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v139/sukhbaatar21a | Not All Memories are Created Equal: Learning to Forget by Expiring | https://proceedings.mlr.press/v139/sukhbaatar21a.html | [
"Sainbayar Sukhbaatar",
"Da Ju",
"Spencer Poff",
"Stephen Roller",
"Arthur Szlam",
"Jason Weston",
"Angela Fan"
] | null | null | Attention mechanisms have shown promising results in sequence modeling tasks that require long-term memory. Recent work investigated mechanisms to reduce the computational cost of preserving and storing memories. However, not all content in the past is equally important to remember. We propose Expire-Span, a method tha... | [] | null | 901 | 2105.06548 | title_snapshot |
v139/summers21a | Nondeterminism and Instability in Neural Network Optimization | https://proceedings.mlr.press/v139/summers21a.html | [
"Cecilia Summers",
"Michael J. Dinneen"
] | null | null | Nondeterminism in neural network optimization produces uncertainty in performance, making small improvements difficult to discern from run-to-run variability. While uncertainty can be reduced by training multiple model copies, doing so is time-consuming, costly, and harms reproducibility. In this work, we establish an ... | [] | null | 902 | 2103.04514 | title_snapshot |
v139/sun21a | AutoSampling: Search for Effective Data Sampling Schedules | https://proceedings.mlr.press/v139/sun21a.html | [
"Ming Sun",
"Haoxuan Dou",
"Baopu Li",
"Junjie Yan",
"Wanli Ouyang",
"Lei Cui"
] | null | null | Data sampling acts as a pivotal role in training deep learning models. However, an effective sampling schedule is difficult to learn due to its inherent high-dimension as a hyper-parameter. In this paper, we propose an AutoSampling method to automatically learn sampling schedules for model training, which consists of t... | [] | null | 903 | 2105.13695 | title_snapshot |
v139/sun21b | What Makes for End-to-End Object Detection? | https://proceedings.mlr.press/v139/sun21b.html | [
"Peize Sun",
"Yi Jiang",
"Enze Xie",
"Wenqi Shao",
"Zehuan Yuan",
"Changhu Wang",
"Ping Luo"
] | null | null | Object detection has recently achieved a breakthrough for removing the last one non-differentiable component in the pipeline, Non-Maximum Suppression (NMS), and building up an end-to-end system. However, what makes for its one-to-one prediction has not been well understood. In this paper, we first point out that one-to... | [] | null | 904 | 2012.05780 | title_snapshot |
v139/sun21c | DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning | https://proceedings.mlr.press/v139/sun21c.html | [
"Wei-Fang Sun",
"Cheng-Kuang Lee",
"Chun-Yi Lee"
] | null | null | In fully cooperative multi-agent reinforcement learning (MARL) settings, the environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of the other agents. To address the above issues, we integrate distributional RL and value function factorization methods ... | [] | null | 905 | 2102.07936 | title_snapshot |
v139/sun21d | Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition | https://proceedings.mlr.press/v139/sun21d.html | [
"Shengyang Sun",
"Jiaxin Shi",
"Andrew Gordon Gordon Wilson",
"Roger B Grosse"
] | null | null | We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability. We propose the harmonic kernel decomposition (HKD), which uses Fourier series to decompose a kernel as a sum of orthogonal kernels. Our variational approximation exp... | [] | null | 906 | 2106.05992 | title_snapshot |
v139/sun21e | Reasoning Over Virtual Knowledge Bases With Open Predicate Relations | https://proceedings.mlr.press/v139/sun21e.html | [
"Haitian Sun",
"Patrick Verga",
"Bhuwan Dhingra",
"Ruslan Salakhutdinov",
"William W Cohen"
] | null | null | We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text. Large Knowledge Bases (KBs) are indispensable for a wide-range of industry applications such as question answering and recommendation. Typically, KBs encode world knowledge in a structured, read... | [] | null | 907 | 2102.07043 | title_snapshot |
v139/sundaram21a | PAC-Learning for Strategic Classification | https://proceedings.mlr.press/v139/sundaram21a.html | [
"Ravi Sundaram",
"Anil Vullikanti",
"Haifeng Xu",
"Fan Yao"
] | null | null | The study of strategic or adversarial manipulation of testing data to fool a classifier has attracted much recent attention. Most previous works have focused on two extreme situations where any testing data point either is completely adversarial or always equally prefers the positive label. In this paper, we generalize... | [] | null | 908 | 2012.03310 | title_snapshot |
v139/suttle21a | Reinforcement Learning for Cost-Aware Markov Decision Processes | https://proceedings.mlr.press/v139/suttle21a.html | [
"Wesley Suttle",
"Kaiqing Zhang",
"Zhuoran Yang",
"Ji Liu",
"David Kraemer"
] | null | null | Ratio maximization has applications in areas as diverse as finance, reward shaping for reinforcement learning (RL), and the development of safe artificial intelligence, yet there has been very little exploration of RL algorithms for ratio maximization. This paper addresses this deficiency by introducing two new, model-... | [] | null | 909 | null | null |
v139/suya21a | Model-Targeted Poisoning Attacks with Provable Convergence | https://proceedings.mlr.press/v139/suya21a.html | [
"Fnu Suya",
"Saeed Mahloujifar",
"Anshuman Suri",
"David Evans",
"Yuan Tian"
] | null | null | In a poisoning attack, an adversary who controls a small fraction of the training data attempts to select that data, so a model is induced that misbehaves in a particular way. We consider poisoning attacks against convex machine learning models and propose an efficient poisoning attack designed to induce a model specif... | [] | null | 910 | 2006.16469 | title_snapshot |
v139/suzuki21a | Generalization Error Bound for Hyperbolic Ordinal Embedding | https://proceedings.mlr.press/v139/suzuki21a.html | [
"Atsushi Suzuki",
"Atsushi Nitanda",
"Jing Wang",
"Linchuan Xu",
"Kenji Yamanishi",
"Marc Cavazza"
] | null | null | Hyperbolic ordinal embedding (HOE) represents entities as points in hyperbolic space so that they agree as well as possible with given constraints in the form of entity $i$ is more similar to entity $j$ than to entity $k$. It has been experimentally shown that HOE can obtain representations of hierarchical data such as... | [] | null | 911 | 2105.10475 | title_snapshot |
v139/swamy21a | Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap | https://proceedings.mlr.press/v139/swamy21a.html | [
"Gokul Swamy",
"Sanjiban Choudhury",
"J. Andrew Bagnell",
"Steven Wu"
] | null | null | We provide a unifying view of a large family of previous imitation learning algorithms through the lens of moment matching. At its core, our classification scheme is based on whether the learner attempts to match (1) reward or (2) action-value moments of the expert’s behavior, with each option leading to differing algo... | [] | null | 912 | 2103.03236 | title_snapshot |
v139/syed21a | Parallel tempering on optimized paths | https://proceedings.mlr.press/v139/syed21a.html | [
