ICML
Collection
Accepted papers for ICML (International Conference on Machine Learning), one dataset per year. • 13 items • Updated
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A New Representation of Successor Features for Transfer across Dissimilar Environments | https://proceedings.mlr.press/v139/abdolshah21a.html | [
"Majid Abdolshah",
"Hung Le",
"Thommen Karimpanal George",
"Sunil Gupta",
"Santu Rana",
"Svetha Venkatesh"
] | null | null | Transfer in reinforcement learning is usually achieved through generalisation across tasks. Whilst many studies have investigated transferring knowledge when the reward function changes, they have assumed that the dynamics of the environments remain consistent. Many real-world RL problems require transfer among environ... | [] | null | 1 | 2107.08426 | title_snapshot | [
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Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling | https://proceedings.mlr.press/v139/abeyrathna21a.html | [
"Kuruge Darshana Abeyrathna",
"Bimal Bhattarai",
"Morten Goodwin",
"Saeed Rahimi Gorji",
"Ole-Christoffer Granmo",
"Lei Jiao",
"Rupsa Saha",
"Rohan K. Yadav"
] | null | null | Using logical clauses to represent patterns, Tsetlin Machine (TM) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. Each TM clause votes for or against a particular class, with classification resolved using a majority vote. While the... | [] | null | 2 | 2009.04861 | title_snapshot | [
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Debiasing Model Updates for Improving Personalized Federated Training | https://proceedings.mlr.press/v139/acar21a.html | [
"Durmus Alp Emre Acar",
"Yue Zhao",
"Ruizhao Zhu",
"Ramon Matas",
"Matthew Mattina",
"Paul Whatmough",
"Venkatesh Saligrama"
] | null | null | We propose a novel method for federated learning that is customized specifically to the objective of a given edge device. In our proposed method, a server trains a global meta-model by collaborating with devices without actually sharing data. The trained global meta-model is then personalized locally by each device to ... | [] | null | 3 | null | null | [
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Memory Efficient Online Meta Learning | https://proceedings.mlr.press/v139/acar21b.html | [
"Durmus Alp Emre Acar",
"Ruizhao Zhu",
"Venkatesh Saligrama"
] | null | null | We propose a novel algorithm for online meta learning where task instances are sequentially revealed with limited supervision and a learner is expected to meta learn them in each round, so as to allow the learner to customize a task-specific model rapidly with little task-level supervision. A fundamental concern arisin... | [] | null | 4 | null | null | [
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Robust Testing and Estimation under Manipulation Attacks | https://proceedings.mlr.press/v139/acharya21a.html | [
"Jayadev Acharya",
"Ziteng Sun",
"Huanyu Zhang"
] | null | null | We study robust testing and estimation of discrete distributions in the strong contamination model. Our results cover both centralized setting and distributed setting with general local information constraints including communication and LDP constraints. Our technique relates the strength of manipulation attacks to the... | [] | null | 5 | 2104.10740 | title_snapshot | [
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GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning | https://proceedings.mlr.press/v139/achituve21a.html | [
"Idan Achituve",
"Aviv Navon",
"Yochai Yemini",
"Gal Chechik",
"Ethan Fetaya"
] | null | null | Gaussian processes (GPs) are non-parametric, flexible, models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially compelling due to the strong representational power induced by the network. However, inference in GPs, whether with or without DKL, can be com... | [] | null | 6 | 2102.07868 | title_snapshot | [
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f-Domain Adversarial Learning: Theory and Algorithms | https://proceedings.mlr.press/v139/acuna21a.html | [
"David Acuna",
"Guojun Zhang",
"Marc T. Law",
"Sanja Fidler"
] | null | null | Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labeled dataset. In this paper, we introduce a novel and general domain-adversarial framework. Specifically, we derive a novel generalization boun... | [] | null | 7 | 2106.11344 | title_snapshot | [
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Towards Rigorous Interpretations: a Formalisation of Feature Attribution | https://proceedings.mlr.press/v139/afchar21a.html | [
"Darius Afchar",
"Vincent Guigue",
"Romain Hennequin"
] | null | null | Feature attribution is often loosely presented as the process of selecting a subset of relevant features as a rationale of a prediction. Task-dependent by nature, precise definitions of "relevance" encountered in the literature are however not always consistent. This lack of clarity stems from the fact that we usually ... | [] | null | 8 | 2104.12437 | title_snapshot | [
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Acceleration via Fractal Learning Rate Schedules | https://proceedings.mlr.press/v139/agarwal21a.html | [
"Naman Agarwal",
"Surbhi Goel",
"Cyril Zhang"
] | null | null | In practical applications of iterative first-order optimization, the learning rate schedule remains notoriously difficult to understand and expensive to tune. We demonstrate the presence of these subtleties even in the innocuous case when the objective is a convex quadratic. We reinterpret an iterative algorithm from t... | [] | null | 9 | 2103.01338 | title_snapshot | [
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A Regret Minimization Approach to Iterative Learning Control | https://proceedings.mlr.press/v139/agarwal21b.html | [
"Naman Agarwal",
"Elad Hazan",
"Anirudha Majumdar",
"Karan Singh"
] | null | null | We consider the setting of iterative learning control, or model-based policy learning in the presence of uncertain, time-varying dynamics. In this setting, we propose a new performance metric, planning regret, which replaces the standard stochastic uncertainty assumptions with worst case regret. Based on recent advance... | [] | null | 10 | 2102.13478 | title_snapshot | [
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Towards the Unification and Robustness of Perturbation and Gradient Based Explanations | https://proceedings.mlr.press/v139/agarwal21c.html | [
"Sushant Agarwal",
"Shahin Jabbari",
"Chirag Agarwal",
"Sohini Upadhyay",
"Steven Wu",
"Himabindu Lakkaraju"
] | null | null | As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two popular post hoc interpretation techniques: SmoothGr... | [] | null | 11 | 2102.10618 | title_snapshot | [
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