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9jRH00HT4-4 | Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media | https://openreview.net/forum?id=9jRH00HT4-4 | [
"Yiyue Qian",
"Yiming Zhang",
"Yanfang Ye",
"Chuxu Zhang"
] | Poster | null | Driven by the considerable profits, the crime of drug trafficking (a.k.a. illicit drug trading) has co-evolved with modern technologies, e.g., social media such as Instagram has become a popular platform for marketing and selling illicit drugs. The activities of online drug trafficking are nimble and resilient, which c... | [
"graph representation learning",
"few-shot learning",
"drug trafficker detection"
] | develop a novel heterogeneous graph model for illicit drug trafficker detection. | 9,359 | null | null |
9FREJhzo1q | A sampling-based circuit for optimal decision making | https://openreview.net/forum?id=9FREJhzo1q | [
"Camille E. Rullán Buxó",
"Cristina Savin"
] | Spotlight | null | Many features of human and animal behavior can be understood in the framework of Bayesian inference and optimal decision making, but the biological substrate of such processes is not fully understood. Neural sampling provides a flexible code for probabilistic inference in high dimensions and explains key features of se... | [
"computational neuroscience",
"neural sampling",
"spiking network",
"optimal decision making"
] | We describe a spiking neural circuit that performs approximately optimal decision making and analyze its computational and representational properties. | 9,345 | null | null |
0FDxsIEv9G | Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation | https://openreview.net/forum?id=0FDxsIEv9G | [
"Liyuan Xu",
"Heishiro Kanagawa",
"Arthur Gretton"
] | Poster | null | Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on outcomes in the presence of unobserved confounding, using proxies (structured side information) for the confounder. This is achieved via two-stage regression: in the first stage, we model relations among the treatment and proxies;... | [
"Causal Inference",
"Deep Learning",
"Off-policy evaluation"
] | null | 9,342 | 2106.03907 | title_snapshot |
V5prUHOrOP4 | Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning | https://openreview.net/forum?id=V5prUHOrOP4 | [
"Milad Abdollahzadeh",
"Touba Malekzadeh",
"Ngai-man Cheung"
] | Poster | null | Multimodal meta-learning is a recent problem that extends conventional few-shot meta-learning by generalizing its setup to diverse multimodal task distributions. This setup makes a step towards mimicking how humans make use of a diverse set of prior skills to learn new skills. Previous work has achieved encouraging per... | [
"Meta-Learning",
"Few-Shot Learning",
"Multi-Task Learning",
"Transference Analysis"
] | This paper proposes a method to quantify knowledge transfer between few-shot tasks. It also proposes a multimodal meta-learner which advances the state-of-the-art with significant margins. | 9,338 | 2110.14202 | title_snapshot |
jar9C-V8GH | Coresets for Time Series Clustering | https://openreview.net/forum?id=jar9C-V8GH | [
"Lingxiao Huang",
"K. Sudhir",
"Nisheeth K Vishnoi"
] | Spotlight | null | We study the problem of constructing coresets for clustering problems with time series data. This problem has gained importance across many fields including biology, medicine, and economics due to the proliferation of sensors facilitating real-time measurement and rapid drop in storage costs. In particular, we consid... | [
"coresets",
"clustering",
"time series data",
"Gaussian mixture model",
"autocorrelation"
] | We present an efficient algorithm to construct coresets for clustering Gaussian mixture time series data. | 9,329 | 2110.15263 | title_snapshot |
a9WXj5XV5mK | Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks | https://openreview.net/forum?id=a9WXj5XV5mK | [
"Maksym Yatsura",
"Jan Hendrik Metzen",
"Matthias Hein"
] | Poster | null | Adversarial attacks based on randomized search schemes have obtained state-of-the-art results in black-box robustness evaluation recently. However, as we demonstrate in this work, their efficiency in different query budget regimes depends on manual design and heuristic tuning of the underlying proposal distributions. W... | [
"black-box adversarial attacks",
"meta-learning"
] | Meta-learned adaptive controllers of the search distribution can make random search based adversarial attacks, in particular Square Attack, more efficient in different query budget regimes. | 9,328 | 2111.01714 | title_snapshot |
soDi-HkzC1 | Generative vs. Discriminative: Rethinking The Meta-Continual Learning | https://openreview.net/forum?id=soDi-HkzC1 | [
"Mohammad Amin Banayeean Zade",
"Rasoul Mirzaiezadeh",
"Hosein Hasani",
"Mahdieh Soleymani Baghshah"
] | Poster | null | Deep neural networks have achieved human-level capabilities in various learning tasks. However, they generally lose performance in more realistic scenarios like learning in a continual manner. In contrast, humans can incorporate their prior knowledge to learn new concepts efficiently without forgetting older ones. In t... | [
"Continual Learning",
"Meta-Learning",
"Meta-Continual Learning",
"Generative Classifier",
"Bayesian Classifier",
"Neuro-Inspired Artificial Intelligence"
] | This paper addresses catastrophic forgetting in continual learning using a meta-learned feature extractor and a generative classifier. | 9,313 | null | null |
Xl1Z1L9DBIJ | Topological Attention for Time Series Forecasting | https://openreview.net/forum?id=Xl1Z1L9DBIJ | [
"Sebastian Zeng",
"Florian Graf",
"Christoph Hofer",
"Roland Kwitt"
] | Poster | null | The problem of (point) forecasting univariate time series is considered. Most approaches, ranging from traditional statistical methods to recent learning-based techniques with neural networks, directly operate on raw time series observations. As an extension, we study whether local topological properties, as captured v... | [
"Time series forecasting",
"Persistent homology",
"Attention",
"Topological Data Analysis"
] | We study whether local topological properties of time series, as captured via persistent homology, can serve as a reliable signal that provides complementary information for learning to forecast. | 9,296 | 2107.09031 | title_snapshot |
ErivP29kYnx | ReSSL: Relational Self-Supervised Learning with Weak Augmentation | https://openreview.net/forum?id=ErivP29kYnx | [
"Mingkai Zheng",
"Shan You",
"Fei Wang",
"Chen Qian",
"Changshui Zhang",
"Xiaogang Wang",
"Chang Xu"
] | Poster | null | Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most of methods mainly focus on the instance level information (\ie, the different augmented images of the same instance should have the same feat... | [
"Self-Supervised Learning"
] | null | 9,291 | 2107.09282 | title_snapshot |
A9HVNx1J8Pc | Online Facility Location with Multiple Advice | https://openreview.net/forum?id=A9HVNx1J8Pc | [
"Matteo Almanza",
"Flavio Chierichetti",
"Silvio Lattanzi",
"Alessandro Panconesi",
"Giuseppe Re"
] | Poster | null | Clustering is a central topic in unsupervised learning and its online formulation has received a lot of attention in recent years. In this paper, we study the classic facility location problem in the presence of multiple machine-learned advice. We design an algorithm with provable performance guarantees such that, if t... | [
"Clustering",
"Facility Location",
"Online Algorithms",
"Machine-Learned Advice",
"Online Clustering"
] | null | 9,270 | null | null |
BFYlnDtJSqW | Efficient Combination of Rematerialization and Offloading for Training DNNs | https://openreview.net/forum?id=BFYlnDtJSqW | [
"Olivier Beaumont",
"Lionel Eyraud-Dubois",
"Alena SHILOVA"
] | Poster | null | Rematerialization and offloading are two well known strategies to save memory during the training phase of deep neural networks, allowing data scientists to consider larger models, batch sizes or higher resolution data. Rematerialization trades memory for computation time, whereas Offloading trades memory for data mo... | [
"combinatorial optimization algorithms",
"dynamic programming",
"rematerialization",
"offloading",
"checkpointing",
"memory constraint",
"training",
"deep neural networks",
"feed forward backpropagation training"
] | We propose an optimized algorithm to compute a sequence of forward / backward / offload / prefetch operations on activations that optimizes training throughput of linearized DNNs under memory constraints. | 9,257 | null | null |
sBBnfOFtPc | Locality defeats the curse of dimensionality in convolutional teacher-student scenarios | https://openreview.net/forum?id=sBBnfOFtPc | [
"Alessandro Favero",
"Francesco Cagnetta",
"Matthieu Wyart"
] | Poster | null | Convolutional neural networks perform a local and translationally-invariant treatment of the data: quantifying which of these two aspects is central to their success remains a challenge. We study this problem within a teacher-student framework for kernel regression, using 'convolutional' kernels inspired by the neural ... | [