"Saifuddin Syed",
"Vittorio Romaniello",
"Trevor Campbell",
"Alexandre Bouchard-Cote"
] | null | null | Parallel tempering (PT) is a class of Markov chain Monte Carlo algorithms that constructs a path of distributions annealing between a tractable reference and an intractable target, and then interchanges states along the path to improve mixing in the target. The performance of PT depends on how quickly a sample from the... | [] | null | 913 | 2102.07720 | title_snapshot |
v139/taghanaki21a | Robust Representation Learning via Perceptual Similarity Metrics | https://proceedings.mlr.press/v139/taghanaki21a.html | [
"Saeid A Taghanaki",
"Kristy Choi",
"Amir Hosein Khasahmadi",
"Anirudh Goyal"
] | null | null | A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream classification task, without overfitting to spurious input features. Extracting such task-relevant predictive information becomes particularly difficult for noisy and high-dimensiona... | [] | null | 914 | 2106.06620 | title_snapshot |
v139/tahmasbi21a | DriftSurf: Stable-State / Reactive-State Learning under Concept Drift | https://proceedings.mlr.press/v139/tahmasbi21a.html | [
"Ashraf Tahmasbi",
"Ellango Jothimurugesan",
"Srikanta Tirthapura",
"Phillip B Gibbons"
] | null | null | When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previously-learned model inaccurate and require training a new model. We present an adaptive learning algorithm that extends previous drift-detection-based methods by incorporating drift detection into a broa... | [] | null | 915 | null | null |
v139/tai21a | Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training | https://proceedings.mlr.press/v139/tai21a.html | [
"Kai Sheng Tai",
"Peter D Bailis",
"Gregory Valiant"
] | null | null | Self-training is a standard approach to semi-supervised learning where the learner’s own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an ... | [] | null | 916 | 2102.08622 | title_snapshot |
v139/takemori21a | Approximation Theory Based Methods for RKHS Bandits | https://proceedings.mlr.press/v139/takemori21a.html | [
"Sho Takemori",
"Masahiro Sato"
] | null | null | The RKHS bandit problem (also called kernelized multi-armed bandit problem) is an online optimization problem of non-linear functions with noisy feedback. Although the problem has been extensively studied, there are unsatisfactory results for some problems compared to the well-studied linear bandit case. Specifically, ... | [] | null | 917 | 2010.12167 | title_snapshot |
v139/takezawa21a | Supervised Tree-Wasserstein Distance | https://proceedings.mlr.press/v139/takezawa21a.html | [
"Yuki Takezawa",
"Ryoma Sato",
"Makoto Yamada"
] | null | null | To measure the similarity of documents, the Wasserstein distance is a powerful tool, but it requires a high computational cost. Recently, for fast computation of the Wasserstein distance, methods for approximating the Wasserstein distance using a tree metric have been proposed. These tree-based methods allow fast compa... | [] | null | 918 | 2101.11520 | title_snapshot |
v139/tan21a | EfficientNetV2: Smaller Models and Faster Training | https://proceedings.mlr.press/v139/tan21a.html | [
"Mingxing Tan",
"Quoc Le"
] | null | null | This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop these models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and parameter effi... | [] | null | 919 | 2104.00298 | title_snapshot |
v139/tandon21a | SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples | https://proceedings.mlr.press/v139/tandon21a.html | [
"Anshoo Tandon",
"Aldric Han",
"Vincent Tan"
] | null | null | We consider learning Ising tree models when the observations from the nodes are corrupted by independent but non-identically distributed noise with unknown statistics. Katiyar et al. (2020) showed that although the exact tree structure cannot be recovered, one can recover a partial tree structure; that is, a structure ... | [] | null | 920 | 2101.08917 | title_snapshot |
v139/tang21a | 1-bit Adam: Communication Efficient Large-Scale Training with Adam’s Convergence Speed | https://proceedings.mlr.press/v139/tang21a.html | [
"Hanlin Tang",
"Shaoduo Gan",
"Ammar Ahmad Awan",
"Samyam Rajbhandari",
"Conglong Li",
"Xiangru Lian",
"Ji Liu",
"Ce Zhang",
"Yuxiong He"
] | null | null | Scalable training of large models (like BERT and GPT-3) requires careful optimization rooted in model design, architecture, and system capabilities. From a system standpoint, communication has become a major bottleneck, especially on commodity systems with standard TCP interconnects that offer limited network bandwidth... | [] | null | 921 | 2102.02888 | title_snapshot |
v139/tang21b | Taylor Expansion of Discount Factors | https://proceedings.mlr.press/v139/tang21b.html | [
"Yunhao Tang",
"Mark Rowland",
"Remi Munos",
"Michal Valko"
] | null | null | In practical reinforcement learning (RL), the discount factor used for estimating value functions often differs from that used for defining the evaluation objective. In this work, we study the effect that this discrepancy of discount factors has during learning, and discover a family of objectives that interpolate valu... | [] | null | 922 | 2106.06170 | title_snapshot |
v139/tao21a | REPAINT: Knowledge Transfer in Deep Reinforcement Learning | https://proceedings.mlr.press/v139/tao21a.html | [
"Yunzhe Tao",
"Sahika Genc",
"Jonathan Chung",
"Tao Sun",
"Sunil Mallya"
] | null | null | Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low. This work proposes REPresentation And INstance Transfer (REPAINT) algorithm for knowled... | [] | null | 923 | 2011.11827 | title_snapshot |
v139/tarmoun21a | Understanding the Dynamics of Gradient Flow in Overparameterized Linear models | https://proceedings.mlr.press/v139/tarmoun21a.html | [
"Salma Tarmoun",
"Guilherme Franca",
"Benjamin D Haeffele",
"Rene Vidal"
] | null | null | We provide a detailed analysis of the dynamics ofthe gradient flow in overparameterized two-layerlinear models. A particularly interesting featureof this model is that its nonlinear dynamics can beexactly solved as a consequence of a large num-ber of conservation laws that constrain the systemto follow particular traje... | [] | null | 924 | null | null |
v139/taskesen21a | Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts | https://proceedings.mlr.press/v139/taskesen21a.html | [
"Bahar Taskesen",
"Man-Chung Yue",
"Jose Blanchet",
"Daniel Kuhn",
"Viet Anh Nguyen"
] | null | null | Least squares estimators, when trained on few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additional labeled training samples from a source distribution that is close to the target distribution. Given available data, we investigate novel ... | [] | null | 925 | 2106.00322 | title_snapshot |
v139/tavares21a | A Language for Counterfactual Generative Models | https://proceedings.mlr.press/v139/tavares21a.html | [
"Zenna Tavares",
"James Koppel",
"Xin Zhang",
"Ria Das",
"Armando Solar-Lezama"
] | null | null | We present Omega, a probabilistic programming language with support for counterfactual inference. Counterfactual inference means to observe some fact in the present, and infer what would have happened had some past intervention been taken, e.g. “given that medication was not effective at dose x, what is the probability... | [] | null | 926 | null | null |
v139/tay21a | Synthesizer: Rethinking Self-Attention for Transformer Models | https://proceedings.mlr.press/v139/tay21a.html | [
"Yi Tay",
"Dara Bahri",
"Donald Metzler",
"Da-Cheng Juan",
"Zhe Zhao",
"Che Zheng"
] | null | null | The dot product self-attention is known to be central and indispensable to state-of-the-art Transformer models. But is it really required? This paper investigates the true importance and contribution of the dot product-based self-attention mechanism on the performance of Transformer models. Via extensive experiments, w... | [] | null | 927 | 2005.00743 | title_judge |
v139/tay21b | OmniNet: Omnidirectional Representations from Transformers | https://proceedings.mlr.press/v139/tay21b.html | [
"Yi Tay",
"Mostafa Dehghani",
"Vamsi Aribandi",
"Jai Gupta",
"Philip M Pham",
"Zhen Qin",
"Dara Bahri",
"Da-Cheng Juan",
"Donald Metzler"
] | null | null | This paper proposes Omnidirectional Representations from Transformers (OMNINET). In OmniNet, instead of maintaining a strictly horizon-tal receptive field, each token is allowed to attend to all tokens in the entire network. This process can also be interpreted as a form of extreme or intensive attention mechanism that... | [] | null | 928 | 2103.01075 | title_snapshot |
v139/teng21a | T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP | https://proceedings.mlr.press/v139/teng21a.html | [
"Jiaye Teng",
"Zeren Tan",
"Yang Yuan"
] | null | null | It is challenging to deal with censored data, where we only have access to the incomplete information of survival time instead of its exact value. Fortunately, under linear predictor assumption, people can obtain guaranteed coverage for the confidence interval of survival time using methods like Cox Regression. However... | [] | null | 929 | 2103.04556 | title_snapshot |
v139/terjek21a | Moreau-Yosida $f$-divergences | https://proceedings.mlr.press/v139/terjek21a.html | [
"Dávid Terjék"
] | null | null | Variational representations of $f$-divergences are central to many machine learning algorithms, with Lipschitz constrained variants recently gaining attention. Inspired by this, we define the Moreau-Yosida approximation of $f$-divergences with respect to the Wasserstein-$1$ metric. The corresponding variational formula... | [] | null | 930 | 2102.13416 | title_snapshot |
v139/teterwak21a | Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers | https://proceedings.mlr.press/v139/teterwak21a.html | [
"Piotr Teterwak",
"Chiyuan Zhang",
"Dilip Krishnan",
"Michael C Mozer"
] | null | null | A discriminatively trained neural net classifier can fit the training data perfectly if all information about its input other than class membership has been discarded prior to the output layer. Surprisingly, past research has discovered that some extraneous visual detail remains in the unnormalized logits. This finding... | [] | null | 931 | 2103.07470 | title_snapshot |
v139/thananjeyan21a | Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism | https://proceedings.mlr.press/v139/thananjeyan21a.html | [
"Brijen Thananjeyan",
"Kirthevasan Kandasamy",
"Ion Stoica",
"Michael Jordan",
"Ken Goldberg",
"Joseph Gonzalez"
] | null | null | We study exploration in stochastic multi-armed bandits when we have access to a divisible resource that can be allocated in varying amounts to arm pulls. We focus in particular on the allocation of distributed computing resources, where we may obtain results faster by allocating more resources per pull, but might have ... | [] | null | 932 | 2011.00330 | title_snapshot |
v139/thin21a | Monte Carlo Variational Auto-Encoders | https://proceedings.mlr.press/v139/thin21a.html | [
"Achille Thin",
"Nikita Kotelevskii",
"Arnaud Doucet",
"Alain Durmus",
"Eric Moulines",
"Maxim Panov"
] | null | null | Variational auto-encoders (VAE) are popular deep latent variable models which are trained by maximizing an Evidence Lower Bound (ELBO). To obtain tighter ELBO and hence better variational approximations, it has been proposed to use importance sampling to get a lower variance estimate of the evidence. However, importanc... | [] | null | 933 | 2106.15921 | title_snapshot |
v139/thygesen21a | Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model | https://proceedings.mlr.press/v139/thygesen21a.html | [
"Christian B Thygesen",
"Christian Skjødt Steenmans",
"Ahmad Salim Al-Sibahi",
"Lys Sanz Moreta",
"Anders Bundgård Sørensen",
"Thomas Hamelryck"
] | null | null | Fragment libraries are often used in protein structure prediction, simulation and design as a means to significantly reduce the vast conformational search space. Current state-of-the-art methods for fragment library generation do not properly account for aleatory and epistemic uncertainty, respectively due to the dynam... | [] | null | 934 | null | null |
v139/tian21a | Understanding self-supervised learning dynamics without contrastive pairs | https://proceedings.mlr.press/v139/tian21a.html | [
"Yuandong Tian",
"Xinlei Chen",
"Surya Ganguli"
] | null | null | While contrastive approaches of self-supervised learning (SSL) learn representations by minimizing the distance between two augmented views of the same data point (positive pairs) and maximizing views from different data points (negative pairs), recent \emph{non-contrastive} SSL (e.g., BYOL and SimSiam) show remarkable... | [] | null | 935 | 2102.06810 | title_snapshot |
v139/tian21b | Online Learning in Unknown Markov Games | https://proceedings.mlr.press/v139/tian21b.html | [
"Yi Tian",
"Yuanhao Wang",
"Tiancheng Yu",
"Suvrit Sra"
] | null | null | We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable. We show that in this challenging setting, achieving sublinear regret against the best response in hindsight is statistically hard. We then consider ... | [] | null | 936 | 2010.15020 | title_snapshot |
v139/tiao21a | BORE: Bayesian Optimization by Density-Ratio Estimation | https://proceedings.mlr.press/v139/tiao21a.html | [
"Louis C Tiao",
"Aaron Klein",
"Matthias W Seeger",
"Edwin V. Bonilla",
"Cedric Archambeau",
"Fabio Ramos"
] | null | null | Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are computed from the posterior predictive of a probabilistic surrogate model. Prevalent among... | [] | null | 937 | 2102.09009 | title_snapshot |
v139/tillinghast21a | Nonparametric Decomposition of Sparse Tensors | https://proceedings.mlr.press/v139/tillinghast21a.html | [
"Conor Tillinghast",
"Shandian Zhe"
] | null | null | Tensor decomposition is a powerful framework for multiway data analysis. Despite the success of existing approaches, they ignore the sparse nature of the tensor data in many real-world applications, explicitly or implicitly assuming dense tensors. To address this model misspecification and to exploit the sparse tensor ... | [] | null | 938 | null | null |
