ICML
Collection
Accepted papers for ICML (International Conference on Machine Learning), one dataset per year. • 13 items • Updated
title stringlengths 14 161 | paper_url stringlengths 45 61 | authors listlengths 1 28 | type stringclasses 0
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values | abstract large_stringlengths 413 4.43k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 1 1.23k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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PAC-Bayesian Bounds on Rate-Efficient Classifiers | https://proceedings.mlr.press/v162/abbas22a.html | [
"Alhabib Abbas",
"Yiannis Andreopoulos"
] | null | null | We derive analytic bounds on the noise invariance of majority vote classifiers operating on compressed inputs. Specifically, starting from recent bounds on the true risk of majority vote classifiers, we extend the applicability of PAC-Bayesian theory to quantify the resilience of majority votes to input noise stemming ... | [] | null | 1 | null | null | [
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Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning | https://proceedings.mlr.press/v162/abbas22b.html | [
"Momin Abbas",
"Quan Xiao",
"Lisha Chen",
"Pin-Yu Chen",
"Tianyi Chen"
] | null | null | Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging due to the innate bilevel problem structure. Specifically, the loss landscape of MAML is much more complex with possibly more saddle points... | [] | null | 2 | 2206.03996 | title_snapshot | [
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An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn | https://proceedings.mlr.press/v162/abbe22a.html | [
"Emmanuel Abbe",
"Elisabetta Cornacchia",
"Jan Hazla",
"Christopher Marquis"
] | null | null | This paper introduces the notion of “Initial Alignment” (INAL) between a neural network at initialization and a target function. It is proved that if a network and a Boolean target function do not have a noticeable INAL, then noisy gradient descent with normalized i.i.d. initialization will not learn in polynomial time... | [] | null | 3 | 2202.12846 | title_snapshot | [
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Active Sampling for Min-Max Fairness | https://proceedings.mlr.press/v162/abernethy22a.html | [
"Jacob D Abernethy",
"Pranjal Awasthi",
"Matthäus Kleindessner",
"Jamie Morgenstern",
"Chris Russell",
"Jie Zhang"
] | null | null | We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimization. The key intuition behind our approach is to use at each timestep a datapoint from the group that is worst off under the current model ... | [] | null | 4 | 2006.06879 | title_snapshot | [
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Meaningfully debugging model mistakes using conceptual counterfactual explanations | https://proceedings.mlr.press/v162/abid22a.html | [
"Abubakar Abid",
"Mert Yuksekgonul",
"James Zou"
] | null | null | Understanding and explaining the mistakes made by trained models is critical to many machine learning objectives, such as improving robustness, addressing concept drift, and mitigating biases. However, this is often an ad hoc process that involves manually looking at the model’s mistakes on many test samples and guessi... | [] | null | 5 | 2106.12723 | title_snapshot | [
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Batched Dueling Bandits | https://proceedings.mlr.press/v162/agarwal22a.html | [
"Arpit Agarwal",
"Rohan Ghuge",
"Viswanath Nagarajan"
] | null | null | The K-armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied. Previous works have only focused on the sequential setting where the policy adapts after every comparison. However, in many applications such as search ranking and recommendation systems, it is ... | [] | null | 6 | 2202.10660 | title_snapshot | [
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Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models. | https://proceedings.mlr.press/v162/agarwal22b.html | [
"Abhineet Agarwal",
"Yan Shuo Tan",
"Omer Ronen",
"Chandan Singh",
"Bin Yu"
] | null | null | Decision trees and random forests (RF) are a cornerstone of modern machine learning practice. Due to their tendency to overfit, trees are typically regularized by a variety of techniques that modify their structure (e.g. pruning). We introduce Hierarchical Shrinkage (HS), a post-hoc algorithm which regularizes the tree... | [] | null | 7 | 2202.00858 | title_judge | [
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Deep equilibrium networks are sensitive to initialization statistics | https://proceedings.mlr.press/v162/agarwala22a.html | [
"Atish Agarwala",
"Samuel S Schoenholz"
] | null | null | Deep equilibrium networks (DEQs) are a promising way to construct models which trade off memory for compute. However, theoretical understanding of these models is still lacking compared to traditional networks, in part because of the repeated application of a single set of weights. We show that DEQs are sensitive to th... | [] | null | 8 | 2207.09432 | title_snapshot | [
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Learning of Cluster-based Feature Importance for Electronic Health Record Time-series | https://proceedings.mlr.press/v162/aguiar22a.html | [
"Henrique Aguiar",
"Mauro Santos",
"Peter Watkinson",
"Tingting Zhu"
] | null | null | The recent availability of Electronic Health Records (EHR) has allowed for the development of algorithms predicting inpatient risk of deterioration and trajectory evolution. However, prediction of disease progression with EHR is challenging since these data are sparse, heterogeneous, multi-dimensional, and multi-modal ... | [] | null | 9 | null | null | [
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On the Convergence of the Shapley Value in Parametric Bayesian Learning Games | https://proceedings.mlr.press/v162/agussurja22a.html | [
"Lucas Agussurja",
"Xinyi Xu",
"Bryan Kian Hsiang Low"
] | null | null | Measuring contributions is a classical problem in cooperative game theory where the Shapley value is the most well-known solution concept. In this paper, we establish the convergence property of the Shapley value in parametric Bayesian learning games where players perform a Bayesian inference using their combined data,... | [] | null | 10 | 2205.07428 | title_snapshot | [
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Individual Preference Stability for Clustering | https://proceedings.mlr.press/v162/ahmadi22a.html | [
"Saba Ahmadi",
"Pranjal Awasthi",
"Samir Khuller",
"Matthäus Kleindessner",
"Jamie Morgenstern",
"Pattara Sukprasert",
"Ali Vakilian"
] | null | null | In this paper, we propose a natural notion of individual preference (IP) stability for clustering, which asks that every data point, on average, is closer to the points in its own cluster than to the points in any other cluster. Our notion can be motivated from several perspectives, including game theory and algorithmi... | [] | null | 11 | 2207.03600 | title_snapshot | [
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