ICML
Collection
Accepted papers for ICML (International Conference on Machine Learning), one dataset per year. • 13 items • Updated
title stringlengths 10 168 | paper_url stringlengths 42 42 | authors listlengths 1 42 | type stringclasses 2
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values | abstract large_stringlengths 417 2.06k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 6 6.84k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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Bayesian Estimation of Differential Privacy | https://openreview.net/forum?id=PwsvGnamYD | [
"Santiago Zanella-Beguelin",
"Lukas Wutschitz",
"Shruti Tople",
"Ahmed Salem",
"Victor Rühle",
"Andrew Paverd",
"Mohammad Naseri",
"Boris Köpf",
"Daniel Jones"
] | Poster | null | Algorithms such as Differentially Private SGD enable training machine learning models with formal privacy guarantees. However, because these guarantees hold with respect to unrealistic adversaries, the protection afforded against practical attacks is typically much better. An emerging strand of work empirically estimat... | [] | null | 6,837 | 2206.05199 | title_snapshot | [
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Adaptive Estimation of Graphical Models under Total Positivity | https://openreview.net/forum?id=USiX9gmGRx | [
"Jiaxi Ying",
"José Vinícius De Miranda Cardoso",
"Daniel P. Palomar"
] | Poster | null | We consider the problem of estimating (diagonally dominant) M-matrices as precision matrices in Gaussian graphical models. Such models have shown interesting properties, e.g., the maximum likelihood estimator exists with as little as two observations in the case of M-matrices, and exists even with one observation in th... | [] | null | 6,836 | 2210.15471 | title_snapshot | [
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GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models | https://openreview.net/forum?id=elL6uw9qOX | [
"Hanjing Wang",
"Man-Kit Sit",
"Congjie He",
"Ying Wen",
"Weinan Zhang",
"Jun Wang",
"Yaodong Yang",
"Luo Mai"
] | Poster | null | This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transformers). With such models, existing systems such as Reverb face considerable bottlenecks in memory, computation, and communication. GEAR, ho... | [] | null | 6,829 | 2310.05205 | title_snapshot | [
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Disentangled Multi-Fidelity Deep Bayesian Active Learning | https://openreview.net/forum?id=jOLIFanYnt | [
"Dongxia Wu",
"Ruijia Niu",
"Matteo Chinazzi",
"Yian Ma",
"Rose Yu"
] | Poster | null | To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a direct mapping from input parameters to simulation outputs at the highest fidelity by actively acquiring data from multiple fidelity levels. H... | [] | null | 6,826 | 2305.04392 | title_snapshot | [
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Understand and Modularize Generator Optimization in ELECTRA-style Pretraining | https://openreview.net/forum?id=ikE60aXe8M | [
"Chengyu Dong",
"Liyuan Liu",
"Hao Cheng",
"Jingbo Shang",
"Jianfeng Gao",
"Xiaodong Liu"
] | Poster | null | Despite the effectiveness of ELECTRA-style pre-training, their performance is dependent on the careful selection of the model size for the auxiliary generator, leading to high trial-and-error costs. In this paper, we present the first systematic study of this problem. Our theoretical investigation highlights the import... | [] | null | 6,816 | null | null | [
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NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations | https://openreview.net/forum?id=cHhGmXDiHp | [
"Yonggan Fu",
"Ye Yuan",
"Souvik Kundu",
"Shang Wu",
"Shunyao Zhang",
"Celine Lin"
] | Poster | null | Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and thus the possibility of instant rendering on new scenes. While adversarial robustness is essential for real-world applications, little study... | [] | null | 6,810 | 2306.06359 | title_snapshot | [
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Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP | https://openreview.net/forum?id=MI5YpKX84O | [
"Jiacheng Guo",
"Zihao Li",
"Huazheng Wang",
"Mengdi Wang",
"Zhuoran Yang",
"Xuezhou Zhang"
] | Poster | null | In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dimensional raw observations to a compact representation and uses it for more efficient exploration and planning. We focus our attention on the... | [] | null | 6,793 | 2306.12356 | title_snapshot | [
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Taming graph kernels with random features | https://openreview.net/forum?id=H21qm4xyk9 | [
"Krzysztof Marcin Choromanski"
] | Oral | null | We introduce in this paper the mechanism of graph random features (GRFs). GRFs can be used to construct unbiased randomized estimators of several important kernels defined on graphs' nodes, in particular the regularized Laplacian kernel. As regular RFs for non-graph kernels, they provide means to scale up kernel method... | [] | null | 6,792 | 2305.00156 | title_snapshot | [
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Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures | https://openreview.net/forum?id=EMN99LtfYA | [
"Oleg Balabanov",
"Matthias Beaupère",
"Laura Grigori",
"Victor Lederer"
] | Poster | null | This article introduces a novel structured random matrix composed blockwise from subsampled randomized Hadamard transforms (SRHTs). The block SRHT is expected to outperform well-known dimension reduction maps, including SRHT and Gaussian matrices on distributed architectures. We prove that a block SRHT with enough rows... | [] | null | 6,779 | 2210.11295 | title_judge | [
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Unconstrained Online Learning with Unbounded Losses | https://openreview.net/forum?id=2K2vEVBm5G | [
"Andrew Jacobsen",
"Ashok Cutkosky"
] | Poster | null | Algorithms for online learning typically require one or more boundedness assumptions: that the domain is bounded, that the losses are Lipschitz, or both. In this paper, we develop a new setting for online learning with unbounded domains and non-Lipschitz losses. For this setting we provide an algorithm which guarantees... | [] | null | 6,766 | 2306.04923 | title_snapshot | [
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Optimistic Planning by Regularized Dynamic Programming | https://openreview.net/forum?id=LctoTBcGUf | [
"Antoine Moulin",
"Gergely Neu"
] | Poster | null | We propose a new method for optimistic planning in infinite-horizon discounted Markov decision processes based on the idea of adding regularization to the updates of an otherwise standard approximate value iteration procedure. This technique allows us to avoid contraction and monotonicity arguments typically required b... | [] | null | 6,761 | 2302.14004 | title_snapshot | [
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