v139/tolpin21a | Probabilistic Programs with Stochastic Conditioning | https://proceedings.mlr.press/v139/tolpin21a.html | [
"David Tolpin",
"Yuan Zhou",
"Tom Rainforth",
"Hongseok Yang"
] | null | null | We tackle the problem of conditioning probabilistic programs on distributions of observable variables. Probabilistic programs are usually conditioned on samples from the joint data distribution, which we refer to as deterministic conditioning. However, in many real-life scenarios, the observations are given as marginal... | [] | null | 939 | 2010.00282 | title_snapshot |
v139/tomen21a | Deep Continuous Networks | https://proceedings.mlr.press/v139/tomen21a.html | [
"Nergis Tomen",
"Silvia-Laura Pintea",
"Jan Van Gemert"
] | null | null | CNNs and computational models of biological vision share some fundamental principles, which opened new avenues of research. However, fruitful cross-field research is hampered by conventional CNN architectures being based on spatially and depthwise discrete representations, which cannot accommodate certain aspects of bi... | [] | null | 940 | 2402.01557 | title_snapshot |
v139/tong21a | Diffusion Earth Mover’s Distance and Distribution Embeddings | https://proceedings.mlr.press/v139/tong21a.html | [
"Alexander Y Tong",
"Guillaume Huguet",
"Amine Natik",
"Kincaid Macdonald",
"Manik Kuchroo",
"Ronald Coifman",
"Guy Wolf",
"Smita Krishnaswamy"
] | null | null | We propose a new fast method of measuring distances between large numbers of related high dimensional datasets called the Diffusion Earth Mover’s Distance (EMD). We model the datasets as distributions supported on common data graph that is derived from the affinity matrix computed on the combined data. In such cases wh... | [] | null | 941 | 2102.12833 | title_snapshot |
v139/touvron21a | Training data-efficient image transformers & distillation through attention | https://proceedings.mlr.press/v139/touvron21a.html | [
"Hugo Touvron",
"Matthieu Cord",
"Matthijs Douze",
"Francisco Massa",
"Alexandre Sablayrolles",
"Herve Jegou"
] | null | null | Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. These high-performing vision transformers are pre-trained with hundreds of millions of images using a large infrastructure, thereby limiting their adoption. In this work, we produce competit... | [] | null | 942 | 2012.12877 | title_snapshot |
v139/trabucco21a | Conservative Objective Models for Effective Offline Model-Based Optimization | https://proceedings.mlr.press/v139/trabucco21a.html | [
"Brandon Trabucco",
"Aviral Kumar",
"Xinyang Geng",
"Sergey Levine"
] | null | null | In this paper, we aim to solve data-driven model-based optimization (MBO) problems, where the goal is to find a design input that maximizes an unknown objective function provided access to only a static dataset of inputs and their corresponding objective values. Such data-driven optimization procedures are the only pra... | [] | null | 943 | 2107.06882 | title_snapshot |
v139/tran21a | Sparse within Sparse Gaussian Processes using Neighbor Information | https://proceedings.mlr.press/v139/tran21a.html | [
"Gia-Lac Tran",
"Dimitrios Milios",
"Pietro Michiardi",
"Maurizio Filippone"
] | null | null | Approximations to Gaussian processes (GPs) based on inducing variables, combined with variational inference techniques, enable state-of-the-art sparse approaches to infer GPs at scale through mini-batch based learning. In this work, we further push the limits of scalability of sparse GPs by allowing large number of ind... | [] | null | 944 | 2011.05041 | title_snapshot |
v139/tran21b | SMG: A Shuffling Gradient-Based Method with Momentum | https://proceedings.mlr.press/v139/tran21b.html | [
"Trang H Tran",
"Lam M Nguyen",
"Quoc Tran-Dinh"
] | null | null | We combine two advanced ideas widely used in optimization for machine learning: \textit{shuffling} strategy and \textit{momentum} technique to develop a novel shuffling gradient-based method with momentum, coined \textbf{S}huffling \textbf{M}omentum \textbf{G}radient (SMG), for non-convex finite-sum optimization proble... | [] | null | 945 | 2011.11884 | title_snapshot |
v139/tran-the21a | Bayesian Optimistic Optimisation with Exponentially Decaying Regret | https://proceedings.mlr.press/v139/tran-the21a.html | [
"Hung Tran-The",
"Sunil Gupta",
"Santu Rana",
"Svetha Venkatesh"
] | null | null | Bayesian optimisation (BO) is a well known algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bounds ranging from $\mathcal{O}(\frac{logN}{\sqrt{N}})$ to $\mathcal O(e^{-\sqrt{N}})$, where $N$ is the number of evaluations. This paper explores the ... | [] | null | 946 | 2105.04332 | title_snapshot |
v139/trauble21a | On Disentangled Representations Learned from Correlated Data | https://proceedings.mlr.press/v139/trauble21a.html | [
"Frederik Träuble",
"Elliot Creager",
"Niki Kilbertus",
"Francesco Locatello",
"Andrea Dittadi",
"Anirudh Goyal",
"Bernhard Schölkopf",
"Stefan Bauer"
] | null | null | The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent dise... | [] | null | 947 | 2006.07886 | title_snapshot |
v139/treutlein21a | A New Formalism, Method and Open Issues for Zero-Shot Coordination | https://proceedings.mlr.press/v139/treutlein21a.html | [
"Johannes Treutlein",
"Michael Dennis",
"Caspar Oesterheld",
"Jakob Foerster"
] | null | null | In many coordination problems, independently reasoning humans are able to discover mutually compatible policies. In contrast, independently trained self-play policies are often mutually incompatible. Zero-shot coordination (ZSC) has recently been proposed as a new frontier in multi-agent reinforcement learning to addre... | [] | null | 948 | 2106.06613 | title_snapshot |
v139/triantafillou21a | Learning a Universal Template for Few-shot Dataset Generalization | https://proceedings.mlr.press/v139/triantafillou21a.html | [
"Eleni Triantafillou",
"Hugo Larochelle",
"Richard Zemel",
"Vincent Dumoulin"
] | null | null | Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purpose of training an adaptable model that can then learn classes from \emph{new datasets} using only a few examples. To this end, we propose t... | [] | null | 949 | 2105.07029 | title_snapshot |
v139/tripuraneni21a | Provable Meta-Learning of Linear Representations | https://proceedings.mlr.press/v139/tripuraneni21a.html | [
"Nilesh Tripuraneni",
"Chi Jin",
"Michael Jordan"
] | null | null | Meta-learning, or learning-to-learn, seeks to design algorithms that can utilize previous experience to rapidly learn new skills or adapt to new environments. Representation learning—a key tool for performing meta-learning—learns a data representation that can transfer knowledge across multiple tasks, which is essentia... | [] | null | 950 | 2002.11684 | title_snapshot |
v139/trouleau21a | Cumulants of Hawkes Processes are Robust to Observation Noise | https://proceedings.mlr.press/v139/trouleau21a.html | [
"William Trouleau",
"Jalal Etesami",
