Stelios Triantafyllou, Adish Singla, Goran Radanovic

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theory, robustness, fairness

Michaël Fanuel, Rémi Bardenet

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optimization, machine learning, kernel methods, interpretability

Byung-Hoon Kim, Jong Chul Ye, Jae-Jin Kim

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deep learning, transformers, graph learning, interpretability

Wei Sun, Aojun Zhou, Sander Stuijk and

Rob Wijnhoven, Andrew Nelson, hongsheng Li, Henk Corporaal

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deep learning

Wenwei Zhang, Jiangmiao Pang, Kai Chen, Chen Change Loy

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vision

Samuel Gruffaz, Pierre-Emmanuel Poulet, Etienne Maheux and

Bruno M Jedynak, Stanley DURRLEMAN

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interpretability

Qingzhong Ai, LIRONG HE, SHIYU LIU, Zenglin Xu

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optimization, generative model, representation learning

Yizhuo Li, Miao Hao, Zonglin Di and

Nitesh Bharadwaj Gundavarapu, Xiaolong Wang

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self-supervised learning, transformers, vision

Kuan-Chieh Wang, YAN FU, Ke Li and

Ashish Khisti, Richard Zemel, Alireza Makhzani

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deep learning, generative model

Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon

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deep learning, generative model

Łukasz Kuciński, Tomasz Korbak, Paweł Kołodziej, Piotr Miłoś

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deep learning, graph learning

Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale and

Mehryar Mohri, Sashank Reddi, Sebastian Stich, Ananda Theertha Suresh

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optimization, reinforcement learning and planning, federated learning

Sarkhan Badirli, Zeynep Akata, George Mohler and

Christine Picard, Mehmet M Dundar

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machine learning, generative model

Terrell Mundhenk, Mikel Landajuela, Ruben Glatt and

Claudio P Santiago, Daniel faissol, Brenden K Petersen

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optimization, reinforcement learning and planning

Ksenia Konyushova, Yutian Chen, Thomas Paine and

Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, Nando de Freitas

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optimization, reinforcement learning and planning, active learning

Hanrui Zhang, Vincent Conitzer

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Marco Federici, Ryota Tomioka, Patrick Forré

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theory, deep learning, machine learning, graph learning, domain adaptation, representation learning

Kimon Fountoulakis, Pan Li, Shenghao Yang

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optimization, graph learning, clustering

Yoon-Yeong Kim, Kyungwoo Song, JoonHo Jang, Il-chul Moon

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deep learning, active learning

Kamal Gupta, Gowthami Somepalli, Anubhav Anubhav and

Vinoj Yasanga Jayasundara Magalle Hewa, Matthias Zwicker, Abhinav Shrivastava

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machine learning, self-supervised learning, transformers

Xinyi Wang, Wenhu Chen, Michael Saxon, William Yang Wang

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deep learning, domain adaptation, causality, language

Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Dan Roy

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graph learning

Runtian Zhai, Chen Dan, Arun Suggala and

J. Zico Kolter, Pradeep Ravikumar

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machine learning, fairness

Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard

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reinforcement learning and planning, robustness, bandits

Jeff Pool, Chong Yu

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optimization

Christian Horvat, Jean-Pascal Pfister

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generative model

Jose Vinicius de Miranda Cardoso, Jiaxi Ying, Daniel Palomar

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graph learning

Pan Zhou, Hanshu Yan, Xiaotong Yuan and

Jiashi Feng, Shuicheng Yan

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deep learning, optimization

Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi and

S. M. Ali Eslami, Oriol Vinyals, Felix Hill

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vision, few shot learning

Minsu Cho, Aditya Balu, Ameya Joshi and

Anjana Deva Prasad, Biswajit Khara, Soumik Sarkar, Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde

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optimization, machine learning

Yangsibo Huang, Samyak Gupta, Zhao Song and

Kai Li, Sanjeev Arora

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privacy, federated learning

Zeru Zhang, Jiayin Jin, Zijie Zhang and

Yang Zhou, Xin Zhao, Jiaxiang Ren, Ji Liu, Lingfei Wu, Ruoming Jin, Dejing Dou

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theory, deep learning, optimization

Nikos Vlassis, Ashok Chandrashekar, Fernando Amat, Nathan Kallus

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optimization, bandits

Marie-Anne Lachaux, Baptiste Roziere, Marc Szafraniec, Guillaume Lample

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self-supervised learning

Mohit Bajaj, Lingyang Chu, Zi Yu Xue and

Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, Yong Zhang

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deep learning, graph learning, interpretability

Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio and

Aaron Courville, Doina Precup, Guillaume Lajoie

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theory, deep learning, optimization, robustness

Rishi Saket

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Jianhong Wang, Wangkun Xu, Yunjie Gu and

Wenbin Song, Tim C Green

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reinforcement learning and planning, graph learning

Weijiang Yu, Haoteng Zheng, Mengfei Li and

Lei Ji, Lijun Wu, Nong Xiao, Nan Duan

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transformers

Xinyi Xu, Lingjuan Lyu, Xingjun Ma and

Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low

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machine learning, fairness, federated learning