neurips 2022 oral presentation

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Neurips 2022: seven microsoft research papers selected for oral presentations.

Published December 5, 2022

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Microsoft is proud to be a platinum sponsor of the 36th annual conference on  Neural Information Processing Systems (opens in new tab) (NeurIPS), which is widely regarded as the world’s most prestigious research conference on artificial intelligence and machine learning.

Microsoft has a strong presence at NeurIPS again this year, with more than 150 of our researchers participating in the conference and 122 of our research papers accepted. Our researchers are also taking part in 10 workshops, four competitions and a tutorial.

In one of the workshops, AI for Science: Progress and Promises , a panel of leading researchers will discuss how artificial intelligence and machine learning have the potential to advance scientific discovery. The panel will include two Microsoft researchers: Max Welling , Vice President and Distinguished Scientist, Microsoft Research AI4Science, who will serve as moderator, and Peter Lee , Corporate Vice President, Microsoft Research and Incubations.

Of the 122 Microsoft research papers accepted for the conference, seven have been selected for oral presentations during the virtual NeurIPS experience the week of December 4 th . The oral presentations provide a deeper dive into each of the featured research topics.

In addition, two other Microsoft research papers received Outstanding Paper Awards for NeurIPS 2022. One of those papers, Gradient Estimation with Discrete Stein Operators , explains how researchers developed a gradient estimator that achieves substantially lower variance than state-of-the-art estimators with the same number of function evaluations, which has the potential to improve problem solving in machine learning. In the other paper, A Neural Corpus Indexer for Document Retrieval , researchers demonstrate that an end-to-end deep neural network that unifies training and indexing stages can significantly improve the recall performance of traditional document retrieval methods.

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Below we have provided the titles, authors and abstracts for all seven of the Microsoft research papers chosen for oral presentations at NeurIPS, with links to additional information for those who want to explore the topics more fully:

Uni[MASK]: Unified Inference in Sequential Decision Problems

Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu , Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann , Matthew Hausknecht, Anca Dragan, Sam Devlin

Abstract :   Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same idea also applies naturally to sequential decision making, where many well-studied tasks like behavior cloning, offline RL, inverse dynamics, and waypoint conditioning correspond to different sequence maskings over a sequence of states, actions, and returns. We introduce the UniMASK framework, which provides a unified way to specify models which can be trained on many different sequential decision-making tasks. We show that a single UniMASK model is often capable of carrying out many tasks with performance similar to or better than single-task models. Additionally, after fine tuning, our UniMASK models consistently outperform comparable single-task models.

K-LITE: Learning Transferable Visual Models with External Knowledge

Sheng Shen, Chunyuan Li , Xiaowei Hu, Yujia Xie, Jianwei Yang , Pengchuan Zhang, Zhe Gan , Lijuan Wang , Lu Yuan , Ce Liu, Kurt Keutzer, Trevor Darrell, Anna Rohrbach, Jianfeng Gao

Abstract : The new generation of state-of-the-art computer vision systems are trained from natural language supervision, ranging from simple object category names to descriptive captions. This form of supervision ensures high generality and usability of the learned visual models, based on the broad concept coverage achieved through large-scale data collection process. Alternatively, we argue that learning with external knowledge about images is a promising way which leverages a much more structured source of supervision and offers sample efficiency.

In this paper, we propose K-LITE (Knowledge-augmented Language-Image Training and Evaluation), a simple strategy to leverage external knowledge for building transferable visual systems: In training, it enriches entities in natural language with WordNet and Wiktionary knowledge, leading to an efficient and scalable approach to learning image representations that uses knowledge about the visual concepts; In evaluation, the natural language is also augmented with external knowledge and then used to reference learned visual concepts (or describe new ones) to enable zero-shot and few-shot transfer of the pre-trained models. We study the performance of K-LITE on two important computer vision problems, image classification and object detection, benchmarking on 20 and 13 different existing datasets, respectively. The proposed knowledge-augmented models show significant improvement in transfer learning performance over existing methods. Our code is released at https://github.com/microsoft/klite (opens in new tab) .