"Deep learning",
"Convolutional Neural Networks",
"Kernel Methods",
"Learning Curves"
] | We compute the learning curves of convolutional kernels in a teacher-student framework. We find that locality is key to beat the curse of dimensionality, while translational invariance is not. | 9,253 | 2106.08619 | title_snapshot |
UlSjqPEkI1V | What Makes Multi-Modal Learning Better than Single (Provably) | https://openreview.net/forum?id=UlSjqPEkI1V | [
"Yu Huang",
"Chenzhuang Du",
"Zihui Xue",
"Xuanyao Chen",
"Hang Zhao",
"Longbo Huang"
] | Poster | null | The world provides us with data of multiple modalities. Intuitively, models fusing data from different modalities outperform their uni-modal counterparts, since more information is aggregated. Recently, joining the success of deep learning, there is an influential line of work on deep multi-modal learning, which has re... | [
"Multi-modal learning theory"
] | We provably show what makes multi-modal outperform unimodal | 9,239 | 2106.04538 | title_snapshot |
VtlGqVzja48 | Identifiability in inverse reinforcement learning | https://openreview.net/forum?id=VtlGqVzja48 | [
"Haoyang Cao",
"Samuel N Cohen",
"Lukasz Szpruch"
] | Poster | null | Inverse reinforcement learning attempts to reconstruct the reward function in a Markov decision problem, using observations of agent actions. As already observed in Russell [1998] the problem is ill-posed, and the reward function is not identifiable, even under the presence of perfect information about optimal behavior... | [
"Inverse reinforcement learning",
"identifiability",
"entropy regularization",
"optimal control",
"Markov decision problem"
] | null | 9,224 | 2106.03498 | title_snapshot |
Ic9vRN3VpZ | Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction | https://openreview.net/forum?id=Ic9vRN3VpZ | [
"Seongjun Yun",
"Seoyoon Kim",
"Junhyun Lee",
"Jaewoo Kang",
"Hyunwoo J. Kim"
] | Poster | null | Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data. They have shown significant improvements over traditional heuristic methods in various tasks such as node classification and graph classification. However, since GNNs heavily rely on smoothed node features r... | [
"Graph Neural Networks",
"Link Prediction",
"Graph Structure",
"Common Neighbors"
] | null | 9,219 | 2206.04216 | title_snapshot |
6wuE1-G4pu6 | Optimal Underdamped Langevin MCMC Method | https://openreview.net/forum?id=6wuE1-G4pu6 | [
"Zhengmian Hu",
"Feihu Huang",
"Heng Huang"
] | Poster | null | In the paper, we study the underdamped Langevin diffusion (ULD) with strongly-convex potential consisting of finite summation of $N$ smooth components, and propose an efficient discretization method, which requires $O(N+d^\frac{1}{3}N^\frac{2}{3}/\varepsilon^\frac{2}{3})$ gradient evaluations to achieve $\varepsilon$-e... | [
"sampling",
"underdamped Langevin diffusion",
"Bayesian methods"
] | We propose an optimal method for estimating ULD with sum decomposible potential function and prove that it is indeed optimal. | 9,214 | null | null |
FM8auLVlRMo | A Faster Maximum Cardinality Matching Algorithm with Applications in Machine Learning | https://openreview.net/forum?id=FM8auLVlRMo | [
"Nathaniel Lahn",
"Sharath Raghvendra",
"Jiacheng Ye"
] | Poster | null | Maximum cardinality bipartite matching is an important graph optimization problem with several applications. For instance, maximum cardinality matching in a $\delta$-disc graph can be used in the computation of the bottleneck matching as well as the $\infty$-Wasserstein and the Lévy-Prokhorov distances between probabil... | [
"bipartite matching",
"Wasserstein distance",
"Levy-Prokhorov distance",
"bottleneck matching",
"unit disc graph matching"
] | null | 9,198 | null | null |
QkljT4mrfs | Partial success in closing the gap between human and machine vision | https://openreview.net/forum?id=QkljT4mrfs | [
"Robert Geirhos",
"Kantharaju Narayanappa",
"Benjamin Mitzkus",
"Tizian Thieringer",
"Matthias Bethge",
"Felix A. Wichmann",
"Wieland Brendel"
] | Oral | null | A few years ago, the first CNN surpassed human performance on ImageNet. However, it soon became clear that machines lack robustness on more challenging test cases, a major obstacle towards deploying machines "in the wild" and towards obtaining better computational models of human visual perception. Here we ask: Are we ... | [
"psychophysics",
"machine vision",
"human vision",
"out-of-distribution generalisation",
"visual perception",
"robustness",
"human behaviour"
] | Data-rich models are closing the gap to human OOD distortion robustness and improve image-level consistency with human psychophysical data. | 9,196 | 2106.07411 | title_snapshot |
2E4AT-qj3Dg | Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks | https://openreview.net/forum?id=2E4AT-qj3Dg | [
"Iulia Duta",
"Andrei Liviu Nicolicioiu",
"Marius Leordeanu"
] | Poster | null | Graph Neural Networks are perfectly suited to capture latent interactions between various entities in the spatio-temporal domain (e.g. videos). However, when an explicit structure is not available, it is not obvious what atomic elements should be represented as nodes. Current works generally use pre-trained object dete... | [
"object-centric",
"graph neural networks",
"spatio-temporal"
] | Discover salient regions, correlated with objects, that are useful for visual relational processing | 9,180 | 2009.08427 | title_snapshot |
a-Lbgfy9RqV | Spot the Difference: Detection of Topological Changes via Geometric Alignment | https://openreview.net/forum?id=a-Lbgfy9RqV | [
"Steffen Czolbe",
"Aasa Feragen",
"Oswin Krause"
] | Poster | null | Geometric alignment appears in a variety of applications, ranging from domain adaptation, optimal transport, and normalizing flows in machine learning; optical flow and learned augmentation in computer vision and deformable registration within biomedical imaging. A recurring challenge is the alignment of domains whose ... | [
"Image registration",
"Geometric transformations",
"Variational Autoencoder",
"VAE",
"Topology"
] | We use a conditional VAE to detect topological changes in images. | 9,160 | 2106.08233 | title_snapshot |
lDzLzhUIwBq | Fine-grained Generalization Analysis of Inductive Matrix Completion | https://openreview.net/forum?id=lDzLzhUIwBq | [
"Antoine Ledent",
"Rodrigo Alves",
"Yunwen Lei",
"Marius Kloft"
] | Poster | null | In this paper, we bridge the gap between the state-of-the-art theoretical results for matrix completion with the nuclear norm and their equivalent in \textit{inductive matrix completion}: (1) In the distribution-free setting, we prove bounds improving the previously best scaling of $O(rd^2)$ to $\widetilde{O}(d^{3/2}\s... | [
"Inductive Matrix Completion",
"Statistical Learning Theory",
"Nuclear Norm Regularisation"
] | We prove distribution-free bounds for inductive matrix completion with rate $\widetilde{O}(d^{3/2}\sqrt{r})$ and provide an inductive analogue of the weighted trace norm which brings the rate down to $\widetilde{O}(rd)$. | 9,157 | null | null |
nzqoh6FN6sF | Estimating the Long-Term Effects of Novel Treatments | https://openreview.net/forum?id=nzqoh6FN6sF | [
"Keith Battocchi",
"Eleanor Dillon",
"Maggie Hei",
"Greg Lewis",
"Miruna Oprescu",
"Vasilis Syrgkanis"
] | Poster | null | Policy makers often need to estimate the long-term effects of novel treatments, while only having historical data of older treatment options. We propose a surrogate-based approach using a long-term dataset where only past treatments were administered and a short-term dataset where novel treatments have been administere... | [
"long-term effects",
"dynamic treatment effects",
"surrogates",
"high-dimensional",
"double machine learning"
] | Estimating long-term causal effects in high-dimensions with short-term surrogates from historical data with dynamic historical treatment policies | 9,156 | 2103.08390 | title_snapshot |
O8Ffv3aRJr | Unbalanced Optimal Transport through Non-negative Penalized Linear Regression | https://openreview.net/forum?id=O8Ffv3aRJr | [
"Laetitia Chapel",
"Rémi Flamary",
"Haoran Wu",
"Cédric Févotte",
"Gilles Gasso"
] | Poster | null | This paper addresses the problem of Unbalanced Optimal Transport (UOT) in which the marginal conditions are relaxed (using weighted penalties in lieu of equality) and no additional regularization is enforced on the OT plan. In this context, we show that the corresponding optimization problem can be reformulated as a no... | [
"Optimal Transport",
"Unbalanced Optimal Transport",
"Penalized Linear Regression",
"Lasso",
"MM algorithms"
] | We propose a reformulation of the Exact Unbalanced Optimal Transport as a Non-negative Linear Regression problem which allows us to devise new algorithms such as the first regularization path for OT and multiplicative algorithms. | 9,155 | 2106.04145 | title_snapshot |
sl_0rQmHxQk | Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation | https://openreview.net/forum?id=sl_0rQmHxQk | [
"Florian Bernard",
"Daniel Cremers",
"Anders Johan Thunberg"
