Google at ICML 2023

Posted by Cat Armato, Program Manager, Google

Groups across Google actively pursue research in the field of machine learning (ML), ranging from theory and application. We build ML systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, algorithm development, and more. We aim to build a more collaborative ecosystem with the broader ML research community through open-sourcing tools and datasets, publishing our work, and actively participating in conferences.

Google is proud to be a Diamond Sponsor of the 40th International Conference on Machine Learning (ICML 2023), a premier annual conference, which is being held this week in Honolulu, Hawaii. As a leader in ML research, Google has a strong presence at this year’s conference with over 120 accepted papers and active involvement in a number of workshops and tutorials. Google is also proud to be a Platinum Sponsor for both the LatinX in AI and Women in Machine Learning workshops. We look forward to sharing some of our extensive ML research and expanding our partnership with the broader ML research community.

Registered for ICML 2023? We hope you’ll visit the Google booth to learn more about the exciting work, creativity, and fun that goes into solving a portion of the field’s most interesting challenges. Visit the @GoogleAI Twitter account to find out about Google booth activities (e.g., demos and Q&A sessions). See Google DeepMind’s blog to learn about their technical participation at ICML 2023.

Take a look below to learn more about the Google research being presented at ICML 2023 (Google affiliations in bold).

Board and Organizing Committee

Board Members include: Corinna Cortes, Hugo Larochelle

Tutorial Chairs include: Hanie Sedghi

Google Research booth activities

Presenters: Bryan Perozzi, Anton Tsitsulin, Brandon Mayer

Title: Unsupervised Graph Embedding @ Google (paper, EXPO workshop)

Tuesday, July 25th at 10:30 AM HST

Presenters: Zheng Xu

Title: Federated Learning of Gboard Language Models with Differential Privacy (paper 1, paper 2, blog post)

Tuesday, July 25th at 3:30 PM HST

Presenters: Thomas Kipf

Title: Self-supervised scene understanding (paper 1, paper 2)

Wednesday, July 26th at 10:30 AM HST

Presenters: Johannes von Oswald, Max Vladymyrov

Title: Transformers learn in-context by gradient descent (paper)

Wednesday, July 26th at 3:30 PM HST

Accepted papers

Scaling Vision Transformers to 22 Billion Parameters (see blog post)

Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetić, Dustin Tran, Thomas Kipf, Mario Lučić, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby

Fast Inference from Transformers via Speculative Decoding

Yaniv Leviathan, Matan Kalman, Yossi Matias

Best of Both Worlds Policy Optimization

Christoph Dann, Chen-Yu Wei, Julian Zimmert

Inflow, Outflow, and Reciprocity in Machine Learning

Mukund Sundararajan, Walid Krichene

Transformers Learn In-Context by Gradient Descent

Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov

Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models

Luke Vilnis, Yury Zemlyanskiy, Patrick Murray*, Alexandre Passos*, Sumit Sanghai

Differentially Private Hierarchical Clustering with Provable Approximation Guarantees (see blog post)

Jacob Imola*, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning

Christopher A. Choquette-Choo, H. Brendan McMahan, Keith Rush, Abhradeep Thakurta

Random Classification Noise Does Not Defeat All Convex Potential Boosters Irrespective of Model Choice

Yishay Mansour, Richard Nock, Robert Williamson

Simplex Random Features

Isaac Reid, Krzysztof Choromanski, Valerii Likhosherstov, Adrian Weller

Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding

Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova

Mu²SLAM: Multitask, Multilingual Speech and Language Models

Yong Cheng, Yu Zhang, Melvin Johnson, Wolfgang Macherey, Ankur Bapna

Robust Budget Pacing with a Single Sample

Santiago Balseiro, Rachitesh Kumar*, Vahab Mirrokni, Balasubramanian Sivan, Di Wang

A Statistical Perspective on Retrieval-Based Models

Soumya Basu, Ankit Singh Rawat, Manzil Zaheer

Approximately Optimal Core Shapes for Tensor Decompositions

Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni

Efficient List-Decodable Regression Using Batches

Abhimanyu Das, Ayush Jain*, Weihao Kong, Rajat Sen

Efficient Training of Language Models Using Few-Shot Learning

Sashank J. Reddi, Sobhan Miryoosefi, Stefani Karp, Shankar Krishnan, Satyen Kale, Seungyeon Kim, Sanjiv Kumar

