paper_id stringlengths 9 13 | title stringlengths 15 153 | paper_url stringlengths 41 45 | authors listlengths 1 21 | type stringclasses 3
values | primary_area stringclasses 0
values | abstract large_stringlengths 310 2.43k | keywords listlengths 1 32 | TL;DR large_stringlengths 23 250 ⌀ | submission_number int64 1 11.7k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
ZarM_uLVyGw | Contrastive Reinforcement Learning of Symbolic Reasoning Domains | https://openreview.net/forum?id=ZarM_uLVyGw | [
"Gabriel Poesia",
"WenXin Dong",
"Noah Goodman"
] | Poster | null | Abstract symbolic reasoning, as required in domains such as mathematics and logic, is a key component of human intelligence. Solvers for these domains have important applications, especially to computer-assisted education. But learning to solve symbolic problems is challenging for machine learning algorithms. Existing ... | [
"reinforcement learning",
"education",
"contrastive learning",
"symbolic reasoning"
] | We propose a novel algorithm for reinforcement learning in discrete symbolic domains based on contrastive learning. | 8,024 | 2106.09146 | title_snapshot |
81Erd42Wimi | Hyperparameter Tuning is All You Need for LISTA | https://openreview.net/forum?id=81Erd42Wimi | [
"Xiaohan Chen",
"Jialin Liu",
"Zhangyang Wang",
"Wotao Yin"
] | Poster | null | Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) introduces the concept of unrolling an iterative algorithm and training it like a neural network. It has had great success on sparse recovery. In this paper, we show that adding momentum to intermediate variables in the LISTA network achieves a better convergen... | [
"LISTA",
"unrolling"
] | null | 8,022 | 2110.15900 | title_snapshot |
yTXtUSV-gk4 | Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis | https://openreview.net/forum?id=yTXtUSV-gk4 | [
"Xiangming Meng",
"Tomoyuki Obuchi",
"Yoshiyuki Kabashima"
] | Poster | null | We theoretically analyze the typical learning performance of $\ell_{1}$-regularized linear regression ($\ell_1$-LinR) for Ising model selection using the replica method from statistical mechanics. For typical random regular graphs in the paramagnetic phase, an accurate estimate of the typical sample complexity of $\ell... | [
"Ising model",
"Lasso",
"replica method",
"statistical physics"
] | Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression | 8,021 | 2102.03988 | title_snapshot |
xRJ_Xqmb6d | Perturb-and-max-product: Sampling and learning in discrete energy-based models | https://openreview.net/forum?id=xRJ_Xqmb6d | [
"Miguel Lazaro-Gredilla",
"Antoine Dedieu",
"Dileep George"
] | Poster | null | Perturb-and-MAP offers an elegant approach to approximately sample from a energy-based model (EBM) by computing the maximum-a-posteriori (MAP) configuration of a perturbed version of the model. Sampling in turn enables learning. However, this line of research has been hindered by the general intractability of the MAP c... | [
"max-product",
"perturb-and-map",
"discrete graphical models",
"energy based models",
"belief revision",
"belief propagation"
] | Perturb-and-MAP combined with max-product yields better performance than Gibbs sampling or LP-based MAP methods on discrete EBMs | 8,020 | 2111.02458 | title_snapshot |
WnJXcebN7hX | An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap | https://openreview.net/forum?id=WnJXcebN7hX | [
"Yuanhao Wang",
"Ruosong Wang",
"Sham M. Kakade"
] | Oral | null | A fundamental question in the theory of reinforcement learning is: suppose the optimal $Q$-function lies in the linear span of a given $d$ dimensional feature mapping, is sample-efficient reinforcement learning (RL) possible? The recent and remarkable result of Weisz et al. (2020) resolves this question in the negative... | [
"Reinforcement Learning",
"Linear Function Approximation",
"Lower Bound"
] | null | 8,007 | 2103.12690 | title_judge |
hhkyM3ib9e6 | Permuton-induced Chinese Restaurant Process | https://openreview.net/forum?id=hhkyM3ib9e6 | [
"Masahiro Nakano",
"Yasuhiro Fujiwara",
"Akisato Kimura",
"Takeshi Yamada",
"Naonori Ueda"
] | Poster | null | This paper proposes the permuton-induced Chinese restaurant process (PCRP), a stochastic process on rectangular partitioning of a matrix. This distribution is suitable for use as a prior distribution in Bayesian nonparametric relational model to find hidden clusters in matrices and network data. Our main contribution i... | [
"Bayesian nonparametrics",
"Chinese restaurant process",
"Relational model"
] | Multi-dimensional extension of the Chinese restaurant process, whose table coordinates arranged by the permuton. | 7,987 | null | null |
Z_J5bCb4Rra | Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals | https://openreview.net/forum?id=Z_J5bCb4Rra | [
"Lang Liu",
"Krishna Pillutla",
"Sean Welleck",
"Sewoong Oh",
"Yejin Choi",
"Zaid Harchaoui"
] | Poster | null | The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance. Divergence frontiers have recently been proposed as an evaluation framework for generative models, due to their ability to measure the quality-diversity trade-off inherent to deep generative modeling... | [
"sample complexity",
"precision-recall",
"generative modeling",
"divergence frontiers"
] | This paper studies the statistical behavior of the divergence frontiers and its statistical summary for evaluating generative models, where the sample complexity and the choice of quantization level are investigated. | 7,985 | 2106.07898 | title_snapshot |
YOc9i6-NrQk | Topological Relational Learning on Graphs | https://openreview.net/forum?id=YOc9i6-NrQk | [
"Yuzhou Chen",
"Baris Coskunuzer",
"Yulia Gel"
] | Poster | null | Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and representation learning. However, GNNs tend to suffer from over-smoothing problems and are vulnerable to graph perturbations. To address these challenges, we propose a novel topological neural framework of topological relational i... | [
"Topological relational learning",
"Graph neural networks",
"Persistence based graph representation",
"Robust graph learning"
] | We introduce a new local topological representation of graph to enhance robustness of Graph Neural Networks in classification tasks and derive its theoretical guarrantees | 7,973 | 2110.15529 | title_snapshot |
5-Of1DTlq | Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization | https://openreview.net/forum?id=5-Of1DTlq | [
"Gavin Zhang",
"Salar Fattahi",
"Richard Y. Zhang"
] | Poster | null | In practical instances of nonconvex matrix factorization, the rank of the true solution $r^{\star}$ is often unknown, so the rank $r$
of the model can be over-specified as $r>r^{\star}$. This over-parameterized regime of matrix factorization significantly slows down the convergence of local search algorithms, from a li... | [
"low-rank matrix factorization",
"matrix sensing",
"nonconvex optimization",
"semidefinite programming",
"gradient descent"
] | We propose a preconditioned gradient descent that enjoys a provable linear convergence rate for over-parameterized low-rank matrix factorization. | 7,969 | 2504.09708 | title_snapshot |
qPOeyokHXT8 | Provably efficient multi-task reinforcement learning with model transfer | https://openreview.net/forum?id=qPOeyokHXT8 | [
"Chicheng Zhang",
"Zhi Wang"
] | Poster | null | We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a group of players concurrently face similar but not necessarily identical MDPs, with a goal of improving their collective performance through inter-player... | [
"Multi-task learning",
"Provably efficient reinforcement learning",
"Model transfer"
] | We formulate a new multi-task episodic reinforcement learning problem, and provide a provably efficient algorithm using model transfer in this setting. | 7,965 | 2107.08622 | title_snapshot |
Rav_oC35ToB | Locality Sensitive Teaching | https://openreview.net/forum?id=Rav_oC35ToB | [
"Zhaozhuo Xu",
"Beidi Chen",
"Chaojian Li",
"Weiyang Liu",
"Le Song",
"Yingyan Lin",
"Anshumali Shrivastava"
] | Poster | null | The emergence of the Internet-of-Things (IoT) sheds light on applying the machine teaching (MT) algorithms for online personalized education on home devices. This direction becomes more promising during the COVID-19 pandemic when in-person education becomes infeasible. However, as one of the most influential and practi... | [
"Locality Sensitive Hashing",
"Machine Teaching",
"Internet-of-Things"
] | An efficient machine teaching algorithm for edge devices. | 7,954 | null | null |