] | Poster | null | We address the non-convex optimisation problem of finding a sparse matrix on the Stiefel manifold (matrices with mutually orthogonal columns of unit length) that maximises (or minimises) a quadratic objective function. Optimisation problems on the Stiefel manifold occur for example in spectral relaxations of various co... | [
"Stiefel manifold",
"quadratic optimisation",
"permutation synchronisation",
"sparsity",
"multi-matching",
"correspondence problems",
"manifold optimisation",
"QR decomposition",
"orthogonal iteration algorithm"
] | A method for finding a globally optimal solution of a quadratic objective function over the Stiefel manifold that is sparse. | 9,144 | 2110.00053 | title_snapshot |
XHHxE-KOK7 | Reinforcement Learning in Reward-Mixing MDPs | https://openreview.net/forum?id=XHHxE-KOK7 | [
"Jeongyeol Kwon",
"Yonathan Efroni",
"Constantine Caramanis",
"Shie Mannor"
] | Poster | null | Learning a near optimal policy in a partially observable system remains an elusive challenge in contemporary reinforcement learning. In this work, we consider episodic reinforcement learning in a reward-mixing Markov decision process (MDP). There, a reward function is drawn from one of $M$ possible reward models at the... | [
"Reinforcement Learning",
"Partially Observable",
"Sample-Complexity",
"Method-of-Moments",
"Latent Confounders",
"Mixture Models"
] | Efficient exploration in Reward-Mixing MDPs without assumptions when $M=2$ | 9,139 | 2110.03743 | title_snapshot |
Oeb2LbHAfJ4 | SketchGen: Generating Constrained CAD Sketches | https://openreview.net/forum?id=Oeb2LbHAfJ4 | [
"Wamiq Reyaz Para",
"Shariq Farooq Bhat",
"Paul Guerrero",
"Tom Kelly",
"Niloy Mitra",
"Leonidas Guibas",
"Peter Wonka"
] | Poster | null | Computer-aided design (CAD) is the most widely used modeling approach for technical design. The typical starting point in these designs is 2D sketches which can later be extruded and combined to obtain complex three-dimensional assemblies. Such sketches are typically composed of parametric primitives, such as points, l... | [
"CAD",
"transformers",
"generative modelling",
"layouts"
] | A generative model to generate constrained CAD models ready for editing. | 9,136 | 2106.02711 | title_snapshot |
StKuQ0-dltN | Double/Debiased Machine Learning for Dynamic Treatment Effects | https://openreview.net/forum?id=StKuQ0-dltN | [
"Greg Lewis",
"Vasilis Syrgkanis"
] | Poster | null | We consider the estimation of treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes. We propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments and apply it to a concrete linea... | [
"dynamic treatment regime",
"high-dimensional",
"treatment effects",
"double machine learning"
] | High dimensional causal inference in the dynamic treatment regime via Neyman orthogonality | 9,135 | null | null |
uTqvj8i3xv | Functional Regularization for Reinforcement Learning via Learned Fourier Features | https://openreview.net/forum?id=uTqvj8i3xv | [
"Alexander Cong Li",
"Deepak Pathak"
] | Poster | null | We propose a simple architecture for deep reinforcement learning by embedding inputs into a learned Fourier basis and show that it improves the sample efficiency of both state-based and image-based RL. We perform infinite-width analysis of our architecture using the Neural Tangent Kernel and theoretically show that tun... | [
"reinforcement learning",
"deep learning",
"neural tangent kernel",
"regularization",
"architecture"
] | We propose a new architecture that can directly control how much it fits high and low target frequencies, and use this to improve off-policy reinforcement learning. | 9,134 | 2112.03257 | title_snapshot |
4orlVaC95Bo | Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data | https://openreview.net/forum?id=4orlVaC95Bo | [
"Dongmin Park",
"Hwanjun Song",
"Minseok Kim",
"Jae-Gil Lee"
] | Poster | null | A deep neural network (DNN) has achieved great success in many machine learning tasks by virtue of its high expressive power. However, its prediction can be easily biased to undesirable features, which are not essential for solving the target task and are even imperceptible to a human, thereby resulting in poor general... | [
"Out-of-Distribution",
"Regularization",
"Task-Agnostic"
] | We propose a novel regularizer that deactivates all undesirable features using OOD examples in the feature extraction layer. | 9,132 | null | null |
JbqW3KmmE6 | Multi-Facet Clustering Variational Autoencoders | https://openreview.net/forum?id=JbqW3KmmE6 | [
"Fabian Falck",
"Haoting Zhang",
"Matthew Willetts",
"George Nicholson",
"Christopher Yau",
"Christopher C. Holmes"
] | Poster | null | Work in deep clustering focuses on finding a single partition of data. However, high-dimensional data, such as images, typically feature multiple interesting characteristics one could cluster over. For example, images of objects against a background could be clustered over the shape of the object and separately by the ... | [
"multi-facet clustering",
"deep clustering",
"variational autoencoders",
"deep generative models"
] | We present theoretical and empirical results for a novel class of variational autoencoders for multi-facet clustering. | 9,110 | 2106.05241 | title_snapshot |
CtaDl9L0bIQ | Group Equivariant Subsampling | https://openreview.net/forum?id=CtaDl9L0bIQ | [
"Jin Xu",
"Hyunjik Kim",
"Tom Rainforth",
"Yee Whye Teh"
] | Poster | null | Subsampling is used in convolutional neural networks (CNNs) in the form of pooling or strided convolutions, to reduce the spatial dimensions of feature maps and to allow the receptive fields to grow exponentially with depth. However, it is known that such subsampling operations are not translation equivariant, unlike c... | [
"group equivariance",
"group invariance",
"convolutional architecture",
"autoencoders",
"representation learning"
] | We propose group equivariant subsampling with applications to equivariant representation learning of objects. | 9,072 | 2106.05886 | title_snapshot |
we8d1FjibAc | Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems | https://openreview.net/forum?id=we8d1FjibAc | [
"Zixiu Wang",
"Yiwen Guo",
"Hu Ding"
] | Spotlight | null | In many machine learning tasks, a common approach for dealing with large-scale data is to build a small summary, {\em e.g.,} coreset, that can efficiently represent the original input. However, real-world datasets usually contain outliers and most existing coreset construction methods are not resilient against outlier... | [
"coreset",
"Continuous-and-Bounded learning",
"outliers",
"dynamic setting"
] | We provide a robust coreset construction method for continuous-and-bounded optimization problems | 9,070 | 2107.00068 | title_snapshot |
1_gaHBaRYt | Fast Federated Learning in the Presence of Arbitrary Device Unavailability | https://openreview.net/forum?id=1_gaHBaRYt | [
"Xinran Gu",
"Kaixuan Huang",
"Jingzhao Zhang",
"Longbo Huang"
] | Poster | null | Federated learning (FL) coordinates with numerous heterogeneous devices to collaboratively train a shared model while preserving user privacy. Despite its multiple advantages, FL faces new challenges. One challenge arises when devices drop out of the training process. In this case, the convergence of popular FL algorit... | [
"Federated Learning",
"Distributed Optimization"
] | We study federated learning algorithms under arbitrary device unavailability and show our proposed MIFA avoids excessive latency induced by inactive devices and achieves minimax optimal convergence rates. | 9,059 | 2106.04159 | title_snapshot |
FFtcBBVIg1T | Overlapping Spaces for Compact Graph Representations | https://openreview.net/forum?id=FFtcBBVIg1T | [
"Kirill Sergeevich Shevkunov",
"Liudmila Prokhorenkova"
] | Poster | null | Various non-trivial spaces are becoming popular for embedding structured data such as graphs, texts, or images. Following spherical and hyperbolic spaces, more general product spaces have been proposed. However, searching for the best configuration of a product space is a resource-intensive procedure, which reduces the... | [
"graph embedding",
"hyperbolic space",
"product space",
"overlapping space"
] | A novel approach to combine metric distances and similarity measures to achieve better vector representations of structured data. | 9,055 | 2007.02445 | title_snapshot |
t5-Mszu1UkO | Information Directed Reward Learning for Reinforcement Learning | https://openreview.net/forum?id=t5-Mszu1UkO | [
"David Lindner",
"Matteo Turchetta",
"Sebastian Tschiatschek",
"Kamil Ciosek",
"Andreas Krause"
] | Poster | null | For many reinforcement learning (RL) applications, specifying a reward is difficult. In this paper, we consider an RL setting where the agent can obtain information about the reward only by querying an expert that can, for example, evaluate individual states or provide binary preferences over trajectories. From such ex... | [
"reward learning",
"reinforcement learning",
"active learning",
"preference learning",
"human feedback"
