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ICML 2024

2,610 papers

SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding

Chanho Park, Namyoon Lee

Foundation Policies with Hilbert Representations

Seohong Park, Tobias Kreiman, Sergey Levine

Mean-field Chaos Diffusion Models

Sungwoo Park, Dongjun Kim, Ahmed Alaa

Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs

Yeonhong Park, Jake Hyun, Sanglyul Cho, Bonggeun Sim, Jae W. Lee

Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs

MoonJeong Park, Jaeseung Heo, Dongwoo Kim

The Linear Representation Hypothesis and the Geometry of Large Language Models

Kiho Park, Yo Joong Choe, Victor Veitch

The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm

Giseung Park, Woohyeon Byeon, Seongmin Kim, Elad Havakuk, Amir Leshem

Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture

Sangjun Park, JinYeong Bak

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel

Position: Topological Deep Learning is the New Frontier for Relational Learning

Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry

Arrows of Time for Large Language Models

Vassilis Papadopoulos, Jérémie Wenger, Clement Hongler

Position: A Call for Embodied AI

Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl

Trainable Transformer in Transformer

Abhishek Panigrahi, Sadhika Malladi, Mengzhou Xia, Sanjeev Arora

Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation

Xianghe Pang, Shuo Tang, Rui Ye, Yuxin Xiong, Bolun Zhang

BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback

Gaurav Pandey, Yatin Nandwani, Tahira Naseem, Mayank Mishra, Guangxuan Xu

A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization

Ashwinee Panda, Xinyu Tang, Saeed Mahloujifar, Vikash Sehwag, Prateek Mittal

Model-Free Robust $φ$-Divergence Reinforcement Learning Using Both Offline and Online Data

Kishan Panaganti, Adam Wierman, Eric Mazumdar

Auto-Encoding Morph-Tokens for Multimodal LLM

Kaihang Pan, Siliang Tang, Juncheng Li, Zhaoyu Fan, Wei Chow

Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation

Michelle Pan, Mariah L Schrum, Vivek Myers, Erdem Biyik, Anca Dragan

RMIB: Representation Matching Information Bottleneck for Matching Text Representations

Haihui Pan, Zhifang Liao, Wenrui Xie, Kun Han

Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to K-Level Stochastic Optimizations

Xiaokang Pan, Xingyu Li, Jin Liu, Tao Sun, Kai Sun

Feedback Loops With Language Models Drive In-Context Reward Hacking

Alexander Pan, Erik Jones, Meena Jagadeesan, Jacob Steinhardt

S^2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting

Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka

MALIBO: Meta-learning for Likelihood-free Bayesian Optimization

Jiarong Pan, Stefan Falkner, Felix Berkenkamp, Joaquin Vanschoren

Counterfactual Image Editing

Yushu Pan, Elias Bareinboim

Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection

Nils Palumbo, Yang Guo, Xi Wu, Jiefeng Chen, Yingyu Liang

Bayesian Program Learning by Decompiling Amortized Knowledge

Alessandro B. Palmarini, Christopher G. Lucas, Siddharth N

Implicit Representations via Operator Learning

Sourav Pal, Harshavardhan Adepu, Clinton Wang, Polina Golland, Vikas Singh

Listenable Maps for Audio Classifiers

Francesco Paissan, Mirco Ravanelli, Cem Subakan

MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent

Kaan Ozkara, Can Karakus, Parameswaran Raman, Mingyi Hong, Shoham Sabach

Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions

Guneykan Ozgul, Xiantao Li, Mehrdad Mahdavi, Chunhao Wang

Structured Chemistry Reasoning with Large Language Models

Siru Ouyang, Zhuosheng Zhang, Bing Yan, Xuan Liu, Yejin Choi

Differentiable Mapper for Topological Optimization of Data Representation

Ziyad Oulhaj, Mathieu Carrière, Bertrand Michel

Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries

Amine Ouasfi, Adnane Boukhayma

Towards Modular LLMs by Building and Reusing a Library of LoRAs

Oleksiy Ostapenko, Zhan Su, Edoardo Ponti, Laurent Charlin, Nicolas Le Roux

Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning

Takayuki Osa, Tatsuya Harada

Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues

Antonio Orvieto, Soham De, Caglar Gulcehre, Razvan Pascanu, Samuel L Smith

Variational Linearized Laplace Approximation for Bayesian Deep Learning

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

Deep Stochastic Mechanics

Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett

Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?

