NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 13 items • Updated
title stringlengths 16 162 | paper_url stringlengths 41 43 | authors listlengths 1 27 | type stringclasses 0
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values | abstract large_stringlengths 393 2.47k | keywords listlengths 0 13 | TL;DR large_stringlengths 4 250 ⌀ | submission_number int64 14 13.1k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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Statistical Learning and Inverse Problems: A Stochastic Gradient Approach | https://openreview.net/forum?id=09QFnDWPF8 | [
"Yuri Fonseca",
"Yuri Saporito"
] | null | null | Inverse problems are paramount in Science and Engineering. In this paper, we consider the setup of Statistical Inverse Problem (SIP) and demonstrate how Stochastic Gradient Descent (SGD) algorithms can be used to solve linear SIP. We provide consistency and finite sample bounds for the excess risk. We also propose a mo... | [
"Statistical Learning",
"Inverse Problems",
"Stochastic Gradient Descent"
] | An algorithm based on stochastic gradient descent for solving linear Inverse Problems under a statistical learning framework. | 13,051 | 2209.14967 | title_snapshot | [
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Efficiency Ordering of Stochastic Gradient Descent | https://openreview.net/forum?id=pnSyqRXx73 | [
"Jie Hu",
"Vishwaraj Doshi",
"Do Young Eun"
] | null | null | We consider the stochastic gradient descent (SGD) algorithm driven by a general stochastic sequence, including i.i.d noise and random walk on an arbitrary graph, among others; and analyze it in the asymptotic sense. Specifically, we employ the notion of `efficiency ordering', a well-analyzed tool for comparing the perf... | [
"Stochastic Gradient Descent",
"Asymptotic Analysis",
"Efficiency Ordering"
] | We introduce the notion of efficiency ordering as an alternative metric for comparing the performance of different stochastic input sequences for Stochastic Gradient Descent algorithm. | 13,028 | 2209.07446 | title_snapshot | [
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Self-Aware Personalized Federated Learning | https://openreview.net/forum?id=EqJ5_hZSqgy | [
"Huili Chen",
"Jie Ding",
"Eric William Tramel",
"Shuang Wu",
"Anit Kumar Sahu",
"Salman Avestimehr",
"Tao Zhang"
] | null | null | In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned. Inspired by Bayesian hierarchical models, we develop a self-aware personalized FL method where each client can ... | [
"Federared Learning",
"Personalization"
] | We propose a new adaptive federated learning algorithm for personalization | 13,014 | 2204.08069 | title_snapshot | [
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Nonnegative Tensor Completion via Integer Optimization | https://openreview.net/forum?id=xnI37HyfoP | [
"Caleb Xavier Bugg",
"Chen Chen",
"Anil Aswani"
] | null | null | Unlike matrix completion, tensor completion does not have an algorithm that is known to achieve the information-theoretic sample complexity rate. This paper develops a new algorithm for the special case of completion for nonnegative tensors. We prove that our algorithm converges in a linear (in numerical tolerance) num... | [
"tensor completion",
"machine learning"
] | We present a new norm for nonnegative tensor completion and demonstrate its usefulness, versus existing methods, through numerical experiments | 13,003 | 2111.04580 | title_snapshot | [
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TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s | https://openreview.net/forum?id=OoNmOfYVhEU | [
"Felix Chern",
"Blake Hechtman",
"Andy Davis",
"Ruiqi Guo",
"David Majnemer",
"Sanjiv Kumar"
] | null | null | This paper presents a novel nearest neighbor search algorithm achieving TPU (Google Tensor Processing Unit) peak performance, outperforming state-of-the-art GPU algorithms with similar level of recall. The design of the proposed algorithm is motivated by an accurate accelerator performance model that takes into account... | [
"TPU",
"K-nearest neighbor search",
"Approximate nearest neighbor search",
"roofline model",
"accelerator"
] | Novel nearest neighbor search algorithm achieving TPU peak performance with recall guarantee. | 12,999 | 2206.14286 | title_snapshot | [
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Equivariant Networks for Crystal Structures | https://openreview.net/forum?id=0Dh8dz4snu | [
"Sékou-Oumar Kaba",
"Siamak Ravanbakhsh"
] | null | null | Supervised learning with deep models has tremendous potential for applications in materials science. Recently, graph neural networks have been used in this context, drawing direct inspiration from models for molecules. However, materials are typically much more structured than molecules, which is a feature that these m... | [
"materials",
"deep learning",
"symmetry",
"equivariance",
"crystals",
"graph neural networks",
"geometric deep learning"
] | A deep model for materials | 12,977 | 2211.15420 | title_snapshot | [
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Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound | https://openreview.net/forum?id=s1yaWFDLxVG | [
"Charles Guille-Escuret",
"Adam Ibrahim",
"Baptiste Goujaud",
"Ioannis Mitliagkas"
] | null | null | The study of first-order optimization is sensitive to the assumptions made on the objective functions.