] | Improving sample efficiency of active reward learning by focusing on learning a good policy. | 9,052 | 2102.12466 | title_snapshot |
KmPSe18DGLs | Statistical Undecidability in Linear, Non-Gaussian Causal Models in the Presence of Latent Confounders | https://openreview.net/forum?id=KmPSe18DGLs | [
"Konstantin Genin"
] | Poster | null | If causal relationships are linear and acyclic and noise terms are independent and Gaussian, causal orientation is not identified from observational data --- even if faithfulness is satisfied (Spirtes et al., 2002). Shimizu et al. (2006) showed that acyclic, linear, {\bf non}-Gaussian (LiNGAM) causal models {\em are} i... | [
"causal discovery",
"linear non-gaussian models",
"faithfulness",
"confounding",
"topology"
] | We refine causal identifiability results in the linear non-Gaussian setting, allowing for the presence of latent confounders. | 9,041 | null | null |
dsevgAwUH4m | Linear Convergence of Gradient Methods for Estimating Structured Transition Matrices in High-dimensional Vector Autoregressive Models | https://openreview.net/forum?id=dsevgAwUH4m | [
"Xiao Lv",
"Wei Cui",
"Yulong Liu"
] | Poster | null | In this paper, we present non-asymptotic optimization guarantees of gradient descent methods for estimating structured transition matrices in high-dimensional vector autoregressive (VAR) models. We adopt the projected gradient descent (PGD) for single-structured transition matrices and the alternating projected gradien... | [
"Vector autoregressive models",
"dirty statistical models",
"linear convergence rate",
"non-asymptotic analysis"
] | null | 9,040 | null | null |
NFurmj-rIWe | Dynamic population-based meta-learning for multi-agent communication with natural language | https://openreview.net/forum?id=NFurmj-rIWe | [
"Abhinav Gupta",
"Marc Lanctot",
"Angeliki Lazaridou"
] | Poster | null | In this work, our goal is to train agents that can coordinate with seen, unseen as well as human partners in a multi-agent communication environment involving natural language. Previous work using a single set of agents has shown great progress in generalizing to known partners, however it struggles when coordinating w... | [
"emergent communication",
"meta-learning",
"population-based methods",
"multi-agent reinforcement learning",
"few-shot generalization",
"human-AI coordination"
] | We propose a dynamic population-based meta-learning method to train agents in a cooperative multi-agent communication environment. | 9,027 | 2110.14241 | title_snapshot |
zImiB39pyUL | BooVAE: Boosting Approach for Continual Learning of VAE | https://openreview.net/forum?id=zImiB39pyUL | [
"Evgenii Egorov",
"Anna Kuzina",
"Evgeny Burnaev"
] | Poster | null | Variational autoencoder (VAE) is a deep generative model for unsupervised learning, allowing to encode observations into the meaningful latent space. VAE is prone to catastrophic forgetting when tasks arrive sequentially, and only the data for the current one is available. We address this problem of continual learning ... | [
"Continual Learning",
"Variational Autoencoder",
"VAE",
"Catastrophic Forgetting",
"Entropy regularisation",
"Boosting",
"Functional regularisation"
] | We propose to used learnable prior to alleviate catastrophic forgetting in continual learning of VAE | 9,024 | 1908.11853 | title_snapshot |
k9iBo3RmCFd | Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks | https://openreview.net/forum?id=k9iBo3RmCFd | [
"Sandesh Kamath",
"Amit Deshpande",
"K Venkata Subrahmanyam",
"Vineeth N. Balasubramanian"
] | Poster | null | (Non-)robustness of neural networks to small, adversarial pixel-wise perturbations, and as more recently shown, to even random spatial transformations (e.g., translations, rotations) entreats both theoretical and empirical understanding. Spatial robustness to random translations and rotations is commonly attained via e... | [
"Adversarial robustness",
"spatial robustness",
"invariance",
"curriculum learning"
] | We show a trade-off between spatial and adversarial robustness theoretically and empirically. And propose a curriculum based training to achieve both of them simultaneously. | 9,022 | 2002.11318 | title_snapshot |
ags1UxpXAl | Powerpropagation: A sparsity inducing weight reparameterisation | https://openreview.net/forum?id=ags1UxpXAl | [
"Jonathan Schwarz",
"Siddhant Jayakumar",
"Razvan Pascanu",
"Peter E. Latham",
"Yee Whye Teh"
] | Poster | null | The training of sparse neural networks is becoming an increasingly important tool for reducing the computational footprint of models at training and evaluation, as well enabling the effective scaling up of models. Whereas much work over the years has been dedicated to specialised pruning techniques, little attention ha... | [
"Sparse Neural Networks",
"Continual Learning"
] | null | 9,020 | 2110.00296 | title_snapshot |
EocGDCLaw-d | Active clustering for labeling training data | https://openreview.net/forum?id=EocGDCLaw-d | [
"Quentin Lutz",
"Elie De Panafieu",
"Maya Stein",
"Alex Scott"
] | Poster | null | Gathering training data is a key step of any supervised learning task, and it is both critical and expensive. Critical, because the quantity and quality of the training data has a high impact on the performance of the learned function. Expensive, because most practical cases rely on humans-in-the-loop to label the data... | [
"clustering",
"active learning",
"annotation",
"training data"
] | Organise human experts to build a training data set for classification using Boolean pairwise queries. | 8,993 | 2110.14521 | title_snapshot |
-OrwaD3bG91 | What Matters for Adversarial Imitation Learning? | https://openreview.net/forum?id=-OrwaD3bG91 | [
"Manu Orsini",
"Anton Raichuk",
"Leonard Hussenot",
"Damien Vincent",
"Robert Dadashi",
"Sertan Girgin",
"Matthieu Geist",
"Olivier Bachem",
"Olivier Pietquin",
"Marcin Andrychowicz"
] | Poster | null | Adversarial imitation learning has become a popular framework for imitation in continuous control. Over the years, several variations of its components were proposed to enhance the performance of the learned policies as well as the sample complexity of the algorithm. In practice, these choices are rarely tested all tog... | [
"imitation learning",
"GAIL",
"continuous control"
] | a large-scale study of adversarial imitation learning algorithms | 8,981 | 2106.00672 | title_snapshot |
DyE8hmj2dse | A Theoretical Analysis of Fine-tuning with Linear Teachers | https://openreview.net/forum?id=DyE8hmj2dse | [
"Gal Shachaf",
"Alon Brutzkus",
"Amir Globerson"
] | Poster | null | Fine-tuning is a common practice in deep learning, achieving excellent generalization results on downstream tasks using relatively little training data. Although widely used in practice, it is not well understood theoretically. Here we analyze the sample complexity of this scheme for regression with linear teachers in ... | [
"deep learning theory",
"learning theory",
"transfer learning",
"fine-tuning"
] | null | 8,956 | 2107.01641 | title_snapshot |
ephWA7KaWmD | GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction | https://openreview.net/forum?id=ephWA7KaWmD | [
"Longyuan Li",
"Jian Yao",
"Li Kevin Wenliang",
"Tong He",
"Tianjun Xiao",
"Junchi Yan",
"David Wipf",
"Zheng Zhang"
] | Poster | null | Learning the distribution of future trajectories conditioned on the past is a crucial problem for understanding multi-agent systems. This is challenging because humans make decisions based on complex social relations and personal intents, resulting in highly complex uncertainties over trajectories. To address this prob... | [
"Trajectory prediction",
"variational Autoencoder",
"deep learning",
"probabilistic"
] | We build a model that disentangles social relations and human intents to generate multimodal future trajectories of multi-agent systems. | 8,952 | null | null |
hyJKKIhfxxT | VAST: Value Function Factorization with Variable Agent Sub-Teams | https://openreview.net/forum?id=hyJKKIhfxxT | [
"Thomy Phan",
"Fabian Ritz",
"Lenz Belzner",
"Philipp Altmann",
"Thomas Gabor",
"Claudia Linnhoff-Popien"
] | Poster | null | Value function factorization (VFF) is a popular approach to cooperative multi-agent reinforcement learning in order to learn local value functions from global rewards. However, state-of-the-art VFF is limited to a handful of agents in most domains. We hypothesize that this is due to the flat factorization scheme, where... | [
"Multi-Agent Learning",
"Reinforcement Learning",
"Value Function Factorization"
] | We propose a hierarchical value function factorization approach based on variable agent sub-teams. | 8,944 | null | null |
ZYJ1r6sStU | On the interplay between data structure and loss function in classification problems | https://openreview.net/forum?id=ZYJ1r6sStU | [
"Stéphane d'Ascoli",
"Marylou Gabrié",
"Levent Sagun",
"Giulio Biroli"
] | Poster | null | One of the central features of modern machine learning models, including deep neural networks, is their generalization ability on structured data in the over-parametrized regime.
In this work, we consider an analytically solvable setup to investigate how properties of data impact learning in classification problems, a... | [
"loss function",
"generalization",
"overparametrization",
"double descent",
"random features"
] | We analyze the interplay between data structure and loss function in random feature classification problems | 8,929 | 2103.05524 | title_snapshot |
Aa5oPXc_1IV | Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias | https://openreview.net/forum?id=Aa5oPXc_1IV | [
"Kaifeng Lyu",
"Zhiyuan Li",
"Runzhe Wang",
"Sanjeev Arora"
] | Poster | null | The generalization mystery of overparametrized deep nets has motivated efforts to understand how gradient descent (GD) converges to low-loss solutions that generalize well. Real-life neural networks are initialized from small random values and trained with cross-entropy loss for classification (unlike the "lazy" or "NT... | [
"margin maximization",
"gradient flow",
"gradient descent",
"implicit bias",
"linearly separable",
"two-layer neural networks"
] | We prove that gradient flow biases two-layer nets to max-margin classifiers on linearly separable data with logistic loss and small initialization, although the margin can be only locally optimal by making simple changes to data assumptions. | 8,917 | 2110.13905 | title_snapshot |
7m6qvNqFjr | Fast rates for prediction with limited expert advice | https://openreview.net/forum?id=7m6qvNqFjr | [
"El Mehdi Saad",
"Gilles Blanchard"
] | Poster | null | We investigate the problem of minimizing the excess generalization error with respect to the best expert prediction in a finite family in the stochastic setting, under limited access to information. We consider that the learner has only access to a limited number of expert advices per training round, as well as for pr... | [
"Online Learning",
"prediction with expert advice",
"high probability bounds",
"budgeted learning"
] | We study the impact of restricted access to information on the generalization error in the setting of prediction with expert advice. | 8,916 | 2110.14485 | title_snapshot |
eQ7Kh-QeWnO | DualNet: Continual Learning, Fast and Slow | https://openreview.net/forum?id=eQ7Kh-QeWnO | [
"Quang Pham",
"Chenghao Liu",
"Steven HOI"
] | Poster | null | According to Complementary Learning Systems (CLS) theory~\cite{mcclelland1995there} in neuroscience, humans do effective \emph{continual learning} through two complementary systems: a fast learning system centered on the hippocampus for rapid learning of the specifics and individual experiences, and a slow learning sys... | [
"Continual learning",
"fast and slow learning"
] | A novel continual learning paradigm of fast and slow learning. | 8,910 | 2110.00175 | title_snapshot |
F1D8buayXQT | Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction | https://openreview.net/forum?id=F1D8buayXQT | [
"Konstantin Schürholt",
"Dimche Kostadinov",
"Damian Borth"
] | Poster | null | Self-Supervised Learning (SSL) has been shown to learn useful and information-preserving representations. Neural Networks (NNs) are widely applied, yet their weight space is still not fully understood. Therefore, we propose to use SSL to learn hyper-representations of the weights of populations of NNs. To that end, we ... | [
"Representation Learning",
"Self-Supervised Learning",
"Weight Space",
"Parameter Space",
"Augmentation",
"Model Zoos"
] | This paper proposes to learn self-supervised representations of the weights of populations of NN models using novel data augmentations and an adapted transformer architecture. | 8,896 | 2110.15288 | title_judge |
7U7JxTiL8gz | Modular Gaussian Processes for Transfer Learning | https://openreview.net/forum?id=7U7JxTiL8gz | [
"Pablo Moreno-Muñoz",
"Antonio Artés",
"Mauricio A Álvarez"
] | Poster | null | We present a framework for transfer learning based on modular variational Gaussian processes (GP). We develop a module-based method that having a dictionary of well fitted GPs, each model being characterised by its hyperparameters, pseudo-inputs and their corresponding posterior densities, one could build ensemble GP m... | [
"Gaussian Processes"
] | Framework for transfer learning based on modular variational Gaussian processes | 8,888 | 2110.13515 | title_snapshot |
ykN3tbJ0qmX | Collapsed Variational Bounds for Bayesian Neural Networks | https://openreview.net/forum?id=ykN3tbJ0qmX | [
"Marcin B. Tomczak",
"Siddharth Swaroop",
"Andrew Y. K. Foong",
"Richard E Turner"
] | Poster | null | Recent interest in learning large variational Bayesian Neural Networks (BNNs) has been partly hampered by poor predictive performance caused by underfitting, and their performance is known to be very sensitive to the prior over weights. Current practice often fixes the prior parameters to standard values or tunes them ... | [
"Bayesian Neural Networks",
"BNNs",
"Bayesian Deep Learning",
"variational inference",
"VI",
"underfitting",
"collapsed ELBO",
"overpruning"
] | We derive a family of collapsed, tighter ELBOs to learn variational posteriors over weights in Bayesian Neural Networks. | 8,882 | null | null |
F9HNBbytcqT | Distributed Machine Learning with Sparse Heterogeneous Data | https://openreview.net/forum?id=F9HNBbytcqT | [
"Dominic Richards",
"Sahand Negahban",
"Patrick Rebeschini"
] | Poster | null | Motivated by distributed machine learning settings such as Federated Learning, we consider the problem of fitting a statistical model across a distributed collection of heterogeneous data sets whose similarity structure is encoded by a graph topology. Precisely, we analyse the case where each node is associated with fi... | [
"Distributed Machine Learning",
"Heterogeneous Data",
"Federated Machine Learning"
] | null | 8,875 | 1912.01417 | title_snapshot |
anxHcl9_sE | On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms | https://openreview.net/forum?id=anxHcl9_sE | [
"Shuyu Cheng",
"Guoqiang Wu",
"Jun Zhu"
] | Poster | null | Zeroth-order (ZO) optimization is widely used to handle challenging tasks, such as query-based black-box adversarial attacks and reinforcement learning. Various attempts have been made to integrate prior information into the gradient estimation procedure based on finite differences, with promising empirical results. Ho... | [
"zeroth-order optimization",
"gradient-free optimization",
"evolutionary strategies",
"convex optimization",
"randomized methods"
] | We conduct convergence analysis on frameworks of ZO algorithms including existing prior-guided random gradient-free methods and a new prior-guided accelerated random search method. | 8,873 | 2107.10110 | title_snapshot |
YN4TMf3sv52 | Exploring Forensic Dental Identification with Deep Learning | https://openreview.net/forum?id=YN4TMf3sv52 | [
"Yuan Liang",
"Weikun Han",
"Liang Qiu",
"Chen Wu",
"Yiting Shao",
"Kun Wang",
"Lei He"
] | Poster | null | Dental forensic identification targets to identify persons with dental traces.
The task is vital for the investigation of criminal scenes and mass disasters because of the resistance of dental structures and the wide-existence of dental imaging.
However, no widely accepted automated solution is available for this labo... | [
"person identification",
"medical imaging",
"forensics"
] | The first in-depth exploration of deep learning for the forensic dental identification. | 8,867 | null | null |
JOOsoL_J6Fc | Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel | https://openreview.net/forum?id=JOOsoL_J6Fc | [
"Dominic Richards",
"Ilja Kuzborskij"
] | Poster | null | We revisit on-average algorithmic stability of Gradient Descent (GD) for training overparameterised shallow neural networks and prove new generalisation and excess risk bounds without the Neural Tangent Kernel (NTK) or Polyak-Łojasiewicz (PL) assumptions. In particular, we show oracle type bounds whic... | [
"Gradient Descent",
"Neural Networks",
"Stability",
"Generalisation",
"Implicit Regularisation",
"Neural Tangent Kernel"
] | null | 8,866 | 2107.12723 | title_snapshot |
ypj3xKoRfmr | Credal Self-Supervised Learning | https://openreview.net/forum?id=ypj3xKoRfmr | [
"Julian Lienen",
"Eyke Hüllermeier"
] | Poster | null | Self-training is an effective approach to semi-supervised learning. The key idea is to let the learner itself iteratively generate "pseudo-supervision" for unlabeled instances based on its current hypothesis. In combination with consistency regularization, pseudo-labeling has shown promising performance in various doma... | [
"semi-supervised learning",
"self-training",
"superset learning",
"credal sets",
"label relaxation",
"pseudo-labeling"
] | We propose a novel semi-supervised learning method that uses credal sets as pseudo-labels for self-supervision. | 8,864 | 2106.11853 | title_snapshot |
YIyYkoJX2eA | Learning to Draw: Emergent Communication through Sketching | https://openreview.net/forum?id=YIyYkoJX2eA | [
"Daniela Mihai",
"Jonathon Hare"
] | Oral | null | Evidence that visual communication preceded written language and provided a basis for it goes back to prehistory, in forms such as cave and rock paintings depicting traces of our distant ancestors. Emergent communication research has sought to explore how agents can learn to communicate in order to collaboratively solv... | [
"Emergent Communication",
"Sketching",
"Drawing",
"Perceptual Loss",
"Interpretability",
"Visual Communication"
] | We use self-supervised play to train artificial agents to communicate by drawing and then show that with the appropriate inductive bias a human can successfully play the same games with the pretrained drawing agent. | 8,863 | 2106.02067 | title_snapshot |
90M-91IZ0JC | Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck | https://openreview.net/forum?id=90M-91IZ0JC | [
"Junho Kim",
"Byung-Kwan Lee",
"Yong Man Ro"
] | Poster | null | Adversarial examples, generated by carefully crafted perturbation, have attracted considerable attention in research fields. Recent works have argued that the existence of the robust and non-robust features is a primary cause of the adversarial examples, and investigated their internal interactions in the feature space... | [
"Adversarial Examples",
"Information Bottleneck",
