ICMLW 2023
927 papers
1-Path-Norm Regularization of Deep Neural Networks
Fabian Latorre, Antoine Bonnet, Paul Rolland, Nadav Hallak, Volkan Cevher A Best Arm Identification Approach for Stochastic Rising Bandits
Alessandro Montenegro, Marco Mussi, Francesco Trovò, Marcello Restelli, Alberto Maria Metelli A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis
Jiaxiang Liu, Tianxiang Hu, Yan Zhang, Xiaotang Gai, Yang Feng, Zuozhu Liu A Comprehensive Analysis of Adapter Efficiency
Nandini Mundra, Sumanth Doddapaneni, Raj Dabre, Anoop Kunchukuttan, Ratish Puduppully, Mitesh M Khapra A Demand-Driven Perspective on Generative Audio AI
Sangshin Oh, Minsung Kang, Hyeongi Moon, Keunwoo Choi, Ben Sangbae Chon A First Order Meta Stackelberg Method for Robust Federated Learning
Yunian Pan, Tao Li, Henger Li, Tianyi Xu, Quanyan Zhu, Zizhan Zheng A Flexible Diffusion Model
Weitao Du, He Zhang, Tao Yang, Yuanqi Du A Generative Model for Text Control in Minecraft
Shalev Lifshitz, Keiran Paster, Harris Chan, Jimmy Ba, Sheila A. McIlraith A Generative Model for Text Control in Minecraft (Abridged Version)
Shalev Lifshitz, Keiran Paster, Harris Chan, Jimmy Ba, Sheila A. McIlraith A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
Guillaume Huguet, Alexander Tong, Edward De Brouwer, Yanlei Zhang, Guy Wolf, Ian Adelstein, Smita Krishnaswamy A Machine Learning Pressure Emulator for Hydrogen Embrittlement
Minh Chau, João Lucas de Sousa Almeida, Elie Alhajjar, Alberto Costa Nogueira Jr A Neural RDE Approach for Continuous-Time Non-Markovian Stochastic Control Problems
Melker Höglund, Emilio Ferrucci, Camilo Hernández, Aitor Muguruza Gonzalez, Cristopher Salvi, Leandro Sánchez-Betancourt, Yufei Zhang A Pipeline for Interpretable Clinical Subtyping with Deep Metric Learning
Haoran Zhang, Qixuan Jin, Thomas Hartvigsen, Miriam Udler, Marzyeh Ghassemi A Ranking Game for Imitation Learning
Harshit Sikchi, Akanksha Saran, Wonjoon Goo, Scott Niekum A Short Review of Automatic Differentiation Pitfalls in Scientific Computing
Jan Hueckelheim, Harshitha Menon, William S. Moses, Bruce Christianson, Paul Hovland, Laurent Hascoet A Simple and yet Fairly Effective Defense for Graph Neural Networks
Sofiane Ennadir, Yassine Abbahaddou, Michalis Vazirgiannis, Henrik Boström A Survey on Knowledge Graphs for Healthcare: Resources, Application Progress, and Promise
Hejie Cui, Jiaying Lu, Shiyu Wang, Ran Xu, Wenjing Ma, Shaojun Yu, Yue Yu, Xuan Kan, Tianfan Fu, Chen Ling, Joyce Ho, Fei Wang, Carl Yang Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Filip Ekström Kelvinius, Dimitar Georgiev, Artur Toshev, Johannes Gasteiger Adapting Blackbox Generative Models via Inversion
Sinjini Mitra, Rakshith Subramanyam, Rushil Anirudh, Jayaraman J. Thiagarajan, Ankita Shukla, Pavan K. Turaga Adaptive Bias Correction for Improved Subseasonal Forecasting
Soukayna Mouatadid, Paulo Orenstein, Genevieve Elaine Flaspohler, Judah Cohen, Miruna Oprescu, Ernest Fraenkel, Lester Mackey Adversarial Training Should Be Cast as a Non-Zero-Sum Game
Alexander Robey, Fabian Latorre, George J. Pappas, Hamed Hassani, Volkan Cevher Adverse Event Prediction Using a Task-Specific Generative Model
Otto Lönnroth, Siddharth Ramchandran, Pekka Tiikkainen, Mine Öğretir, Jussi Leinonen, Harri Lähdesmäki Algorithms for Optimal Adaptation ofDiffusion Models to Reward Functions
Krishnamurthy Dj Dvijotham, Shayegan Omidshafiei, Kimin Lee, Katherine M. Collins, Deepak Ramachandran, Adrian Weller, Mohammad Ghavamzadeh, Milad Nasr, Ying Fan, Jeremiah Zhe Liu Aligned Diffusion Schrödinger Bridges
Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, Maria Rodriguez Martinez, Andreas Krause, Charlotte Bunne An Empirical Analysis Towards Replacing Vocabulary-Rigid Embeddings by a Vocabulary-Free Mechanism
Alejandro Rodriguez Perez, Korn Sooksatra, Pablo Rivas, Ernesto Quevedo Caballero, Javier S. Turek, Gisela Bichler, Tomas Cerny, Laurie Giddens, Stacie Petter An Exact Kernel Equivalence for Finite Classification Models
Brian Wesley Bell, Michael Geyer, David Glickenstein, Amanda S Fernandez, Juston Moore Analyzing the Sample Complexity of Model-Free Opponent Shaping
Kitty Fung, Qizhen Zhang, Chris Lu, Timon Willi, Jakob Nicolaus Foerster Annealed Biological Sequence Optimization
Yuxuan Song, Botian Wang, Hao Zhou, Wei-Ying Ma Answering Causal Questions with Augmented LLMs
Nick Pawlowski, James Vaughan, Joel Jennings, Cheng Zhang Are Visual Recognition Models Robust to Image Compression?