Andreas Opedal, Alessandro Stolfo, Haruki Shirakami, Ying Jiao, Ryan Cotterell

Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions

Harrie Oosterhuis, Lijun Lyu, Avishek Anand

SILVER: Single-loop variance reduction and application to federated learning

Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki

Adaptive Proximal Gradient Methods Are Universal Without Approximation

Konstantinos Oikonomidis, Emanuel Laude, Puya Latafat, Andreas Themelis, Panagiotis Patrinos

Linear Explanations for Individual Neurons

Tuomas Oikarinen, Tsui-Wei Weng

The Perception-Robustness Tradeoff in Deterministic Image Restoration

Guy Ohayon, Tomer Michaeli, Michael Elad

Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network

Hyunseok Oh, Youngki Lee

On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning

Jeongheon Oh, Kibok Lee

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Samir Obando Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother

In value-based deep reinforcement learning, a pruned network is a good network

Johan Samir Obando Ceron, Aaron Courville, Pablo Samuel Castro

Sparser, Better, Deeper, Stronger: Improving Static Sparse Training with Exact Orthogonal Initialization

Aleksandra Nowak, Łukasz Gniecki, Filip Szatkowski, Jacek Tabor

f-Divergence Based Classification: Beyond the Use of Cross-Entropy

Nicola Novello, Andrea M Tonello

DITTO: Diffusion Inference-Time T-Optimization for Music Generation

Zachary Novack, Julian McAuley, Taylor Berg-Kirkpatrick, Nicholas J. Bryan

Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills

Kolby Nottingham, Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Sameer Singh, Peter Clark

RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching

Divya Nori, Wengong Jin

Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling

Ruijia Niu, Dongxia Wu, Kai Kim, Yian Ma, Duncan Watson-Parris

GFlowNet Training by Policy Gradients

Puhua Niu, Shili Wu, Mingzhou Fan, Xiaoning Qian

Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming

Xinlei Niu, Christian Walder, Jing Zhang, Charles Patrick Martin

Test-Time Model Adaptation with Only Forward Passes

Shuaicheng Niu, Chunyan Miao, Guohao Chen, Pengcheng Wu, Peilin Zhao

Risk Aware Benchmarking of Large Language Models

Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti, Kristjan Greenewald, Brian Belgodere

Understanding the Impact of Introducing Constraints at Inference Time on Generalization Error

Masaaki Nishino, Kengo Nakamura, Norihito Yasuda

Indirectly Parameterized Concrete Autoencoders

Alfred Nilsson, Klas Wijk, Sai Bharath Chandra Gutha, Erik Englesson, Alexandra Hotti

REMEDI: Corrective Transformations for Improved Neural Entropy Estimation

Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy, Pierre Nyquist

RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation

Mahdi Nikdan, Soroush Tabesh, Elvir Crnčević, Dan Alistarh

Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences

Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban, Georgios Tzannetos, Goran Radanovic

Nearest Neighbour Score Estimators for Diffusion Generative Models

Matthew Niedoba, Dylan Green, Saeid Naderiparizi, Vasileios Lioutas, Jonathan Wilder Lavington