"Feature Disentanglement",
"Feature Interpretation"
] | We present a way of distilling the robust and non-robust features in adversarial examples, using Information Bottleneck. | 8,856 | 2204.02735 | title_snapshot |
3-GCM92yaB3 | The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation | https://openreview.net/forum?id=3-GCM92yaB3 | [
"Thibault Sejourne",
"François-Xavier Vialard",
"Gabriel Peyré"
] | Poster | null | Comparing metric measure spaces (i.e. a metric space endowed with a probability distribution) is at the heart of many machine learning problems. The most popular distance between such metric measure spaces is the Gromov-Wasserstein (GW) distance, which is the solution of a quadratic assignment problem. The GW distance ... | [
"Optimal transport",
"Quadratic assignment problem",
"Gromov-Wasserstein"
] | We propose a generalization of the Gromov-Wasserstein distance to unbalanced input data with a GPU-friendly algorithm. | 8,855 | 2009.04266 | title_snapshot |
_RSgXL8gNnx | Batch Normalization Orthogonalizes Representations in Deep Random Networks | https://openreview.net/forum?id=_RSgXL8gNnx | [
"Hadi Daneshmand",
"Amir Joudaki",
"Francis Bach"
] | Spotlight | null | This paper underlines an elegant property of batch-normalization (BN): Successive batch normalizations with random linear updates make samples increasingly orthogonal. We establish a non-asymptotic characterization of the interplay between depth, width, and the orthogonality of deep representations. More precisely, we ... | [
"Batch normalization",
"Theory of deep neural networks",
"Markov chains",
"Random Neural Networks",
"Optimization for neural networks"
] | We prove that successive batch normalizations, together with random linear layers, incrementally orthogonalize representations of samples. | 8,850 | 2106.03970 | title_snapshot |
0NXUSlb6oEu | Improving Robustness using Generated Data | https://openreview.net/forum?id=0NXUSlb6oEu | [
"Sven Gowal",
"Sylvestre-Alvise Rebuffi",
"Olivia Wiles",
"Florian Stimberg",
"Dan Andrei Calian",
"Timothy Mann"
] | Poster | null | Recent work argues that robust training requires substantially larger datasets than those required for standard classification. On CIFAR-10 and CIFAR-100, this translates into a sizable robust-accuracy gap between models trained solely on data from the original training set and those trained with additional data extrac... | [
"adversarial",
"robustness",
"generative"
] | We identity conditions under which incorporating generated data can improve robustness to adversarial examples, and demonstrate that it is possible to significantly improve upon the state-of-the-art without the need for external data. | 8,839 | 2110.09468 | title_snapshot |
kAm9By0R5ME | A Causal Lens for Controllable Text Generation | https://openreview.net/forum?id=kAm9By0R5ME | [
"Zhiting Hu",
"Li Erran Li"
] | Poster | null | Controllable text generation concerns two fundamental tasks of wide applications, namely generating text of given attributes (i.e., attribute-conditional generation), and minimally editing existing text to possess desired attributes (i.e., text attribute transfer). Extensive prior work has largely studied the two probl... | [
"causal inference",
"controllable text generation",
"text style transfer",
"natural language processing"
] | The first unified causal framework for controllable text generation that unifies two fundamental tasks and mitigates generation biases | 8,838 | 2201.09119 | title_snapshot |
i_Q1yrOegLY | Revisiting Deep Learning Models for Tabular Data | https://openreview.net/forum?id=i_Q1yrOegLY | [
"Yury Gorishniy",
"Ivan Rubachev",
"Valentin Khrulkov",
"Artem Babenko"
] | Poster | null | The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets. However, the proposed models are usually not properly compared to each other and existing works often use different benchmarks and experiment protocols. As a result... | [
"tabular data",
"architecture",
"DNN"
] | Compared many DL models for tabular data, identified a strong baseline (ResNet) and proposed a powerful Transformer-based model. | 8,834 | 2106.11959 | title_snapshot |
pX7gwTNljqa | Non-Gaussian Gaussian Processes for Few-Shot Regression | https://openreview.net/forum?id=pX7gwTNljqa | [
"Marcin Sendera",
"Jacek Tabor",
"Aleksandra Nowak",
"Andrzej Bedychaj",
"Massimiliano Patacchiola",
"Tomasz Trzcinski",
"Przemysław Spurek",
"Maciej Zieba"
] | Poster | null | Gaussian Processes (GPs) have been widely used in machine learning to model distributions over functions, with applications including multi-modal regression, time-series prediction, and few-shot learning. GPs are particularly useful in the last application since they rely on Normal distributions and enable closed-form ... | [
"Meta-Learning",
"Few-Shot Learning",
"Few-Shot Regression",
"Normalizing Flows",
"Gaussian Processes"
] | In this work we address the limitations of Gaussian Processes in Few-Shot Regression by leveraging the flexibility of Normalizing Flows to modulate the posterior predictive distribution. | 8,826 | 2110.13561 | title_snapshot |
vERYhbX_6Y | Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes | https://openreview.net/forum?id=vERYhbX_6Y | [
"Sara Sangalli",
"Ertunc Erdil",
"Andeas Hötker",
"Olivio F Donati",
"Ender Konukoglu"
] | Poster | null | Deep neural networks (DNNs) are notorious for making more mistakes for the classes that have substantially fewer samples than the others during training. Such class imbalance is ubiquitous in clinical applications and very crucial to handle because the classes with fewer samples most often correspond to critical cases ... | [
"Constrained optimization",
"class imbalance",
"augmented Lagrangian method"
] | null | 8,820 | 2102.12894 | title_snapshot |
F-H4oe3MXXI | Denoising Normalizing Flow | https://openreview.net/forum?id=F-H4oe3MXXI | [
"Christian Horvat",
"Jean-Pascal Pfister"
] | Poster | null | Normalizing flows (NF) are expressive as well as tractable density estimation methods whenever the support of the density is diffeomorphic to the entire data-space. However, real-world data sets typically live on (or very close to) low-dimensional manifolds thereby challenging the applicability of standard NF on real-w... | [
"Normalizing Flow",
"Manifold Learning",
"Density Estimation"
] | We propose an NF for simultaneous manifold and density-on-manifold learning. | 8,819 | null | null |
_nRSyha2SP | Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages | https://openreview.net/forum?id=_nRSyha2SP | [
"Xinyun Chen",
"Dawn Song",
"Yuandong Tian"
] | Poster | null | Program synthesis from input-output (IO) examples has been a long-standing challenge. While recent works demonstrated limited success on domain-specific languages (DSL), it remains highly challenging to apply them to real-world programming languages, such as C. Due to complicated syntax and token variation, there are t... | [
"neural program synthesis",
"program execution"
] | null | 8,812 | 2107.00101 | title_judge |
ot2ORiBqTa1 | Going Beyond Linear Transformers with Recurrent Fast Weight Programmers | https://openreview.net/forum?id=ot2ORiBqTa1 | [
"Kazuki Irie",
"Imanol Schlag",
"Róbert Csordás",
"Jürgen Schmidhuber"
] | Poster | null | Transformers with linearised attention (''linear Transformers'') have demonstrated the practical scalability and effectiveness of outer product-based Fast Weight Programmers (FWPs) from the '90s. However, the original FWP formulation is more general than the one of linear Transformers: a slow neural network (NN) contin... | [
"Transformers",
"memory augmented recurrent neural networks",
"fast weight programmers"
] | We augment linear Transformers with recurrent connections. | 8,811 | 2106.06295 | title_snapshot |
vMWHOumNj5 | A Unified Approach to Fair Online Learning via Blackwell Approachability | https://openreview.net/forum?id=vMWHOumNj5 | [
"Evgenii E Chzhen",
"Christophe Giraud",
"Gilles Stoltz"
] | Spotlight | null | We provide a setting and a general approach to fair online learning with stochastic sensitive and non-sensitive contexts.
The setting is a repeated game between the Player and Nature, where at each stage both pick actions based on the contexts. Inspired by the notion of unawareness, we assume that the Player can only a... | [
"online learning",
"fairness",
"Blackwell approachability",
"calibration"
] | We provide a general approachability-theorem based tool to tacke faire online adversarial learning (with stochastic contexts) and illustrate its application by working out several examples. | 8,801 | 2106.12242 | title_snapshot |
Esd7tGH3Spl | Evaluating Efficient Performance Estimators of Neural Architectures | https://openreview.net/forum?id=Esd7tGH3Spl | [
"Xuefei Ning",
"Changcheng Tang",
"Wenshuo Li",
"Zixuan Zhou",
"Shuang Liang",
"Huazhong Yang",
"Yu Wang"
] | Poster | null | Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS). To reduce the architecture training costs in NAS, one-shot estimators (OSEs) amortize the architecture training costs by sharing the parameters of one supernet between all architectures. Recent... | [
"Neural architecture search (NAS)",
"Parameter sharing",
"One-shot NAS",
"Zero-shot NAS"
] | We conduct a comprehensive empirical study on how and why the one-shot / zero-shot estimators in NAS have biases & variances. | 8,797 | 2008.03064 | title_snapshot |
zlhpIYub2d0 | Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems | https://openreview.net/forum?id=zlhpIYub2d0 | [
"Jiayu Chen",
"Yuanxin Zhang",
"Yuanfan Xu",
"Huimin Ma",
"Huazhong Yang",
"Jiaming Song",
"Yu Wang",
"Yi Wu"
] | Poster | null | We introduce an automatic curriculum algorithm, Variational Automatic Curriculum Learning (VACL), for solving challenging goal-conditioned cooperative multi-agent reinforcement learning problems. We motivate our curriculum learning paradigm through a variational perspective, where the learning objective can be decompos... | [
"multi-agent reinforcement learning",
"curriculum learning",
"variational inference"
] | We introduce a curriculum learning algorithm for solving goal-conditioned cooperative multi-agent reinforcement learning problems. | 8,794 | 2111.04613 | title_snapshot |
-_D-ss8su3 | Novel Upper Bounds for the Constrained Most Probable Explanation Task | https://openreview.net/forum?id=-_D-ss8su3 | [
"Tahrima Rahman",
"Sara Rouhani",
"Vibhav Giridhar Gogate"
] | Poster | null | We propose several schemes for upper bounding the optimal value of the constrained most probable explanation (CMPE) problem. Given a set of discrete random variables, two probabilistic graphical models defined over them and a real number $q$, this problem involves finding an assignment of values to all the variables s... | [
"Discrete Optimization",
"Probabilistic Graphical Models",
"Constrained Most Probable Explanation",
"Explainable AI"
] | A novel method that integrates fast knapsack algorithms, mini buckets and Lagrange relaxations and decompositions to yield upper bounds on the optimal value of a hard discrete constrained optimization problem. | 8,793 | null | null |
cAw860ncLRW | Anti-Backdoor Learning: Training Clean Models on Poisoned Data | https://openreview.net/forum?id=cAw860ncLRW | [
"Yige Li",
"Xixiang Lyu",
"Nodens Koren",
"Lingjuan Lyu",
"Bo Li",
"Xingjun Ma"
] | Poster | null | Backdoor attack has emerged as a major security threat to deep neural networks (DNNs). While existing defense methods have demonstrated promising results on detecting or erasing backdoors, it is still not clear whether robust training methods can be devised to prevent the backdoor triggers being injected into the train... | [
"Backdoor Defense",
"Deep Neural Networks",
"Anti-backdoor learning"
] | We present the first anti-backdoor learning method that allows one to train clean models out of backdoor-poisoned data. | 8,783 | 2110.11571 | title_snapshot |
_CmrI7UrmCl | G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators | https://openreview.net/forum?id=_CmrI7UrmCl | [
"Yunhui Long",
"Boxin Wang",
"Zhuolin Yang",
"Bhavya Kailkhura",
"Aston Zhang",
"Carl A. Gunter",
"Bo Li"
] | Poster | null | Recent advances in machine learning have largely benefited from the massive accessible training data. However, large-scale data sharing has raised great privacy concerns. In this work, we propose a novel privacy-preserving data Generative model based on the PATE framework (G-PATE), aiming to train a scalable differenti... | [
"Differential Privacy",
"Generative Models"
] | We propose a novel privacy-preserving data Generative model based on the PATE framework (G-PATE) to generate high-dimensional differentially private data while preserving high data utility. | 8,782 | 1906.09338 | title_snapshot |
m4k66oJFK9P | INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding | https://openreview.net/forum?id=m4k66oJFK9P | [
"Shuwen Liu",
"Bernardo Cuenca Grau",
"Ian Horrocks",
"Egor V. Kostylev"
] | Poster | null | The aim of knowledge graph (KG) completion is to extend an incomplete KG with missing triples. Popular approaches based on graph embeddings typically work by first representing the KG in a vector space, and then applying a predefined scoring function to the resulting vectors to complete the KG. These approaches work we... | [
"Knowledge Graph Completion",
"Graph Neural Networks"
] | We propose a novel GNN-based approach with pair-wise encoding for inductive knowledge graph completion problem. | 8,745 | null | null |
bqGK5PyI6-N | Compacter: Efficient Low-Rank Hypercomplex Adapter Layers | https://openreview.net/forum?id=bqGK5PyI6-N | [
"Rabeeh Karimi mahabadi",
"James Henderson",
"Sebastian Ruder"
] | Poster | null | Adapting large-scale pretrained language models to downstream tasks via fine-tuning is the standard method for achieving state-of-the-art performance on NLP benchmarks. However, fine-tuning all weights of models with millions or billions of parameters is sample-inefficient, unstable in low-resource settings, and wastef... | [
"parameter-compact fine-tuning methods",
"adapters",
"low-rank methods",
"hypercomplex multiplication layers",
"adapting large-scale language models"
] | We have proposed Compacter, a light-weight fine-tuning method for large-scale language models and demonstrated that despite learning 2127.66x fewer parameters than standard fine-tuning, it obtains comparable performance. | 8,743 | 2106.04647 | title_snapshot |
MckiHYXsBT | Learning to Select Exogenous Events for Marked Temporal Point Process | https://openreview.net/forum?id=MckiHYXsBT | [
"Ping Zhang",
"Rishabh K Iyer",
"Ashish V. Tendulkar",
"Gaurav Aggarwal",
"Abir De"
] | Poster | null | Marked temporal point processes (MTPPs) have emerged as a powerful modeling
tool for a wide variety of applications which are characterized using discrete
events localized in continuous time. In this context, the events are of two types
endogenous events which occur due to the influence of the previous events and
exoge... | [
"Temporal point process",
"time series",
"event selection"
] | It learns to select exogenous events from a set of events in the context of marked temporal point processes | 8,721 | null | null |
Bix4uw5GcbE | Learning rule influences recurrent network representations but not attractor structure in decision-making tasks | https://openreview.net/forum?id=Bix4uw5GcbE | [
"Brandon J McMahan",
"Michael Kleinman",
"Jonathan Kao"
] | Poster | null | Recurrent neural networks (RNNs) are popular tools for studying computational dynamics in neurobiological circuits. However, due to the dizzying array of design choices, it is unclear if computational dynamics unearthed from RNNs provide reliable neurobiological inferences. Understanding the effects of design choices o... | [
"RNN",
"learning rules",
"hyperparameters",
"RNN dynamics",
"decision-making",
"computational neuroscience",
"interpretability",
"biological models"
] | RNNs adopt similar dynamical mechanisms when trained with different learning rules on sufficiently complex tasks | 8,704 | null | null |
1XwPDFrJObw | Towards Instance-Optimal Offline Reinforcement Learning with Pessimism | https://openreview.net/forum?id=1XwPDFrJObw | [
"Ming Yin",
"Yu-Xiang Wang"
] | Poster | null | We study the \emph{offline reinforcement learning} (offline RL) problem, where the goal is to learn a reward-maximizing policy in an unknown \emph{Markov Decision Process} (MDP) using the data coming from a policy $\mu$. In particular, we consider the sample complexity problems of offline RL for the finite horizon MDP... | [
"Theory",
"Reinforcement Learning Theory",
"Markov Decision Process Theory"
] | null | 8,703 | 2110.08695 | title_snapshot |
lEf52hTHq0Q | Escape saddle points by a simple gradient-descent based algorithm | https://openreview.net/forum?id=lEf52hTHq0Q | [
"Chenyi Zhang",
"Tongyang Li"
] | Poster | null | Escaping saddle points is a central research topic in nonconvex optimization. In this paper, we propose a simple gradient-based algorithm such that for a smooth function $f\colon\mathbb{R}^n\to\mathbb{R}$, it outputs an $\epsilon$-approximate second-order stationary point in $\tilde{O}(\log n/\epsilon^{1.75})$ iteratio... | [
"saddle points",
"gradient descent",
"stochastic optimization",
"nonconvex optimization",
"negative curvature finding"
] | We propose a simple gradient-based algorithm to find an eps-approx. second-order stationary point of an n-dim function in ~O(log n/eps^1.75) iterations, achieving poly-speedup in log n. It's also applicable to stochastic optimization. | 8,698 | 2111.14069 | title_snapshot |
xV6ZDMwRspN | Unlabeled Principal Component Analysis | https://openreview.net/forum?id=xV6ZDMwRspN | [
"Yunzhen Yao",
"Liangzu Peng",
"Manolis C. Tsakiris"
] | Poster | null | We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-defined algebraic problem in the sense that the only matrices of m... | [
"unlabeled sensing",
"linear regression without correspondences",
"robust principal component analysis",
"algebraic geometry"
] | null | 8,690 | null | null |
c1p817YZAx6 | Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning | https://openreview.net/forum?id=c1p817YZAx6 | [
"Hyunsoo Chung",
"Jungtaek Kim",
"Boris Knyazev",
"Jinhwi Lee",
"Graham W. Taylor",
"Jaesik Park",
"Minsu Cho"
] | Poster | null | Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possible combinations. To address such a problem, we introduce a novel formulation, combinatorial construction, which requires a building agent to... | [
"Combinatorial construction",
"Sequential assembly",
"Deep reinforcement learning"
] | We introduce a novel problem formulation, combinatorial construction, which requires a building agent to assemble unit primitives sequentially, given incomplete partial target information. | 8,675 | 2110.15481 | title_snapshot |
QcwJmp1sTnk | Agent Modelling under Partial Observability for Deep Reinforcement Learning | https://openreview.net/forum?id=QcwJmp1sTnk | [
"Georgios Papoudakis",
"Filippos Christianos",
"Stefano V Albrecht"
] | Poster | null | Modelling the behaviours of other agents is essential for understanding how agents interact and making effective decisions. Existing methods for agent modelling commonly assume knowledge of the local observations and chosen actions of the modelled agents during execution. To eliminate this assumption, we extract repres... | [
"agent modelling",
"deep reinforcement learning",
"partial observability",
"opponent modelling"
] | null | 8,671 | 2006.09447 | title_snapshot |
VuzPO_TZHPc | Associative Memories via Predictive Coding | https://openreview.net/forum?id=VuzPO_TZHPc | [
"Tommaso Salvatori",
"Yuhang Song",
"Yujian Hong",
"Lei Sha",
"Simon Frieder",
"Zhenghua Xu",
"Rafal Bogacz",
"Thomas Lukasiewicz"
] | Poster | null | Associative memories in the brain receive and store patterns of activity registered by the sensory neurons, and are able to retrieve them when necessary. Due to their importance in human intelligence, computational models of associative memories have been developed for several decades now. In this paper, we present a n... | [
"Deep Learning",
"Associative Memory",
"Cognitive Science"
] | null | 8,669 | 2109.08063 | title_snapshot |
GWRkOYr4jxQ | Luna: Linear Unified Nested Attention | https://openreview.net/forum?id=GWRkOYr4jxQ | [
"Xuezhe Ma",
"Xiang Kong",
"Sinong Wang",
"Chunting Zhou",
"Jonathan May",
"Hao Ma",
"Luke Zettlemoyer"