Fully Dynamic Submodular Maximization Over Matroids

Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam

GFlowNet-EM for Learning Compositional Latent Variable Models

Edward J Hu, Nikolay Malkin, Moksh Jain, Katie Everett, Alexandros Graikos, Yoshua Bengio

Improved Online Learning Algorithms for CTR Prediction in Ad Auctions

Zhe Feng, Christopher Liaw, Zixin Zhou

Large Language Models Struggle to Learn Long-Tail Knowledge

Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel

Multi-channel Autobidding with Budget and ROI Constraints

Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni

Multi-layer Neural Networks as Trainable Ladders of Hilbert Spaces

Zhengdao Chen

On User-Level Private Convex Optimization

Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang

PAC Generalization via Invariant Representations

Advait U Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice

Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Menard, Mohammad Gheshlaghi Azar, Remi Munos, Olivier Pietquin, Matthieu Geist,Csaba Szepesvari, Wataru Kumagai, Yutaka Matsuo

Speeding Up Bellman Ford via Minimum Violation Permutations

Silvio Lattanzi, Ola Svensson, Sergei Vassilvitskii

Statistical Indistinguishability of Learning Algorithms

Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas

Test-Time Adaptation with Slot-Centric Models

Mihir Prabhudesai, Anirudh Goyal, Sujoy Paul, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gaurav Aggarwal, Thomas Kipf, Deepak Pathak, Katerina Fragkiadaki>

Algorithms for Bounding Contribution for Histogram Estimation Under User-Level Privacy

Yuhan Liu*, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser

Bandit Online Linear Optimization with Hints and Queries

Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

CLUTR: Curriculum Learning via Unsupervised Task Representation Learning

Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff, Nathaniel Alexis, Aleksandra Faust, Pieter Abbeel, Ion Stoica

CSP: Self-Supervised Contrastive Spatial Pre-training for Geospatial-Visual Representations

Gengchen Mai, Ni Lao, Yutong He, Jiaming Song, Stefano Ermon

Ewald-Based Long-Range Message Passing for Molecular Graphs

Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann

Fast (1+ε)-Approximation Algorithms for Binary Matrix Factorization

Ameya Velingker, Maximilian Vötsch, David Woodruff, Samson Zhou

Federated Linear Contextual Bandits with User-Level Differential Privacy

Ruiquan Huang, Huanyu Zhang, Luca Melis, Milan Shen, Meisam Hejazinia, Jing Yang

Investigating the Role of Model-Based Learning in Exploration and Transfer

Jacob C Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Theophane Weber, Jessica B Hamrick

Label Differential Privacy and Private Training Data Release

Robert Busa-Fekete, Andres Munoz, Umar Syed, Sergei Vassilvitskii

Lifelong Language Pretraining with Distribution-Specialized Experts

Wuyang Chen*, Yanqi Zhou, Nan Du, Yanping Huang, James Laudon, Zhifeng Chen, Claire Cui

Multi-User Reinforcement Learning with Low Rank Rewards

Dheeraj Mysore Nagaraj, Suhas S Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain

Multi-View Masked World Models for Visual Robotic Manipulation

Younggyo Seo, Junsu Kim, Stephen James, Kimin Lee, Jinwoo Shin, Pieter Abbeel

PaLM-E: An Embodied Multimodal Language Model (see blog post)

Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter,Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence

Private Federated Learning with Autotuned Compression

Enayat Ullah*, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh

Refined Regret for Adversarial MDPs with Linear Function Approximation

Yan Dai, Haipeng Luo, Chen-Yu Wei, Julian Zimmert

Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory

Justin Cui, Ruoche Wan, Si Si, Cho-Jui Hsieh

SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance

Amit Attia, Tomer Koren

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation

Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney

Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features

Chieh Hubert Lin, Hung-Yu Tseng, Hsin-Ying Lee, Maneesh Kumar Singh, Ming-Hsuan Yang

User-Level Private Stochastic Convex Optimization with Optimal Rates

Raef Bassily, Ziteng Sun

A Simple Zero-Shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models

James Urquhart Allingham*, Jie Ren, Michael W Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan

Can Large Language Models Reason About Program Invariants?

Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, Pengcheng Yin

Concurrent Shuffle Differential Privacy Under Continual Observation

Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer

Constant Matters: Fine-Grained Error Bound on Differentially Private Continual Observation

Hendrik Fichtenberger, Monika Henzinger, Jalaj Upadhyay

Cross-Entropy Loss Functions: Theoretical Analysis and Applications

Anqi Mao, Mehryar Mohri, Yutao Zhong

Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation

Orin Levy, Alon Cohen, Asaf Cassel, Yishay Mansour

Fairness in Streaming Submodular Maximization Over a Matroid Constraint

Marwa El Halabi, Federico Fusco, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski

The Flan Collection: Designing Data and Methods for Effective Instruction Tuning (see blog post)

Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V Le, Barret Zoph, Jason Wei, Adam Roberts

Graph Reinforcement Learning for Network Control via Bi-level Optimization

Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira

Learning-Augmented Private Algorithms for Multiple Quantile Release

Mikhail Khodak*, Kareem Amin, Travis Dick, Sergei Vassilvitskii

LegendreTron: Uprising Proper Multiclass Loss Learning

Kevin H Lam, Christian Walder, Spiridon Penev, Richard Nock

Measuring the Impact of Programming Language Distribution

Gabriel Orlanski*, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishabh Singh, Michele Catasta*

Multi-task Differential Privacy Under Distribution Skew

Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Thakurta, Li Zhang

Muse: Text-to-Image Generation via Masked Generative Transformers

Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, José Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan

On the Convergence of Federated Averaging with Cyclic Client Participation

Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang

Optimal Stochastic Non-smooth Non-convex Optimization Through Online-to-Non-convex Conversion

Ashok Cutkosky, Harsh Mehta, Francesco Orabona

Out-of-Domain Robustness via Targeted Augmentations

Irena Gao, Shiori Sagawa, Pang Wei Koh, Tatsunori Hashimoto, Percy Liang

Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models

Jamil Arbas, Hassan Ashtiani, Christopher Liaw

Pre-computed Memory or On-the-Fly Encoding? A Hybrid Approach to Retrieval Augmentation Makes the Most of Your Compute

Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Joshua Ainslie, Sumit Sanghai, Fei Sha, William W. Cohen

Scalable Adaptive Computation for Iterative Generation

Allan Jabri*, David J. Fleet, Ting Chen

Scaling Spherical CNNs

Carlos Esteves, Jean-Jacques Slotine, Ameesh Makadia

STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition

Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa, Amir Yazdanbakhsh

Stratified Adversarial Robustness with Rejection

Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, Somesh Jha

When Does Privileged information Explain Away Label Noise?

Guillermo Ortiz-Jimenez*, Mark Collier, Anant Nawalgaria, Alexander D’Amour, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

Adaptive Computation with Elastic Input Sequence

Fuzhao Xue*, Valerii Likhosherstov, Anurag Arnab, Neil Houlsby, Mostafa Dehghani, Yang You

Can Neural Network Memorization Be Localized?

Pratyush Maini, Michael C. Mozer, Hanie Sedghi, Zachary C. Lipton, J. Zico Kolter, Chiyuan Zhang

Controllability-Aware Unsupervised Skill Discovery

Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel

Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network

Yadi Cao, Menglei Chai, Minchen Li, Chenfanfu Jiang

Federated Heavy Hitter Recovery Under Linear Sketching

Adria Gascon, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh

Graph Generative Model for Benchmarking Graph Neural Networks

Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Russ Salakhutdinov

H-Consistency Bounds for Pairwise Misranking Loss Surrogates

Anqi Mao, Mehryar Mohri, Yutao Zhong

Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation

Uri Sherman, Tomer Koren, Yishay Mansour

Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames

Ondrej Biza*, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Thomas Kipf

Multi-task Off-Policy Learning from Bandit Feedback

Joey Hong, Branislav Kveton, Manzil Zaheer, Sumeet Katariya, Mohammad Ghavamzadeh

Optimal No-Regret Learning for One-Sided Lipschitz Functions

Paul Duetting, Guru Guruganesh, Jon Schneider, Joshua Ruizhi Wang

Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games

Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He

Regret Minimization and Convergence to Equilibria in General-Sum Markov Games

Liad Erez, Tal Lancewicki, Uri Sherman, Tomer Koren, Yishay Mansour

Reinforcement Learning Can Be More Efficient with Multiple Rewards

Christoph Dann, Yishay Mansour, Mehryar Mohri

Reinforcement Learning with History-Dependent Dynamic Contexts

Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutlier

User-Defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems

Marc Anton Finzi*, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Nunez

Discrete Key-Value Bottleneck

Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf

DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm

Lisang Ding, Kexin Jin, Bicheng Ying, Kun Yuan, Wotao Yin

Exphormer: Sparse Transformers for Graphs

Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop

Fast, Differentiable and Sparse Top-k: A Convex Analysis Perspective

Michael Eli Sander*, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel

Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation

Aditya Mate, Bryan Wilder, Aparna Taneja, Milind Tambe

In Search for a Generalizable Method for Source Free Domain Adaptation

Malik Boudiaf*, Tom Denton, Bart van Merrienboer, Vincent Dumoulin, Eleni Triantafillou