Compositional Text-to-Image Generation with Dense Blob Representations

Weili Nie, Sifei Liu, Morteza Mardani, Chao Liu, Benjamin Eckart

Online Cascade Learning for Efficient Inference over Streams

Lunyiu Nie, Zhimin Ding, Erdong Hu, Christopher Jermaine, Swarat Chaudhuri

How Transformers Learn Causal Structure with Gradient Descent

Eshaan Nichani, Alex Damian, Jason D. Lee

Risk-Sensitive Reward-Free Reinforcement Learning with CVaR

Xinyi Ni, Guanlin Liu, Lifeng LAI

On the Nonlinearity of Layer Normalization

Yunhao Ni, Yuxin Guo, Junlong Jia, Lei Huang

NExT: Teaching Large Language Models to Reason about Code Execution

Ansong Ni, Miltiadis Allamanis, Arman Cohan, Yinlin Deng, Kensen Shi

On The Statistical Complexity of Offline Decision-Making

Thanh Nguyen-Tang, Raman Arora

Sliced Wasserstein with Random-Path Projecting Directions

Khai Nguyen, Shujian Zhang, Tam Le, nhật Hồ

Novel Spectral Algorithms for the Partial Credit Model

Duc Nguyen, Anderson Ye Zhang

Differentially private exact recovery for stochastic block models

Dung Nguyen, Anil Kumar Vullikanti

PIDformer: Transformer Meets Control Theory

Tam Minh Nguyen, Cesar A Uribe, Tan Minh Nguyen, Richard Baraniuk

Generative Conditional Distributions by Neural (Entropic) Optimal Transport

Bao Nguyen, Binh Nguyen, Hieu Trung Nguyen, Viet Anh Nguyen

Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks

Duy Minh Ho Nguyen, Nina Lukashina, Tai Nguyen, An Thai Le, TrungTin Nguyen

On Least Square Estimation in Softmax Gating Mixture of Experts

Huy Nguyen, nhật Hồ, Alessandro Rinaldo

Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems

Ta Duy Nguyen, Alina Ene

Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design

Quan Nguyen, Adji Bousso Dieng

PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling

Phong C.H. Nguyen, Xinlun Cheng, Shahab Azarfar, Pradeep Seshadri, Yen T. Nguyen

A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts

Huy Nguyen, Pedram Akbarian, TrungTin Nguyen, nhật Hồ

Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?

Huy Nguyen, Pedram Akbarian, nhật Hồ

Measuring Stochastic Data Complexity with Boltzmann Influence Functions

Nathan Hoyen Ng, Roger Baker Grosse, Marzyeh Ghassemi

Score-Based Causal Discovery of Latent Variable Causal Models

Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes

Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization

Gergely Neu, Nneka Okolo

On The Fairness Impacts of Hardware Selection in Machine Learning

Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran, Sara Hooker, Ferdinando Fioretto

A Computational Framework for Solving Wasserstein Lagrangian Flows

Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu

SelfVC: Voice Conversion With Iterative Refinement using Self Transformations

Paarth Neekhara, Shehzeen Samarah Hussain, Rafael Valle, Boris Ginsburg, Rishabh Ranjan

Stable Differentiable Causal Discovery

Achille Nazaret, Justin Hong, Elham Azizi, David Blei

Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference

Piotr Nawrot, Adrian Łańcucki, Marcin Chochowski, David Tarjan, Edoardo Ponti

Equivariant Deep Weight Space Alignment

Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik, Nadav Dym

Box Facets and Cut Facets of Lifted Multicut Polytopes

Lucas Fabian Naumann, Jannik Irmai, Shengxian Zhao, Bjoern Andres

Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning

Michal Nauman, Michał Bortkiewicz, Piotr Miłoś, Tomasz Trzcinski, Mateusz Ostaszewski

PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs

Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang

Time Weaver: A Conditional Time Series Generation Model

Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep P. Chinchali

Solving Poisson Equations using Neural Walk-on-Spheres

Hong Chul Nam, Julius Berner, Anima Anandkumar

Density Ratio Estimation with Doubly Strong Robustness

Ryosuke Nagumo, Hironori Fujisawa

PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect

Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi