ICLR 2023

1,573 papers

Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning

Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-chun Zhu

Masked Frequency Modeling for Self-Supervised Visual Pre-Training

Jiahao Xie, Wei Li, Xiaohang Zhan, Ziwei Liu, Yew-Soon Ong

Meta Knowledge Condensation for Federated Learning

Ping Liu, Xin Yu, Joey Tianyi Zhou

Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference

Souvik Kundu, Shunlin Lu, Yuke Zhang, Jacqueline Tiffany Liu, Peter Anthony Beerel

Test-Time Robust Personalization for Federated Learning

Liangze Jiang, Tao LIN

Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path

Yihan Du, Siwei Wang, Longbo Huang

Collaborative Pure Exploration in Kernel Bandit

Yihan Du, Wei Chen, Yuko Kuroki, Longbo Huang

Sound Randomized Smoothing in Floating-Point Arithmetic

Václav Voráček, Matthias Hein

LDMIC: Learning-based Distributed Multi-view Image Coding

Xinjie Zhang, Jiawei Shao, Jun Zhang

An Additive Instance-Wise Approach to Multi-class Model Interpretation

Vy Vo, Van Nguyen, Trung Le, Quan Hung Tran, Gholamreza Haffari

SCoMoE: Efficient Mixtures of Experts with Structured Communication

Zhiyuan Zeng, Deyi Xiong

Approximate Bayesian Inference with Stein Functional Variational Gradient Descent

Tobias Pielok, Bernd Bischl, David Rügamer

Estimating individual treatment effects under unobserved confounding using binary instruments

Dennis Frauen, Stefan Feuerriegel

Denoising Masked Autoencoders Help Robust Classification

QuanLin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang

Deep Variational Implicit Processes

Luis A. Ortega, Simon Rodriguez Santana, Daniel Hernández-lobato

Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representations

Ali Hummos

Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance

Xueyi Liu, Ji Zhang, Ruizhen Hu, Haibin Huang, He Wang

Towards Understanding Why Mask Reconstruction Pretraining Helps in Downstream Tasks

Jiachun Pan, Pan Zhou, Shuicheng Yan

Deep Ensembles for Graphs with Higher-order Dependencies

Steven Krieg, William Burgis, Patrick Soga, Nitesh Chawla

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales

ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation

Jianye Hao, Pengyi Li, Hongyao Tang, YAN ZHENG, Xian Fu

Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction

Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato

Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning

Bo Wan, Yongfei Liu, Desen Zhou, Tinne Tuytelaars, Xuming He

Robustness to corruption in pre-trained Bayesian neural networks

Xi Wang, Laurence Aitchison

Deep Ranking Ensembles for Hyperparameter Optimization

Abdus Salam Khazi, Sebastian Pineda Arango, Josif Grabocka

Fair Attribute Completion on Graph with Missing Attributes

Dongliang Guo, Zhixuan Chu, Sheng Li

Reliability of CKA as a Similarity Measure in Deep Learning

MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf

Riemannian Metric Learning via Optimal Transport

Christopher Scarvelis, Justin Solomon

Automated Data Augmentations for Graph Classification

Youzhi Luo, Michael Curtis McThrow, Wing Yee Au, Tao Komikado, Kanji Uchino

Composing Ensembles of Pre-trained Models via Iterative Consensus

Shuang Li, Yilun Du, Joshua B. Tenenbaum, Antonio Torralba, Igor Mordatch

Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models

Meng Liu, Haoran Liu, Shuiwang Ji

Linear Connectivity Reveals Generalization Strategies

Jeevesh Juneja, Rachit Bansal, Kyunghyun Cho, João Sedoc, Naomi Saphra

Understanding Why Generalized Reweighting Does Not Improve Over ERM

Runtian Zhai, Chen Dan, J Zico Kolter, Pradeep Kumar Ravikumar

Discovering Informative and Robust Positives for Video Domain Adaptation

Chang Liu, Kunpeng Li, Michael Stopa, Jun Amano, Yun Fu

Generate rather than Retrieve: Large Language Models are Strong Context Generators

Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju

General Neural Gauge Fields

Fangneng Zhan, Lingjie Liu, Adam Kortylewski, Christian Theobalt

BrainBERT: Self-supervised representation learning for intracranial recordings

Christopher Wang, Vighnesh Subramaniam, Adam Uri Yaari, Gabriel Kreiman, Boris Katz

The KFIoU Loss for Rotated Object Detection

Xue Yang, Yue Zhou, Gefan Zhang, Jirui Yang, Wentao Wang

Plateau in Monotonic Linear Interpolation --- A "Biased" View of Loss Landscape for Deep Networks

Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge

StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training

Yuechen Yu, Yulin Li, Chengquan Zhang, Xiaoqiang Zhang, Zengyuan Guo

Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Shuo Yang, Zeke Xie, Hanyu Peng, Min Xu, Mingming Sun

ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor

Wanqi Xue, Qingpeng Cai, Ruohan Zhan, Dong Zheng, Peng Jiang

Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information

Yulun Wu, Rob Barton, Zichen Wang, Vassilis N. Ioannidis, Carlo De Donno

EVC: Towards Real-Time Neural Image Compression with Mask Decay

Wang Guo-Hua, Jiahao Li, Bin Li, Yan Lu

Learning to Generate Columns with Application to Vertex Coloring

Yuan Sun, Andreas T Ernst, Xiaodong Li, Jake Weiner

CodeT: Code Generation with Generated Tests

Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin

Visual Imitation Learning with Patch Rewards

Minghuan Liu, Tairan He, Weinan Zhang, Shuicheng Yan, Zhongwen Xu

Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems

Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen

Self-Supervised Set Representation Learning for Unsupervised Meta-Learning

Dong Bok Lee, Seanie Lee, Kenji Kawaguchi, Yunji Kim, Jihwan Bang

Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning

Sasha Salter, Kristian Hartikainen, Walter Goodwin, Ingmar Posner

Learning Symbolic Models for Graph-structured Physical Mechanism

Hongzhi Shi, Jingtao Ding, Yufan Cao, Quanming Yao, Li Liu

Continual Transformers: Redundancy-Free Attention for Online Inference

Lukas Hedegaard, Arian Bakhtiarnia, Alexandros Iosifidis

Strategic Classification with Graph Neural Networks

Itay Eilat, Ben Finkelshtein, Chaim Baskin, Nir Rosenfeld

Truthful Self-Play

Shohei Ohsawa

Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam

Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He

InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised Learning

Zhuoran Yu, Yin Li, Yong Jae Lee

Spherical Sliced-Wasserstein

Clément Bonet, Paul Berg, Nicolas Courty, François Septier, Lucas Drumetz

Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization

Stamatios Lefkimmiatis, Iaroslav Sergeevich Koshelev

Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks

SHUAI ZHANG, Meng Wang, Pin-Yu Chen, Sijia Liu, Songtao Lu

SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation

Qiang Wan, Zilong Huang, Jiachen Lu, Gang Yu, Li Zhang

Kernel Neural Optimal Transport

Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev

Robust Active Distillation

Cenk Baykal, Khoa Trinh, Fotis Iliopoulos, Gaurav Menghani, Erik Vee

Sparse Token Transformer with Attention Back Tracking

Heejun Lee, Minki Kang, Youngwan Lee, Sung Ju Hwang

Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?

Kaiqi Zhang, Yu-Xiang Wang

Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?

Runpei Dong, Zekun Qi, Linfeng Zhang, Junbo Zhang, Jianjian Sun

Critic Sequential Monte Carlo

Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas, Matthew Niedoba, Yunpeng Liu

NORM: Knowledge Distillation via N-to-One Representation Matching

Xiaolong Liu, Luking Li, CHAO LI, Anbang Yao

Switch-NeRF: Learning Scene Decomposition with Mixture of Experts for Large-scale Neural Radiance Fields

Zhenxing Mi, Dan Xu

Selective Annotation Makes Language Models Better Few-Shot Learners

Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang

Approximate Vanishing Ideal Computations at Scale

Elias Samuel Wirth, Hiroshi Kera, Sebastian Pokutta

FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities

Takashi Matsubara, Takaharu Yaguchi

Learning without Prejudices: Continual Unbiased Learning via Benign and Malignant Forgetting