xfDXF0I_bt | Locally Valid and Discriminative Prediction Intervals for Deep Learning Models | https://openreview.net/forum?id=xfDXF0I_bt | [
"Zhen Lin",
"Shubhendu Trivedi",
"Jimeng Sun"
] | Poster | null | Crucial for building trust in deep learning models for critical real-world applications is efficient and theoretically sound uncertainty quantification, a task that continues to be challenging. Useful uncertainty information is expected to have two key properties: It should be valid (guaranteeing coverage) and discrimi... | [
"Deep Learning",
"Local Validity",
"Conformal Prediction",
"Uncertainty Quantification",
"Prediction Interval"
] | null | 7,941 | 2106.00225 | title_snapshot |
jBQaRXpEgO | Can Information Flows Suggest Targets for Interventions in Neural Circuits? | https://openreview.net/forum?id=jBQaRXpEgO | [
"Praveen Venkatesh",
"Sanghamitra Dutta",
"Neil Mehta",
"Pulkit Grover"
] | Poster | null | Motivated by neuroscientific and clinical applications, we empirically examine whether observational measures of information flow can suggest interventions. We do so by performing experiments on artificial neural networks in the context of fairness in machine learning, where the goal is to induce fairness in the system... | [
"information flow",
"interventions",
"neuroscience",
"information theory",
"explainability",
"fairness"
] | To test whether observational measures of information flow can predict the outcomes of interventions in neuroscience, we perform analogous experiments on artificial neural networks in a fairness context and analyze bias-accuracy tradeoffs. | 7,933 | 2111.05299 | title_snapshot |
iPHnzuU6S94 | Neural Active Learning with Performance Guarantees | https://openreview.net/forum?id=iPHnzuU6S94 | [
"Zhilei Wang",
"Pranjal Awasthi",
"Christoph Dann",
"Ayush Sekhari",
"Claudio Gentile"
] | Poster | null | We investigate the problem of active learning in the streaming setting in non-parametric regimes, where the labels are stochastically generated from a class of functions on which we make no assumptions whatsoever. We rely on recently proposed Neural Tangent Kernel (NTK) approximation tools to construct a suitable neura... | [
"Active learning",
"Deep Neural Networks",
"Neural Tangent Kernel",
"Selective Sampling",
"theoretical guarantees"
] | We investigate online active learning in non-parametric regimes under the NTK approximation, and derive theoretical guarantees for the proposed algorithms. | 7,927 | 2106.03243 | title_snapshot |
l41jc6kUfKr | Pointwise Bounds for Distribution Estimation under Communication Constraints | https://openreview.net/forum?id=l41jc6kUfKr | [
"Wei-Ning Chen",
"Peter Kairouz",
"Ayfer Ozgur"
] | Poster | null | We consider the problem of estimating a $d$-dimensional discrete distribution from its samples observed under a $b$-bit communication constraint. In contrast to most previous results that largely focus on the global minimax error, we study the local behavior of the estimation error and provide \emph{pointwise} bounds t... | [
"distributed estimation",
"communication complexity",
"instance-optimal"
] | We characterize instance-optimal bounds on estimating distributions under communication constraints. | 7,923 | 2110.03189 | title_snapshot |
6mUrD5rg-UU | Does enforcing fairness mitigate biases caused by subpopulation shift? | https://openreview.net/forum?id=6mUrD5rg-UU | [
"Subha Maity",
"Debarghya Mukherjee",
"Mikhail Yurochkin",
"Yuekai Sun"
] | Poster | null | Many instances of algorithmic bias are caused by subpopulation shifts. For example, ML models often perform worse on demographic groups that are underrepresented in the training data. In this paper, we study whether enforcing algorithmic fairness during training improves the performance of the trained model in the \emp... | [
"algorithmic fairness",
"domain adaptation",
"sub-population shift",
"fairness accuracy trade-off"
] | The paper presents a precise characterization of trade-off between accuracy and fairness under sub-population shift. | 7,919 | 2011.03173 | title_snapshot |
FEhntTXAeHN | Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning | https://openreview.net/forum?id=FEhntTXAeHN | [
"Jongjin Park",
"Younggyo Seo",
"Chang Liu",
"Li Zhao",
"Tao Qin",
"Jinwoo Shin",
"Tie-Yan Liu"
] | Poster | null | Behavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations. However, behavioral cloning often suffers from the causal confusion problem where a policy relies on the noticeable effect of expert actions due to the strong correlation but not the cause we desir... | [
"imitation learning",
"behavioral cloning",
"causal confusion",
"regularization"
] | We propose an object-aware regularization technique to address the causal confusion problem in imitation learning. | 7,914 | 2110.14118 | title_snapshot |
-mGv2KxQ43D | Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning | https://openreview.net/forum?id=-mGv2KxQ43D | [
"Kai Wang",
"Sanket Shah",
"Haipeng Chen",
"Andrew Perrault",
"Finale Doshi-Velez",
"Milind Tambe"
] | Spotlight | null | In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved. Recent work on decision-focused learning shows that embedding the optimization ... | [
"Optimization",
"predict-then-optimize",
"decision-focused learning",
"differentiable optimization",
"reinforcement learning",
"MDP",
"low-rank approximation",
"Woodbury matrix identity",
"KKT conditions",
"optimality conditions",
"sequential decision problems"
] | We extend decision-focused learning to MDPs with missing parameters. The key novelty is to approximate Hessian to address the high computational cost of differentiating through MDP layers, which arises from large state-action and policy spaces. | 7,910 | 2106.03279 | title_judge |
KvjtYlrmAj | Stateful ODE-Nets using Basis Function Expansions | https://openreview.net/forum?id=KvjtYlrmAj | [
"Alejandro Francisco Queiruga",
"N. Benjamin Erichson",
"Liam Hodgkinson",
"Michael W. Mahoney"
] | Poster | null | The recently-introduced class of ordinary differential equation networks (ODE-Nets) establishes a fruitful connection between deep learning and dynamical systems. In this work, we reconsider formulations of the weights as continuous-in-depth functions using linear combinations of basis functions which enables us to lev... | [
"Neural ODEs",
"Dynamical Systems",
"Differential Equations"
] | We take advantage of basis transformations to introduce stateful normalization layers as well as a methodology for compressing ODE-Nets. | 7,904 | 2106.10820 | title_snapshot |
nehzxAdyJxF | Evaluating model performance under worst-case subpopulations | https://openreview.net/forum?id=nehzxAdyJxF | [
"Mike Li",
"Hongseok Namkoong",
"Shangzhou Xia"
] | Poster | null | The performance of ML models degrades when the training population is different from that seen under operation. Towards assessing distributional robustness, we study the worst-case performance of a model over all subpopulations of a given size, defined with respect to core attributes $Z$. This notion of robustness can ... | [
"reliability",
"robustness",
"safety",
"distribution shift",
"subpopulation shift",
"fairness"
] | We develop a diagnostic for evaluating model performance under subpopulation shifts. | 7,897 | 2407.01316 | title_snapshot |
8YSqxvRhi-Q | Online Meta-Learning via Learning with Layer-Distributed Memory | https://openreview.net/forum?id=8YSqxvRhi-Q | [
"Sudarshan Babu",
"Pedro Henrique Pamplona Savarese",
"Michael Maire"
] | Poster | null | We demonstrate that efficient meta-learning can be achieved via end-to-end training of deep neural networks with memory distributed across layers. The persistent state of this memory assumes the entire burden of guiding task adaptation. Moreover, its distributed nature is instrumental in orchestrating adaptation. Ab... | [
"Meta-learning",
"Memory models",
"Online learning"
] | Efficient and simplified meta-learning via distributed memory networks. | 7,881 | null | null |
_y2G1-i7L8 | Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models | https://openreview.net/forum?id=_y2G1-i7L8 | [
"Kohei Miyaguchi"
] | Poster | null | We consider the problem of offline policy evaluation~(OPE) with Markov decision processes~(MDPs), where the goal is to estimate the utility of given decision-making policies based on static datasets. Recently, theoretical understanding of OPE has been rapidly advanced under (approximate) realizability assumptions, i.e.... | [
"reinforcement learning",
"offline policy evaluation",
"linear function approximation"
] | We characterize the error of a simple linear OPE method when the function approximation may be completely wrong. As a result, we found a new interpretation and new error bounds. | 7,879 | null | null |
j4oYd8SGop | Inverse-Weighted Survival Games | https://openreview.net/forum?id=j4oYd8SGop | [
"Xintian Han",
"Mark Goldstein",
"Aahlad Manas Puli",
"Thomas Wies",
"Adler J Perotte",
"Rajesh Ranganath"
] | Poster | null | Deep models trained through maximum likelihood have achieved state-of-the-art results for survival analysis. Despite this training scheme, practitioners evaluate models under other criteria, such as binary classification losses at a chosen set of time horizons, e.g. Brier score (BS) and Bernoulli log likelihood (BLL). ... | [
"Survival Analysis",
"Healthcare",
"Censoring",
"Games",
"IPCW",
"Brier Score"
] | A new inverse-weighted training method in survival analysis for optimizing criteria such as Brier score under censoring. | 7,867 | 2111.08175 | title_snapshot |
Tqx7nJp7PR | MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers | https://openreview.net/forum?id=Tqx7nJp7PR | [
"Krishna Pillutla",
"Swabha Swayamdipta",
"Rowan Zellers",
"John Thickstun",
"Sean Welleck",
"Yejin Choi",
"Zaid Harchaoui"
] | Oral | null | As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce Mauve, a comparison measure for open-ended text generation, which directly compares the learnt distribution from a text generation model to the distribut... | [
"Open-ended generation",
"neural text generation",
"evaluation",
"divergence frontier",
"human judgement"
] | A comparison measure for open-ended text generation by directly comparing the distribution of neural machine-generated text to that of human-written text. | 7,853 | 2102.01454 | title_snapshot |
iNUKmzaL-M5 | Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling | https://openreview.net/forum?id=iNUKmzaL-M5 | [
"Chen Tang",
"Wei Zhan",
"Masayoshi Tomizuka"
] | Poster | null | Multi-agent behavior modeling and trajectory forecasting are crucial for the safe navigation of autonomous agents in interactive scenarios. Variational Autoencoder (VAE) has been widely applied in multi-agent interaction modeling to generate diverse behavior and learn a low-dimensional representation for interacting sy... | [
"multi-agent interaction modeling",
"variational autoencoder",
"applications"
] | null | 7,851 | 2112.00298 | title_snapshot |
VJ7u6SbqorK | MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge | https://openreview.net/forum?id=VJ7u6SbqorK | [
"Geng Yuan",
"Xiaolong Ma",
"Wei Niu",
"Zhengang Li",
"Zhenglun Kong",
"Ning Liu",
"Yifan Gong",
"Zheng Zhan",
"Chaoyang He",
"Qing Jin",
"Siyue Wang",
"Minghai Qin",
"Bin Ren",
"Yanzhi Wang",
"Sijia Liu",
"Xue Lin"
] | Spotlight | null | Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory-Economic Sparse Training (MEST) framework targeting for accurate and fast execution on edge devices. The proposed MEST framework consists o... | [
"dynamic sparse training",
"memory-economic training",
"edge device training",
"mobile training acceleration"
] | null | 7,849 | 2110.14032 | title_snapshot |
rm0I5y2zkG8 | Learning to delegate for large-scale vehicle routing | https://openreview.net/forum?id=rm0I5y2zkG8 | [
"Sirui Li",
"Zhongxia Yan",
"Cathy Wu"
] | Spotlight | null | Vehicle routing problems (VRPs) form a class of combinatorial problems with wide practical applications. While previous heuristic or learning-based works achieve decent solutions on small problem instances, their performance deteriorates in large problems. This article presents a novel learning-augmented local search f... | [
"machine learning",
"combinatorial optimization",
"vehicle routing",
"decomposition"
] | null | 7,840 | 2107.04139 | title_snapshot |
QZpx42n0BWr | FINE Samples for Learning with Noisy Labels | https://openreview.net/forum?id=QZpx42n0BWr | [
"Taehyeon Kim",
"Jongwoo Ko",
"Sangwook Cho",
"JinHwan Choi",
"Se-Young Yun"
] | Poster | null | Modern deep neural networks (DNNs) become frail when the datasets contain noisy (incorrect) class labels. Robust techniques in the presence of noisy labels can be categorized into two folds: developing noise-robust functions or using noise-cleansing methods by detecting the noisy data. Recently, noise-cleansing methods... | [
"Learning with noisy labels",
"Image Classification",
"Deep Neural Networks",
"Eigen Decomposition"
] | null | 7,839 | 2102.11628 | title_snapshot |
YzasumDKCWV | Modified Frank Wolfe in Probability Space | https://openreview.net/forum?id=YzasumDKCWV | [
"Carson Kent",
"Jiajin Li",
"Jose Blanchet",
"Peter Glynn"
] | Poster | null | We propose a novel Frank-Wolfe (FW) procedure for the optimization of infinite-dimensional functionals of probability measures - a task which arises naturally in a wide range of areas including statistical learning (e.g. variational inference) and artificial intelligence (e.g. generative adversarial networks). Our FW p... | [
"Frank-Wolfe",
"Variational Methods",
"Wasserstein Gradient Flows",
"Distributionally Robust Optimization"
] | null | 7,838 | null | null |
lS-ocNKV-u | Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits | https://openreview.net/forum?id=lS-ocNKV-u | [
"Orestis Papadigenopoulos",
"Constantine Caramanis"
] | Poster | null | A recent line of research focuses on the study of stochastic multi-armed bandits (MAB), in the case where temporal correlations of specific structure are imposed between the player's actions and the reward distributions of the arms. These correlations lead to (sub-)optimal solutions that exhibit interesting dynamical p... | [
"non-stationary",
"multi-armed",
"bandits",
"blocking",
"regret",
"submodular",
"welfare",
"maximization",
"approximation"
] | null | 7,834 | 2102.00321 | title_judge |
owQmPJ9q9u | Controlling Neural Networks with Rule Representations | https://openreview.net/forum?id=owQmPJ9q9u | [
"Sungyong Seo",
"Sercan O Arik",
"Jinsung Yoon",
"Xiang Zhang",
"Kihyuk Sohn",
"Tomas Pfister"
] | Poster | null | We propose a novel training method that integrates rules into deep learning, in a way the strengths of the rules are controllable at inference. Deep Neural Networks with Controllable Rule Representations (DeepCTRL) incorporates a rule encoder into the model coupled with a rule-based objective, enabling a shared represe... | [
"Controlling neural networks",
"Incorporating rules",
"Unsupervised adaptation"
] | We propose a novel training method to integrate rules into deep learning, in a way their strengths are controllable at inference. | 7,829 | 2106.07804 | title_snapshot |
t9gKUW9T8fX | On Model Calibration for Long-Tailed Object Detection and Instance Segmentation | https://openreview.net/forum?id=t9gKUW9T8fX | [
"Tai-Yu Pan",
"Cheng Zhang",
"YANDONG LI",
"Hexiang Hu",
"Dong Xuan",
"Soravit Changpinyo",
"Boqing Gong",
"Wei-Lun Chao"
] | Poster | null | Vanilla models for object detection and instance segmentation suffer from the heavy bias toward detecting frequent objects in the long-tailed setting. Existing methods address this issue mostly during training, e.g., by re-sampling or re-weighting. In this paper, we investigate a largely overlooked approach --- post-pr... | [
"Long-tailed distribution",
"Object detection",
"Instance segmentation",
"Calibration"
] | We propose a simple and effective post-processing calibration method for long-tailed object detection and instance segmentation | 7,823 | 2107.02170 | title_snapshot |
napaTaDQ0lY | Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions | https://openreview.net/forum?id=napaTaDQ0lY | [
"Yin Tat Lee",
"Ruoqi Shen",
"Kevin Tian"
] | Oral | null | We give lower bounds on the performance of two of the most popular sampling methods in practice, the Metropolis-adjusted Langevin algorithm (MALA) and multi-step Hamiltonian Monte Carlo (HMC) with a leapfrog integrator, when applied to well-conditioned distributions. Our main result is a nearly-tight lower bound of $\w... | [
"sampling",
"computational statistics",
"Bayesian methods",
"Langevin dynamics",
"Hamiltonian Monte Carlo"
] | We give lower bounds showing the current analyses of MALA for sampling well-conditioned distributions are nearly-tight, and that HMC incurs a polynomial dimension dependence for any number of steps. | 7,820 | 2106.05480 | title_snapshot |
15HPeY8MGQ | Bayesian Adaptation for Covariate Shift | https://openreview.net/forum?id=15HPeY8MGQ | [
"Aurick Zhou",
"Sergey Levine"
] | Poster | null | When faced with distribution shift at test time, deep neural networks often make inaccurate predictions with unreliable uncertainty estimates.
While improving the robustness of neural networks is one promising approach to mitigate this issue, an appealing alternate to robustifying networks against all possible test-tim... | [
"uncertainty estimation",
"calibration",
"test-time adaptation",
"distribution shift"
] | We present a Bayesian approach to test-time adaptation in order to improve both accuracy and calibration under distribution shift. | 7,810 | 2109.12746 | title_judge |
l-0rLXvctI | Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem | https://openreview.net/forum?id=l-0rLXvctI | [
"Adarsh Barik",
"Jean Honorio"
] | Spotlight | null | In this paper, we study the problem of fair sparse regression on a biased dataset where bias depends upon a hidden binary attribute. The presence of a hidden attribute adds an extra layer of complexity to the problem by combining sparse regression and clustering with unknown binary labels. The corresponding optimizatio... | [
"Fairness",
"Invexity",
"Combinatorial Optimization",
"Primal Dual Witness Method"
] | We provide an invex relaxation with provable theoretical guarantees for a combinatorial problem while ensuring fairness. | 7,805 | 2102.09704 | title_snapshot |
1LCtHgPC-l4 | Representation Learning on Spatial Networks | https://openreview.net/forum?id=1LCtHgPC-l4 | [
"Zheng Zhang",
"Liang Zhao"
] | Poster | null | Spatial networks are networks for which the nodes and edges are constrained by geometry and embedded in real space, which has crucial effects on their topological properties. Although tremendous success has been achieved in spatial and network representation separately in recent years, there exist very little works on ... | [
"Spatial networks",
"graph neural network",
"representation learning"
] | We propose a new message passing neural network model with theoretical guarantee to handle the spatial-graph topology coupled network. | 7,803 | null | null |
jZ6FlEB78CG | Gradual Domain Adaptation without Indexed Intermediate Domains | https://openreview.net/forum?id=jZ6FlEB78CG | [
"Hong-You Chen",
"Wei-Lun Chao"
] | Poster | null | The effectiveness of unsupervised domain adaptation degrades when there is a large discrepancy between the source and target domains. Gradual domain adaption (GDA) is one promising way to mitigate such an issue, by leveraging additional unlabeled data that gradually shift from the source to the target. Through sequenti... | [
"Gradual Domain Adaptation",
"classification"
] | We propose a novel algorithm IDOL to bypass the need of pre-defined domain sequences in gradual domain adaptation (GDA). | 7,795 | 2207.04587 | title_snapshot |
3qMwV98zLIk | FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling | https://openreview.net/forum?id=3qMwV98zLIk | [
"Bowen Zhang",
"Yidong Wang",
"Wenxin Hou",
"Hao Wu",
"Jindong Wang",
"Manabu Okumura",
"Takahiro Shinozaki"
] | Poster | null | The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learn... | [
"semi-supervised learning",
"pseudo labeling",
"consistency regularization"
] | A simple and cost-free approach that can dramatically boost the performance and convergence speed of semi-supervised learning. | 7,790 | 2110.08263 | title_snapshot |
QgNAUqQLh4 | Structured Dropout Variational Inference for Bayesian Neural Networks | https://openreview.net/forum?id=QgNAUqQLh4 | [
"Son Nguyen",
"Duong Nguyen",
"Khai Nguyen",
"Khoat Than",
"Hung Bui",
"Nhat Ho"
] | Poster | null | Approximate inference in Bayesian deep networks exhibits a dilemma of how to yield high fidelity posterior approximations while maintaining computational efficiency and scalability. We tackle this challenge by introducing a novel variational structured approximation inspired by the Bayesian interpretation of Dropout re... | [
"Bayesian neural networks",
"Variational Dropout",
"Structured Approximate inference"
] | Improving approximate inference in Bayesian neural networks with Variational Structured Dropout | 7,786 | 2102.07927 | title_snapshot |
-Z7FuZGUzv | You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership | https://openreview.net/forum?id=-Z7FuZGUzv | [
"Xuxi Chen",
"Tianlong Chen",
"Zhenyu Zhang",
"Zhangyang Wang"
] | Poster | null | Despite tremendous success in many application scenarios, the training and inference costs of using deep learning are also rapidly increasing over time. The lottery ticket hypothesis (LTH) emerges as a promising framework to leverage a special sparse subnetwork (i.e., $\textit{winning ticket}$) instead of a full model ... | [
"Lottery Ticket Hypothesis",
"Ownership Verification"
] | A topology-based ownership verification mechanism that can prevent lottery-ticket theft under various verification schemes and attacks | 7,781 | 2111.00162 | title_snapshot |
iKYO63MOWwi | Online Active Learning with Surrogate Loss Functions | https://openreview.net/forum?id=iKYO63MOWwi | [
"Giulia DeSalvo",
"Claudio Gentile",
"Tobias Sommer Thune"
] | Spotlight | null | We derive a novel active learning algorithm in the streaming setting for binary classification tasks. The algorithm leverages
weak labels to minimize the number of label requests, and trains a model to optimize a surrogate loss on a resulting set of labeled and
weak-labeled points. Our algorithm jointly admits two cr... | [
"active learning",
"streaming",
"weak labels"
] | We introduce a novel active learning algorithm with provable guarantees that works with surrogate loss functions and achieving compelling experimental performances | 7,774 | null | null |
m8KpGet0Etq | Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training | https://openreview.net/forum?id=m8KpGet0Etq | [
"Anup Sarma",
"Sonali Singh",
"Huaipan Jiang",
"Rui Zhang",
"Mahmut Kandemir",
"Chita Das"
] | Poster | null | Recurrent Neural Networks (RNNs), more specifically their Long Short-Term Memory (LSTM) variants, have been widely used as a deep learning tool for tackling sequence-based learning tasks in text and speech. Training of such LSTM applications is computationally intensive due to the recurrent nature of hidden state compu... | [
"LSTM",
"TRAINING",
"DROPOUT",
"STRUCTURED",
"SPARSITY"
] | null | 7,771 | 2106.12089 | title_snapshot |
BR5bZGhzrel | The Value of Information When Deciding What to Learn | https://openreview.net/forum?id=BR5bZGhzrel | [
"Dilip Arumugam",
"Benjamin Van Roy"
] | Poster | null | All sequential decision-making agents explore so as to acquire knowledge about a particular target. It is often the responsibility of the agent designer to construct this target which, in rich and complex environments, constitutes a onerous burden; without full knowledge of the environment itself, a designer may forge ... | [
"Multi-armed bandits",
"sequential decision-making under uncertainty",
"information theory",
"efficient exploration",
"reinforcement learning"
] | Coupling information-theoretic principles for the design of optimal learning targets and optimal information acquisition. | 7,756 | 2110.13973 | title_snapshot |
tNT4APQ0Wgj | Time-independent Generalization Bounds for SGLD in Non-convex Settings | https://openreview.net/forum?id=tNT4APQ0Wgj | [
"Tyler Farghly",
"Patrick Rebeschini"
] | Poster | null | We establish generalization error bounds for stochastic gradient Langevin dynamics (SGLD) with constant learning rate under the assumptions of dissipativity and smoothness, a setting that has received increased attention in the sampling/optimization literature. Unlike existing bounds for SGLD in non-convex settings, ou... | [
"SGLD",
"Langevin",
"stochastic gradient",
"generalization",
"stability",
"non-convex",
"wasserstein",
"optimization"
] | In the setting of non-convex learning, we derive generalization error bounds for SGLD that are time-independent and decay to zero as the sample size increases | 7,751 | 2111.12876 | title_snapshot |
dTp-VUFDIB | PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning | https://openreview.net/forum?id=dTp-VUFDIB | [
"Yining Hong",
"Li Yi",
"Joshua B. Tenenbaum",
"Antonio Torralba",
"Chuang Gan"
] | Poster | null | A critical aspect of human visual perception is the ability to parse visual scenes into individual objects and further into object parts, forming part-whole hierarchies. Such composite structures could induce a rich set of semantic concepts and relations, thus playing an important role in the interpretation and organiz... | [
"Part",
"visual reasoning",
"relational reasoning",
"concept learning",
"model diagnostics"
] | We introduce a new dataset for Part-based Conceptual, Relational, and Physical Reasoning | 7,750 | 2112.05136 | title_snapshot |
vCWztO0ppL | A Convergence Analysis of Gradient Descent on Graph Neural Networks | https://openreview.net/forum?id=vCWztO0ppL | [
"Pranjal Awasthi",
"Abhimanyu Das",
"Sreenivas Gollapudi"
] | Poster | null | Graph Neural Networks~(GNNs) are a powerful class of architectures for solving learning problems on graphs. While many variants of GNNs have been proposed in the literature and have achieved strong empirical performance, their theoretical properties are less well understood. In this work we study the convergence proper... | [
"Learning Theory",
"Graph Neural Networks",
"Convergence Analysis"
] | We provide a convergence analysis of gradient descent for graph neural networks. | 7,747 | null | null |
7RIYO406DB- | Dynamic influence maximization | https://openreview.net/forum?id=7RIYO406DB- | [
"Binghui Peng"
] | Poster | null | We initiate a systematic study on {\em dynamic influence maximization} (DIM). In the DIM problem, one maintains a seed set $S$ of at most $k$ nodes in a dynamically involving social network, with the goal of maximizing the expected influence spread while minimizing the amortized updating cost. We consider two evolution... | [
"Influence maximization",
"submodular maximization",
"dynamic algorithm"
] | Near optimal algorithm/hardness for dynamic influence maximization | 7,745 | 2110.12602 | title_snapshot |