"Matthias Grossglauser",
"Negar Kiyavash",
"Patrick Thiran"
] | null | null | Multivariate Hawkes processes (MHPs) are widely used in a variety of fields to model the occurrence of causally related discrete events in continuous time. Most state-of-the-art approaches address the problem of learning MHPs from perfect traces without noise. In practice, the process through which events are collected... | [] | null | 951 | null | null |
v139/tulsiani21a | PixelTransformer: Sample Conditioned Signal Generation | https://proceedings.mlr.press/v139/tulsiani21a.html | [
"Shubham Tulsiani",
"Abhinav Gupta"
] | null | null | We propose a generative model that can infer a distribution for the underlying spatial signal conditioned on sparse samples e.g. plausible images given a few observed pixels. In contrast to sequential autoregressive generative models, our model allows conditioning on arbitrary samples and can answer distributional quer... | [] | null | 952 | 2103.15813 | title_snapshot |
v139/upadhyay21a | A Framework for Private Matrix Analysis in Sliding Window Model | https://proceedings.mlr.press/v139/upadhyay21a.html | [
"Jalaj Upadhyay",
"Sarvagya Upadhyay"
] | null | null | We perform a rigorous study of private matrix analysis when only the last $W$ updates to matrices are considered useful for analysis. We show the existing framework in the non-private setting is not robust to noise required for privacy. We then propose a framework robust to noise and use it to give first efficient $o(W... | [] | null | 953 | null | null |
v139/usmanova21a | Fast Projection Onto Convex Smooth Constraints | https://proceedings.mlr.press/v139/usmanova21a.html | [
"Ilnura Usmanova",
"Maryam Kamgarpour",
"Andreas Krause",
"Kfir Levy"
] | null | null | The Euclidean projection onto a convex set is an important problem that arises in numerous constrained optimization tasks. Unfortunately, in many cases, computing projections is computationally demanding. In this work, we focus on projection problems where the constraints are smooth and the number of constraints is sig... | [] | null | 954 | 2109.09835 | title_snapshot |
v139/ustimenko21a | SGLB: Stochastic Gradient Langevin Boosting | https://proceedings.mlr.press/v139/ustimenko21a.html | [
"Aleksei Ustimenko",
"Liudmila Prokhorenkova"
] | null | null | This paper introduces Stochastic Gradient Langevin Boosting (SGLB) - a powerful and efficient machine learning framework that may deal with a wide range of loss functions and has provable generalization guarantees. The method is based on a special form of the Langevin diffusion equation specifically designed for gradie... | [] | null | 955 | 2001.07248 | title_snapshot |
v139/vaezipoor21a | LTL2Action: Generalizing LTL Instructions for Multi-Task RL | https://proceedings.mlr.press/v139/vaezipoor21a.html | [
"Pashootan Vaezipoor",
"Andrew C Li",
"Rodrigo A Toro Icarte",
"Sheila A. Mcilraith"
] | null | null | We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments. Instructions are expressed in a well-known formal language {–} linear temporal logic (LTL) {–} and can specify a diversity of complex, temporally extended behaviours, including conditionals and... | [] | null | 956 | 2102.06858 | title_snapshot |
v139/van-gorp21a | Active Deep Probabilistic Subsampling | https://proceedings.mlr.press/v139/van-gorp21a.html | [
"Hans Van Gorp",
"Iris Huijben",
"Bastiaan S Veeling",
"Nicola Pezzotti",
"Ruud J. G. Van Sloun"
] | null | null | Subsampling a signal of interest can reduce costly data transfer, battery drain, radiation exposure and acquisition time in a wide range of problems. The recently proposed Deep Probabilistic Subsampling (DPS) method effectively integrates subsampling in an end-to-end deep learning model, but learns a static pattern for... | [] | null | 957 | null | null |
v139/vedantam21a | CURI: A Benchmark for Productive Concept Learning Under Uncertainty | https://proceedings.mlr.press/v139/vedantam21a.html | [
"Ramakrishna Vedantam",
"Arthur Szlam",
"Maximillian Nickel",
"Ari Morcos",
"Brenden M Lake"
] | null | null | Humans can learn and reason under substantial uncertainty in a space of infinitely many compositional, productive concepts. For example, if a scene with two blue spheres qualifies as “daxy,” one can reason that the underlying concept may require scenes to have “only blue spheres” or “only spheres” or “only two objects.... | [] | null | 958 | 2010.02855 | title_snapshot |
v139/verma21a | Towards Domain-Agnostic Contrastive Learning | https://proceedings.mlr.press/v139/verma21a.html | [
"Vikas Verma",
"Thang Luong",
"Kenji Kawaguchi",
"Hieu Pham",
"Quoc Le"
] | null | null | Despite recent successes, most contrastive self-supervised learning methods are domain-specific, relying heavily on data augmentation techniques that require knowledge about a particular domain, such as image cropping and rotation. To overcome such limitation, we propose a domain-agnostic approach to contrastive learni... | [] | null | 959 | 2011.04419 | title_snapshot |
v139/verma21b | Sparsifying Networks via Subdifferential Inclusion | https://proceedings.mlr.press/v139/verma21b.html | [
"Sagar Verma",
"Jean-Christophe Pesquet"
] | null | null | Sparsifying deep neural networks is of paramount interest in many areas, especially when those networks have to be implemented on low-memory devices. In this article, we propose a new formulation of the problem of generating sparse weights for a pre-trained neural network. By leveraging the properties of standard nonli... | [] | null | 960 | null | null |
v139/vicol21a | Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies | https://proceedings.mlr.press/v139/vicol21a.html | [
"Paul Vicol",
"Luke Metz",
"Jascha Sohl-Dickstein"
] | null | null | Unrolled computation graphs arise in many scenarios, including training RNNs, tuning hyperparameters through unrolled optimization, and training learned optimizers. Current approaches to optimizing parameters in such computation graphs suffer from high variance gradients, bias, slow updates, or large memory usage. We i... | [] | null | 961 | 2112.13835 | title_snapshot |
v139/vincent-cuaz21a | Online Graph Dictionary Learning | https://proceedings.mlr.press/v139/vincent-cuaz21a.html | [
"Cédric Vincent-Cuaz",
"Titouan Vayer",
"Rémi Flamary",
"Marco Corneli",
"Nicolas Courty"
] | null | null | Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements. Yet, this analysis is not amenable in the context of graph learning, as graphs usually belong to different metric spaces. We fill this gap by proposing a new online Graph Dictionary Learnin... | [] | null | 962 | 2102.06555 | title_snapshot |
v139/vlastelica21a | Neuro-algorithmic Policies Enable Fast Combinatorial Generalization | https://proceedings.mlr.press/v139/vlastelica21a.html | [
"Marin Vlastelica",
"Michal Rolinek",
"Georg Martius"
] | null | null | Although model-based and model-free approaches to learning the control of systems have achieved impressive results on standard benchmarks, generalization to task variations is still lacking. Recent results suggest that generalization for standard architectures improves only after obtaining exhaustive amounts of data. W... | [] | null | 963 | 2102.07456 | title_snapshot |
v139/vos21a | Efficient Training of Robust Decision Trees Against Adversarial Examples | https://proceedings.mlr.press/v139/vos21a.html | [
"Daniël Vos",
"Sicco Verwer"
] | null | null | Current state-of-the-art algorithms for training robust decision trees have high runtime costs and require hours to run. We present GROOT, an efficient algorithm for training robust decision trees and random forests that runs in a matter of seconds to minutes. Where before the worst-case Gini impurity was computed iter... | [] | null | 964 | 2012.10438 | title_snapshot |
v139/voynov21a | Object Segmentation Without Labels with Large-Scale Generative Models | https://proceedings.mlr.press/v139/voynov21a.html | [
"Andrey Voynov",
"Stanislav Morozov",
"Artem Babenko"
] | null | null | The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing high-quality representations for transfer on downstream tasks. Furthermore, recent works also employed these representations in a fully unsupervised setup for image classification, reducing th... | [] | null | 965 | 2006.04988 | title_snapshot |
v139/vreugdenhil21a | Principal Component Hierarchy for Sparse Quadratic Programs | https://proceedings.mlr.press/v139/vreugdenhil21a.html | [
"Robbie Vreugdenhil",
"Viet Anh Nguyen",
"Armin Eftekhari",
"Peyman Mohajerin Esfahani"
] | null | null | We propose a novel approximation hierarchy for cardinality-constrained, convex quadratic programs that exploits the rank-dominating eigenvectors of the quadratic matrix. Each level of approximation admits a min-max characterization whose objective function can be optimized over the binary variables analytically, while ... | [] | null | 966 | 2105.12022 | title_snapshot |
v139/wadia21a | Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization | https://proceedings.mlr.press/v139/wadia21a.html | [
"Neha Wadia",
"Daniel Duckworth",
"Samuel S Schoenholz",
"Ethan Dyer",
"Jascha Sohl-Dickstein"
] | null | null | Machine learning is predicated on the concept of generalization: a model achieving low error on a sufficiently large training set should also perform well on novel samples from the same distribution. We show that both data whitening and second order optimization can harm or entirely prevent generalization. In general, ... | [] | null | 967 | 2008.07545 | title_snapshot |
v139/wagener21a | Safe Reinforcement Learning Using Advantage-Based Intervention | https://proceedings.mlr.press/v139/wagener21a.html | [
"Nolan C Wagener",
"Byron Boots",
"Ching-An Cheng"
] | null | null | Many sequential decision problems involve finding a policy that maximizes total reward while obeying safety constraints. Although much recent research has focused on the development of safe reinforcement learning (RL) algorithms that produce a safe policy after training, ensuring safety during training as well remains ... | [] | null | 968 | 2106.09110 | title_snapshot |
v139/wagenmaker21a | Task-Optimal Exploration in Linear Dynamical Systems | https://proceedings.mlr.press/v139/wagenmaker21a.html | [
"Andrew J Wagenmaker",
"Max Simchowitz",
"Kevin Jamieson"
] | null | null | Exploration in unknown environments is a fundamental problem in reinforcement learning and control. In this work, we study task-guided exploration and determine what precisely an agent must learn about their environment in order to complete a particular task. Formally, we study a broad class of decision-making problems... | [] | null | 969 | 2102.05214 | title_snapshot |
v139/wan21a | Learning and Planning in Average-Reward Markov Decision Processes | https://proceedings.mlr.press/v139/wan21a.html | [
"Yi Wan",
"Abhishek Naik",
"Richard S Sutton"
] | null | null | We introduce learning and planning algorithms for average-reward MDPs, including 1) the first general proven-convergent off-policy model-free control algorithm without reference states, 2) the first proven-convergent off-policy model-free prediction algorithm, and 3) the first off-policy learning algorithm that converg... | [] | null | 970 | 2006.16318 | title_snapshot |
v139/wan21b | Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces | https://proceedings.mlr.press/v139/wan21b.html | [
"Xingchen Wan",
"Vu Nguyen",
"Huong Ha",
"Binxin Ru",
"Cong Lu",
"Michael A. Osborne"
] | null | null | High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian optimisation methods on continuous domains, domains that are categorical, or that mix continuous and categorical variables, remain challenging. We propose a novel solution—we combine local o... | [] | null | 971 | 2102.07188 | title_snapshot |
v139/wang21a | Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model | https://proceedings.mlr.press/v139/wang21a.html | [
"Zi Wang"
] | null | null | Knowledge distillation (KD) is a successful approach for deep neural network acceleration, with which a compact network (student) is trained by mimicking the softmax output of a pre-trained high-capacity network (teacher). In tradition, KD usually relies on access to the training samples and the parameters of the white... | [] | null | 972 | 2106.03310 | title_snapshot |
v139/wang21b | Fairness of Exposure in Stochastic Bandits | https://proceedings.mlr.press/v139/wang21b.html | [
"Lequn Wang",
"Yiwei Bai",
"Wen Sun",
"Thorsten Joachims"
] | null | null | Contextual bandit algorithms have become widely used for recommendation in online systems (e.g. marketplaces, music streaming, news), where they now wield substantial influence on which items get shown to users. This raises questions of fairness to the items — and to the sellers, artists, and writers that benefit from ... | [] | null | 973 | 2103.02735 | title_snapshot |
v139/wang21c | A Proxy Variable View of Shared Confounding | https://proceedings.mlr.press/v139/wang21c.html | [
"Yixin Wang",
"David Blei"
] | null | null | Causal inference from observational data can be biased by unobserved confounders. Confounders{—}the variables that affect both the treatments and the outcome{—}induce spurious non-causal correlations between the two. Without additional conditions, unobserved confounders generally make causal quantities hard to identify... | [] | null | 974 | null | null |
v139/wang21d | Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss | https://proceedings.mlr.press/v139/wang21d.html | [
"Jiali Wang",
"He Chen",
"Rujun Jiang",
"Xudong Li",
"Zihao Li"
] | null | null | The Stackelberg prediction game (SPG) has been extensively used to model the interactions between the learner and data provider in the training process of various machine learning algorithms. Particularly, SPGs played prominent roles in cybersecurity applications, such as intrusion detection, banking fraud detection, s... | [] | null | 975 | 2105.05531 | title_snapshot |
v139/wang21e | Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework | https://proceedings.mlr.press/v139/wang21e.html | [
