Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. - Kelvin Xu; Jimmy Ba; Ryan Kiros; Kyunghyun Cho; Aaron C. Courville; Ruslan Salakhutdinov; Richard S. Zemel; Yoshua Bengio (ICML, 2015)

On Using Monolingual Corpora in Neural Machine Translation. - Çaglar Gülçehre; Orhan Firat; Kelvin Xu; Kyunghyun Cho; Loïc Barrault; Huei-Chi Lin; Fethi Bougares; Holger Schwenk; Yoshua Bengio (CoRR, 2015)

A Controller Recognizer Framework: How necessary is recognition for control? - Marcin Moczulski; Kelvin Xu; Aaron C. Courville; KyungHyun Cho (CoRR, 2015)

Theano: A Python framework for fast computation of mathematical expressions. - Rami Al-Rfou; Guillaume Alain; Amjad Almahairi; Christof Angermüller; Dzmitry Bahdanau; Nicolas Ballas; Frédéric Bastien; Justin Bayer; Anatoly Belikov; Alexander Belopolsky; Yoshua Bengio; Arnaud Bergeron; et al. (CoRR, 2016)

An Actor-Critic Algorithm for Sequence Prediction. - Dzmitry Bahdanau; Philemon Brakel; Kelvin Xu; Anirudh Goyal; Ryan Lowe; Joelle Pineau; Aaron C. Courville; Yoshua Bengio (ICLR (Poster), 2017)

Unsupervised Perceptual Rewards for Imitation Learning. - Pierre Sermanet; Kelvin Xu; Sergey Levine (ICLR (Workshop), 2017)

Bridging the Gap Between Value and Policy Based Reinforcement Learning. - Ofir Nachum; Mohammad Norouzi 002; Kelvin Xu; Dale Schuurmans (NIPS, 2017)

On integrating a language model into neural machine translation. - Çaglar Gülçehre; Orhan Firat; Kelvin Xu; Kyunghyun Cho; Yoshua Bengio (Comput. Speech Lang., 2017)

Trust-PCL: An Off-Policy Trust Region Method for Continuous Control. - Ofir Nachum; Mohammad Norouzi 002; Kelvin Xu; Dale Schuurmans (ICLR (Poster), 2018)

Probabilistic Model-Agnostic Meta-Learning. - Chelsea Finn; Kelvin Xu; Sergey Levine (NeurIPS, 2018)

Learning a Prior over Intent via Meta-Inverse Reinforcement Learning. - Kelvin Xu; Ellis Ratner; Anca D. Dragan; Sergey Levine; Chelsea Finn (ICML, 2019)

Privacy-Preserving Fall Detection with Deep Learning on mmWave Radar Signal. - Yangfan Sun; Renlong Hang; Zhu Li 001; Mouqing Jin; Kelvin Xu (VCIP, 2019)

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. - Eleni Triantafillou; Tyler Zhu; Vincent Dumoulin; Pascal Lamblin; Kelvin Xu; Ross Goroshin; Carles Gelada; Kevin Swersky; Pierre-Antoine Manzagol; Hugo Larochelle (CoRR, 2019)

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. - Eleni Triantafillou; Tyler Zhu; Vincent Dumoulin; Pascal Lamblin; Utku Evci; Kelvin Xu; Ross Goroshin; Carles Gelada; Kevin Swersky; Pierre-Antoine Manzagol; Hugo Larochelle (ICLR, 2020)

Continual Learning of Control Primitives : Skill Discovery via Reset-Games. - Kelvin Xu; Siddharth Verma; Chelsea Finn; Sergey Levine (NeurIPS, 2020)

Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention. - Abhishek Gupta 004; Justin Yu; Tony Z. Zhao; Vikash Kumar; Aaron Rovinsky; Kelvin Xu; Thomas Devlin; Sergey Levine (ICRA, 2021)

Autonomous Reinforcement Learning: Formalism and Benchmarking. - Archit Sharma; Kelvin Xu; Nikhil Sardana; Abhishek Gupta 004; Karol Hausman; Sergey Levine; Chelsea Finn (ICLR, 2022)

Towards Adaptive, Continual Embodied Agents - Kelvin Xu (2022)

Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance. - Kelvin Xu; Zheyuan Hu 003; Ria Doshi; Aaron Rovinsky; Vikash Kumar; Abhishek Gupta 004; Sergey Levine (ICRA, 2023)

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? - C. Daniel Freeman; Laura Culp; Aaron Parisi; Maxwell L. Bileschi; Gamaleldin F. Elsayed; Alex Rizkowsky; Isabelle Simpson; Alex Alemi; Azade Nova; Ben Adlam; Bernd Bohnet; Gaurav Mishra; et al. (CoRR, 2023)

LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models. - Marwa Abdulhai; Isadora White; Charlie Snell; Charles Sun; Joey Hong; Yuexiang Zhai; Kelvin Xu; Sergey Levine (CoRR, 2023)

ContMulti-objective Optimization Model for Momentum Change Based on Genetic Algorithm. - Shuo Zhang; Ziqi Kong; Kelvin Xu; Guangxiao Shi; Zixiao Kong; Xia Li; Jinjin Zan (ICIC (1), 2024)

Small-scale proxies for large-scale Transformer training instabilities. - Mitchell Wortsman; Peter J. Liu; Lechao Xiao; Katie E. Everett; Alexander A. Alemi; Ben Adlam; John D. Co-Reyes; Izzeddin Gur; Abhishek Kumar; Roman Novak; Jeffrey Pennington; Jascha Sohl-Dickstein; et al. (ICLR, 2024)

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters. - Charlie Snell; Jaehoon Lee 001; Kelvin Xu; Aviral Kumar (CoRR, 2024)

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. - Jiri Hron; Laura Culp; Gamaleldin F. Elsayed; Rosanne Liu; Ben Adlam; Maxwell L. Bileschi; Bernd Bohnet; JD Co-Reyes; Noah Fiedel; C. Daniel Freeman; Izzeddin Gur; Kathleen Kenealy; et al. (CoRR, 2024)

Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries. - Kiran Vodrahalli; Santiago Ontanon; Nilesh Tripuraneni; Kelvin Xu; Sanil Jain; Rakesh Shivanna; Jeffrey Hui; Nishanth Dikkala; Mehran Kazemi; Bahare Fatemi; Rohan Anil; Ethan Dyer; et al. (CoRR, 2024)

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. - Avi Singh; John D. Co-Reyes; Rishabh Agarwal; Ankesh Anand; Piyush Patil; Xavier Garcia; Peter J. Liu; James Harrison; Jaehoon Lee 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)

Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning. - Charlie Victor Snell; Jaehoon Lee 001; Kelvin Xu; Aviral Kumar (ICLR, 2025)