Neural Information Processing Systems Foundation, Inc. (NeurIPS)

Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What Makes Graph Neural Networks Miscalibrated?

Daniel Cremers, Hans Hao-Hsun Hsu, Yuesong Shen, Christian Tomani p13775-13786 from Advances in Neural Information Processing Systems 35

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What Makes Partial-Label Learning Algorithms Effective?

Xin Geng, Yangfan Liu, Jiaqi Lv, Gang Niu, Masashi Sugiyama, Shiyu Xia, Miao Xu, Ning Xu, Min-Ling Zhang p89513-89534 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What makes unlearning hard and what to do about it

George-Octavian Barbulescu, Meghdad Kurmanji, Eleni Triantafillou, Peter Triantafillou, Kairan Zhao p12293-12333 from Advances in Neural Information Processing Systems 37

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What Matters in Graph Class Incremental Learning? An Information Preservation Perspective

Qinghua Hu, Jialu Li, Wanyu Lin, Yu Wang, Pengfei Zhu p26195-26223 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What matters when building vision-language models?

Matthieu Cord, Hugo Laurençon, Victor Sanh, Léo Tronchon p87874-87907 from Advances in Neural Information Processing Systems 37

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What Planning Problems Can A Relational Neural Network Solve?

Leslie Kaelbling, Tomás Lozano-Pérez, Jiayuan Mao, Josh Tenenbaum p59522-59542 from Advances in Neural Information Processing Systems 36

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What Rotary Position Embedding Can Tell Us: Identifying Query and Key Weights Corresponding to Basic Syntactic or High-level Semantic Information

Yiting Chen, Junchi Yan p54507-54528 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction

Ingo Bax, Guillaume Berger, Apratim Bhattacharyya, Cornelius Böhm, Florian Dietrichkeit, Mingu Lee, Xuanlin Li, Pulkit Madan, Roland Memisevic, Antoine Mercier, Sunny Panchal, Reza Pourreza, Mark Todorovich p75853-75882 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What Truly Matters in Trajectory Prediction for Autonomous Driving?

Panpan Cai, David Hsu, Tran Phong, Haoran Wu, Cunjun Yu, Sifa Zheng p71327-71339 from Advances in Neural Information Processing Systems 36

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What type of inference is planning?

Dileep George, Li Ku, Miguel Lázaro-Gredilla, Kevin Murphy p116705-116742 from Advances in Neural Information Processing Systems 37

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What Variables Affect Out-of-Distribution Generalization in Pretrained Models?

Jhair Gallardo, Yousuf Harun, Christopher Kanan, Giri Krishnan, Kyungbok Lee p56479-56525 from Advances in Neural Information Processing Systems 37

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What You See is What You Classify: Black Box Attributions

Radhakrishna Achanta, Fernando Perez-Cruz, Nathanael Perraudin, Steven Stalder, Michele Volpi p84-94 from Advances in Neural Information Processing Systems 35

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What You See is What You Get: Principled Deep Learning Via Distributional Generalization

Jaroslaw Blasiok, Bogdan Kulynych, Preetum Nakkiran, Yao-Yuan Yang, Yaodong Yu p2168-2183 from Advances in Neural Information Processing Systems 35

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What You See is What You Read? Improving Text-Image Alignment Evaluation

Roee Aharoni, Yonatan Bitton, Soravit Changpinyo, Jonathan Herzig, Oran Lang, Eran Ofek, Idan Szpektor, Michal Yarom p1601-1619 from Advances in Neural Information Processing Systems 36

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What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment

Nathan Kallus p15996-16009 from Advances in Neural Information Processing Systems 35

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What’s Left? Concept Grounding with Logic-Enhanced Foundation Models

Joy Hsu, Jiayuan Mao, Josh Tenenbaum, Jiajun Wu p38798-38814 from Advances in Neural Information Processing Systems 36

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When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture

Yiwen Guo, Yichuan Mo, Yisen Wang, Yifei Wang, Dongxian Wu p18599-18611 from Advances in Neural Information Processing Systems 35

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When are dynamical systems learned from time series data statistically accurate?

Nisha Chandramoorthy, Jeongjin Park, Nicole Yang p43975-44008 from Advances in Neural Information Processing Systems 37

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

When are ensembles really effective?

Liam Hodgkinson, Hyunsuk Kim, Michael Mahoney, Ryan Theisen, Yaoqing Yang p15015-15026 from Advances in Neural Information Processing Systems 36

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When Are Local Queries Useful for Robust Learning?

Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell p33920-33933 from Advances in Neural Information Processing Systems 35

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

When Are Offline Two-Player Zero-Sum Markov Games Solvable?

Qiwen Cui, Simon Du p25779-25791 from Advances in Neural Information Processing Systems 35

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality

Jose Blanchet, Haoxuan Chen, Yiping Lu, Lexing Ying p36566-36578 from Advances in Neural Information Processing Systems 36

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

When Can We Track Significant Preference Shifts in Dueling Bandits?

Arpit Agarwal, Joe Suk p38347-38369 from Advances in Neural Information Processing Systems 36

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

When Combinatorial Thompson Sampling Meets Approximation Regret

Pierre Perrault p17639-17651 from Advances in Neural Information Processing Systems 35

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Neural Information Processing Systems Foundation, Inc. (NeurIPS)

What Makes Graph Neural Networks Miscalibrated?

What Makes Partial-Label Learning Algorithms Effective?

What makes unlearning hard and what to do about it

What Matters in Graph Class Incremental Learning? An Information Preservation Perspective

What matters when building vision-language models?

What Planning Problems Can A Relational Neural Network Solve?

What Rotary Position Embedding Can Tell Us: Identifying Query and Key Weights Corresponding to Basic Syntactic or High-level Semantic Information

What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction

What Truly Matters in Trajectory Prediction for Autonomous Driving?

What type of inference is planning?

What Variables Affect Out-of-Distribution Generalization in Pretrained Models?

What You See is What You Classify: Black Box Attributions

What You See is What You Get: Principled Deep Learning Via Distributional Generalization

What You See is What You Read? Improving Text-Image Alignment Evaluation

What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment

What’s Left? Concept Grounding with Logic-Enhanced Foundation Models

When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture

When are dynamical systems learned from time series data statistically accurate?

When are ensembles really effective?

When Are Local Queries Useful for Robust Learning?

When Are Offline Two-Player Zero-Sum Markov Games Solvable?

When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality

When Can We Track Significant Preference Shifts in Dueling Bandits?

When Combinatorial Thompson Sampling Meets Approximation Regret

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