"Wenxiao Wang",
"Minghao Chen",
"Shuai Zhao",
"Long Chen",
"Jinming Hu",
"Haifeng Liu",
"Deng Cai",
"Xiaofei He",
"Wei Liu"
] | null | null | Most neural network pruning methods, such as filter-level and layer-level prunings, prune the network model along one dimension (depth, width, or resolution) solely to meet a computational budget. However, such a pruning policy often leads to excessive reduction of that dimension, thus inducing a huge accuracy loss. To... | [] | null | 976 | 2010.04879 | title_snapshot |
v139/wang21f | Explainable Automated Graph Representation Learning with Hyperparameter Importance | https://proceedings.mlr.press/v139/wang21f.html | [
"Xin Wang",
"Shuyi Fan",
"Kun Kuang",
"Wenwu Zhu"
] | null | null | Current graph representation (GR) algorithms require huge demand of human experts in hyperparameter tuning, which significantly limits their practical applications, leading to an urge for automated graph representation without human intervention. Although automated machine learning (AutoML) serves as a good candidate f... | [] | null | 977 | null | null |
v139/wang21g | Self-Tuning for Data-Efficient Deep Learning | https://proceedings.mlr.press/v139/wang21g.html | [
"Ximei Wang",
"Jinghan Gao",
"Mingsheng Long",
"Jianmin Wang"
] | null | null | Deep learning has made revolutionary advances to diverse applications in the presence of large-scale labeled datasets. However, it is prohibitively time-costly and labor-expensive to collect sufficient labeled data in most realistic scenarios. To mitigate the requirement for labeled data, semi-supervised learning (SSL)... | [] | null | 978 | 2102.12903 | title_snapshot |
v139/wang21h | Label Distribution Learning Machine | https://proceedings.mlr.press/v139/wang21h.html | [
"Jing Wang",
"Xin Geng"
] | null | null | Although Label Distribution Learning (LDL) has witnessed extensive classification applications, it faces the challenge of objective mismatch – the objective of LDL mismatches that of classification, which has seldom been noticed in existing studies. Our goal is to solve the objective mismatch and improve the classifica... | [] | null | 979 | null | null |
v139/wang21i | AlphaNet: Improved Training of Supernets with Alpha-Divergence | https://proceedings.mlr.press/v139/wang21i.html | [
"Dilin Wang",
"Chengyue Gong",
"Meng Li",
"Qiang Liu",
"Vikas Chandra"
] | null | null | Weight-sharing neural architecture search (NAS) is an effective technique for automating efficient neural architecture design. Weight-sharing NAS builds a supernet that assembles all the architectures as its sub-networks and jointly trains the supernet with the sub-networks. The success of weight-sharing NAS heavily re... | [] | null | 980 | 2102.07954 | title_snapshot |
v139/wang21j | Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time | https://proceedings.mlr.press/v139/wang21j.html | [
"Weichen Wang",
"Jiequn Han",
"Zhuoran Yang",
"Zhaoran Wang"
] | null | null | Recent years have witnessed the success of multi-agent reinforcement learning, which has motivated new research directions for mean-field control (MFC) and mean-field game (MFG), as the multi-agent system can be well approximated by a mean-field problem when the number of agents grows to be very large. In this paper, w... | [] | null | 981 | 2008.06845 | title_snapshot |
v139/wang21k | SG-PALM: a Fast Physically Interpretable Tensor Graphical Model | https://proceedings.mlr.press/v139/wang21k.html | [
"Yu Wang",
"Alfred Hero"
] | null | null | We propose a new graphical model inference procedure, called SG-PALM, for learning conditional dependency structure of high-dimensional tensor-variate data. Unlike most other tensor graphical models the proposed model is interpretable and computationally scalable to high dimension. Physical interpretability follows fro... | [] | null | 982 | 2105.12271 | title_snapshot |
v139/wang21l | Deep Generative Learning via Schrödinger Bridge | https://proceedings.mlr.press/v139/wang21l.html | [
"Gefei Wang",
"Yuling Jiao",
"Qian Xu",
"Yang Wang",
"Can Yang"
] | null | null | We propose to learn a generative model via entropy interpolation with a Schr{ö}dinger Bridge. The generative learning task can be formulated as interpolating between a reference distribution and a target distribution based on the Kullback-Leibler divergence. At the population level, this entropy interpolation is charac... | [] | null | 983 | 2106.10410 | title_snapshot |
v139/wang21m | Robust Inference for High-Dimensional Linear Models via Residual Randomization | https://proceedings.mlr.press/v139/wang21m.html | [
"Y. Samuel Wang",
"Si Kai Lee",
"Panos Toulis",
"Mladen Kolar"
] | null | null | We propose a residual randomization procedure designed for robust inference using Lasso estimates in the high-dimensional setting. Compared to earlier work that focuses on sub-Gaussian errors, the proposed procedure is designed to work robustly in settings that also include heavy-tailed covariates and errors. Moreover,... | [] | null | 984 | 2106.07717 | title_snapshot |
v139/wang21n | A Modular Analysis of Provable Acceleration via Polyak’s Momentum: Training a Wide ReLU Network and a Deep Linear Network | https://proceedings.mlr.press/v139/wang21n.html | [
"Jun-Kun Wang",
"Chi-Heng Lin",
"Jacob D Abernethy"
] | null | null | Incorporating a so-called “momentum” dynamic in gradient descent methods is widely used in neural net training as it has been broadly observed that, at least empirically, it often leads to significantly faster convergence. At the same time, there are very few theoretical guarantees in the literature to explain this app... | [] | null | 985 | 2010.01618 | title_snapshot |
v139/wang21o | Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method | https://proceedings.mlr.press/v139/wang21o.html | [
"Peng Wang",
"Huikang Liu",
"Zirui Zhou",
"Anthony Man-Cho So"
] | null | null | In this paper, we study the problem of exact community recovery in the symmetric stochastic block model, where a graph of $n$ vertices is randomly generated by partitioning the vertices into $K \ge 2$ equal-sized communities and then connecting each pair of vertices with probability that depends on their community memb... | [] | null | 986 | 2106.05644 | title_snapshot |
v139/wang21p | ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation | https://proceedings.mlr.press/v139/wang21p.html | [
"Mengfan Wang",
"Boyu Lyu",
"Guoqiang Yu"
] | null | null | The variance-stabilizing transformation (VST) problem is to transform heteroscedastic data to homoscedastic data so that they are more tractable for subsequent analysis. However, most of the existing approaches focus on finding an analytical solution for a certain parametric distribution, which severely limits the appl... | [] | null | 987 | null | null |
v139/wang21q | The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks | https://proceedings.mlr.press/v139/wang21q.html | [
"Bohan Wang",
"Qi Meng",
"Wei Chen",
"Tie-Yan Liu"
