Google Research at ICLR 2024

Conformal Risk Control
Anastasios N. Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei, Tal Schuster

Learning to Reject Meets Long-tail Learning
Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Neha Gupta, Sanjiv Kumar

On Bias-Variance Alignment in Deep Models
Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar

Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs
Aakash Lahoti, Stefani Karp, Ezra Winston, Aarti Singh, Yuanzhi Li

Massively Scalable Inverse Reinforcement Learning in Google Maps (see blog post)
Matt Barnes, Matthew Abueg, Oliver F. Lange, Matt Deeds, Jason Trader, Denali Molitor, Markus Wulfmeier, Shawn O'Banion

A Benchmark for Learning to Translate a New Language from One Grammar Book
Garrett Tanzer, Mirac Suzgun, Eline Visser, Dan Jurafsky, Luke Melas-Kyriazi

On the Foundations of Shortcut Learning
Katherine Hermann, Hossein Mobahi, Thomas Fel, Michael Curtis Mozer

Dictionary Contrastive Learning for Efficient Local Supervision without Auxiliary Networks
Suhwan Choi, Myeongho Jeon, Yeonjung Hwang, Jeonglyul Oh, Sungjun Lim, Joonseok Lee, Myungjoo Kang

Distributionally Robust Optimization with Bias and Variance Reduction
Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaid Harchaoui

Learning Performance-Improving Code Edits
Alexander G Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh

Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
Tianhong Li*, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang*, Dilip Krishnan

DyST: Towards Dynamic Neural Scene Representations on Real-World Videos
Maximilian Seitzer, Sjoerd van Steenkiste, Thomas Kipf, Klaus Greff, Mehdi S. M. Sajjadi

DreamFlow: High-Quality Text-to-3D Generation by Approximating Probability Flow
Kyungmin Lee, Kihyuk Sohn, Jinwoo Shin

Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
Somnath Basu Roy Chowdhury*, Nicholas Monath, Ahmad Beirami, Rahul Kidambi, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi

Privacy Amplification for Matrix Mechanisms
Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
Haolin Liu, Chen-Yu Wei, Julian Zimmert

CausalLM is not Optimal for in-Context Learning
Nan Ding, Tomer Levinboim, Jialin Wu, Sebastian Goodman, Radu Soricut

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Ratsch, Guy Tennenholtz

Neural SDF Flow for 3D Reconstruction of Dynamic Scenes
Wei Mao, Richard Hartley, Mathieu Salzmann, Miaomiao Liu

Retrieval-Enhanced Contrastive Vision-Text Models
Ahmet Iscen, Mathilde Caron, Alireza Fathi, Cordelia Schmid

TEMPO: Prompt-Based Generative Pre-trained Transformer for Time Series Forecasting
Defu Cao, Furong Jia, Sercan Ö. Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu

The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning
Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite

Combining Axes Pre-Conditioners through Kronecker Approximation for Deep Learning
Sai Surya Duvvuri*, Fnu Devvrit, Rohan Anil, Cho-Jui Hsieh, Inderjit S Dhillon

Demystifying Embedding Spaces using Large Language Models
Guy Tennenholtz, Yinlam Chow, ChihWei Hsu, Jihwan Jeong, Lior Shani, Azamat Tulepbergenov, Deepak Ramachandran, Martin Mladenov, Craig Boutilier

Denoising Diffusion via Image-Based Rendering
Titas Anciukevičius, Fabian Manhardt, Federico Tombari, Paul Henderson

Don't Trust: Verify — Grounding LLM Quantitative Reasoning with Auto Formalization
Jin Peng Zhou*, Charles E Staats*, Wenda Li, Christian Szegedy*, Kilian Q Weinberger, Yuhuai Wu*

Enabling Language Models to Implicitly Learn Self-Improvement
Ziqi Wang*, Le Hou , Tianjian Lu , Yuexin Wu , Yunxuan Li, Hongkun Yu, Heng Ji

The Importance of Feature Pre-Processing for Differentially Private Linear Optimization
Ziteng Sun, Ananda Theertha Suresh, Aditya Krishna Menon

OmniControl: Control Any Joint at Any Time for Human Motion Generation
Yiming Xie, Varun Jampani, Lei Zhong, Deqing Sun, Huaizu Jiang

Perceptual Group Tokenizer: Building Perception with Iterative Grouping
Zhiwei Deng, Ting Chen, Yang Li

Scalable Neural Network Kernels
Arijit Sehanobish, Krzysztof Marcin Choromanski, Yunfan Zhao, Avinava Dubey, Valerii Likhosherstov

Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning
Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan

Adaptive Regret for Bandits Made Possible: Two Queries Suffice
Zhou Lu, Qiuyi Zhang, Xinyi Chen, Fred Zhang, David Woodruff, Elad Hazan

Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
Shengyi Huang, Jiayi Weng, Rujikorn Charakorn, Min Lin, Zhongwen Xu, Santiago Ontanon

CoBIT: A Contrastive Bi-Directional Image-Text Generation Model
Haoxuan You*, Mandy Guo, Zhecan Wang, Kai-Wei Chang, Jason Michael Baldridge, Jiahui Yu

LabelDP-Pro: Learning with Label Differential Privacy via Projections
Badih Ghazi, Yangsibo Huang*, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

Learning to Reject with a Fixed Predictor: Application to Decontextualization
Christopher Mohri, Daniel Andor, Eunsol Choi, Michael Collins, Anqi Mao, Yutao Zhong

A Restoration Network as an Implicit Prior
Yuyang Hu, Mauricio Delbracio, Peyman Milanfar, Ulugbek Kamilov

Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM
Eliya Nachmani, Alon Levkovitch*, Roy Hirsch, Julian Salazar, Chulayuth Asawaroengchai, Soroosh Mariooryad, Ehud Rivlin, RJ Skerry-Ryan, Michelle Tadmor Ramanovich

Conformal Language Modeling
Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, Regina Barzilay

The Hidden Language of Diffusion Models
Hila Chefer*, Oran Lang, Mor Geva, Volodymyr Polosukhin, Assaf Shocher, Michal Irani, Inbar Mosseri, Lior Wolf

Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
Shreyas Havaldar, Navodita Sharma, Shubhi Sareen, Karthikeyan Shanmugam, Aravindan Raghuveer

Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets
Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes, Nadav Cohen, Amir Globerson

LLM Augmented LLMs: Expanding Capabilities through Composition
Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, Nitish Gupta, Shikhar Vashishth, Sriram Ganapathy, Abhishek Bapna, Prateek Jain, Partha Talukdar

Transformers can Optimally Learn Regression Mixture Models
Reese Pathak*, Rajat Sen, Weihao Kong, Abhimanyu Das

AGILE3D: Attention Guided Interactive Multi-Object 3D Segmentation
Yuanwen Yue, Sabarinath Mahadevan, Jonas Schult, Francis Engelmann, Bastian Leibe, Konrad Schindler, Theodora Kontogianni

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
Christopher A. Choquette-Choo, Krishnamurthy (Dj) Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta

Discovering Modular Solutions that Generalize Compositionally
Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wolczyk, Alexandra Maria Proca, Johannes Von Oswald, Razvan Pascanu, Joao Sacramento, Angelika Steger

DistillSpec: Improving Speculative Decoding via Knowledge Distillation
Yongchao Zhou*, Kaifeng Lyu*, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal

FeatUp: A Model-Agnostic Framework for Features at Any Resolution
Stephanie Fu, Mark Hamilton, Laura E. Brandt, Axel Feldmann, Zhoutong Zhang, William T. Freeman

Language Model Cascades: Token-Level Uncertainty and Beyond
Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar

Learning Thresholds with Latent Values and Censored Feedback
Jiahao Zhang, Tao Lin, Weiqiang Zheng, Zhe Feng, Yifeng Teng, Xiaotie Deng

MBR and QE Fine-Tuning: Training-Time Distillation of the Best and Most Expensive Decoding Methods
Mara Finkelstein, Markus Freitag

Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models
Sheng Shen*, Le Hou, Yanqi Zhou, Nan Du, Shayne Longpre*, Jason Wei*, Hyung Won Chung*, Barret Zoph*, William Fedus*, Xinyun Chen, Tu Vu*, Yuexin Wu, Wuyang Chen*, Albert Webson, Yunxuan Li, Vincent Y Zhao, Hongkun Yu, Kurt Keutzer, Trevor Darrell, Denny Zhou

Statistical Rejection Sampling Improves Preference Optimization
Tianqi Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J. Liu, Jialu Liu

Talk Like a Graph: Encoding Graphs for Large Language Models (see blog post)
Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi

When Scaling Meets LLM Fine-Tuning: The Effect of Data, Model and Fine-Tuning Methods
Biao Zhang, Zhongtao Liu, Colin Cherry, Orhan Firat

Davidsonian Scene Graph: Improving Reliability in Fine-Grained Evaluation for Text-to-Image Generation
Jaemin Cho*, Yushi Hu, Roopal Garg, Peter Anderson, Ranjay Krishna, Jason Baldridge, Mohit Bansal, Jordi Pont-Tuset, Su Wang

Learning from Aggregate Responses: Instance Level versus Bag Level Loss Functions
Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu

Magnushammer: A Transformer-Based Approach to Premise Selection
Maciej Mikuła, Szymon Tworkowski, Szymon Antoniak, Bartosz Piotrowski, Albert Q. Jiang, Jin Peng Zhou*, Christian Szegedy*, Łukasz Kuciński, Piotr Miłoś, Yuhuai Wu*

RETSim: Resilient and Efficient Text Similarity
Marina Zhang, Owen Skipper Vallis, Aysegul Bumin*, Tanay Vakharia, Elie Bursztein

Think Before you Speak: Training Language Models with Pause Tokens
Sachin Goyal*, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan

Two-Stage LLM Fine-Tuning with Less Specialization and More Generalization
Yihan Wang*, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, Sanjiv Kumar

Finite Scalar Quantization: VQ-VAE Made Simple
Fabian Mentzer, David Minnen, Eirikur Agustsson, Michael Tschannen

HyperAttention: Long-Context Attention in Near-Linear Time
Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David Woodruff, Amir Zandieh

The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric
Daniel Severo*, Lucas Theis, Johannes Ballé

Dual Associated Encoder for Face Restoration
Yu-Ju Tsai, Yu-Lun Liu, Lu Qi, Kelvin C. K. Chan, Ming-Hsuan Yang

Locality-Aware Graph Rewiring in GNNs
Christopher Fifty, Dennis Duan, Ronald Guenther Junkins, Ehsan Amid, Jure Leskovec, Christopher Re, Sebastian Thrun

Context-Aware Meta-Learning
Tian Li, Manzil Zaheer, Ziyu Liu, Sashank Reddi, Brendan McMahan, Virginia Smith

Learning Model Uncertainty as Variance-Minimizing Instance Weights
Nishant Jain, Karthikeyan Shanmugham, Pradeep Shenoy

Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Han Zhou*, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine A Heller, Subhrajit Roy

Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding (see blog post)
Zilong Wang*, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, Tomas Pfister

A Differentially Private Clustering Algorithm for Well-Clustered Graphs
Weiqiang He, Hendrik Fichtenberger, Pan Peng

Dual-Encoders for Extreme Multi-Label Classification
Nilesh Gupta, Devvrit Khatri, Ankit Singh Rawat, Srinadh Bhojanapalli, Prateek Jain, Inderjit S Dhillon

Idempotent Generative Network
Assaf Shocher, Amil V Dravid, Yossi Gandelsman, Inbar Mosseri, Michael Rubinstein, Alexei A Efros

OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Francis Engelmann, Fabian Manhardt, Michael Niemeyer, Keisuke Tateno, Marc Pollefeys, Federico Tombari

Plugin Estimators for Selective Classification with Out-of-Distribution Detection
Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar

Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning
Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos, Konstantinos E. Nikolakakis, Amin Karbasi, Dionysis Kalogerias, Nezihe Merve Gürel, Theodoros Rekatsinas

Diffusion Sampling with Momentum for Mitigating Divergence Artifacts
Suttisak Wizadwongsa, Worameth Chinchuthakun, Pramook Khungurn, Amit Raj, Supasorn Suwajanakorn

DORSal: Diffusion for Object-Centric Representations of Scenes et al.
Allan Jabri*, Sjoerd van Steenkiste, Emiel Hoogeboom, Mehdi S. M. Sajjadi, Thomas Kipf

Language Model Beats Diffusion - Tokenizer is Key to Visual Generation
Lijun Yu*, Jose Lezama, Nitesh Bharadwaj Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, Boqing Gong, Ming-Hsuan Yang, Irfan Essa, David A Ross, Lu Jiang

Functional Interpolation for Relative Positions Improves Long Context Transformers
Shanda Li*, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontanon, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli

Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding
Yuanhao Xiong*, Long Zhao, Boqing Gong, Ming-Hsuan Yang, Florian Schroff, Ting Liu, Cho-Jui Hsieh, Liangzhe Yuan

Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
Mauricio Delbracio, Peyman Milanfar

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, Yonghui Wu

SPADE: Semi-Supervised Anomaly Detection Under Distribution Mismatch
Jinsung Yoon, Kihyuk Sohn, Chun Liang Li, Sercan Ö. Arik, Tomas Pfister