Extreme Compression for Pre-trained Transformers Made Simple and Efficient

Xiaoxia Wu, Zhewei Yao, Minjia Zhang , Conglong Li , Yuxiong He

Abstract : Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices. However, to preserve the accuracy for such aggressive compression schemes, cutting-edge methods usually introduce complicated compression pipelines, e.g., multi-stage expensive knowledge distillation with extensive hyperparameter tuning. Also, they oftentimes focus less on smaller transformer models that have already been heavily compressed via knowledge distillation and lack a systematic study to show the effectiveness of their methods.

In this paper, we perform a very comprehensive systematic study to measure the impact of many key hyperparameters and training strategies from previous. As a result, we find out that previous baselines for ultra-low bit precision quantization are significantly under-trained. Based on our study, we propose a simple yet effective compression pipeline for extreme compression.

Our simplified pipeline demonstrates that:

(1) we can skip the pre-training knowledge distillation to obtain a 5-layer \bert while achieving better performance than previous state-of-the-art methods, like TinyBERT;

(2) extreme quantization plus layer reduction is able to reduce the model size by 50x, resulting in new state-of-the-art results on GLUE tasks.

On the Complexity of Adversarial Decision Making

Dylan J Foster , Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan

Abstract : A central problem in online learning and decision making—from bandits to reinforcement learning—is to understand what modeling assumptions lead to sample-efficient learning guarantees. We consider a general adversarial decision-making framework that encompasses (structured) bandit problems with adversarial rewards and reinforcement learning problems with adversarial dynamics. Our main result is to show—via new upper and lower bounds—that the Decision-Estimation Coefficient, a complexity measure introduced by Foster et al. in the stochastic counterpart to our setting, is necessary and sufficient to obtain low regret for adversarial decision making. However, compared to the stochastic setting, one must apply the Decision-Estimation Coefficient to the convex hull of the class of models (or, hypotheses) under consideration. This establishes that the price of accommodating adversarial rewards or dynamics is governed by the behavior of the model class under convexification, and recovers a number of existing results –both positive and negative. En route to obtaining these guarantees, we provide new structural results that connect the Decision-Estimation Coefficient to variants of other well-known complexity measures, including the Information Ratio of Russo and Van Roy and the Exploration-by-Optimization objective of Lattimore and György.

Maximum Class Separation as Inductive Bias in One Matrix

Tejaswi Kasarla, Gertjan J. Burghouts, Max van Spengler, Elise van der Pol , Rita Cucchiara, Pascal Mettes

Abstract : Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not equipped with this inductive bias and therefore many alternative solutions have been proposed through differential optimization. Current approaches tend to optimize classification and separation jointly: aligning inputs with class vectors and separating class vectors angularly.

This paper proposes a simple alternative: encoding maximum separation as an inductive bias in the network by adding one fixed matrix multiplication before computing the softmax activations. The main observation behind our approach is that separation does not require optimization but can be solved in closed-form prior to training and plugged into a network. We outline a recursive approach to obtain the matrix consisting of maximally separable vectors for any number of classes, which can be added with negligible engineering effort and computational overhead. Despite its simple nature, this one matrix multiplication provides real impact. We show that our proposal directly boosts classification, long-tailed recognition, out-of-distribution detection, and open-set recognition, from CIFAR to ImageNet. We find empirically that maximum separation works best as a fixed bias; making the matrix learnable adds nothing to the performance. The closed-form implementation and code to reproduce the experiments are available on GitHub.

Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis

Tim Pearce , Jong-Hyeon Jeong, Yichen Jia, Jun Zhu

Abstract : This paper considers doing quantile regression on censored data using neural networks (NNs). This adds to the survival analysis toolkit by allowing direct prediction of the target variable, along with a distribution-free characterization of uncertainty, using a flexible function approximator. We begin by showing how an algorithm popular in linear models can be applied to NNs. However, the resulting procedure is inefficient, requiring sequential optimization of an individual NN at each desired quantile. Our major contribution is a novel algorithm that simultaneously optimizes a grid of quantiles output by a single NN. To offer theoretical insight into our algorithm, we show firstly that it can be interpreted as a form of expectation-maximization, and secondly that it exhibits a desirable `self-correcting’ property. Experimentally, the algorithm produces quantiles that are better calibrated than existing methods on 10 out of 12 real datasets.

Learning (Very) Simple Generative Models Is Hard

Sitan Chen, Jerry Li , Yuanzhi Li

Abstract : Motivated by the recent empirical successes of deep generative models, we study the computational complexity of the following unsupervised learning problem. For an unknown neural network \(F:\mathbb{R}^d\to\mathbb{R}^{d’}\), let \(D\) be the distribution over \(\mathbb{R}^{d’}\) given by pushing the standard Gaussian \(\mathcal{N}(0,\textrm{Id}_d)\) through \(F\). Given i.i.d. samples from \(D\), the goal is to output \({any}\) distribution close to \(D\) in statistical distance.

We show under the statistical query (SQ) model that no polynomial-time algorithm can solve this problem even when the output coordinates of \(F\) are one-hidden-layer ReLU networks with \(\log(d)\) neurons. Previously, the best lower bounds for this problem simply followed from lower bounds for \(supervised\) \(learning\) and required at least two hidden layers and \(poly(d)\) neurons [Daniely-Vardi ’21, Chen-Gollakota-Klivans-Meka ’22].

The key ingredient in our proof is an ODE-based construction of a compactly supported, piecewise-linear function \(f\) with polynomially-bounded slopes such that the pushforward of \(\mathcal{N}(0,1)\) under \(f\) matches all low-degree moments of \(\mathcal{N}(0,1)\).

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BibTeX Record

MEDICAL IMAGING MEETS NeurIPS

An official NeurIPS Workshop - 2 December 2022 -   In Person (with hybrid provided)

MedNeurIPS will be returning in 2023! The New link is here: https://sites.google.com/view/med-neurips2023/home

Consider joining us for a day at the intersection of machine learning and medical imaging.  .

Submissions:   Thursday, Sep 29, 2022 Monday, Oct 3, 2022

Notifications:  Thursday, Oct 20, 2022     Tu es day, Nov 1 , 2022

Paper Final Version:   Monday, Nov 10, 2022

Poster Submissios: Friday, Nov 18, 2022

Workshop Date: Friday, Dec 2, 2022

Call for Abstracts

We invite submissions of extended abstracts for oral and poster presentation during the workshop. Submitting an abstract is an ideal way of engaging with the workshop and to showcase research in the area of machine learning for medical imaging . Submitted work can be of preliminary nature and we also invite perspectives and position papers to generate discussions about recent trends and major challenges. Please check the submissions page for more details .

'Medical Imaging meets NeurIPS' is a satellite workshop established in 2017. The workshop aims to bring researchers together from the medical image computing and machine learning communities. The objective is to discuss the major challenges in the field and opportunities for joining forces. This year the workshop will feature online oral and poster sessions with an emphasis on audience interactions. In addition, there will be a series of high-profile invited speakers from industry, academia, engineering and medical sciences giving an overview of recent advances, challenges, latest technology and efforts for sharing clinical data.

Medical imaging is facing a major crisis with an ever increasing complexity and volume of data and immense economic pressure. The interpretation of medical images pushes human abilities to the limit with the risk that critical patterns of disease go undetected. Machine learning has emerged as a key technology for developing novel tools in computer aided diagnosis, therapy and intervention. Still, progress is slow compared to other fields of visual recognition which is mainly due to the domain complexity and constraints in clinical applications which require most robust, accurate, and reliable solutions. The workshop aims to raise the awareness of the unmet needs in machine learning for successful applications in medical imaging.