] | null | null | Despite their overwhelming capacity to overfit, deep neural networks trained by specific optimization algorithms tend to generalize relatively well to unseen data. Recently, researchers explained it by investigating the implicit bias of optimization algorithms. A remarkable progress is the work (Lyu & Li, 2019), which ... | [] | null | 988 | 2012.06244 | title_snapshot |
v139/wang21r | Robust Learning for Data Poisoning Attacks | https://proceedings.mlr.press/v139/wang21r.html | [
"Yunjuan Wang",
"Poorya Mianjy",
"Raman Arora"
] | null | null | We investigate the robustness of stochastic approximation approaches against data poisoning attacks. We focus on two-layer neural networks with ReLU activation and show that under a specific notion of separability in the RKHS induced by the infinite-width network, training (finite-width) networks with stochastic gradie... | [] | null | 989 | null | null |
v139/wang21s | SketchEmbedNet: Learning Novel Concepts by Imitating Drawings | https://proceedings.mlr.press/v139/wang21s.html | [
"Alexander Wang",
"Mengye Ren",
"Richard Zemel"
] | null | null | Sketch drawings capture the salient information of visual concepts. Previous work has shown that neural networks are capable of producing sketches of natural objects drawn from a small number of classes. While earlier approaches focus on generation quality or retrieval, we explore properties of image representations le... | [] | null | 990 | 2009.04806 | title_snapshot |
v139/wang21t | Directional Bias Amplification | https://proceedings.mlr.press/v139/wang21t.html | [
"Angelina Wang",
"Olga Russakovsky"
] | null | null | Mitigating bias in machine learning systems requires refining our understanding of bias propagation pathways: from societal structures to large-scale data to trained models to impact on society. In this work, we focus on one aspect of the problem, namely bias amplification: the tendency of models to amplify the biases ... | [] | null | 991 | 2102.12594 | title_snapshot |
v139/wang21u | An exact solver for the Weston-Watkins SVM subproblem | https://proceedings.mlr.press/v139/wang21u.html | [
"Yutong Wang",
"Clayton Scott"
] | null | null | Recent empirical evidence suggests that the Weston-Watkins support vector machine is among the best performing multiclass extensions of the binary SVM. Current state-of-the-art solvers repeatedly solve a particular subproblem approximately using an iterative strategy. In this work, we propose an algorithm that solves t... | [] | null | 992 | 2102.05640 | title_snapshot |
v139/wang21v | SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II | https://proceedings.mlr.press/v139/wang21v.html | [
"Xiangjun Wang",
"Junxiao Song",
"Penghui Qi",
"Peng Peng",
"Zhenkun Tang",
"Wei Zhang",
"Weimin Li",
"Xiongjun Pi",
"Jujie He",
"Chao Gao",
"Haitao Long",
"Quan Yuan"
] | null | null | AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms and systems, and especially the tremendous amount of computation needed are ... | [] | null | 993 | 2012.13169 | title_snapshot |
v139/wang21w | Quantum algorithms for reinforcement learning with a generative model | https://proceedings.mlr.press/v139/wang21w.html | [
"Daochen Wang",
"Aarthi Sundaram",
"Robin Kothari",
"Ashish Kapoor",
"Martin Roetteler"
] | null | null | Reinforcement learning studies how an agent should interact with an environment to maximize its cumulative reward. A standard way to study this question abstractly is to ask how many samples an agent needs from the environment to learn an optimal policy for a $\gamma$-discounted Markov decision process (MDP). For such ... | [] | null | 994 | 2112.08451 | title_snapshot |
v139/wang21x | Matrix Completion with Model-free Weighting | https://proceedings.mlr.press/v139/wang21x.html | [
"Jiayi Wang",
"Raymond K. W. Wong",
"Xiaojun Mao",
"Kwun Chuen Gary Chan"
] | null | null | In this paper, we propose a novel method for matrix completion under general non-uniform missing structures. By controlling an upper bound of a novel balancing error, we construct weights that can actively adjust for the non-uniformity in the empirical risk without explicitly modeling the observation probabilities, and... | [] | null | 995 | 2106.05850 | title_snapshot |
v139/wang21y | UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data | https://proceedings.mlr.press/v139/wang21y.html | [
"Chengyi Wang",
"Yu Wu",
"Yao Qian",
"Kenichi Kumatani",
"Shujie Liu",
"Furu Wei",
"Michael Zeng",
"Xuedong Huang"
] | null | null | In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both labeled and unlabeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representati... | [] | null | 996 | 2101.07597 | title_snapshot |
v139/wang21z | Instabilities of Offline RL with Pre-Trained Neural Representation | https://proceedings.mlr.press/v139/wang21z.html | [
"Ruosong Wang",
"Yifan Wu",
"Ruslan Salakhutdinov",
"Sham Kakade"
] | null | null | In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn) policies in scenarios where the data are collected from a distribution that substantially differs from that of the target policy to be evaluated. Recent theoretical advances have shown that such sample-efficient offline RL is... | [] | null | 997 | 2103.04947 | title_snapshot |
v139/wang21aa | Learning to Weight Imperfect Demonstrations | https://proceedings.mlr.press/v139/wang21aa.html | [
"Yunke Wang",
"Chang Xu",
"Bo Du",
"Honglak Lee"
] | null | null | This paper investigates how to weight imperfect expert demonstrations for generative adversarial imitation learning (GAIL). The agent is expected to perform behaviors demonstrated by experts. But in many applications, experts could also make mistakes and their demonstrations would mislead or slow the learning process o... | [] | null | 998 | null | null |
v139/wang21ab | Evolving Attention with Residual Convolutions | https://proceedings.mlr.press/v139/wang21ab.html | [
"Yujing Wang",
"Yaming Yang",
"Jiangang Bai",
"Mingliang Zhang",
"Jing Bai",
"Jing Yu",
"Ce Zhang",
"Gao Huang",
"Yunhai Tong"
] | null | null | Transformer is a ubiquitous model for natural language processing and has attracted wide attentions in computer vision. The attention maps are indispensable for a transformer model to encode the dependencies among input tokens. However, they are learned independently in each layer and sometimes fail to capture precise ... | [] | null | 999 | 2102.12895 | title_snapshot |
v139/wang21ac | Guarantees for Tuning the Step Size using a Learning-to-Learn Approach | https://proceedings.mlr.press/v139/wang21ac.html | [
"Xiang Wang",
"Shuai Yuan",
"Chenwei Wu",
"Rong Ge"
] | null | null | Choosing the right parameters for optimization algorithms is often the key to their success in practice. Solving this problem using a learning-to-learn approach—using meta-gradient descent on a meta-objective based on the trajectory that the optimizer generates—was recently shown to be effective. However, the meta-opti... | [] | null | 1,000 | 2006.16495 | title_snapshot |
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