Myeongho Jeon, Hyoje Lee, Yedarm Seong, Myungjoo Kang

TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang

Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling

Penghao Wu, Li Chen, Hongyang Li, Xiaosong Jia, Junchi Yan

Light Sampling Field and BRDF Representation for Physically-based Neural Rendering

Jing Yang, Hanyuan Xiao, Wenbin Teng, Yunxuan Cai, Yajie Zhao

Towards Robust Object Detection Invariant to Real-World Domain Shifts

Qi Fan, Mattia Segu, Yu-Wing Tai, Fisher Yu, Chi-Keung Tang

Combating Exacerbated Heterogeneity for Robust Models in Federated Learning

Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu

Learning Locality and Isotropy in Dialogue Modeling

Han Wu, Haochen Tan, Mingjie Zhan, Gangming Zhao, Shaoqing Lu

Particle-based Variational Inference with Preconditioned Functional Gradient Flow

Hanze Dong, Xi Wang, LIN Yong, Tong Zhang

Everybody Needs Good Neighbours: An Unsupervised Locality-based Method for Bias Mitigation

Xudong Han, Timothy Baldwin, Trevor Cohn

POPGym: Benchmarking Partially Observable Reinforcement Learning

Steven Morad, Ryan Kortvelesy, Matteo Bettini, Stephan Liwicki, Amanda Prorok

Deep Reinforcement Learning for Cost-Effective Medical Diagnosis

zheng Yu, Yikuan Li, Joseph Chahn Kim, Kaixuan Huang, Yuan Luo

GReTo: Remedying dynamic graph topology-task discordance via target homophily

Zhengyang Zhou, Qihe Huang, Gengyu Lin, Kuo Yang, LEI BAI

Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity

Clare Elizabeth Heinbaugh, Emilio Luz-Ricca, Huajie Shao

CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning

Sheng Yue, Guanbo Wang, Wei Shao, Zhaofeng Zhang, Sen Lin

Graph Domain Adaptation via Theory-Grounded Spectral Regularization

Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen

Spatial Attention Kinetic Networks with E(n)-Equivariance

Yuanqing Wang, John Chodera

Learning to Jointly Share and Prune Weights for Grounding Based Vision and Language Models

Shangqian Gao, Burak Uzkent, Yilin Shen, Heng Huang, Hongxia Jin

Robust Fair Clustering: A Novel Fairness Attack and Defense Framework

Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu

A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis

Damien Ferbach, Christos Tsirigotis, Gauthier Gidel, Joey Bose

Sparse Random Networks for Communication-Efficient Federated Learning

Berivan Isik, Francesco Pase, Deniz Gündüz, Tsachy Weissman, Zorzi Michele

DFlow: Learning to Synthesize Better Optical Flow Datasets via a Differentiable Pipeline

Kwon Byung-Ki, Nam Hyeon-Woo, Ji-Yun Kim, Tae-Hyun Oh

Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal Retrieval

Zhenghao Liu, Chenyan Xiong, Yuanhuiyi Lv, Zhiyuan Liu, Ge Yu

Dataless Knowledge Fusion by Merging Weights of Language Models

Xisen Jin, Xiang Ren, Daniel Preoţiuc-Pietro, Pengxiang Cheng

Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?

Jie Ren, Zhanpeng Zhou, Qirui Chen, Quanshi Zhang

Understanding Zero-shot Adversarial Robustness for Large-Scale Models

Chengzhi Mao, Scott Geng, Junfeng Yang, Xin Wang, Carl Vondrick

Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes

Miao Lu, Yifei Min, Zhaoran Wang, Zhuoran Yang

Information-Theoretic Analysis of Unsupervised Domain Adaptation

Ziqiao Wang, Yongyi Mao

Parallel Deep Neural Networks Have Zero Duality Gap

Yifei Wang, Tolga Ergen, Mert Pilanci

Mega: Moving Average Equipped Gated Attention

Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui