Google at NeurIPS 2023

This week the 37th annual Conference on Neural Information Processing Systems (NeurIPS 2023), the biggest machine learning conference of the year, kicks off in New Orleans, LA. Google is proud to be a Diamond Level sponsor of NeurIPS this year and will have a strong presence with >170 accepted papers, two keynote talks, and additional contributions to the broader research community through organizational support and involvement in >20 workshops and tutorials. Google is also proud to be a Platinum Sponsor for both the Women in Machine Learning and LatinX in AI workshops. We look forward to sharing some of our extensive ML research and expanding our partnership with the broader ML research community.

Attending for NeurIPS 2023 in person? Come visit the Google Research booth to learn more about the exciting work we’re doing to solve some of the field’s most interesting challenges. Visit the @GoogleAI X (Twitter) account to find out about Google booth activities (e.g., demos and Q&A sessions).

You can learn more about our latest cutting edge work being presented at the conference in the list below (Google affiliations highlighted in bold). And see Google DeepMind’s blog to learn more about their participation at NeurIPS 2023.

Board & Organizing Committee

NeurIPS Board: Corinna Cortes
Advisory Board: John C. Platt

Senior Area Chair: Inderjit S. Dhillon

Creative AI Chair: Isabelle Guyon

Program Chair: Amir Globerson

Datasets and Benchmarks Chair: Remi Denton

Google Research Booth Demo/Q&A Schedule

This schedule is subject to change. Please visit the Google booth (#215) for more information.

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

Presenter: Yonatan Bitton

Monday, Dec 11 | 12:15PM – 1:45PM

Talk like a Graph: Encoding Graphs for Large Language Models
Presenters: Bahar Fatemi, Jonathan Halcrow, Bryan Perozzi
Monday, Dec 11 | 4:00PM – 4:45PM

VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use
Presenter: Yonatan Bitton
Monday, Dec 11 | 4:00PM – 4:45PM

MLCommons Croissant
Presenters: Omar Benjelloun, Meg Risdal, Lora Aroyo
Tuesday, Dec 12 | 9:15AM – 10:00AM

DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model
Presenter: Xiuye Gu
Tuesday, Dec 12 | 12:45PM – 2:15PM

Embedding Large Graphs
Presenters: Bryan Perozzi, Anton Tsitsulin
Tuesday, Dec 12 | 3:20PM – 3:40PM

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
Presenter: Krishna Pillutla
Tuesday, Dec 12 | 3:20PM – 3:40PM

Presenter: Tao Tu
Tuesday, Dec 12 | 4:45PM – 5:15PM

StyleDrop: Text-to-Image Generation in Any Style
Presenters: Kihyuk Sohn, Lu Jiang, Irfan Essa
Tuesday, Dec 12 | 4:45PM – 5:15PM

DICES Dataset: Diversity in Conversational AI Evaluation for Safety
Presenters: Lora Aroyo, Alicia Parrish, Vinodkumar Prabhakaran
Wednesday, Dec 13 | 9:15AM – 10:00AM

Resonator: Scalable Game-Based Evaluation of Large Models
Presenters: Erin Drake Kajioka, Michal Todorovic
Wednesday, Dec 13 | 12:45PM – 2:15PM

Adversarial Nibbler
Presenter: Lora Aroyo
Wednesday, Dec 13 | 12:45PM – 2:15PM

Towards Generalist Biomedical AI
Presenter: Tao Tu
Wednesday, Dec 13 | 3:15PM – 3:30PM

Conditional Adaptors
Presenter: Junwen Bai
Wednesday, Dec 13 | 3:15PM – 3:30PM

Patient Assistance with Multimodal RAG
Presenters: Ryan Knuffman, Milica Cvetkovic
Wednesday, Dec 13 | 4:15PM – 5:00PM

How Hessian Structure Explains Mysteries in Sharpness Regularization
Presenter: Hossein Mobahi
Wednesday, Dec 13 | 4:15PM – 5:00PM