z9Xs6T0y9Eg | Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes | https://openreview.net/forum?id=z9Xs6T0y9Eg | [
"Brian Brubach",
"Nathaniel Grammel",
"Will Ma",
"Aravind Srinivasan"
] | Poster | null | Matching is one of the most fundamental and broadly applicable problems across many domains. In these diverse real-world applications, there is often a degree of uncertainty in the input which has led to the study of stochastic matching models. Here, each edge in the graph has a known, independent probability of existi... | [
"Stochastic Optimization",
"Combinatorial Optimization",
"Discrete Optimization",
"Approximation Algorithms",
"Matching",
"Stochastic Matching",
"Prophet Inequality",
"Prophet Secretary",
"Contention Resolution"
] | null | 7,742 | 2106.06892 | title_snapshot |
7BlQMwp_44p | ReLU Regression with Massart Noise | https://openreview.net/forum?id=7BlQMwp_44p | [
"Ilias Diakonikolas",
"Jongho Park",
"Christos Tzamos"
] | Poster | null | We study the fundamental problem of ReLU regression, where the goal is to fit Rectified Linear Units (ReLUs) to data. This supervised learning task is efficiently solvable in the realizable setting, but is known to be computationally hard with adversarial label noise. In this work, we focus on ReLU regression in the Ma... | [
"Robust learning"
] | This work studies ReLU regression with semi-random noise. | 7,733 | 2109.04623 | title_snapshot |
jVzGglbNuW5 | Learning Markov State Abstractions for Deep Reinforcement Learning | https://openreview.net/forum?id=jVzGglbNuW5 | [
"Cameron Allen",
"Neev Parikh",
"Omer Gottesman",
"George Konidaris"
] | Poster | null | A fundamental assumption of reinforcement learning in Markov decision processes (MDPs) is that the relevant decision process is, in fact, Markov. However, when MDPs have rich observations, agents typically learn by way of an abstract state representation, and such representations are not guaranteed to preserve the Mark... | [
"State Abstraction",
"Representation Learning",
"Markov Decision Processes",
"Reinforcement Learning",
"Deep RL",
"Markov Property"
] | We introduce a theoretically-grounded and practical training objective for learning Markov abstract state representations that does not require pixel reconstruction, transition modeling, or reward information. | 7,724 | 2106.04379 | title_snapshot |
5KWmB6JePx | End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering | https://openreview.net/forum?id=5KWmB6JePx | [
"Devendra Singh Sachan",
"Siva Reddy",
"William L. Hamilton",
"Chris Dyer",
"Dani Yogatama"
] | Poster | null | We present an end-to-end differentiable training method for retrieval-augmented open-domain question answering systems that combine information from multiple retrieved documents when generating answers. We model retrieval decisions as latent variables over sets of relevant documents. Since marginalizing over sets of re... | [
"open-domain question answering",
"information retrieval",
"closed-book QA"
] | We propose an algorithm to train a multi-document reader and retriever for open-domain question answering in an end-to-end fashion. | 7,723 | 2106.05346 | title_snapshot |
nUtLCcV24hL | Reinforcement Learning Enhanced Explainer for Graph Neural Networks | https://openreview.net/forum?id=nUtLCcV24hL | [
"Caihua Shan",
"Yifei Shen",
"Yao Zhang",
"Xiang Li",
"Dongsheng Li"
] | Poster | null | Graph neural networks (GNNs) have recently emerged as revolutionary technologies for machine learning tasks on graphs. In GNNs, the graph structure is generally incorporated with node representation via the message passing scheme, making the explanation much more challenging. Given a trained GNN model, a GNN explainer ... | [
"Graph neural networks",
"Interpretability",
"Reinforcement Learning"
] | We generate explanations for graph neural networks by a reinforcement learning enhanced method. | 7,722 | null | null |
e8WWUBeafM | Bellman-consistent Pessimism for Offline Reinforcement Learning | https://openreview.net/forum?id=e8WWUBeafM | [
"Tengyang Xie",
"Ching-An Cheng",
"Nan Jiang",
"Paul Mineiro",
"Alekh Agarwal"
] | Oral | null | The use of pessimism, when reasoning about datasets lacking exhaustive exploration has recently gained prominence in offline reinforcement learning. Despite the robustness it adds to the algorithm, overly pessimistic reasoning can be equally damaging in precluding the discovery of good policies, which is an issue for t... | [
"offline reinforcement learning",
"Bellman-consistent pessimism",
"sample complexity bounds",
"linear MDP",
"function approximation"
] | We introduce the notion of Bellman-consistent pessimism for general function approximation in offline reinforcement learning. | 7,718 | 2106.06926 | title_snapshot |
h7-XixPCAL | Structured Denoising Diffusion Models in Discrete State-Spaces | https://openreview.net/forum?id=h7-XixPCAL | [
"Jacob Austin",
"Daniel D. Johnson",
"Jonathan Ho",
"Daniel Tarlow",
"Rianne van den Berg"
] | Poster | null | Denoising diffusion probabilistic models (DDPMs) [Ho et al. 2021] have shown impressive results on image and waveform generation in continuous state spaces. Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete data that generalize the multinomial di... | [
"diffusion models",
"generative models",
"text generation",
"probabilistic models"
] | We explore diffusion models for discrete data, exploiting structured transition matrices to achieve strong results on image and text generation. | 7,715 | 2107.03006 | title_snapshot |
D5APl1Yixnc | Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning | https://openreview.net/forum?id=D5APl1Yixnc | [
"Xin Zhang",
"Zhuqing Liu",
"Jia Liu",
"Zhengyuan Zhu",
"Songtao Lu"
] | Poster | null | Cooperative multi-agent reinforcement learning (MARL) has received increasing attention in recent years and has found many scientific and engineering applications. However, a key challenge arising from many cooperative MARL algorithm designs (e.g., the actor-critic framework) is the policy evaluation problem, which can... | [
"Decentralized Mini-max Optimization",
"Variance Reduction",
"Nonlinear Function Approximation",
"Policy Evaluation"
] | null | 7,704 | null | null |
XeM4Lld0zTR | Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses | https://openreview.net/forum?id=XeM4Lld0zTR | [
"Haipeng Luo",
"Chen-Yu Wei",
"Chung-Wei Lee"
] | Poster | null | Policy optimization is a widely-used method in reinforcement learning. Due to its local-search nature, however, theoretical guarantees on global optimality often rely on extra assumptions on the Markov Decision Processes (MDPs) that bypass the challenge of global exploration. To eliminate the need of such assumptions, ... | [
"adversarial MDP",
"policy optimization",
"bandit feedback",
"dilated bonuses"
] | null | 7,703 | 2107.08346 | title_snapshot |
Wkq4hKpGxWv | Auditing Black-Box Prediction Models for Data Minimization Compliance | https://openreview.net/forum?id=Wkq4hKpGxWv | [
"Bashir Rastegarpanah",
"Krishna P. Gummadi",
"Mark Crovella"
] | Spotlight | null | In this paper, we focus on auditing black-box prediction models for compliance with the GDPR’s data minimization principle. This principle restricts prediction models to use the minimal information that is necessary for performing the task at hand. Given the challenge of the black-box setting, our key idea is to check ... | [
"Responsible ML",
"Privacy",
"Data Minimization"
] | This paper proposes a new operational definition of the data minimization principle (eg, from GDPR) that is appropriate for auditing black-box prediction models, and develops efficient algorithms for that setting. | 7,696 | null | null |
8gmBGNeOfT | Observation-Free Attacks on Stochastic Bandits | https://openreview.net/forum?id=8gmBGNeOfT | [
"Yinglun Xu",
"Bhuvesh Kumar",
"Jacob Abernethy"
] | Poster | null | We study data corruption attacks on stochastic multi arm bandit algorithms. Existing attack methodologies assume that the attacker can observe the multi arm bandit algorithm's realized behavior which is in contrast to the adversaries modeled in the robust multi arm bandit algorithms literature. To the best of our knowl... | [
"Multi arm bandit",
"Data poisoning attack"
] | null | 7,694 | null | null |
NP-9Ppxdca | Tracking People with 3D Representations | https://openreview.net/forum?id=NP-9Ppxdca | [
"Jathushan Rajasegaran",
"Georgios Pavlakos",
"Angjoo Kanazawa",
"Jitendra Malik"
] | Poster | null | We present a novel approach for tracking multiple people in video. Unlike past approaches which employ 2D representations, we focus on using 3D representations of people, located in three-dimensional space. To this end, we develop a method, Human Mesh and Appearance Recovery (HMAR) which in addition to extracting the 3... | [
"Tracking",
"3D",
"Humans",
"Transformers"
] | null | 7,691 | 2111.07868 | title_snapshot |
R4NeFnapYQZ | Reinforcement Learning based Disease Progression Model for Alzheimer’s Disease | https://openreview.net/forum?id=R4NeFnapYQZ | [
"Krishnakant V. Saboo",
"Anirudh Choudhary",
"Yurui Cao",
"Gregory Worrell",
"David T Jones",
"Ravi Iyer"
] | Poster | null | We model Alzheimer’s disease (AD) progression by combining differential equations (DEs) and reinforcement learning (RL) with domain knowledge. DEs provide relationships between some, but not all, factors relevant to AD. We assume that the missing relationships must satisfy general criteria about the working of the bra... | [
"disease progression model",
"Alzheimer's disease",
"cognition trajectory prediction",
"reinforcement learning",
"differential equations"
] | We model Alzheimer's disease progression by combining differential equation-based models with RL using domain knowledge to (i) predict individualized disease progression 10 years into the future, and (ii) understand processes underlying the disease. | 7,684 | 2106.16187 | title_snapshot |
ngdcA1tlDvj | Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning | https://openreview.net/forum?id=ngdcA1tlDvj | [
"Jiani Huang",