João Maria Janeiro, Stanislav Frolov, Alaaeldin El-Nouby, Jakob Verbeek Asynchronous Algorithmic Alignment with Cocycles
Andrew Joseph Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Veličković Audio-Journey: Efficient Visual+LLM-Aided Audio Encodec Diffusion
Juncheng B Li, Jackson Sam Michaels, Laura Yao, Lijun Yu, Zach Wood-Doughty, Florian Metze Auditing for Human Expertise
Rohan Alur, Loren Laine, Darrick Li, Manish Raghavan, Devavrat Shah, Dennis Shung Augmenting Bayesian Optimization with Preference-Based Expert Feedback
Daolang Huang, Louis Filstroff, Petrus Mikkola, Runkai Zheng, Milica Todorovic, Samuel Kaski Auto-Aligning Multiagent Incentives with Global Objectives
Minae Kwon, John P Agapiou, Edgar A. Duéñez-Guzmán, Romuald Elie, Georgios Piliouras, Kalesha Bullard, Ian Gemp Backdoor Attacks for In-Context Learning with Language Models
Nikhil Kandpal, Matthew Jagielski, Florian Tramèr, Nicholas Carlini BatchGFN: Generative Flow Networks for Batch Active Learning
Shreshth A Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery
Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong Benchmarking Adversarial Robustness of Compressed Deep Learning Models
Brijesh Vora, Kartik Patwari, Syed Mahbub Hafiz, Zubair Shafiq, Chen-Nee Chuah Benchmarking the Reliability of Post-Training Quantization: A Particular Focus on Worst-Case Performance
Zhihang Yuan, Jiawei Liu, Jiaxiang Wu, Dawei Yang, Qiang Wu, Guangyu Sun, Wenyu Liu, Xinggang Wang, Bingzhe Wu Beyond Intuition, a Framework for Applying GPs to Real-World Data
Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex Gardner, S. T. John, Hong Ge, Richard E Turner Black Box Adversarial Prompting for Foundation Models
Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner Borda Regret Minimization for Generalized Linear Dueling Bandits
Yue Wu, Tao Jin, Qiwei Di, Hao Lou, Farzad Farnoud, Quanquan Gu Bridging Equational Properties and Patterns on Graphs: An AI-Based Approach
Oguzhan Keskin, Alisia Maria Lupidi, Stefano Fioravanti, Lucie Charlotte Magister, Pietro Barbiero, Pietro Lio, Francesco Giannini Building Community Driven Libraries of Natural Programs
Leonardo Hernandez Cano, Yewen Pu, Robert D. Hawkins, Joshua B. Tenenbaum, Armando Solar-Lezama Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine Calibrating Language Models via Augmented Prompt Ensembles
Mingjian Jiang, Yangjun Ruan, Sicong Huang, Saifei Liao, Silviu Pitis, Roger Baker Grosse, Jimmy Ba Can Euclidean Symmetry Help in Reinforcement Learning and Planning
Linfeng Zhao, Owen Lewis Howell, Jung Yeon Park, Xupeng Zhu, Robin Walters, Lawson L.S. Wong Can Public Large Language Models Help Private Cross-Device Federated Learning?
Boxin Wang, Yibo Jacky Zhang, Yuan Cao, Bo Li, Hugh Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer Can Public Large Language Models Help Private Cross-Device Federated Learning?
Boxin Wang, Yibo Jacky Zhang, Yuan Cao, Bo Li, Hugh Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer Can Strong Structural Encoding Reduce the Importance of Message Passing?