] | Poster | null | The quadratic computational and memory complexities of the Transformer's attention mechanism have limited its scalability for modeling long sequences. In this paper, we propose Luna, a linear unified nested attention mechanism that approximates softmax attention with two nested linear attention functions, yielding onl... | [
"Efficient Attention",
"Transformer",
"Linear Attention"
] | Linear Unified Nested Attention | 8,658 | 2106.01540 | title_snapshot |
ERzpLwEDOY | Bandits with many optimal arms | https://openreview.net/forum?id=ERzpLwEDOY | [
"Rianne de Heide",
"James Cheshire",
"Pierre MENARD",
"Alexandra Carpentier"
] | Poster | null | We consider a stochastic bandit problem with a possibly infinite number of arms. We write $p^*$ for the proportion of optimal arms and $\Delta$ for the minimal mean-gap between optimal and sub-optimal arms. We characterize the optimal learning rates both in the cumulative regret setting, and in the best-arm identificat... | [
"Infinitely-Armed Bandit Models",
"Cumulative Regret",
"Best-Arm Identification"
] | Stochastic bandit problems with possibly infinitely many arms but a fixed proportion of optimal arms. | 8,657 | 2103.12452 | title_snapshot |
wgeK563QgSw | Offline Reinforcement Learning as One Big Sequence Modeling Problem | https://openreview.net/forum?id=wgeK563QgSw | [
"Michael Janner",
"Qiyang Li",
"Sergey Levine"
] | Spotlight | null | Reinforcement learning (RL) is typically viewed as the problem of estimating single-step policies (for model-free RL) or single-step models (for model-based RL), leveraging the Markov property to factorize the problem in time. However, we can also view RL as a sequence modeling problem: predict a sequence of actions th... | [
"Reinforcement learning",
"transformers"
] | null | 8,655 | 2106.02039 | title_snapshot |
bhdntUKwA1 | Parallel and Efficient Hierarchical k-Median Clustering | https://openreview.net/forum?id=bhdntUKwA1 | [
"Vincent Cohen-Addad",
"Silvio Lattanzi",
"Ashkan Norouzi-Fard",
"Christian Sohler",
"Ola Svensson"
] | Poster | null | As a fundamental unsupervised learning task, hierarchical clustering has been extensively studied in the past decade. In particular, standard metric formulations as hierarchical $k$-center, $k$-means, and $k$-median received a lot of attention and the problems have been studied extensively in different models of compu... | [
"Clustering",
"k-Median",
"Hierarchical"
] | In this work we propose an efficient algorithm for Hierarchical k-Median Clustering problem in distributed setting. | 8,652 | null | null |
ebQXflQre5a | AutoBalance: Optimized Loss Functions for Imbalanced Data | https://openreview.net/forum?id=ebQXflQre5a | [
"Mingchen Li",
"Xuechen Zhang",
"Christos Thrampoulidis",
"Jiasi Chen",
"Samet Oymak"
] | Poster | null | Imbalanced datasets are commonplace in modern machine learning problems. The presence of under-represented classes or groups with sensitive attributes results in concerns about generalization and fairness. Such concerns are further exacerbated by the fact that large capacity deep nets can perfectly fit the training dat... | [
"Label imbalance",
"fairness",
"bilevel optimization",
"personalization"
] | null | 8,648 | 2201.01212 | title_snapshot |
AklttWFnxS9 | Maximum Likelihood Training of Score-Based Diffusion Models | https://openreview.net/forum?id=AklttWFnxS9 | [
"Yang Song",
"Conor Durkan",
"Iain Murray",
"Stefano Ermon"
] | Spotlight | null | Score-based diffusion models synthesize samples by reversing a stochastic process that diffuses data to noise, and are trained by minimizing a weighted combination of score matching losses. The log-likelihood of score-based diffusion models can be tractably computed through a connection to continuous normalizing flows,... | [
"generative models",
"density estimation",
"score matching",
"score-based generative models",
"diffusion models",
"stochastic differential equations",
"normalizing flows",
"neural ODEs",
"likelihood",
"continuous normalizing flows"
] | Score-based generative models can achieve state-of-the-art likelihoods when re-weighting the training objective. | 8,646 | 2101.09258 | title_snapshot |
HhUmPH22Vpn | CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration | https://openreview.net/forum?id=HhUmPH22Vpn | [
"Hao Yu",
"Fu Li",
"Mahdi Saleh",
"Benjamin Busam",
"Slobodan Ilic"
] | Poster | null | We study the problem of extracting correspondences between a pair of point clouds for registration. For correspondence retrieval, existing works benefit from matching sparse keypoints detected from dense points but usually struggle to guarantee their repeatability. To address this issue, we present CoFiNet - Coarse-to-... | [
"Point cloud Matching",
"Point Cloud Registration",
"Coarse-to-fine Mechanism",
"Deep Neural Networks"
] | We present CoFiNet - Coarse-to-Fine Network which extracts hierarchical correspondences from coarse to fine for point cloud registration. | 8,643 | 2110.14076 | title_judge |
9lwprXiGdR4 | Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs | https://openreview.net/forum?id=9lwprXiGdR4 | [
"Jiafan He",
"Dongruo Zhou",
"Quanquan Gu"
] | Poster | null | We study the reinforcement learning problem for discounted Markov Decision Processes (MDPs) under the tabular setting. We propose a model-based algorithm named UCBVI-$\gamma$, which is based on the \emph{optimism in the face of uncertainty principle} and the Bernstein-type bonus. We show that UCBVI-$\gamma$ achieves a... | [
"reinforcement learning",
"discounted MDP",
"Bernstein inequality"
] | null | 8,634 | 2010.00587 | title_snapshot |
_hKvtsqItc | The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers | https://openreview.net/forum?id=_hKvtsqItc | [
"Fabian Latorre",
"Leello Tadesse Dadi",
"Paul Rolland",
"Volkan Cevher"
] | Poster | null | It has been recently observed that neural networks, unlike kernel methods, enjoy a reduced sample complexity when the distribution is isotropic (i.e., when the covariance matrix is the identity). We find that this sensitivity to the data distribution is not exclusive to neural networks, and the same phenomenon can be o... | [
"intrinsic dimension",
"generalization",
"rademacher complexity",
"statistical learning theory"
] | We show that the complexity of nuclear-norm regularized quadratic functions adapts to the intrinsic dimension of the data distribution. | 8,633 | null | null |
4bKbEP9b65v | Littlestone Classes are Privately Online Learnable | https://openreview.net/forum?id=4bKbEP9b65v | [
"Noah Golowich",
"Roi Livni"
] | Spotlight | null | We consider the problem of online classification under a privacy constraint. In this setting a learner observes sequentially a stream of labelled examples $(x_t, y_t)$, for $1 \leq t \leq T$, and returns at each iteration $t$ a hypothesis $h_t$ which is used to predict the label of each new example $x_t$. The learn... | [
"differential privacy",
"online classification",
"online learning"
] | We provide a differentially private online learning algorithm for Littlestone classes with finite mistake bound. | 8,629 | 2106.13513 | title_snapshot |
M0J1c3PqwKZ | Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition | https://openreview.net/forum?id=M0J1c3PqwKZ | [
"Yulin Wang",
"Rui Huang",
"Shiji Song",
"Zeyi Huang",
"Gao Huang"
] | Poster | null | Vision Transformers (ViT) have achieved remarkable success in large-scale image recognition. They split every 2D image into a fixed number of patches, each of which is treated as a token. Generally, representing an image with more tokens would lead to higher prediction accuracy, while it also results in drastically inc... | [
"Vision Transformer",
"Efficient Inference",
"Image Recognition"
] | We develop a Dynamic Vision Transformer (DVT) to automatically configure a proper number of tokens for each individual image, leading to a significant improvement in computational efficiency, both theoretically and empirically. | 8,621 | 2105.15075 | title_snapshot |
ui4xChWcA4R | Shape Registration in the Time of Transformers | https://openreview.net/forum?id=ui4xChWcA4R | [
"Giovanni Trappolini",
"Luca Cosmo",
"Luca Moschella",
"Riccardo Marin",
"Simone Melzi",
"Emanuele Rodolà"
] | Poster | null | In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed approach is data-driven and adopts for the first time the transformers architecture in the registration task.
Our method is general and applies to different settings. Given a fixed template... | [
"3D Registration",
"Point Clouds",
"Deep Learning",
"Transformers",
"Shape Matching"
] | We propose a transformer based architecture to tackle the problem of non–rigid registration, with a novel surface attention mechanisms better suited to exploit the local geometric priors of the underlying structure. | 8,618 | 2106.13679 | title_snapshot |
ejmqyWW0MK6 | Mixability made efficient: Fast online multiclass logistic regression | https://openreview.net/forum?id=ejmqyWW0MK6 | [
"Rémi Jézéquel",
"Pierre Gaillard",
"Alessandro Rudi"
] | Spotlight | null | Mixability has been shown to be a powerful tool to obtain algorithms with optimal regret. However, the resulting methods often suffer from high computational complexity which has reduced their practical applicability. For example, in the case of multiclass logistic regression, the aggregating forecaster (see Foster et ... | [
"Online learning",
"Logistic regression"
] | null | 8,596 | 2110.03960 | title_snapshot |
admg0sZZm1e | Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation | https://openreview.net/forum?id=admg0sZZm1e | [
"Arno Solin",
"Ella Maija Tamir",
"Prakhar Verma"
] | Poster | null | Simulation-based techniques such as variants of stochastic Runge–Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning. These methods are general-purpose and used with parametric and non-parametric models, and neural SDEs. Stochastic Runge–Kutta relies on the us... | [
"Stochastic differential equation",
"approximative inference",
"neural SDE",
"Gaussian process"
] | Gaussian approximations are a fast and more scalable option to stochastic Runge–Kutta methods for SDEs in ML | 8,590 | 2110.15739 | title_snapshot |
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