"Ziyang Li",
"Binghong Chen",
"Karan Samel",
"Mayur Naik",
"Le Song",
"Xujie Si"
] | Poster | null | Deep learning and symbolic reasoning are complementary techniques for an intelligent system. However, principled combinations of these techniques have limited scalability, rendering them ill-suited for real-world applications. We propose Scallop, a system that builds upon probabilistic deductive databases, to bridge th... | [
"probabilistic database",
"logic reasoning",
"neural symbolic"
] | Scalable Differentiable Reasoning Using A Probabilistic Deductive Database | 7,681 | null | null |
yMf3SLah5-y | Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings | https://openreview.net/forum?id=yMf3SLah5-y | [
"Ming Yin",
"Yu-Xiang Wang"
] | Poster | null | This work studies the statistical limits of uniform convergence for offline policy evaluation (OPE) problems with model-based methods (for episodic MDP) and provides a unified framework towards optimal learning for several well-motivated offline tasks. Uniform OPE $\sup_\Pi|Q^\pi-\hat{Q}^\pi|<\epsilon$ is a stronger me... | [
"Theory",
"Reinforcement learning theory",
"Markov decision process theory"
] | null | 7,677 | 2105.06029 | title_snapshot |
VzuIzbRDrum | CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation | https://openreview.net/forum?id=VzuIzbRDrum | [
"YUSUKE TASHIRO",
"Jiaming Song",
"Yang Song",
"Stefano Ermon"
] | Poster | null | The imputation of missing values in time series has many applications in healthcare and finance. While autoregressive models are natural candidates for time series imputation, score-based diffusion models have recently outperformed existing counterparts including autoregressive models in many tasks such as image genera... | [
"time series imputation",
"generative modeling",
"deep learning",
"score-based diffusion models"
] | We propose a novel probabilistic time series imputation method which utilize conditional score-based diffusion models. | 7,676 | 2107.03502 | title_snapshot |
2OqZZAqxnn | Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning | https://openreview.net/forum?id=2OqZZAqxnn | [
"Youngkyu Hong",
"Eunho Yang"
] | Poster | null | Datasets for training machine learning models tend to be biased unless the data is collected with complete care. In such a biased dataset, models are susceptible to making predictions based on the biased features of the data. The biased model fails to generalize to the case where correlations between biases and targets... | [
"Debiasing",
"Fairness",
"Contrastive Learning"
] | Debiased classification by bias-contrastive learning and bias-balanced regression. | 7,662 | null | null |
hqDb8d65Vfh | Efficiently Identifying Task Groupings for Multi-Task Learning | https://openreview.net/forum?id=hqDb8d65Vfh | [
"Christopher Fifty",
"Ehsan Amid",
"Zhe Zhao",
"Tianhe Yu",
"Rohan Anil",
"Chelsea Finn"
] | Spotlight | null | Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naively training all tasks together in one model often degrades performance, and exhaustively searching through combinations of task groupings can be prohibitively expensive. As a result, effi... | [
"multi-task learning",
"task groupings",
"which tasks should train together"
] | An efficient approach to determine which tasks should train together in multi-task learning networks. | 7,658 | 2109.04617 | title_snapshot |
CzVPfeqPOBu | Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization | https://openreview.net/forum?id=CzVPfeqPOBu | [
"Qi Zhu",
"Carl Yang",
"Yidan Xu",
"Haonan Wang",
"Chao Zhang",
"Jiawei Han"
] | Poster | null | Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs. Some recent work started to study the pre-training of GNNs. However, none of them provide theoretical insights into the design of their frameworks, or clear requirem... | [
"graph neural network",
"transfer learning",
"pre-training",
"domain adaptation",
"theoretical guarantee"
] | We propose a theoretically guaranteed and practically effective GNN model for graph transfer learning | 7,656 | 2009.05204 | title_snapshot |
UMrf6F4Tg9c | Fast Abductive Learning by Similarity-based Consistency Optimization | https://openreview.net/forum?id=UMrf6F4Tg9c | [
"Yu-Xuan Huang",
"Wang-Zhou Dai",
"Le-Wen Cai",
"Stephen Muggleton",
"Yuan Jiang"
] | Poster | null | To utilize the raw inputs and symbolic knowledge simultaneously, some recent neuro-symbolic learning methods use abduction, i.e., abductive reasoning, to integrate sub-symbolic perception and logical inference. While the perception model, e.g., a neural network, outputs some facts that are inconsistent with the symboli... | [
"abductive learning",
"abduction",
"symbolic learning",
"neuro-symbolic learning"
] | We propose a fast neuro-symbolic learning method that can perform logical abduction while considering the similarity among training instances in feature space. | 7,649 | null | null |
fU7-so5RRhW | Clockwork Variational Autoencoders | https://openreview.net/forum?id=fU7-so5RRhW | [
"Vaibhav Saxena",
"Jimmy Ba",
"Danijar Hafner"
] | Poster | null | Deep learning has enabled algorithms to generate realistic images. However, accurately predicting long video sequences requires understanding long-term dependencies and remains an open challenge. While existing video prediction models succeed at generating sharp images, they tend to fail at accurately predicting far in... | [
"temporal abstraction",
"variational inference",
"recurrent neural networks",
"deep learning"
] | null | 7,636 | 2102.09532 | title_snapshot |
C0bV8xGhsz | Last-iterate Convergence in Extensive-Form Games | https://openreview.net/forum?id=C0bV8xGhsz | [
"Chung-Wei Lee",
"Christian Kroer",
"Haipeng Luo"
] | Poster | null | Regret-based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games. However, most regret-based algorithms, including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence. Inspired by recent advances on last-... | [
"last-iterate convergence",
"optimistic algorithms",
"extensive-form games",
"no-regret learning",
"two-player zero-sum games",
"online learning",
"game dynamics"
] | null | 7,634 | 2106.14326 | title_snapshot |
e5RK939Zz1S | Adversarial Robustness with Semi-Infinite Constrained Learning | https://openreview.net/forum?id=e5RK939Zz1S | [
"Alexander Robey",
"Luiz F. O. Chamon",
"George J. Pappas",
"Hamed Hassani",
"Alejandro Ribeiro"
] | Poster | null | Despite strong performance in numerous applications, the fragility of deep learning to input perturbations has raised serious questions about its use in safety-critical domains. While adversarial training can mitigate this issue in practice, state-of-the-art methods are increasingly application-dependent, heuristic in... | [
"Robust learning",
"adversarial robustness",
"constrained learning",
"semi-infinite programming"
] | null | 7,631 | 2110.15767 | title_snapshot |
S2-j0ZegyrE | Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent | https://openreview.net/forum?id=S2-j0ZegyrE | [
"Xingchao Liu",
"Xin Tong",
"qiang liu"
] | Spotlight | null | Finding diverse and representative Pareto solutions from the Pareto front is a key challenge in multi-objective optimization (MOO). In this work, we propose a novel gradient-based algorithm for profiling Pareto front by using Stein variational gradient descent (SVGD). We also provide a counterpart of our method based o... | [
"Pareto Front",
"Sampling",
"Multi-objective Learning"
] | We design Multi-objective Stein Variational Gradient Descent that can profile the whole Pareto front with particles. | 7,628 | null | null |
MT0pTKLyzkT | Self-Supervised GANs with Label Augmentation | https://openreview.net/forum?id=MT0pTKLyzkT | [
"Liang Hou",
"Huawei Shen",
"Qi Cao",
"Xueqi Cheng"
] | Poster | null | Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environment. However, the separate self-supervised tasks in existing self-supervised GANs cause a goal inconsis... | [
"generative adversarial networks",
"self-supervised learning",
"label augmentation"
] | We propose a novel self-supervised GAN without causing any undesired goal to the generator. | 7,625 | 2106.08601 | title_snapshot |
0DBYkHfkZlk | Parameter-free HE-friendly Logistic Regression | https://openreview.net/forum?id=0DBYkHfkZlk | [
"Junyoung Byun",
"Woojin Lee",
"Jaewook Lee"
] | Poster | null | Privacy in machine learning has been widely recognized as an essential ethical and legal issue, because the data used for machine learning may contain sensitive information. Homomorphic encryption has recently attracted attention as a key solution to preserve privacy in machine learning applications. However, current a... | [
"homomorphic encryption",
"logistic regression",
"parameter-free"
] | We propose an effective privacy-preserving logistic regression method which is free from hyperparameter selection. | 7,623 | null | null |
824xC-SgWgU | Efficient Training of Retrieval Models using Negative Cache | https://openreview.net/forum?id=824xC-SgWgU | [
"Erik Lindgren",
"Sashank J. Reddi",
"Ruiqi Guo",
"Sanjiv Kumar"
] | Poster | null | Factorized models, such as two tower neural network models, are widely used for scoring (query, document) pairs in information retrieval tasks. These models are typically trained by optimizing the model parameters to score relevant ``positive" pairs higher than the irrelevant ``negative" ones. While a large set of nega... | [
"dual encoders",
"extreme classification",
"negative sampling",
"recommender systems"
] | We develop a streaming negative cache for efficient training of retrieval models. | 7,622 | null | null |
5Ya8PbvpZ9 | BARTScore: Evaluating Generated Text as Text Generation | https://openreview.net/forum?id=5Ya8PbvpZ9 | [
"Weizhe Yuan",
"Graham Neubig",
"Pengfei Liu"
] | Poster | null | A wide variety of NLP applications, such as machine translation, summarization, and dialog, involve text generation. One major challenge for these applications is how to evaluate whether such generated texts are actually fluent, accurate, or effective. In this work, we conceptualize the evaluation of generated text as ... | [