Registration

Registration for the workshop can be done via the NeurIPS conference website .

Any questions, please contact email: [email protected]

A computer science student at Princeton University

• Princeton, NJ

Talks and presentations

Video presentation at neurips 2022.

October 22, 2022

In this talk, I use 5 minutes to present our paper Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering . You can visit my talk online here

In this talk, I use 5 minutes to present our paper BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression . You can visit my talk online here .

Presentation at FLOW: Federated Learning One World Seminar

August 11, 2021

In this talk, I presented my work with Zhize Li @KAUST and Peter Richtárik on our paper FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning .

Oral presentation at AAAI 2020

February 11, 2020

In this talk, I presented my work with Prof. Wei Chen @MSRA on our paper Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting . Because of the virus in China, I cannot go the the AAAI main conference, and I will give my oral presentation remotely. The paper can be downloaded here . The PPT is available at here .

Oral presentation at ECML/PKDD 2019

September 19, 2019

In this talk, I presented my work with Prof. Wei Chen @MSRA on our paper Stochastic One-Sided Full-Information Bandit . The paper can be downloaded here .

Oral in Workshop: AI for Science: Progress and Promises

Oral presentation 1.

Registration

Visit the MyStuff page using the link below, choose your 2022 registration, and visit the payment section to generate a registration receipt or certificate of attendance. 

Announcements

• Read the NeurIPS blog for conference updates and subscribe to the NeurIPS Newletter for infrequent updates. 

Careers Site

The NeurIPS Careers site is open but no longer accepting new job poster applications. 

Conference (Virtual) Site Schedule Invited Talks Posters Workshops Competitions Affinity Workshops Tutorials Papers Socials Expo Lightning Talks and Panels Featured Papers New In ML - Mentorship

Important Dates

If you have questions about supporting the conference, please contact us .

View NeurIPS 2022 exhibitors » Become an 2023 Exhibitor (not currently taking applications) Exhibitor Info »

Organizing Committee

General chair, program chair, workshop chair, tutorial chair, competition chair, datasets and benchmarks chair, diversity, inclusion & accessibility chair, affinity workshop chair, expo chairs, outreach chair, ethics review chair, communication chair, social chair, journal chair, hybrid workflow chair, workflow manager, logistics and it, mission statement.

The Neural Information Processing Systems Foundation is a non-profit corporation whose purpose is to foster the exchange of research advances in Artificial Intelligence and Machine Learning, principally by hosting an annual interdisciplinary academic conference with the highest ethical standards for a diverse and inclusive community.

About the Conference

The conference was founded in 1987 and is now a multi-track interdisciplinary annual meeting that includes invited talks, demonstrations, symposia, and oral and poster presentations of refereed papers. Along with the conference is a professional exposition focusing on machine learning in practice, a series of tutorials, and topical workshops that provide a less formal setting for the exchange of ideas.

More about the Neural Information Processing Systems foundation »

Datasets and Benchmarks

In 2021, NeurIPS introduced a new track, Datasets and Benchmarks. The first year of that track, 2021, has its own proceedings, accessible by the link below. From 2022 on, the Datasets and Benchmarks papers are in the main NeurIPS proceedings.

• Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
• Advances in Neural Information Processing Systems 35 (NeurIPS 2022)
• Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
• Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
• Advances in Neural Information Processing Systems 32 (NeurIPS 2019)
• Advances in Neural Information Processing Systems 31 (NeurIPS 2018)
• Advances in Neural Information Processing Systems 30 (NIPS 2017)
• Advances in Neural Information Processing Systems 29 (NIPS 2016)
• Advances in Neural Information Processing Systems 28 (NIPS 2015)
• Advances in Neural Information Processing Systems 27 (NIPS 2014)
• Advances in Neural Information Processing Systems 26 (NIPS 2013)
• Advances in Neural Information Processing Systems 25 (NIPS 2012)
• Advances in Neural Information Processing Systems 24 (NIPS 2011)
• Advances in Neural Information Processing Systems 23 (NIPS 2010)
• Advances in Neural Information Processing Systems 22 (NIPS 2009)
• Advances in Neural Information Processing Systems 21 (NIPS 2008)
• Advances in Neural Information Processing Systems 20 (NIPS 2007)
• Advances in Neural Information Processing Systems 19 (NIPS 2006)
• Advances in Neural Information Processing Systems 18 (NIPS 2005)
• Advances in Neural Information Processing Systems 17 (NIPS 2004)
• Advances in Neural Information Processing Systems 16 (NIPS 2003)
• Advances in Neural Information Processing Systems 15 (NIPS 2002)
• Advances in Neural Information Processing Systems 14 (NIPS 2001)
• Advances in Neural Information Processing Systems 13 (NIPS 2000)
• Advances in Neural Information Processing Systems 12 (NIPS 1999)
• Advances in Neural Information Processing Systems 11 (NIPS 1998)
• Advances in Neural Information Processing Systems 10 (NIPS 1997)
• Advances in Neural Information Processing Systems 9 (NIPS 1996)
• Advances in Neural Information Processing Systems 8 (NIPS 1995)
• Advances in Neural Information Processing Systems 7 (NIPS 1994)
• Advances in Neural Information Processing Systems 6 (NIPS 1993)
• Advances in Neural Information Processing Systems 5 (NIPS 1992)
• Advances in Neural Information Processing Systems 4 (NIPS 1991)
• Advances in Neural Information Processing Systems 3 (NIPS 1990)
• Advances in Neural Information Processing Systems 2 (NIPS 1989)
• Advances in Neural Information Processing Systems 1 (NIPS 1988)
• Neural Information Processing Systems 0 (NIPS 1987)

Name Change Policy

Requests for name changes in the electronic proceedings will be accepted with no questions asked. However name changes may cause bibliographic tracking issues. Authors are asked to consider this carefully and discuss it with their co-authors prior to requesting a name change in the electronic proceedings.

Use the "Report an Issue" link to request a name change.

NeurIPS Newsletter – October 2023

Communications Chairs 2023 2023 Conference

Welcome to the October edition of the monthly NeurIPS Newsletter! This newsletter was sent out by email to all subscribers on October 26, 2023, but we reposting on our blog as a monthly digest. To receive this newsletter in your email inbox directly, subscribe at: https://neurips.cc/Profile/subscribe . For more information on email preferences, visit:  https://neurips.cc/FAQ/EmailPreferences

The NeurIPS Newsletter aims to provide an easy way to keep up to date with NeurIPS events and planning progress, respond to requests for feedback and participation, and find information about new initiatives. Notably, this newsletter will focus on NeurIPS 2023, which will return to New Orleans from Sunday, December 10th – Saturday, December 16th, 2023.

Newsletter includes:

• Keynote Speakers
• Journal Track
• Competition Track
• Affinity Groups
• Accepted Papers
• Financial Aid
• Childcare and Other Amenities

1. Keynote Speakers

We are delighted to announce this year’s keynote speakers: 

• Björn Ommer,  Mon 11 Dec
• Lora Aroyo, Tue 12 Dec
• Linda Smith, Tue 12 Dec 
• Jelani Nelson, Wed 13 Dec 
• LLM Panel (Sasha Rush, Angela Fan, Aakanksha Chowdhery, Jie Tang, Percy Liang), Wed 13 Dec
• Christopher Ré, Thu 14 Dec
• Susan Murphy, Thu 14 Dec 

More information about the seven invited talks will be coming soon.

2. Journal Track

This year we have a total of 45 papers for the Journal track. Out of these, 21 papers have been sourced from the Journal of Machine Learning Research (JMLR), while the remaining 24 papers are contributions from the ML Reproducibility Challenge (MLRC) 2022. We eagerly await your presence at the poster sessions.

