006.10412 | title_snapshot |
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