"Pre-trained models",
"Text Generation",
"Sequence-to-Sequence"
] | Using text generation probability to evaluate the quality of generated text. | 7,605 | 2106.11520 | title_snapshot |
0qnPBmvJSaf | Monte Carlo Tree Search With Iteratively Refining State Abstractions | https://openreview.net/forum?id=0qnPBmvJSaf | [
"Samuel Sokota",
"Caleb Ho",
"Zaheen Farraz Ahmad",
"J Zico Kolter"
] | Poster | null | Decision-time planning is the process of constructing a transient, local policy with the intent of using it to make the immediate decision. Monte Carlo tree search (MCTS), which has been leveraged to great success in Go, chess, shogi, Hex, Atari, and other settings, is perhaps the most celebrated decision-time planning... | [
"decision-time planning",
"stochasticity",
"reinforcement learning",
"tree search"
] | An augmentation of MCTS for stochastic settings | 7,599 | null | null |
-646c8bpgPl | Visual Adversarial Imitation Learning using Variational Models | https://openreview.net/forum?id=-646c8bpgPl | [
"Rafael Rafailov",
"Tianhe Yu",
"Aravind Rajeswaran",
"Chelsea Finn"
] | Poster | null | Reward function specification, which requires considerable human effort and iteration, remains a major impediment for learning behaviors through deep reinforcement learning. In contrast, providing visual demonstrations of desired behaviors presents an easier and more natural way to teach agents. We consider a setting w... | [
"imitation learning",
"visual imitation learning",
"deep reinforcement learning"
] | We train a distribution-matching imitation-learning algorithm using variational models of image-based environments. | 7,596 | 2107.08829 | title_snapshot |
_61Qh8tULj_ | Conflict-Averse Gradient Descent for Multi-task learning | https://openreview.net/forum?id=_61Qh8tULj_ | [
"Bo Liu",
"Xingchao Liu",
"Xiaojie Jin",
"Peter Stone",
"qiang liu"
] | Poster | null | The goal of multi-task learning is to enable more efficient learning than single task learning by sharing model structures for a diverse set of tasks. A standard multi-task learning objective is to minimize the average loss across all tasks. While straightforward, using this objective often results in much worse final ... | [
"multi-task learning"
] | we propose a new gradient manipulation method for multi-task learning that mitigates the conflicting gradient problem while still provably converging to an optimum point of the average loss. | 7,590 | 2110.14048 | title_snapshot |
lHvy0DLYWm | When Is Generalizable Reinforcement Learning Tractable? | https://openreview.net/forum?id=lHvy0DLYWm | [
"Dhruv Malik",
"Yuanzhi Li",
"Pradeep Kumar Ravikumar"
] | Poster | null | Agents trained by reinforcement learning (RL) often fail to generalize beyond the environment they were trained in, even when presented with new scenarios that seem similar to the training environment. We study the query complexity required to train RL agents that generalize to multiple environments. Intuitively, tract... | [
"reinforcement learning",
"generalization"
] | We prove lower and upper bounds on the sample complexity required by RL to generalize to multiple environments. | 7,570 | 2101.00300 | title_snapshot |
NWYlZ5z8Q-R | See More for Scene: Pairwise Consistency Learning for Scene Classification | https://openreview.net/forum?id=NWYlZ5z8Q-R | [
"Gongwei Chen",
"Xinhang Song",
"Bohan Wang",
"Shuqiang Jiang"
] | Poster | null | Scene classification is a valuable classification subtask and has its own characteristics which still needs more in-depth studies. Basically, scene characteristics are distributed over the whole image, which cause the need of “seeing” comprehensive and informative regions. Previous works mainly focus on region discover... | [
"pairwise learning",
"scene classification",
"convolution neural networks"
] | From the perspective of the focus area, we explore a new way to understand scene classification and propose a new learning scheme with a tailored loss in accordance with the empirical knowledge about scenes. | 7,555 | null | null |
4CRpaV4pYp | Medical Dead-ends and Learning to Identify High-Risk States and Treatments | https://openreview.net/forum?id=4CRpaV4pYp | [
"Mehdi Fatemi",
"Taylor W. Killian",
"Jayakumar Subramanian",
"Marzyeh Ghassemi"
] | Poster | null | Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal decision-making policy identification via reinforcement learning. In data-constrained offline settings, both approaches may fail as they assume fully optimal behavior or rely on exploring altern... | [
"medical deadends",
"treatment security",
"reinforcement learning",
"offline rl",
"sepsis",
"healthcare"
] | We present a methodology with formal proof to learn security with respect to dead-end states, where all future treatments lead to death. | 7,546 | 2110.04186 | title_snapshot |
ySFGlFjgIfN | Towards Robust Bisimulation Metric Learning | https://openreview.net/forum?id=ySFGlFjgIfN | [
"Mete Kemertas",
"Tristan Ty Aumentado-Armstrong"
] | Poster | null | Learned representations in deep reinforcement learning (DRL) have to extract task-relevant information from complex observations, balancing between robustness to distraction and informativeness to the policy. Such stable and rich representations, often learned via modern function approximation techniques, can enable pr... | [
"reinforcement learning",
"bisimulation",
"state abstraction",
"sparse rewards",
"state similarity metrics",
"representation learning",
"continuous control"
] | We theoretically analyze on-policy bisimulation metric learning, and improve its performance in distracting and sparse reward environments. | 7,537 | 2110.14096 | title_snapshot |
VvUldGZ3izR | ELLA: Exploration through Learned Language Abstraction | https://openreview.net/forum?id=VvUldGZ3izR | [
"Suvir Mirchandani",
"Siddharth Karamcheti",
"Dorsa Sadigh"
] | Poster | null | Building agents capable of understanding language instructions is critical to effective and robust human-AI collaboration. Recent work focuses on training these agents via reinforcement learning in environments with synthetic language; however, instructions often define long-horizon, sparse-reward tasks, and learning p... | [
"instruction following",
"reward shaping",
"reinforcement learning"
] | We introduce a reward shaping approach for guiding exploration in instruction following settings by correlating complex, high-level instructions with simple, low-level behaviors. | 7,528 | 2103.05825 | title_snapshot |
gISH-80g05u | BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation | https://openreview.net/forum?id=gISH-80g05u | [
"Mingcong Liu",
"Qiang Li",
"Zekui Qin",
"Guoxin Zhang",
"Pengfei Wan",
"Wen Zheng"
] | Poster | null | Generative Adversarial Networks (GANs) have made a dramatic leap in high-fidelity image synthesis and stylized face generation. Recently, a layer-swapping mechanism has been developed to improve the stylization performance. However, this method is incapable of fitting arbitrary styles in a single model and requires hun... | [
"GAN",
"image generation",
"stylized face synthesis",
"arbitrary style transfer"
] | We present BlendGAN for arbitrary stylized face generation, which provides both latent-guided and reference-guided synthesis to generate high-quality natural and stylized face image pairs. | 7,526 | 2110.11728 | title_snapshot |
G1jmxFOtY_ | Learning with User-Level Privacy | https://openreview.net/forum?id=G1jmxFOtY_ | [
"Daniel Asher Nathan Levy",
"Ziteng Sun",
"Kareem Amin",
"Satyen Kale",
"Alex Kulesza",
"Mehryar Mohri",
"Ananda Theertha Suresh"
] | Poster | null | We propose and analyze algorithms to solve a range of learning tasks under user-level differential privacy constraints. Rather than guaranteeing only the privacy of individual samples, user-level DP protects a user's entire contribution ($m \ge 1$ samples), providing more stringent but more realistic protection against... | [
"privacy",
"user-level",
"optimization",
"sco",
"learning",
"minimax",
"lower bounds",
"differential privacy"
] | How to solve learning tasks under a more stringent but more realistic notion of privacy where each user contributes $m\ge 1$ samples and we wish to protect information leaks at the user-level. | 7,522 | 2102.11845 | title_snapshot |
hMY6nm9lld | Predicting Molecular Conformation via Dynamic Graph Score Matching | https://openreview.net/forum?id=hMY6nm9lld | [
"Shitong Luo",
"Chence Shi",
"Minkai Xu",
"Jian Tang"
] | Poster | null | Predicting stable 3D conformations from 2D molecular graphs has been a long-standing challenge in computational chemistry. Recently, machine learning approaches have demonstrated very promising results compared to traditional experimental and physics-based simulation methods. These approaches mainly focus on modeling t... | [
"Molecular conformation generation",
"score matching",
"generative model"
] | we propose Dynamic Graph Score Matching (DGSM) for molecular conformation prediction, which models both the local and long-range interactions within molecules. | 7,517 | null | null |
VMAfyuC3uXP | Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration | https://openreview.net/forum?id=VMAfyuC3uXP | [
"Yan Sun",
"Wenjun Xiong",
"Faming Liang"
] | Poster | null | Deep learning has powered recent successes of artificial intelligence (AI). However, the deep neural network, as the basic model of deep learning, has suffered from issues such as local traps and miscalibration. In this paper, we provide a new framework for sparse deep learning, which has the above issues addressed in... | [
"Asymptotic Normality",
"Posterior Consistency",
"Prior Annealing",
"Structure Selection",
"Uncertainty Quantification"
] | null | 7,516 | 2110.00653 | title_snapshot |
rJwDMui8DI | Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex | https://openreview.net/forum?id=rJwDMui8DI | [
"Colin Conwell",
"David Mayo",
"Andrei Barbu",
"Michael A Buice",
"George A. Alvarez",
"Boris Katz"
] | Poster | null | How well do deep neural networks fare as models of mouse visual cortex? A majority of research to date suggests results far more mixed than those produced in the modeling of primate visual cortex. Here, we perform a large-scale benchmarking of dozens of deep neural network models in mouse visual cortex with both repres... | [
"neuro_ai",
"deep neural networks",
"rodent visual cortex",
"mouse brains",
"optical physiology"
] | we offer a large-scale benchmarking of hundreds of deep neural network models in mouse visual cortex using multiple methods of comparison (encoding models, RSA) | 7,514 | null | null |
2lBhfVPYOM | (Almost) Free Incentivized Exploration from Decentralized Learning Agents | https://openreview.net/forum?id=2lBhfVPYOM | [
"Chengshuai Shi",
"Haifeng Xu",
"Wei Xiong",
"Cong Shen"
] | Poster | null | Incentivized exploration in multi-armed bandits (MAB) has witnessed increasing interests and many progresses in recent years, where a principal offers bonuses to agents to do explorations on her behalf. However, almost all existing studies are confined to temporary myopic agents. In this work, we break this barrier and... | [
"Incentivized Exploration",
"Principal Agent Models",
"Multi-armed Bandits"
] | This work studied incentivized exploration in multi-armed bandits with decentralized self-interested agents, and found that when there are sufficiently many learning agents involved, the exploration process of the principal can be (almost) free. | 7,509 | 2110.14628 | title_snapshot |
WL7pr00_fnJ | Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? | https://openreview.net/forum?id=WL7pr00_fnJ | [
"Xiaolong Ma",
"Geng Yuan",
"Xuan Shen",
"Tianlong Chen",
"Xuxi Chen",
"Xiaohan Chen",
"Ning Liu",
"Minghai Qin",
"Sijia Liu",
"Zhangyang Wang",
"Yanzhi Wang"
] | Poster | null | There have been long-standing controversies and inconsistencies over the experiment setup and criteria for identifying the "winning ticket" in literature. To reconcile such, we revisit the definition of lottery ticket hypothesis, with comprehensive and more rigorous conditions. Under our new definition, we show concret... | [
"Lottery ticket hypothesis"
] | null | 7,496 | 2107.00166 | title_snapshot |
vLvsnP64VC0 | Dynamic Trace Estimation | https://openreview.net/forum?id=vLvsnP64VC0 | [
"Prathamesh Dharangutte",
"Christopher P Musco"
] | Poster | null | We study a dynamic version of the implicit trace estimation problem. Given access to an oracle for computing matrix-vector multiplications with a dynamically changing matrix A, our goal is to maintain an accurate approximation to A's trace using as few multiplications as possible. We present a practical algorithm for s... | [
"randomized numerical linear algebra",
"trace estimation",
"dynamic algorithms"
] | null | 7,490 | 2110.13752 | title_snapshot |
FYDE3I9fev0 | Influence Patterns for Explaining Information Flow in BERT | https://openreview.net/forum?id=FYDE3I9fev0 | [
"Kaiji Lu",
"Zifan Wang",
"Piotr Mardziel",
"Anupam Datta"
] | Poster | null | While attention is all you need may be proving true, we do not know why: attention-based transformer models such as BERT are superior but how information flows from input tokens to output predictions are unclear. We introduce influence patterns, abstractions of sets of paths through a transformer model. Patterns qua... | [
"BERT",
"interpretability",
"attention",
"information flow",
"Transformers"
] | This paper introduces a new technique for explaining the information flow in BERT's computational graph using gradient-based method. | 7,478 | 2011.00740 | title_snapshot |
CRFSrgYtV7m | MERLOT: Multimodal Neural Script Knowledge Models | https://openreview.net/forum?id=CRFSrgYtV7m | [
"Rowan Zellers",
"Ximing Lu",
"Jack Hessel",
"Youngjae Yu",
"Jae Sung Park",
"Jize Cao",
"Ali Farhadi",
"Yejin Choi"
] | Oral | null | As humans, we understand events in the visual world contextually, performing multimodal reasoning across time to make inferences about the past, present, and future. We introduce MERLOT, a model that learns multimodal script knowledge by watching millions of YouTube videos with transcribed speech -- in an entirely labe... | [
"commonsense reasoning",
"representation learning",
"vision and language"
] | We learn multimodal, temporal representations about “how the world works” through videos. | 7,477 | 2106.02636 | title_snapshot |
z-l1kpDXs88 | TokenLearner: Adaptive Space-Time Tokenization for Videos | https://openreview.net/forum?id=z-l1kpDXs88 | [
"Michael S Ryoo",
"AJ Piergiovanni",
"Anurag Arnab",
"Mostafa Dehghani",
"Anelia Angelova"
] | Poster | null | In this paper, we introduce a novel visual representation learning which relies on a handful of adaptively learned tokens, and which is applicable to both image and video understanding tasks. Instead of relying on hand-designed splitting strategies to obtain visual tokens and processing a large number of densely sample... | [
"visual representation",
"transformers",
"video understanding"
] | Instead of relying on hand-designed splitting strategies to obtain visual tokens and processing a large number of densely sampled patches for attention, TokenLearner learns to mine important tokens in visual data. | 7,474 | null | null |
KzYIEQ_B1BX | Continuous Latent Process Flows | https://openreview.net/forum?id=KzYIEQ_B1BX | [
"Ruizhi Deng",
"Marcus A Brubaker",
"Greg Mori",
"Andreas Lehrmann"
] | Poster | null | Partial observations of continuous time-series dynamics at arbitrary time stamps exist in many disciplines. Fitting this type of data using statistical models with continuous dynamics is not only promising at an intuitive level but also has practical benefits, including the ability to generate continuous trajectories a... | [
"continuous dynamics",
"time series",
"stochastic differential equation",
"normalizing flow"
] | null | 7,470 | 2106.15580 | title_snapshot |
kLWGdQYsmC5 | Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training | https://openreview.net/forum?id=kLWGdQYsmC5 | [
"Shangshu Qian",
"Hung Viet Pham",
"Thibaud Lutellier",
"Zeou Hu",
"Jungwon Kim",
"Lin Tan",
"Yaoliang Yu",
"Jiahao Chen",
"Sameena Shah"
] | Poster | null | Deep learning (DL) systems have been gaining popularity in critical tasks such as credit evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL software implementations introduce variance: identical DL training runs (i.e., identical network, data, configuration, software, and hardware)... | [
"DL software implementation",
"fairness variance",
"empirical study"
] | We perform a large-scale empirical study on DL software implementations' impact on the fairness of DL systems. | 7,459 | null | null |
HbaQ4FEh-6 | Adapting to function difficulty and growth conditions in private optimization | https://openreview.net/forum?id=HbaQ4FEh-6 | [
"Hilal Asi",
"Daniel Asher Nathan Levy",
"John Duchi"
] | Poster | null | We develop algorithms for private stochastic convex optimization that adapt to the hardness of the specific function we wish to optimize. While previous work provide worst-case bounds for arbitrary convex functions, it is often the case that the function at hand belongs to a smaller class that enjoys faster rates. Conc... | [
"privacy",
"differential privacy",
"adaptivity",
"optimization",
"convex",
"learning",
"sco",
"lower bounds",
"minimax",
"growth"
] | We provide private adaptive algorithms that adapt to the growth of the actual instance and improves upon standard worst-case rates and show that on easier functions, the extra error incurred by privacy shrinks significantly. | 7,457 | 2108.02391 | title_snapshot |
djbC2A4uTHP | Consistency Regularization for Variational Auto-Encoders | https://openreview.net/forum?id=djbC2A4uTHP | [
"Samarth Sinha",
"Adji Bousso Dieng"
] | Poster | null | Variational Auto-Encoders (VAEs) are a powerful approach to unsupervised learning. They enable scalable approximate posterior inference in latent-variable models using variational inference. A VAE posits a variational family parameterized by a deep neural network---called an encoder---that takes data as input. This enc... | [
"Variational Auto-Encoders"
] | A simple and general regularization technique to improve representations learned by VAEs and generalize better. | 7,454 | 2105.14859 | title_snapshot |
W-agFo22-TS | TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks | https://openreview.net/forum?id=W-agFo22-TS | [
"YU LI",
"Min Li",
"Qiuxia LAI",
"Yannan Liu",
"Qiang Xu"
] | Poster | null | Deep learning (DL) systems are notoriously difficult to test and debug due to the lack of correctness proof and the huge test input space to cover. Given the ubiquitous unlabeled test data and high labeling cost, in this paper, we propose a novel test prioritization technique, namely TestRank, which aims at revealing m... | [
"Deep Neural Networks",
"Debugging",
"Testing",
"Graph Neural Network",
"Test input prioritization"
] | A novel test input prioritization technique for efficient testing and debugging of deep neural network | 7,453 | 2105.10113 | title_snapshot |
10anajdGZm | Provably Faster Algorithms for Bilevel Optimization | https://openreview.net/forum?id=10anajdGZm | [
"Junjie Yang",
"Kaiyi Ji",
"Yingbin Liang"
] | Spotlight | null | Bilevel optimization has been widely applied in many important machine learning applications such as hyperparameter optimization and meta-learning. Recently, several momentum-based algorithms have been proposed to solve bilevel optimization problems faster. However, those momentum-based algorithms do not achieve provab... | [
"Bilevel Optimization",
"Momentum",
"Recursive Gradient Estimator",
"Hessian Vector Computation"
] | This paper proposes two bilevel optimizers that provably outperform all existing algorithms by the order of magnitude. | 7,452 | 2106.04692 | title_snapshot |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.