ICML 2023

1,828 papers

SpeedDETR: Speed-aware Transformers for End-to-end Object Detection

Peiyan Dong, Zhenglun Kong, Xin Meng, Peng Zhang, Hao Tang

Towards Understanding and Reducing Graph Structural Noise for GNNs

Mingze Dong, Yuval Kluger

On Data Manifolds Entailed by Structural Causal Models

Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Georgios Arvanitidis, Bernhard Schölkopf

Polynomial Preconditioning for Gradient Methods

Nikita Doikov, Anton Rodomanov

Second-Order Optimization with Lazy Hessians

Nikita Doikov, El Mahdi Chayti, Martin Jaggi

PixelAsParam: A Gradient View on Diffusion Sampling with Guidance

Anh-Dung Dinh, Daochang Liu, Chang Xu

Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning

Ziluo Ding, Wanpeng Zhang, Junpeng Yue, Xiangjun Wang, Tiejun Huang

Open-Vocabulary Universal Image Segmentation with MaskCLIP

Zheng Ding, Jieke Wang, Zhuowen Tu

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

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

Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models

Wenhao Ding, Tong Che, Ding Zhao, Marco Pavone

Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models

Nikolaos Dimitriadis, Pascal Frossard, Francois Fleuret

Subset-Based Instance Optimality in Private Estimation

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

Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space

Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi, Adil SALIM

Improving Graph Generation by Restricting Graph Bandwidth

Nathaniel Lee Diamant, Alex M Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia

Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals

Ilias Diakonikolas, Daniel Kane, Lisheng Ren

Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA

Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas

On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology

Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lió

Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path

Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu

A Large-Scale Study of Probabilistic Calibration in Neural Network Regression

Victor Dheur, Souhaib Ben Taieb

Efficient Parametric Approximations of Neural Network Function Space Distance

Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Baker Grosse

Fairness in Matching under Uncertainty

Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova

The case for 4-bit precision: k-bit Inference Scaling Laws

Tim Dettmers, Luke Zettlemoyer

Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing

Aditya Desai, Keren Zhou, Anshumali Shrivastava

Hyperbolic Image-text Representations

Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Shanmukha Ramakrishna Vedantam

Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement

Ailin Deng, Miao Xiong, Bryan Hooi

Confidence and Dispersity Speak: Characterizing Prediction Matrix for Unsupervised Accuracy Estimation

Weijian Deng, Yumin Suh, Stephen Gould, Liang Zheng

Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks

Shikuang Deng, Hao Lin, Yuhang Li, Shi Gu

Multi-channel Autobidding with Budget and ROI Constraints

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

Uncertainty Estimation by Fisher Information-based Evidential Deep Learning

Danruo DENG, Guangyong Chen, Yang Yu, Furui Liu, Pheng-Ann Heng

Optimizing NOTEARS Objectives via Topological Swaps

Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar

Blossom: an Anytime Algorithm for Computing Optimal Decision Trees

Emir Demirović, Emmanuel Hebrard, Louis Jean

Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration

Blaise Delattre, Quentin Barthélemy, Alexandre Araujo, Alexandre Allauzen

Scaling Vision Transformers to 22 Billion Parameters

Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek

Learning-Rate-Free Learning by D-Adaptation

Aaron Defazio, Konstantin Mishchenko

Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation

Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla

High Fidelity Image Counterfactuals with Probabilistic Causal Models

Fabio De Sousa Ribeiro, Tian Xia, Miguel Monteiro, Nick Pawlowski, Ben Glocker

The Value of Out-of-Distribution Data

Ashwin De Silva, Rahul Ramesh, Carey Priebe, Pratik Chaudhari, Joshua T Vogelstein

Continuous Spatiotemporal Transformer

Antonio Henrique de Oliveira Fonseca, Emanuele Zappala, Josue Ortega Caro, David van Dijk

