More from the Same Authors

• 2021 : CommonsenseQA 2.0: Exposing the Limits of AI through Gamification »
  Alon Talmor · Ori Yoran · Ronan Le Bras · Chandra Bhagavatula · Yoav Goldberg · Yejin Choi · Jonathan Berant
• 2021 : NaturalProofs: Mathematical Theorem Proving in Natural Language »
  Sean Welleck · Jiacheng Liu · Ronan Le Bras · Hanna Hajishirzi · Yejin Choi · Kyunghyun Cho
• 2021 : Towards Grounded Natural Language Proof Generation »
  Sean Welleck · Jiacheng Liu · Yejin Choi
• 2021 : Cutting Down on Prompts and Parameters:Simple Few-Shot Learning with Language Models »
  Robert Logan · Ivana Balazevic · Eric Wallace · Fabio Petroni · Sameer Singh · Sebastian Riedel
• 2022 Poster: Optimality and Stability in Non-Convex Smooth Games »
  Guojun Zhang · Pascal Poupart · Yaoliang Yu
• 2022 : DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low Rank Adaptation »
  Mojtaba Valipour · Mehdi Rezaghoizadeh · Ivan Kobyzev · Ali Ghodsi
• 2022 : SymbolicGPT: A Generative Transformer Model for Symbolic Regression »
  Mojtaba Valipour · Bowen You · Maysum H Panju · Ali Ghodsi
• 2022 : Improved Knowledge Distillation by Utilizing Backward Pass Knowledge in Neural Networks »
  Aref Jafari · Mehdi Rezaghoizadeh · Ali Ghodsi
• 2022 : Attribute Controlled Dialogue Prompting »
  Runcheng Liu · Ahmad Rashid · Ivan Kobyzev · Mehdi Rezaghoizadeh · Pascal Poupart
• 2022 : Quantifying Social Biases Using Templates is Unreliable »
  Preethi Seshadri · Pouya Pezeshkpour · Sameer Singh
• 2022 : Information-Theoretic Evaluation of Free-Text Rationales with Conditional $\mathcal{V}$-Information »
  Hanjie Chen · Faeze Brahman · Xiang Ren · Yangfeng Ji · Yejin Choi · Swabha Swayamdipta
• 2022 : TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations »
  Dylan Slack · Satyapriya Krishna · Himabindu Lakkaraju · Sameer Singh
• 2022 : Geometric attacks on batch normalization »
  Amur Ghose · Apurv Gupta · Yaoliang Yu · Pascal Poupart
• 2022 : Contributed Talk: TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations »
  Dylan Slack · Satyapriya Krishna · Himabindu Lakkaraju · Sameer Singh
• 2022 Spotlight: Optimality and Stability in Non-Convex Smooth Games »
  Guojun Zhang · Pascal Poupart · Yaoliang Yu
• 2022 : Attribute Controlled Dialogue Prompting »
  Runcheng Liu · Ahmad Rashid · Ivan Kobyzev · Mehdi Rezaghoizadeh · Pascal Poupart
• 2022 : Fine-grained Interactive Vision Language Pre-training »
  Lu Hou · Lu Hou
• 2022 Workshop: Second Workshop on Efficient Natural Language and Speech Processing (ENLSP-II) »
  Mehdi Rezagholizadeh · Peyman Passban · Yue Dong · Lili Mou · Pascal Poupart · Ali Ghodsi · Qun Liu
• 2022 Poster: GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale »
  Tim Dettmers · Mike Lewis · Younes Belkada · Luke Zettlemoyer
• 2022 Poster: COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics »
  Lianhui Qin · Sean Welleck · Daniel Khashabi · Yejin Choi
• 2022 Poster: Towards Efficient Post-training Quantization of Pre-trained Language Models »
  Haoli Bai · Lu Hou · Lifeng Shang · Xin Jiang · Irwin King · Michael R Lyu
• 2022 Poster: Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models »
  Kushal Tirumala · Aram Markosyan · Luke Zettlemoyer · Armen Aghajanyan
• 2022 Poster: Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark »
  Jiaxi Gu · Xiaojun Meng · Guansong Lu · Lu Hou · Niu Minzhe · Xiaodan Liang · Lewei Yao · Runhui Huang · Wei Zhang · Xin Jiang · Chunjing XU · Hang Xu
• 2022 Poster: Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game »
  Guiliang Liu · Yudong Luo · Oliver Schulte · Pascal Poupart
• 2022 Poster: Improving Policy Learning via Language Dynamics Distillation »
  Victor Zhong · Jesse Mu · Luke Zettlemoyer · Edward Grefenstette · Tim Rocktäschel
• 2022 Poster: QUARK: Controllable Text Generation with Reinforced Unlearning »
  Ximing Lu · Sean Welleck · Jack Hessel · Liwei Jiang · Lianhui Qin · Peter West · Prithviraj Ammanabrolu · Yejin Choi
• 2022 Poster: NaturalProver: Grounded Mathematical Proof Generation with Language Models »
  Sean Welleck · Jiacheng Liu · Ximing Lu · Hannaneh Hajishirzi · Yejin Choi
• 2021 : Best Papers and Closing Remarks »
  Ali Ghodsi · Pascal Poupart
• 2021 : Battling with Larger Models through Grounding and Searching »
  Yejin Choi
• 2021 : Toward Efficient Training of Large Language Models with Balanced Conditional Compute »
  Luke Zettlemoyer
• 2021 : Benchmarks for Multi-objective Hyperparameter Optimization »
  Kevin Duh
• 2021 : How to Win LMs and Influence Predictions: Using Short Phrases to Control NLP Models »
  Sameer Singh
• 2021 : Cutting Down on Prompts and Parameters:Simple Few-Shot Learning with Language Models »
  Robert Logan · Ivana Balazevic · Eric Wallace · Fabio Petroni · Sameer Singh · Sebastian Riedel
• 2021 : Opening of the Afternoon Session »
  Ali Ghodsi
• 2021 : Compression and Acceleration of Pre-trained Language Models »
  Lu Hou
• 2021 Workshop: Efficient Natural Language and Speech Processing (Models, Training, and Inference) »
  Mehdi Rezaghoizadeh · Lili Mou · Yue Dong · Pascal Poupart · Ali Ghodsi · Qun Liu
• 2021 : Opening Speech »
  Pascal Poupart
• 2021 Oral: MERLOT: Multimodal Neural Script Knowledge Models »
  Rowan Zellers · Ximing Lu · Jack Hessel · Youngjae Yu · Jae Sung Park · Jize Cao · Ali Farhadi · Yejin Choi
• 2021 Poster: Luna: Linear Unified Nested Attention »
  Xuezhe Ma · Xiang Kong · Sinong Wang · Chunting Zhou · Jonathan May · Hao Ma · Luke Zettlemoyer
• 2021 : NaturalProofs: Mathematical Theorem Proving in Natural Language »
  Sean Welleck · Jiacheng Liu · Ronan Le Bras · Hanna Hajishirzi · Yejin Choi · Kyunghyun Cho
• 2021 Poster: Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals »
  Lang Liu · Krishna Pillutla · Sean Welleck · Sewoong Oh · Yejin Choi · Zaid Harchaoui
• 2021 Poster: Reliable Post hoc Explanations: Modeling Uncertainty in Explainability »
  Dylan Slack · Anna Hilgard · Sameer Singh · Himabindu Lakkaraju
• 2021 : PYLON: A PyTorch Framework for Learning with Constraints »
  Kareem Ahmed · Tao Li · Nu Mai Thy Ton · Quan Guo · Kai-Wei Chang · Parisa Kordjamshidi · Vivek Srikumar · Guy Van den Broeck · Sameer Singh
• 2021 Poster: MERLOT: Multimodal Neural Script Knowledge Models »
  Rowan Zellers · Ximing Lu · Jack Hessel · Youngjae Yu · Jae Sung Park · Jize Cao · Ali Farhadi · Yejin Choi
• 2021 Poster: Quantifying and Improving Transferability in Domain Generalization »
  Guojun Zhang · Han Zhao · Yaoliang Yu · Pascal Poupart
• 2021 Poster: Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning »
  Guiliang Liu · Xiangyu Sun · Oliver Schulte · Pascal Poupart
• 2021 Poster: SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark »
  Victor Zhong · Austin W. Hanjie · Sida Wang · Karthik Narasimhan · Luke Zettlemoyer
• 2021 Poster: MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers »
  Krishna Pillutla · Swabha Swayamdipta · Rowan Zellers · John Thickstun · Sean Welleck · Yejin Choi · Zaid Harchaoui
• 2021 Poster: Counterfactual Explanations Can Be Manipulated »
  Dylan Slack · Anna Hilgard · Himabindu Lakkaraju · Sameer Singh
• 2021 : CommonsenseQA 2.0: Exposing the Limits of AI through Gamification »
  Alon Talmor · Ori Yoran · Ronan Le Bras · Chandra Bhagavatula · Yoav Goldberg · Yejin Choi · Jonathan Berant
• 2021 Oral: MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers »
  Krishna Pillutla · Swabha Swayamdipta · Rowan Zellers · John Thickstun · Sean Welleck · Yejin Choi · Zaid Harchaoui
• 2020 : Panel Discussion & Closing »
  Yejin Choi · Alexei Efros · Chelsea Finn · Kristen Grauman · Quoc V Le · Yann LeCun · Ruslan Salakhutdinov · Eric Xing
• 2020 : QA: Yejin Choi »
  Yejin Choi
• 2020 : Invited Talk: Yejin Choi »
  Yejin Choi
• 2020 : Invited talk - De-noising Sequence-to-Sequence Pre-training »
  Luke Zettlemoyer
• 2020 : Adversarial, Socially Aware, and Commonsensical Data »
  Yejin Choi
• 2020 Poster: Learning Agent Representations for Ice Hockey »
  Guiliang Liu · Oliver Schulte · Pascal Poupart · Mike Rudd · Mehrsan Javan
• 2020 Tutorial: (Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities Q&A »
  Himabindu Lakkaraju · Julius Adebayo · Sameer Singh
• 2020 Poster: Learning Dynamic Belief Graphs to Generalize on Text-Based Games »
  Ashutosh Adhikari · Xingdi Yuan · Marc-Alexandre Côté · Mikuláš Zelinka · Marc-Antoine Rondeau · Romain Laroche · Pascal Poupart · Jian Tang · Adam Trischler · Will Hamilton
• 2020 Poster: DynaBERT: Dynamic BERT with Adaptive Width and Depth »
  Lu Hou · Zhiqi Huang · Lifeng Shang · Xin Jiang · Xiao Chen · Qun Liu
• 2020 Poster: Pre-training via Paraphrasing »
  Mike Lewis · Marjan Ghazvininejad · Gargi Ghosh · Armen Aghajanyan · Sida Wang · Luke Zettlemoyer
• 2020 Spotlight: DynaBERT: Dynamic BERT with Adaptive Width and Depth »
  Lu Hou · Zhiqi Huang · Lifeng Shang · Xin Jiang · Xiao Chen · Qun Liu
• 2020 Tutorial: (Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities »
  Himabindu Lakkaraju · Julius Adebayo · Sameer Singh
• 2019 : Invited Talk (Yejin Choi) »
  Yejin Choi
• 2019 : Yejin Choi »
  Yejin Choi
• 2019 Workshop: KR2ML - Knowledge Representation and Reasoning Meets Machine Learning »
  Veronika Thost · Christian Muise · Kartik Talamadupula · Sameer Singh · Christopher Ré
• 2019 Poster: Defending Against Neural Fake News »
  Rowan Zellers · Ari Holtzman · Hannah Rashkin · Yonatan Bisk · Ali Farhadi · Franziska Roesner · Yejin Choi
• 2019 Demonstration: AllenNLP Interpret: Explaining Predictions of NLP Models »