Learning Rate Schedules in the Presence of Distribution Shift

Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah

Not All Semantics Are Created Equal: Contrastive Self-Supervised Learning with Automatic Temperature Individualization

Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang

On the Relationship Between Explanation and Prediction: A Causal View

Amir-Hossein Karimi*, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim

On the Role of Attention in Prompt-Tuning

Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, Christos Thrampoulidis

PLay: Parametrically Conditioned Layout Generation Using Latent Diffusion

Chin-Yi Cheng, Forrest Huang, Gang Li, Yang Li

The Power of Learned Locally Linear Models for Nonlinear Policy Optimization

Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu

Relevant Walk Search for Explaining Graph Neural Networks

Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus Robert Muller,Shinichi Nakajima

Repository-Level Prompt Generation for Large Language Models of Code

Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

Robust and Private Stochastic Linear Bandits

Vasileios Charisopoulos*, Hossein Esfandiari, Vahab Mirrokni

Simple Diffusion: End-to-End Diffusion for High Resolution Images

Emiel Hoogeboom, Jonathan Heek, Tim Salimans

Tied-Augment: Controlling Representation Similarity Improves Data Augmentation

Emirhan Kurtulus, Zichao Li, Yann Dauphin, Ekin D. Cubuk

Why Is Public Pre-Training Necessary for Private Model Training?

Arun Ganesh, Mahdi Haghifam*, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang

A Connection Between One-Step RL and Critic Regularization in Reinforcement Learning

Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov

Beyond Uniform Lipschitz Condition in Differentially Private Optimization

Rudrajit Das*, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi

Efficient Graph Field Integrators Meet Point Clouds

Krzysztof Choromanski, Arijit Sehanobish, Han Lin, Yunfan Zhao, Eli Berger, Tetiana Parshakova, Alvin Pan, David Watkins, Tianyi Zhang, Valerii Likhosherstov, Somnath Basu Roy Chowdhury, Avinava Dubey, Deepali Jain, Tamas Sarlos, Snigdha Chaturvedi, Adrian Weller

Fast as CHITA: Neural Network Pruning with Combinatorial Optimization

Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder

Jump-Start Reinforcement Learning (see blog post)

Ikechukwu Uchendu*, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman

Learning in POMDPs is Sample-Efficient with Hindsight Observability

Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang

Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single

Paul Vicol

Masked Trajectory Models for Prediction, Representation, and Control

Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran

Overcoming Simplicity Bias in Deep Networks Using a Feature Sieve

Rishabh Tiwari, Pradeep Shenoy

Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions

Boxiang Lyu, Zhe Feng, Zachary Robertson, Sanmi Koyejo

Predictive Flows for Faster Ford-Fulkerson

Sami Davies, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang

Scaling Laws for Multilingual Neural Machine Translation

Patrick Fernandes, Behrooz Ghorbani, Xavier Garcia, Markus Freitag, Orhan Firat

Sequential Monte Carlo Learning for Time Series Structure Discovery

Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka

Stochastic Gradient Succeeds for Bandits

Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvari, Dale Schuurmans

Subset-Based Instance Optimality in Private Estimation

Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh

The Unreasonable Effectiveness of Few-Shot Learning for Machine Translation

Xavier Garcia, Yamini Bansal, Colin Cherry, George Foster, Maxim Krikun, Melvin Johnson, Orhan Firat

Tutorials

Self-Supervised Learning in Vision: from Research Advances to Best Practices

Xinlei Chen, Ishan Misra, Randall Balestriero, Mathilde Caron, Christoph Feichtenhofer, Mark Ibrahim

How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy (see blog post)

Sergei Vassilvitskii, Natalia Ponomareva, Zheng Xu

Recent Advances in the Generalization Theory of Neural Networks

Tengyu Ma, Alex Damian