Keynote Speakers

Affinity Workshops

Women in ML
Google Sponsored – Platinum

LatinX in AI
Google Sponsored – Platinum

New in ML
Organizer: Isabelle Guyon


AI for Accelerated Materials Design (AI4Mat-2023)
Fireside Chat: Gowoon Cheon

Associative Memory & Hopfield Networks in 2023
Panelist: Blaise Agüera y Arcas

Information-Theoretic Principles in Cognitive Systems (InfoCog)
Speaker: Alexander Alemi

Machine Learning and the Physical Sciences
Speaker: Alexander Alemi

UniReps: Unifying Representations in Neural Models
Organizer: Mathilde Caron

Robustness of Zero/Few-shot Learning in Foundation Models (R0-FoMo)
Speaker: Partha Talukdar
Organizer: Ananth Balashankar, Yao Qin, Ahmad Beirami

Workshop on Diffusion Models
Speaker: Tali Dekel

Algorithmic Fairness through the Lens of Time
Roundtable Lead: Stephen Pfohl
Organizer: Golnoosh Farnadi

Backdoors in Deep Learning: The Good, the Bad, and the Ugly
Organizer: Eugene Bagdasaryan

OPT 2023: Optimization for Machine Learning
Organizer: Cristóbal Guzmán

Machine Learning for Creativity and Design
Speaker: Aleksander Holynski, Alexander Mordvintsev

Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models
Speaker: Matt Barnes

Machine Learning for Audio
Organizer: Shrikanth Narayanan

Federated Learning in the Age of Foundation Models (FL@FM-NeurIPS’23)
Speaker: Cho-Jui Hsieh, Zheng Xu

Socially Responsible Language Modelling Research (SoLaR)
Panelist: Vinodkumar Prabhakaran

I Can’t Believe It’s Not Better (ICBINB): Failure Modes in the Age of Foundation Models
Advisory Board: Javier Antorán

Machine Learning for Systems
Organizer: Yawen Wang
Competition Committee: Bryan Perozzi, Sami Abu-el-haija
Steering Committee: Milad Hashemi

Self-Supervised Learning: Theory and Practice
Organizer: Mathilde Caron


NeurIPS 2023 Machine Unlearning Competition
Organizer: Isabelle Guyon, Peter Kairouz

Lux AI Challenge Season 2 NeurIPS Edition
Organizer: Bovard Doerschuk-Tiberi, Addison Howard


Data-Centric AI for Reliable and Responsible AI: From Theory to Practice
Isabelle Guyon, Nabeel Seedat, Mihaela va der Schaar

Creative AI Track

Creative AI Performances 1 & 2
Speaker: Erin Drake Kajioka, Yonatan Bitton

Organizer: Isabelle Guyon
Performance 1: Mon, Dec 11 | 6:30PM – 8:30PM, Lobby Stage
Performance 2: Thu, Dec 14 | 7:00PM – 9:00PM, Lobby Stage

Creative AI Sessions 1 – 3
Speaker: Erin Drake Kajioka, Yonatan Bitton
Organizer: Isabelle Guyon
Session 1: Tue, Dec 12 | 3:05PM – 3:40PM, Hall D2
Session 2: Wed, Dec 13 | 10:45AM – 2:15PM, Hall D2
Session 3: Thu, Dec 14 | 10:45 AM – 2:15PM, Hall D2

Creative AI Videos
Organizer: Isabelle Guyon

Expo Talks

Graph Learning Meets Artificial Intelligence
Speaker: Bryan Perozzi

Resonator: Music Space
Speakers: Erin Drake Kajioka, Michal Todorovic

Empirical Rigor in ML as a Massively Parallelizable Challenge
Speaker: Megan Risdal (Kaggle)

Oral Talks

Ordering-based Conditions for Global Convergence of Policy Gradient Methods
Jincheng Mei, Bo Dai, Alekh Agarwal, Mohammad Ghavamzadeh*, Csaba Szepesvari, Dale Schuurmans

Private Everlasting Prediction
Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan

User-Level Differential Privacy With Few Examples Per User
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang

DataComp: In Search of the Next Generation of Multimodal Datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt

Optimal Learners for Realizable Regression: PAC Learning and Online Learning
Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas

The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation
Saurabh Saxena, Charles Herrmann, Junhwa Hur, Abhishek Kar, Mohammad Norouzi*, Deqing Sun, David J. Fleet

Journal Track

Graph Clustering with Graph Neural Networks
Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller

Spotlight Papers

Alternating Updates for Efficient Transformers (see blog post)
Cenk Baykal, Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh*, Rina Panigrahy, Xin Wang

Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun

Is Learning in Games Good for the Learners?
William Brown, Jon Schneider, Kiran Vodrahalli

Participatory Personalization in Classification
Hailey Joren, Chirag Nagpal, Katherine Heller, Berk Ustun

Tight Risk Bounds for Gradient Descent on Separable Data
Matan Schliserman, Tomer Koren

Counterfactual Memorization in Neural Language Models
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini

Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
Zhong Yi Wan, Ricardo Baptista, Anudhyan Boral, Yi-Fan Chen, John Anderson, Fei Sha, Leonardo Zepeda-Nunez

Faster Margin Maximization Rates for Generic Optimization Methods
Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob Abernethy

From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
Peter Shaw, Mandar Joshi, James Cohan, Jonathan Berant, Panupong Pasupat, Hexiang Hu, Urvashi Khandelwal, Kenton Lee, Kristina N Toutanova

PAC Learning Linear Thresholds from Label Proportions
Anand Brahmbhatt, Rishi Saket, Aravindan Raghuveer

SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs
Lijun Yu*, Yong Cheng, Zhiruo Wang, Vivek Kumar, Wolfgang Macherey, Yanping Huang, David Ross, Irfan Essa, Yonatan Bisk, Ming-Hsuan Yang, Kevin Murphy, Alexander Hauptmann, Lu Jiang

Adaptive Data Analysis in a Balanced Adversarial Model
Kobbi Nissim, Uri Stemmer, Eliad Tsfadia

Lexinvariant Language Models
Qian Huang, Eric Zelikman, Sarah Chen, Yuhuai Wu, Gregory Valiant, Percy Liang

On Quantum Backpropagation, Information Reuse, and Cheating Measurement Collapse
Amira Abbas, Robbie King, Hsin-Yuan Huang, William J. Huggins, Ramis Movassagh, Dar Gilboa, Jarrod McClean

Private Estimation Algorithms for Stochastic Block Models and Mixture Models
Hongjie Chen, Vincent Cohen-Addad, Tommaso d’Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel

Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
Aniket Das, Dheeraj Nagaraj

Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
Arun Ganesh, Daogao Liu*, Sewoong Oh, Abhradeep Guha Thakurta

Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
Pritam Sarkar, Ahmad Beirami, Ali Etemad

AIMS: All-Inclusive Multi-Level Segmentation for Anything
Lu Qi, Jason Kuen, Weidong Guo, Jiuxiang Gu, Zhe Lin, Bo Du, Yu Xu, Ming-Hsuan Yang

DreamHuman: Animatable 3D Avatars from Text
Nikos Kolotouros, Thiemo Alldieck, Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Fieraru, Cristian Sminchisescu

Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts
Chaoqi Wang, Ziyu Ye, Zhe Feng, Ashwinkumar Badanidiyuru, Haifeng Xu

Learning List-Level Domain-Invariant Representations for Ranking
Ruicheng Xian*, Honglei Zhuang, Zhen Qin, Hamed Zamani*, Jing Lu, Ji Ma, Kai Hui, Han Zhao, Xuanhui Wang, Michael Bendersky

Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
Liang Zhang, Junchi Yang, Amin Karbasi, Niao He

Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach, Ruoxi Wang, Lichan Hong, Ed Chi, Derek Cheng

Proximity-Informed Calibration for Deep Neural Networks
Miao Xiong, Ailin Deng, Pang Wei Koh, Jiaying Wu, Shen Li, Jianqing Xu, Bryan Hooi


Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization
Adel Javanmard, Vahab Mirrokni

Better Private Linear Regression Through Better Private Feature Selection
Travis Dick, Jennifer Gillenwater*, Matthew Joseph

Binarized Neural Machine Translation
Yichi Zhang, Ankush Garg, Yuan Cao, Łukasz Lew, Behrooz Ghorbani*, Zhiru Zhang, Orhan Firat

BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information
Mehran Kazemi, Quan Yuan, Deepti Bhatia, Najoung Kim, Xin Xu, Vaiva Imbrasaite, Deepak Ramachandran

Boosting with Tempered Exponential Measures
Richard Nock, Ehsan Amid, Manfred Warmuth

Concept Algebra for (Score-Based) Text-Controlled Generative Models
Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch

Deep Contract Design via Discontinuous Networks
Tonghan Wang, Paul Dütting, Dmitry Ivanov, Inbal Talgam-Cohen, David C. Parkes

Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
Cheng-Ju Ho, Chen-Hsuan Tai, Yen-Yu Lin, Ming-Hsuan Yang, Yi-Hsuan Tsai

Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, Matthew Walter

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
Anastasia Koloskova*, Ryan McKenna, Zachary Charles, J Keith Rush, Hugh Brendan McMahan

Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products
Tamas Sarlos, Xingyou Song, David P. Woodruff, Qiuyi (Richard) Zhang

Module-wise Adaptive Distillation for Multimodality Foundation Models

Chen Liang, Jiahui Yu, Ming-Hsuan Yang, Matthew Brown, Yin Cui, Tuo Zhao, Boqing Gong, Tianyi Zhou

Multi-Swap k-Means++
Lorenzo Beretta, Vincent Cohen-Addad, Silvio Lattanzi, Nikos Parotsidis

OpenMask3D: Open-Vocabulary 3D Instance Segmentation
Ayça Takmaz, Elisabetta Fedele, Robert Sumner, Marc Pollefeys, Federico Tombari, Francis Engelmann

Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
Dami Choi*, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani

PopSign ASL v1.0: An Isolated American Sign Language Dataset Collected via Smartphones
Thad Starner, Sean Forbes, Matthew So, David Martin, Rohit Sridhar, Gururaj Deshpande, Sam Sepah, Sahir Shahryar, Khushi Bhardwaj, Tyler Kwok, Daksh Sehgal, Saad Hassan, Bill Neubauer, Sofia Vempala, Alec Tan, Jocelyn Heath, Unnathi Kumar, Priyanka Mosur, Tavenner Hall, Rajandeep Singh, Christopher Cui, Glenn Cameron, Sohier Dane, Garrett Tanzer

Semi-Implicit Denoising Diffusion Models (SIDDMs)
Yanwu Xu*, Mingming Gong, Shaoan Xie, Wei Wei, Matthias Grundmann, Kayhan Batmanghelich, Tingbo Hou

State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding
Devleena Das, Sonia Chernova, Been Kim

StoryBench: A Multifaceted Benchmark for Continuous Story Visualization
Emanuele Bugliarello*, Hernan Moraldo, Ruben Villegas, Mohammad Babaeizadeh, Mohammad Taghi Saffar, Han Zhang, Dumitru Erhan, Vittorio Ferrari, Pieter-Jan Kindermans, Paul Voigtlaender

Subject-driven Text-to-Image Generation via Apprenticeship Learning
Wenhu Chen, Hexiang Hu, Yandong Li, Nataniel Ruiz, Xuhui Jia, Ming-Wei Chang, William W. Cohen

TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao*, Bahare Fatemi, Mike Burrows, Charith Mendis*, Bryan Perozzi

Training Chain-of-Thought via Latent-Variable Inference
Du Phan, Matthew D. Hoffman, David Dohan*, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous

Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints
Jayadev Acharya, Clement L. Canonne, Ziteng Sun, Himanshu Tyagi

What You See is What You Read? Improving Text-Image Alignment Evaluation
Michal Yarom, Yonatan Bitton, Soravit Changpinyo, Roee Aharoni, Jonathan Herzig, Oran Lang, Eran Ofek, Idan Szpektor

When Does Confidence-Based Cascade Deferral Suffice?
Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar

Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Filip Ekström Kelvinius, Dimitar Georgiev, Artur Petrov Toshev, Johannes Gasteiger

AVIS: Autonomous Visual Information Seeking with Large Language Model Agent
Ziniu Hu*, Ahmet Iscen, Chen Sun, Kai-Wei Chang, Yizhou Sun, David Ross, Cordelia Schmid, Alireza Fathi

Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing “Spurious” Correlations
Qingyao Sun, Kevin Patrick Murphy, Sayna Ebrahimi, Alexander D’Amour

Collaborative Score Distillation for Consistent Visual Editing
Subin Kim, Kyungmin Lee, June Suk Choi, Jongheon Jeong, Kihyuk Sohn, Jinwoo Shin

CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graphs
Guangyao Zhai, Evin Pınar Örnek, Shun-Cheng Wu, Yan Di, Federico Tombari, Nassir Navab, Benjamin Busam

Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
Amit Daniely, Nathan Srebro, Gal Vardi

A Computationally Efficient Sparsified Online Newton Method
Fnu Devvrit*, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S Dhillon

DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field
Chenyangguang Zhang, Yan Di, Ruida Zhang, Guangyao Zhai, Fabian Manhardt, Federico Tombari, Xiangyang Ji

Double Auctions with Two-sided Bandit Feedback
Soumya Basu, Abishek Sankararaman

Grammar Prompting for Domain-Specific Language Generation with Large Language Models
Bailin Wang, Zi Wang, Xuezhi Wang, Yuan Cao, Rif A. Saurous, Yoon Kim

Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training
Rie Johnson, Tong Zhang*

Large Graph Property Prediction via Graph Segment Training
Kaidi Cao*, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis*, Jure Leskovec, Bryan Perozzi

On Computing Pairwise Statistics with Local Differential Privacy
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

On Student-teacher Deviations in Distillation: Does it Pay to Disobey?
Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar

Optimal Cross-learning for Contextual Bandits with Unknown Context Distributions
Jon Schneider, Julian Zimmert

Near-Optimal k-Clustering in the Sliding Window Model
David Woodruff, Peilin Zhong, Samson Zhou

Post Hoc Explanations of Language Models Can Improve Language Models
Satyapriya Krishna, Jiaqi Ma, Dylan Z Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju

Recommender Systems with Generative Retrieval
Shashank Rajput*, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, Maheswaran Sathiamoorthy

Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh*, Kangwook Lee, Kimin Lee*

Replicable Clustering
Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou

Replicability in Reinforcement Learning
Amin Karbasi, Grigoris Velegkas, Lin Yang, Felix Zhou

Riemannian Projection-free Online Learning
Zihao Hu, Guanghui Wang, Jacob Abernethy

Sharpness-Aware Minimization Leads to Low-Rank Features
Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion

What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models
Khashayar Gatmiry, Zhiyuan Li, Ching-Yao Chuang, Sashank Reddi, Tengyu Ma, Stefanie Jegelka

Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization
Jui-Nan Yen, Sai Surya Duvvuri, Inderjit S Dhillon, Cho-Jui Hsieh

Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain

Boundary Guided Learning-Free Semantic Control with Diffusion Models
Ye Zhu, Yu Wu, Zhiwei Deng, Olga Russakovsky, Yan Yan

Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
Tao Lei, Junwen Bai, Siddhartha Brahma, Joshua Ainslie, Kenton Lee, Yanqi Zhou, Nan Du*, Vincent Y. Zhao, Yuexin Wu, Bo Li, Yu Zhang, Ming-Wei Chang

Conformal Prediction for Time Series with Modern Hopfield Networks
Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter

Does Visual Pretraining Help End-to-End Reasoning?
Chen Sun, Calvin Luo, Xingyi Zhou, Anurag Arnab, Cordelia Schmid

Effective Robustness Against Natural Distribution Shifts for Models with Different Training Data
Zhouxing Shi*, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel*, Yao Qin

Improving Neural Network Representations Using Human Similarity Judgments
Lukas Muttenthaler*, Lorenz Linhardt, Jonas Dippel, Robert A. Vandermeulen, Katherine Hermann, Andrew K. Lampinen, Simon Kornblith

Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency
Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala

Mnemosyne: Learning to Train Transformers with Transformers
Deepali Jain, Krzysztof Choromanski, Avinava Dubey, Sumeet Singh, Vikas Sindhwani, Tingnan Zhang, Jie Tan

Nash Regret Guarantees for Linear Bandits
Ayush Sawarni, Soumyabrata Pal, Siddharth Barman

A Near-Linear Time Algorithm for the Chamfer Distance
Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten.

On Differentially Private Sampling from Gaussian and Product Distributions
Badih Ghazi, Xiao Hu*, Ravi Kumar, Pasin Manurangsi

On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh*, Marek Petrik

ResMem: Learn What You Can and Memorize the Rest
Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Sanjiv Kumar

Responsible AI (RAI) Games and Ensembles
Yash Gupta, Runtian Zhai, Arun Suggala, Pradeep Ravikumar

RoboCLIP: One Demonstration Is Enough to Learn Robot Policies
Sumedh A Sontakke, Jesse Zhang, Sébastien M. R. Arnold, Karl Pertsch, Erdem Biyik, Dorsa Sadigh, Chelsea Finn, Laurent Itti

Robust Concept Erasure via Kernelized Rate-Distortion Maximization
Somnath Basu Roy Chowdhury, Nicholas Monath, Kumar Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi

Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
Alexander Bukharin, Yan Li, Yue Yu, Qingru Zhang, Zhehui Chen, Simiao Zuo, Chao Zhang, Songan Zhang, Tuo Zhao

Simplicity Bias in 1-Hidden Layer Neural Networks
Depen Morwani*, Jatin Batra, Prateek Jain, Praneeth Netrapalli

SLaM: Student-Label Mixing for Distillation with Unlabeled Examples
Vasilis Kontonis, Fotis Iliopoulos, Khoa Trinh, Cenk Baykal, Gaurav Menghani, Erik Vee

SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding
Paul-Edouard Sarlin*, Eduard Trulls, Marc Pollefeys, Jan Hosang, Simon Lynen

SOAR: Improved Indexing for Approximate Nearest Neighbor Search
Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo, Sanjiv Kumar

StyleDrop: Text-to-Image Synthesis of Any Style
Kihyuk Sohn, Lu Jiang, Jarred Barber, Kimin Lee*, Nataniel Ruiz, Dilip Krishnan, Huiwen Chang*, Yuanzhen Li, Irfan Essa, Michael Rubinstein, Yuan Hao, Glenn Entis, Irina Blok, Daniel Castro Chin

Three Towers: Flexible Contrastive Learning with Pretrained Image Models
Jannik Kossen*, Mark Collier, Basil Mustafa, Xiao Wang, Xiaohua Zhai, Lucas Beyer, Andreas Steiner, Jesse Berent, Rodolphe Jenatton, Efi Kokiopoulou

Two-Stage Learning to Defer with Multiple Experts
Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong

AdANNS: A Framework for Adaptive Semantic Search
Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan, Qingqing Cao, Sham Kakade, Prateek Jain, Ali Farhadi

Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer
Bowen Tan*, Yun Zhu, Lijuan Liu, Eric Xing, Zhiting Hu, Jindong Chen

Causal-structure Driven Augmentations for Text OOD Generalization
Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David Blei

Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel
Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller

Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence
Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, Trevor Darrell

Diffusion Self-Guidance for Controllable Image Generation
Dave Epstein, Allan Jabri, Ben Poole, Alexei A Efros, Aleksander Holynski

Fully Dynamic k-Clustering in Õ(k) Update Time
Sayan Bhattacharya, Martin Nicolas Costa, Silvio Lattanzi, Nikos Parotsidis

Improving CLIP Training with Language Rewrites
Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian

k-Means Clustering with Distance-Based Privacy
Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong

LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
Weixi Feng, Wanrong Zhu, Tsu-Jui Fu, Varun Jampani, Arjun Reddy Akula, Xuehai He, Sugato Basu, Xin Eric Wang, William Yang Wang

Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
Dhawal Gupta*, Yinlam Chow, Azamat Tulepbergenov, Mohammad Ghavamzadeh*, Craig Boutilier