3. Competitions Track

NeurIPS Competitions promote innovative research to solve open problems in Machine Learning and foster collaboration across different scientific disciplines by providing an opportunity to showcase research and compete with other leading researchers. The Competition track workshops will be held on Friday Dec 15th and Saturday Dec 16th 2023. Some of the sessions will be held in a hybrid format, while others will be purely virtual.  Be sure to check them out to see what approaches rise to the top of the leaderboard!

Check out the draft program currently up here: https://neurips.cc/virtual/2023/events/Competition . 

The Competition Track chairs want to thank everyone who organized and participated in this year’s competitions. 

4. Workshops

Each of our 58 workshops is associated with its own dedicated website, and is linked to the NeurIPS 2023 virtual website https://neurips.cc/virtual/2023/events/workshop . We thank all authors for contributing workshop submissions, and all organizers and reviewers for their hard work. Workshops are now entering the workshop submission reviewing stage. The decisions are expected to be announced before October 27, 2023 AoE. Stay tuned for updates!

While the workshop organizers are super busy preparing top-notch program agendas, we invite attendees and participants to read our blog post to understand more about the workshop preparation process: https://blog.neurips.cc/2023/09/12/your-neurips-workshop-was-accepted-now-what/

In 2023, we have strongly encouraged all NeurIPS workshops to prioritize having in-person speakers (except for exceptional circumstances), while still welcoming and facilitating both in-person and virtual attendance. As in previous years, virtual attendees will be able to watch the livestreams on the NeurIPS virtual website. We invite you to join our stellar workshops – many of which include outstanding speakers and roundtable discussions as well as mentoring and networking opportunities – regardless of whether you plan to attend in-person or virtually!

5. Affinity Groups

Nine affinity groups are organizing workshops/socials at NeurIPS 2023 on Monday, and are working hard to create spaces for marginalized communities at the conference. They are progressively populating their pages on the NeurIPS schedule with events and papers. Most workshops’ call for contributions have closed or are closing soon; please check their websites or get in contact with any relevant groups if you would still like to submit your work. To showcase these contributions, we are organizing an in-person and a virtual joint affinity poster session, which will emphasize learning from and across affinity groups. Beyond the workshops and poster session, many affinity groups are hosting receptions and socials at the venue or local restaurants/bars (more details coming soon). We are working with the NeurIPS staff and DIA chairs to provide childcare/lactation/quiet/work rooms and compile answers to FAQs about accessibility and gender inclusivity.

6. Accepted Papers 

The list of 3586 accepted papers and poster schedule are now available on the website: https://neurips.cc/virtual/2023/papers.html?filter=titles . 

These include papers from both the main track and the datasets and benchmarks track. At the conference there will be daily poster sessions (one morning and one afternoon session), as well as oral presentations. The schedule is available at https://neurips.cc/virtual/2023/calendar . 

For authors of accepted papers, please note that the SlidesLive video deadline is Nov 13.

7. Town Hall

On Thursday during NeurIPS the Communications Chairs will host the annual Town Hall between 7:00-9:00 p.m. as an opportunity to connect with and ask questions to the NeurIPS organizers and the Board. If you have questions for this year’s organizers please submit them to [email protected]   so we can discuss these publicly during the conference in New Orleans. 

8. Financial Aid

Financial aid applications have closed. The NeurIPS DIA Chairs and Logistics Team have begun sending out rolling notifications. For the latest info about financial aid, visit this updating page: https://neurips.cc/Conferences/2023/FinancialAssistance

9. Child Care

NeurIPS is proud to provide free on-site child care. Please register before the November 30 deadline and familiarize yourself with the cancelation policy to avoid no-show fees. For details and how to register visit: https://neurips.cc/Conferences/2023/ChildCare

Other amenities will include a prayer room and quiet rooms.

Thanks and looking forward to seeing you at the conference,

Alice Oh & Tristan Naumann

NeurIPS 2023 General Chairs

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