These assumptions induce complexity classes which play a key role in worst-case analysis, including
the fundamental concept of algorithm optimality. Recent work argues that strong convexity and
smoothness—popular assu... | [
"First-Order Optimization",
"Non-Convex",
"Deterministic",
"Gradient Descent",
"Restricted Secant Inequality",
"Error Bounds"
] | We show that Gradient Descent is exactly optimal on a class of functions relevant to machine learning using Performance Estimation Problems | 12,967 | 2203.00342 | title_snapshot | [
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Decoupled Context Processing for Context Augmented Language Modeling | https://openreview.net/forum?id=02dbnEbEFn | [
"Zonglin Li",
"Ruiqi Guo",
"Sanjiv Kumar"
] | null | null | Language models can be augmented with context retriever to incorporate knowledge from large external databases. By leveraging retrieved context, the neural network does not have to memorize the massive amount of world knowledge within its internal parameters, leading to better parameter efficiency, interpretability and... | [
"Retrieval Augmentation",
"Encoder-Decoder",
"Language Modeling",
"Efficiency"
] | null | 12,953 | 2210.05758 | title_snapshot | [
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Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction | https://openreview.net/forum?id=7eUOC9fEIRO | [
"Dilip Arumugam",
"Satinder Singh"
] | null | null | The Bayes-Adaptive Markov Decision Process (BAMDP) formalism pursues the Bayes-optimal solution to the exploration-exploitation trade-off in reinforcement learning. As the computation of exact solutions to Bayesian reinforcement-learning problems is intractable, much of the literature has focused on developing suitable... | [
"Bayes-Adaptive Markov Decision Process",
"Bayesian reinforcement learning",
"Exploration",
"Planning"
] | null | 12,944 | 2210.16872 | title_snapshot | [
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Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions | https://openreview.net/forum?id=BUMiizPcby6 | [
"Antonio Terpin",
"Nicolas Lanzetti",
"Batuhan Yardim",
"Florian Dorfler",
"Giorgia Ramponi"
] | null | null | Policy Optimization (PO) algorithms have been proven particularly suited to handle the high-dimensionality of real-world continuous control tasks. In this context, Trust Region Policy Optimization methods represent a popular approach to stabilize the policy updates. These usually rely on the Kullback-Leibler (KL) diver... | [
"Trust region policy optimization",
"optimal transport"
] | null | 12,920 | 2210.11137 | title_snapshot | [
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Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings | https://openreview.net/forum?id=kpSAfnHSgXR | [
"Dongxu Zhang",
"Michael Boratko",
"Cameron N Musco",
"Andrew McCallum"
] | null | null | Modeling directed graphs with differentiable representations is a fundamental requirement for performing machine learning on graph-structured data. Geometric embedding models (e.g. hyperbolic, cone, and box embeddings) excel at this task, exhibiting useful inductive biases for directed graphs. However, modeling directe... | [
"graph representation learning",
"geometric representation learning",
"directed graphs",
"cyclic graphs",
"transitivity"
] | null | 12,916 | null | null | [
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