Optimal Unbiased Randomizers for Regression with Label Differential Privacy
Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Jacob Leeman, Pasin Manurangsi, Avinash V Varadarajan, Chiyuan Zhang

Paraphrasing Evades Detectors of AI-generated Text, but Retrieval Is an Effective Defense
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, Mohit Iyyer

ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
Shuyang Sun*, Weijun Wang, Qihang Yu*, Andrew Howard, Philip Torr, Liang-Chieh Chen*

Robust and Actively Secure Serverless Collaborative Learning
Nicholas Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang

SpecTr: Fast Speculative Decoding via Optimal Transport
Ziteng Sun, Ananda Theertha Suresh, Jae Hun Ro, Ahmad Beirami, Himanshu Jain, Felix Yu

Structured Prediction with Stronger Consistency Guarantees
Anqi Mao, Mehryar Mohri, Yutao Zhong

Affinity-Aware Graph Networks
Ameya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Veličković, Sreenivas Gollapudi

ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections
Chun-Han Yao*, Amit Raj, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein, Ming-Hsuan Yang, Varun Jampani

Black-Box Differential Privacy for Interactive ML
Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer

Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
Haolin Liu, Chen-Yu Wei, Julian Zimmert

DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model
Xiuye Gu, Yin Cui*, Jonathan Huang, Abdullah Rashwan, Xuan Yang, Xingyi Zhou, Golnaz Ghiasi, Weicheng Kuo, Huizhong Chen, Liang-Chieh Chen*, David Ross

Easy Learning from Label Proportions
Robert Busa-Fekete, Heejin Choi*, Travis Dick, Claudio Gentile, Andres Munoz Medina

Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks
Eeshaan Jain, Tushar Nandy, Gaurav Aggarwal, Ashish Tendulkar, Rishabh Iyer, Abir De

Faster Differentially Private Convex Optimization via Second-Order Methods
Arun Ganesh, Mahdi Haghifam*, Thomas Steinke, Abhradeep Guha Thakurta

Finding Safe Zones of Markov Decision Processes Policies
Lee Cohen, Yishay Mansour, Michal Moshkovitz

Focused Transformer: Contrastive Training for Context Scaling
Szymon Tworkowski, Konrad Staniszewski, Mikołaj Pacek, Yuhuai Wu*, Henryk Michalewski, Piotr Miłoś

Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
Abhin Shah, Karthikeyan Shanmugam, Murat Kocaoglu

H-Consistency Bounds: Characterization and Extensions
Anqi Mao, Mehryar Mohri, Yutao Zhong

Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation
David Brandfonbrener, Ofir Nachum, Joan Bruna

Most Neural Networks Are Almost Learnable
Amit Daniely, Nathan Srebro, Gal Vardi

Multiclass Boosting: Simple and Intuitive Weak Learning Criteria
Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran

NeRF Revisited: Fixing Quadrature Instability in Volume Rendering
Mikaela Angelina Uy, Kiyohiro Nakayama, Guandao Yang, Rahul Krishna Thomas, Leonidas Guibas, Ke Li

Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
Wei-Ning Chen, Dan Song, Ayfer Ozgur, Peter Kairouz

Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
Jingfeng Wu*, Wennan Zhu, Peter Kairouz, Vladimir Braverman

RETVec: Resilient and Efficient Text Vectorizer
Elie Bursztein, Marina Zhang, Owen Skipper Vallis, Xinyu Jia, Alexey Kurakin

Symbolic Discovery of Optimization Algorithms
Xiangning Chen*, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V. Le

A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence
Junyi Zhang, Charles Herrmann, Junhwa Hur, Luisa F. Polania, Varun Jampani, Deqing Sun, Ming-Hsuan Yang

A Trichotomy for Transductive Online Learning
Steve Hanneke, Shay Moran, Jonathan Shafer

A Unified Fast Gradient Clipping Framework for DP-SGD
William Kong, Andres Munoz Medina

Unleashing the Power of Randomization in Auditing Differentially Private ML
Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh

(Amplified) Banded Matrix Factorization: A unified approach to private training
Christopher A Choquette-Choo, Arun Ganesh, Ryan McKenna, H Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu

Adversarial Resilience in Sequential Prediction via Abstention
Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty

Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception
Hassan Akbari, Dan Kondratyuk, Yin Cui, Rachel Hornung, Huisheng Wang, Hartwig Adam

Android in the Wild: A Large-Scale Dataset for Android Device Control
Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy Lillicrap

Benchmarking Robustness to Adversarial Image Obfuscations
Florian Stimberg, Ayan Chakrabarti, Chun-Ta Lu, Hussein Hazimeh, Otilia Stretcu, Wei Qiao, Yintao Liu, Merve Kaya, Cyrus Rashtchian, Ariel Fuxman, Mehmet Tek, Sven Gowal

Building Socio-culturally Inclusive Stereotype Resources with Community Engagement
Sunipa Dev, Jaya Goyal, Dinesh Tewari, Shachi Dave, Vinodkumar Prabhakaran

Consensus and Subjectivity of Skin Tone Annotation for ML Fairness
Candice Schumann, Gbolahan O Olanubi, Auriel Wright, Ellis Monk Jr*, Courtney Heldreth, Susanna Ricco

Counting Distinct Elements Under Person-Level Differential Privacy
Alexander Knop, Thomas Steinke

DICES Dataset: Diversity in Conversational AI Evaluation for Safety
Lora Aroyo, Alex S. Taylor, Mark Diaz, Christopher M. Homan, Alicia Parrish, Greg Serapio-García, Vinodkumar Prabhakaran, Ding Wang

Does Progress on ImageNet Transfer to Real-world Datasets?
Alex Fang, Simon Kornblith, Ludwig Schmidt

Estimating Generic 3D Room Structures from 2D Annotations
Denys Rozumnyi*, Stefan Popov, Kevis-kokitsi Maninis, Matthias Nießner, Vittorio Ferrari

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang

MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Derrick Xin, Aditya Kusupati, Romi Stella, Ankur Bapna, Orhan Firat

Mechanic: A Learning Rate Tuner
Ashok Cutkosky, Aaron Defazio, Harsh Mehta

NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations
Varun Jampani, Kevis-kokitsi Maninis, Andreas Engelhardt, Arjun Karpur, Karen Truong, Kyle Sargent, Stefan Popov, Andre Araujo, Ricardo Martin Brualla, Kaushal Patel, Daniel Vlasic, Vittorio Ferrari, Ameesh Makadia, Ce Liu*, Yuanzhen Li, Howard Zhou

Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations
Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Nunez, James Lottes, Qing Wang, Yi-Fan Chen, John Roberts Anderson, Fei Sha

Restart Sampling for Improving Generative Processes
Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi Jaakkola

Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
Fan Yao, Chuanhao Li, Karthik Abinav Sankararaman, Yiming Liao, Yan Zhu, Qifan Wang, Hongning Wang, Haifeng Xu

Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union
Zifu Wang, Maxim Berman, Amal Rannen-Triki, Philip Torr, Devis Tuia, Tinne Tuytelaars, Luc Van Gool, Jiaqian Yu, Matthew B. Blaschko

RoboHive: A Unified Framework for Robot Learning
Vikash Kumar, Rutav Shah, Gaoyue Zhou, Vincent Moens, Vittorio Caggiano, Abhishek Gupta, Aravind Rajeswaran

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data
Mélisande Teng, Amna Elmustafa, Benjamin Akera, Yoshua Bengio, Hager Radi, Hugo Larochelle, David Rolnick

Sparsity-Preserving Differentially Private Training of Large Embedding Models
Badih Ghazi, Yangsibo Huang*, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan

Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett

Universality and Limitations of Prompt Tuning
Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh

Unsupervised Semantic Correspondence Using Stable Diffusion
Eric Hedlin, Gopal Sharma, Shweta Mahajan, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, Kwang Moo Yi

YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus
Dave Uthus, Garrett Tanzer, Manfred Georg

The Noise Level in Linear Regression with Dependent Data
Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

* Work done while at Google