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

On the Robustness of Randomized Ensembles to Adversarial Perturbations

Hassan Dbouk, Naresh Shanbhag

Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling

Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne

The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data

Thomas Davies, Zhengchao Wan, Ruben J Sanchez-Garcia

Predictive Flows for Faster Ford-Fulkerson

Sami Davies, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang

Fast Combinatorial Algorithms for Min Max Correlation Clustering

Sami Davies, Benjamin Moseley, Heather Newman

Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information

Sam Daulton, Maximilian Balandat, Eytan Bakshy

Interval Bound Interpolation for Few-shot Learning with Few Tasks

Shounak Datta, Sankha Subhra Mullick, Anish Chakrabarty, Swagatam Das

Understanding Self-Distillation in the Presence of Label Noise

Rudrajit Das, Sujay Sanghavi

Beyond Uniform Lipschitz Condition in Differentially Private Optimization

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

Efficient List-Decodable Regression using Batches

Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen

Image generation with shortest path diffusion

Ayan Das, Stathi Fotiadis, Anil Batra, Farhang Nabiei, Fengting Liao

Best of Both Worlds Policy Optimization

Christoph Dann, Chen-Yu Wei, Julian Zimmert

Reinforcement Learning Can Be More Efficient with Multiple Rewards

Christoph Dann, Yishay Mansour, Mehryar Mohri

Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data

Hien Dang, Tho Tran Huu, Stanley Osher, Hung The Tran, nhật Hồ

Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation

Ronghao Dang, Lu Chen, Liuyi Wang, Zongtao He, Chengju Liu

Efficient displacement convex optimization with particle gradient descent

Hadi Daneshmand, Jason D. Lee, Chi Jin

Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning

Brett Daley, Martha White, Christopher Amato, Marlos C. Machado

Moderately Distributional Exploration for Domain Generalization

Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian

MultiRobustBench: Benchmarking Robustness Against Multiple Attacks

Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen

Refined Regret for Adversarial MDPs with Linear Function Approximation

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

Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning

Yanbo Dai, Songze Li

From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning

Edwige Cyffers, Aurélien Bellet, Debabrota Basu

Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps

Marco Cuturi, Michal Klein, Pierre Ablin

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

Ashok Cutkosky, Harsh Mehta, Francesco Orabona

In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation

Alicia Curth, Mihaela van der Schaar

Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions

Alicia Curth, Alihan Hüyük, Mihaela van der Schaar

Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation

Yiming Cui, Linjie Yang, Haichao Yu

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

Justin CUI, Ruochen Wang, Si Si, Cho-jui Hsieh

IRNeXt: Rethinking Convolutional Network Design for Image Restoration

Yuning Cui, Wenqi Ren, Sining Yang, Xiaochun Cao, Alois Knoll

A General Representation Learning Framework with Generalization Performance Guarantees

Junbiao Cui, Jianqing Liang, Qin Yue, Jiye Liang

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Hugo Cui, Florent Krzakala, Lenka Zdeborová

Rethinking Weak Supervision in Helping Contrastive Learning

Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang

Learning to Maximize Mutual Information for Dynamic Feature Selection

Ian Connick Covert, Wei Qiu, MingYu Lu, Na Yoon Kim, Nathan J White

A Mathematical Model for Curriculum Learning for Parities

Elisabetta Cornacchia, Elchanan Mossel

Task-specific experimental design for treatment effect estimation

Bethany Connolly, Kim Moore, Tobias Schwedes, Alexander Adam, Gary Willis

Inferring Relational Potentials in Interacting Systems

Armand Comas, Yilun Du, Christian Fernandez Lopez, Sandesh Ghimire, Mario Sznaier

K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs

Andrea Coletta, Svitlana Vyetrenko, Tucker Balch

Spatial Implicit Neural Representations for Global-Scale Species Mapping

Elijah Cole, Grant Van Horn, Christian Lange, Alexander Shepard, Patrick Leary

Few-Sample Feature Selection via Feature Manifold Learning

David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon

Sequential Strategic Screening

Lee Cohen, Saeed Sharifi -Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani

Distribution Free Prediction Sets for Node Classification

Jase Clarkson

A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations

Bilal Chughtai, Lawrence Chan, Neel Nanda

InfoOT: Information Maximizing Optimal Transport

Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis

Multiply Robust Off-policy Evaluation and Learning under Truncation by Death

Jianing Chu, Shu Yang, Wenbin Lu

Shape-Guided Dual-Memory Learning for 3D Anomaly Detection

Yu-Min Chu, Chieh Liu, Ting-I Hsieh, Hwann-Tzong Chen, Tyng-luh Liu

Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks

Xu Chu, Yujie Jin, Xin Wang, Shanghang Zhang, Yasha Wang

Unifying Molecular and Textual Representations via Multi-task Language Modelling

Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino

What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective

Rhea Chowers, Yair Weiss

Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks

Mohammed Nowaz Rabbani Chowdhury, SHUAI ZHANG, Meng Wang, Sijia Liu, Pin-Yu Chen

Forget Unlearning: Towards True Data-Deletion in Machine Learning

Rishav Chourasia, Neil Shah

ContraBAR: Contrastive Bayes-Adaptive Deep RL

Era Choshen, Aviv Tamar

Efficient Graph Field Integrators Meet Point Clouds

Krzysztof Marcin Choromanski, Arijit Sehanobish, Han Lin, Yunfan Zhao, Eli Berger

Taming graph kernels with random features

Krzysztof Marcin Choromanski

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning

Christopher A. Choquette-Choo, Hugh Brendan McMahan, J Keith Rush, Abhradeep Guha Thakurta

Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions

Nicolas Chopin, Andras Fulop, Jeremy Heng, Alexandre H. Thiery

New metrics and search algorithms for weighted causal DAGs

Davin Choo, Kirankumar Shiragur

Active causal structure learning with advice

Davin Choo, Themistoklis Gouleakis, Arnab Bhattacharyya

Concept-based Explanations for Out-of-Distribution Detectors

Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha

Restoration based Generative Models

Jaemoo Choi, Yesom Park, Myungjoo Kang

Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model

Young-Geun Choi, Gi-Soo Kim, Yunseo Choi, Wooseong Cho, Myunghee Cho Paik

Is Overfitting Necessary for Implicit Video Representation?

Hee Min Choi, Hyoa Kang, Dokwan Oh