ICLR 2023

1,573 papers

Quantized Compressed Sensing with Score-Based Generative Models

Xiangming Meng, Yoshiyuki Kabashima

CUTS: Neural Causal Discovery from Irregular Time-Series Data

Yuxiao Cheng, Runzhao Yang, Tingxiong Xiao, Zongren Li, Jinli Suo

Data augmentation alone can improve adversarial training

Lin Li, Michael W. Spratling

Language Models are Realistic Tabular Data Generators

Vadim Borisov, Kathrin Sessler, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci

Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams.

Cuiyu Liu, Xiao Chuanfu, Mingshuo Ding, Chao Yang

Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation

Martin Zong, Zengyu Qiu, Xinzhu Ma, Kunlin Yang, Chunya Liu

Shuang Wu, Jian Yao, Haobo Fu, Ye Tian, Chao Qian

Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data

Fuxiang Zhang, Chengxing Jia, Yi-Chen Li, Lei Yuan, Yang Yu

Learning Vortex Dynamics for Fluid Inference and Prediction

Yitong Deng, Hong-Xing Yu, Jiajun Wu, Bo Zhu

Λ-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among Cells

Sajad Movahedi, Melika Adabinejad, Ayyoob Imani, Arezou Keshavarz, Mostafa Dehghani

Bayesian Oracle for bounding information gain in neural encoding models

Konstantin-Klemens Lurz, Mohammad Bashiri, Edgar Y. Walker, Fabian H. Sinz

NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs

Jinsong Chen, Kaiyuan Gao, Gaichao Li, Kun He

Scaling Pareto-Efficient Decision Making via Offline Multi-Objective RL

Baiting Zhu, Meihua Dang, Aditya Grover

Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy

Jinsong Zhang, Qiang Fu, Xu Chen, Lun Du, Zelin Li

Policy-Based Self-Competition for Planning Problems

Jonathan Pirnay, Quirin Göttl, Jakob Burger, Dominik Gerhard Grimm

Leveraging Unlabeled Data to Track Memorization

Mahsa Forouzesh, Hanie Sedghi, Patrick Thiran

Neural-based classification rule learning for sequential data

Marine Collery, Philippe Bonnard, François Fages, Remy Kusters

Almost Linear Constant-Factor Sketching for ℓ_1 and Logistic Regression

Alexander Munteanu, Simon Omlor, David Woodruff

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning

Mikayel Samvelyan, Akbir Khan, Michael D Dennis, Minqi Jiang, Jack Parker-Holder

Gradient Gating for Deep Multi-Rate Learning on Graphs

T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra

Global Explainability of GNNs via Logic Combination of Learned Concepts

Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Lió, Andrea Passerini

Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees

Florent Delgrange, Ann Nowe, Guillermo Pérez

Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes

Christian Alexander Haase, Christoph Hertrich, Georg Loho

Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems

Zhongyuan Zhao, Ananthram Swami, Santiago Segarra

MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors

Chen Huang, Hanlin Goh, Jiatao Gu, Joshua M. Susskind

The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks

Mor Shpigel Nacson, Rotem Mulayoff, Greg Ongie, Tomer Michaeli, Daniel Soudry

How Sharpness-Aware Minimization Minimizes Sharpness?

Kaiyue Wen, Tengyu Ma, Zhiyuan Li

Active Learning for Object Detection with Evidential Deep Learning and Hierarchical Uncertainty Aggregation

Younghyun Park, Wonjeong Choi, Soyeong Kim, Dong-Jun Han, Jaekyun Moon

Learning ReLU networks to high uniform accuracy is intractable

Julius Berner, Philipp Grohs, Felix Voigtlaender

A law of adversarial risk, interpolation, and label noise

Daniel Paleka, Amartya Sanyal

Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization

Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu

SLTUNET: A Simple Unified Model for Sign Language Translation

Biao Zhang, Mathias Müller, Rico Sennrich

User-Interactive Offline Reinforcement Learning

Phillip Swazinna, Steffen Udluft, Thomas Runkler

Scaffolding a Student to Instill Knowledge

Anil Kag, Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama

Learnable Graph Convolutional Attention Networks

Adrián Javaloy, Pablo Sanchez Martin, Amit Levi, Isabel Valera

FastFill: Efficient Compatible Model Update

Florian Jaeckle, Fartash Faghri, Ali Farhadi, Oncel Tuzel, Hadi Pouransari

Explaining RL Decisions with Trajectories

Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang, Chirag Agarwal

Online Low Rank Matrix Completion

Soumyabrata Pal, Prateek Jain

Contrastive Learning for Unsupervised Domain Adaptation of Time Series

Yilmazcan Ozyurt, Stefan Feuerriegel, Ce Zhang

Anti-Symmetric DGN: a stable architecture for Deep Graph Networks

Alessio Gravina, Davide Bacciu, Claudio Gallicchio

Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms

Ximing Li, Chendi Wang, Guang Cheng

Consolidator: Mergable Adapter with Group Connections for Visual Adaptation

Tianxiang Hao, Hui Chen, Yuchen Guo, guiguang ding

Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach

Minyoung Kim, Da Li, Timothy Hospedales

TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations

Haoxuan Li, Yan Lyu, Chunyuan Zheng, Peng Wu

DAVA: Disentangling Adversarial Variational Autoencoder

Benjamin Estermann, Roger Wattenhofer

Learning Input-agnostic Manipulation Directions in StyleGAN with Text Guidance

Yoonjeon Kim, Hyunsu Kim, Junho Kim, Yunjey Choi, Eunho Yang

$\mathscr{N}$-WL: A New Hierarchy of Expressivity for Graph Neural Networks

Qing Wang, Dillon Ze Chen, Asiri Wijesinghe, Shouheng Li, Muhammad Farhan

Sampling-free Inference for Ab-Initio Potential Energy Surface Networks

Nicholas Gao, Stephan Günnemann

Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning

Deyao Zhu, Li Erran Li, Mohamed Elhoseiny

Optimizing Spca-based Continual Learning: A Theoretical Approach

Chunchun Yang, Malik Tiomoko, Zengfu Wang

Revisit Finetuning strategy for Few-Shot Learning to Transfer the Emdeddings

Heng Wang, Tan Yue, Xiang Ye, Zihang He, Bohan Li

CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, Jie Tang

Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling

Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu

Robust Explanation Constraints for Neural Networks

Matthew Robert Wicker, Juyeon Heo, Luca Costabello, Adrian Weller

Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning

Peizhong Ju, Yingbin Liang, Ness Shroff

Defending against Adversarial Audio via Diffusion Model

Shutong Wu, Jiongxiao Wang, Wei Ping, Weili Nie, Chaowei Xiao

Sampling-based inference for large linear models, with application to linearised Laplace

Javier Antoran, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz

StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random

Haoxuan Li, Chunyuan Zheng, Peng Wu

PASHA: Efficient HPO and NAS with Progressive Resource Allocation

Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau

Holistic Adversarially Robust Pruning

Qi Zhao, Christian Wressnegger

That Label's got Style: Handling Label Style Bias for Uncertain Image Segmentation

Kilian Zepf, Eike Petersen, Jes Frellsen, Aasa Feragen

DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training

Wei Li, Linchao Zhu, Longyin Wen, Yi Yang

Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints

David Henry Mguni, Aivar Sootla, Juliusz Krzysztof Ziomek, Oliver Slumbers, Zipeng Dai

Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners

Seonghyeon Ye, Doyoung Kim, Joel Jang, Joongbo Shin, Minjoon Seo

Learning Fast and Slow for Online Time Series Forecasting

Quang Pham, Chenghao Liu, Doyen Sahoo, Steven Hoi

Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles

Martin Bjerke, Lukas Schott, Kristopher T Jensen, Claudia Battistin, David A. Klindt

Structure by Architecture: Structured Representations without Regularization

Felix Leeb, Giulia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer

On Explaining Neural Network Robustness with Activation Path

Ziping Jiang

A GNN-Guided Predict-and-Search Framework for Mixed-Integer Linear Programming

Qingyu Han, Linxin Yang, Qian Chen, Xiang Zhou, Dong Zhang

LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval

Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Xiaolong Huang

Short-Term Memory Convolutions

Grzegorz Stefański, Krzysztof Arendt, Paweł Daniluk, Bartłomiej Jasik, Artur Szumaczuk

Fundamental Limits in Formal Verification of Message-Passing Neural Networks

Marco Sälzer, Martin Lange

Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs

Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang

Improving the imputation of missing data with Markov Blanket discovery

Yang Liu, Anthony Constantinou

Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer

Zhun Yang, Adam Ishay, Joohyung Lee

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization

Alan Jeffares, Tennison Liu, Jonathan Crabbe, Fergus Imrie, Mihaela van der Schaar

DynaMS: Dyanmic Margin Selection for Efficient Deep Learning

Jiaxing Wang, Yong Li, Jingwei Zhuo, Xupeng Shi, Weizhong Zhang

DiffusER: Diffusion via Edit-based Reconstruction

Machel Reid, Vincent Josua Hellendoorn, Graham Neubig

Multi-objective optimization via equivariant deep hypervolume approximation

Jim Boelrijk, Bernd Ensing, Patrick Forré

Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection

Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, Qianru Sun

Unbiased Supervised Contrastive Learning

Carlo Alberto Barbano, Benoit Dufumier, Enzo Tartaglione, Marco Grangetto, Pietro Gori

The Curious Case of Benign Memorization

Sotiris Anagnostidis, Gregor Bachmann, Lorenzo Noci, Thomas Hofmann

Learning topology-preserving data representations

Ilya Trofimov, Daniil Cherniavskii, Eduard Tulchinskii, Nikita Balabin, Evgeny Burnaev

Sequential Learning of Neural Networks for Prequential MDL

Jörg Bornschein, Yazhe Li, Marcus Hutter

Representational Dissimilarity Metric Spaces for Stochastic Neural Networks

Lyndon Duong, Jingyang Zhou, Josue Nassar, Jules Berman, Jeroen Olieslagers

Why adversarial training can hurt robust accuracy

Jacob Clarysse, Julia Hörrmann, Fanny Yang

Copy is All You Need

Tian Lan, Deng Cai, Yan Wang, Heyan Huang, Xian-Ling Mao

Leveraging Importance Weights in Subset Selection

Gui Citovsky, Giulia DeSalvo, Sanjiv Kumar, Srikumar Ramalingam, Afshin Rostamizadeh

Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation

Bariscan Bozkurt, Ateş İsfendiyaroğlu, Cengiz Pehlevan, Alper Tunga Erdogan

Efficient Planning in a Compact Latent Action Space

zhengyao jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel

Enhancing the Inductive Biases of Graph Neural ODE for Modeling Physical Systems

Suresh Bishnoi, Ravinder Bhattoo, Jayadeva Jayadeva, Sayan Ranu, N M Anoop Krishnan

Contrastive Meta-Learning for Partially Observable Few-Shot Learning

Adam Jelley, Amos Storkey, Antreas Antoniou, Sam Devlin

Imitating Human Behaviour with Diffusion Models

Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun

Understanding weight-magnitude hyperparameters in training binary networks

Joris Quist, Yunqiang Li, Jan van Gemert

This Looks Like It Rather Than That: ProtoKNN For Similarity-Based Classifiers

Yuki Ukai, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi

Latent Graph Inference using Product Manifolds

Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Lió

Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach

Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim

Identifiability Results for Multimodal Contrastive Learning

Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E Vogt

A View From Somewhere: Human-Centric Face Representations

Jerone Theodore Alexander Andrews, Przemyslaw Joniak, Alice Xiang

CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning

Samuel Maddock, Alexandre Sablayrolles, Pierre Stock