  Jens Tuyls · Eric Wallace · Matt Gardner · Junlin Wang · Sameer Singh · Sanjay Subramanian
• 2019 Poster: Normalization Helps Training of Quantized LSTM »
  Lu Hou · Jinhua Zhu · James Kwok · Fei Gao · Tao Qin · Tie-Yan Liu
• 2018 Workshop: Reinforcement Learning under Partial Observability »
  Joni Pajarinen · Chris Amato · Pascal Poupart · David Hsu
• 2018 Poster: Deep Homogeneous Mixture Models: Representation, Separation, and Approximation »
  Priyank Jaini · Pascal Poupart · Yaoliang Yu
• 2018 Poster: Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks »
  Agastya Kalra · Abdullah Rashwan · Wei-Shou Hsu · Pascal Poupart · Prashant Doshi · George Trimponias
• 2018 Poster: Unsupervised Video Object Segmentation for Deep Reinforcement Learning »
  Vikash Goel · Jameson Weng · Pascal Poupart
• 2018 Poster: Monte-Carlo Tree Search for Constrained POMDPs »
  Jongmin Lee · Geon-Hyeong Kim · Pascal Poupart · Kee-Eung Kim
• 2017 : End-to-end Learning for Broad Coverage Semantics: SRL, Coreference, and Beyond »
  Luke Zettlemoyer
• 2016 Poster: Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics »
  Wei-Shou Hsu · Pascal Poupart
• 2016 Poster: A Unified Approach for Learning the Parameters of Sum-Product Networks »
  Han Zhao · Pascal Poupart · Geoffrey Gordon
• 2015 Workshop: Machine Learning Systems »
  Alex Beutel · Tianqi Chen · Sameer Singh · Elaine Angelino · Markus Weimer · Joseph Gonzalez
• 2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
  Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden
• 2014 Workshop: 4th Workshop on Automated Knowledge Base Construction (AKBC) »
  Sameer Singh · Fabian M Suchanek · Sebastian Riedel · Partha Pratim Talukdar · Kevin Murphy · Christopher Ré · William Cohen · Tom Mitchell · Andrew McCallum · Jason E Weston · Ramanathan Guha · Boyan Onyshkevych · Hoifung Poon · Oren Etzioni · Ari Kobren · Arvind Neelakantan · Peter Clark
• 2014 Demonstration: A Visual and Interactive IDE for Probabilistic Programming »
  Sameer Singh · Luke Hewitt · Tim Rocktäschel · Sebastian Riedel
• 2013 Workshop: Big Learning : Advances in Algorithms and Data Management »
  Xinghao Pan · Haijie Gu · Joseph Gonzalez · Sameer Singh · Yucheng Low · Joseph Hellerstein · Derek G Murray · Raghu Ramakrishnan · Michael Jordan · Christopher Ré
• 2012 Workshop: Big Learning : Algorithms, Systems, and Tools »
  Sameer Singh · John Duchi · Yucheng Low · Joseph E Gonzalez
• 2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
  Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu
• 2009 Poster: FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs »
  Andrew McCallum · Karl Schultz · Sameer Singh
• 2009 Poster: Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference »
  Michael Wick · Khashayar Rohanimanesh · Sameer Singh · Andrew McCallum
• 2009 Spotlight: Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference »
  Michael Wick · Khashayar Rohanimanesh · Sameer Singh · Andrew McCallum
• 2008 Poster: Multi-Agent Filtering with Infinitely Nested Beliefs »
  Luke Zettlemoyer · Brian Milch · Leslie Kaelbling
• 2008 Spotlight: Multi-Agent Filtering with Infinitely Nested Beliefs »
  Luke Zettlemoyer · Brian Milch · Leslie Kaelbling