Floor Eijkelboom, Erik J Bekkers, Michael M. Bronstein, Francesco Di Giovanni Categorical SDEs with Simplex Diffusion
Pierre Harvey Richemond, Sander Dieleman, Arnaud Doucet Causal Discovery with Language Models as Imperfect Experts
Stephanie Long, Alexandre Piché, Valentina Zantedeschi, Tibor Schuster, Alexandre Drouin Caveats of Neural Persistence in Deep Neural Networks
Leander Girrbach, Anders Christensen, Ole Winther, Zeynep Akata, A. Sophia Koepke Certifying Ensembles: A General Certification Theory with S-Lipschitzness
Aleksandar Petrov, Francisco Eiras, Amartya Sanyal, Philip Torr, Adel Bibi Characterizing the Optimal $0-1$ Loss for Multi-Class Classification with a Test-Time Attacker
Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji, Daniel Cullina, Ben Y. Zhao, Haitao Zheng, Prateek Mittal ChatGPT-Powered Conversational Drug Editing Using Retrieval and Domain Feedback
Shengchao Liu, Jiongxiao Wang, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo, Chaowei Xiao Classifier Robustness Enhancement via Test-Time Transformation
Tsachi Blau, Roy Ganz, Chaim Baskin, Michael Elad, Alex M. Bronstein ClimaX: A Foundation Model for Weather and Climate
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, Aditya Grover CM-GAN: Stabilizing GAN Training with Consistency Models
Haoye Lu, Yiwei Lu, Dihong Jiang, Spencer Ryan Szabados, Sun Sun, Yaoliang Yu Co-Dream: Collaborative Data Synthesis with Decentralized Models
Abhishek Singh, Gauri Gupta, Charles Lu, Yogesh Koirala, Sheshank Shankar, Mohammed Ehab, Ramesh Raskar Collaborative Score Distillation for Consistent Visual Synthesis
Subin Kim, Kyungmin Lee, June Suk Choi, Jongheon Jeong, Kihyuk Sohn, Jinwoo Shin Competing Bandits in Non-Stationary Matching Markets
Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran, Tara Javidi, Arya Mazumdar Complementing a Policy with a Different Observation Space
Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu Compositional Interfaces for Compositional Generalization
Jelena Luketina, Jack Lanchantin, Sainbayar Sukhbaatar, Arthur Szlam Concept Algebra for Score-Based Conditional Model
Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch Concept Bottleneck Generative Models
Aya Abdelsalam Ismail, Julius Adebayo, Hector Corrada Bravo, Stephen Ra, Kyunghyun Cho Concept-Aware Clustering for Decentralized Deep Learning Under Temporal Shift
Edvin Listo Zec, Emilie Klefbom, Marcus Toftås, Martin Johan Willbo, Olof Mogren Conditional Diffusion Replay for Continual Learning in Medical Settings
Yewon Byun, Saurabh Garg, Sanket Vaibhav Mehta, Praveer Singh, Jayashree Kalpathy-cramer, Bryan Wilder, Zachary Chase Lipton Consistent Explanations in the Face of Model Indeterminacy via Ensembling
Dan Ley, Leonard Tang, Matthew Nazari, Hongjin Lin, Suraj Srinivas, Himabindu Lakkaraju Constant Memory Attention Block
Leo Feng, Frederick Tung, Hossein Hajimirsadeghi, Yoshua Bengio, Mohamed Osama Ahmed Continual Pre-Training of Large Language Models: How to Re-Warm Your Model?
Kshitij Gupta, Benjamin Thérien, Adam Ibrahim, Mats Leon Richter, Quentin Gregory Anthony, Eugene Belilovsky, Irina Rish, Timothée Lesort Continuous Vector Quantile Regression
Sanketh Vedula, Irene Tallini, Aviv A. Rosenberg, Marco Pegoraro, Emanuele Rodolà, Yaniv Romano, Alexander Bronstein Coupled Gradient Flows for Strategic Non-Local Distribution Shift
Lauren Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Julien Niklas Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes Otterbach, Martin Genzel Data Models for Dataset Drift Controls in Machine Learning with Optical Images
Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jerome Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti Deceptive Alignment Monitoring
Andres Carranza, Dhruv Bhandarkar Pai, Rylan Schaeffer, Arnuv Tandon, Sanmi Koyejo Deep Equilibrium Based Neural Operators for Steady-State PDEs
Tanya Marwah, Ashwini Pokle, J Zico Kolter, Zachary Chase Lipton, Jianfeng Lu, Andrej Risteski Deep Learning Approach for Cardiac Electrophysiology Model Correction
Victoriya Kashtanova, Mihaela Pop, Patrick Gallinari, Maxime Sermesant Deep Networks as Paths on the Manifold of Neural Representations
Richard D Lange, Devin Kwok, Jordan Kyle Matelsky, Xinyue Wang, David Rolnick, Konrad Kording Designing Discontinuities
Ibtihal Ferwana, Suyong Park, Ting-Yi Wu, Lav R. Varshney Diagnostically Lossless Compression of Medical Images
Rogier Van der Sluijs, Maya Varma, Jip Prince, Curtis Langlotz, Akshay S Chaudhari Differentiable Causal Discovery with Smooth Acyclic Orientations
Riccardo Massidda, Francesco Landolfi, Martina Cinquini, Davide Bacciu Differentiable Clustering and Partial Fenchel-Young Losses
Lawrence Stewart, Francis Bach, Felipe Llinares-López, Quentin Berthet Differentiable MaxSAT Message Passing
Francesco Alesiani, Cristóbal Corvalán Morbiducci, Markus Zopf Differentiable Search of Evolutionary Trees
Ramith Hettiarachchi, Avi Z Swartz, Sergey Ovchinnikov Differentiable Search of Evolutionary Trees from Leaves
Ramith Hettiarachchi, Avi Z Swartz, Sergey Ovchinnikov Differentiable Set Partitioning
Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E Vogt Differentiable Sorting for Censored Time-to-Event Data
Andre Vauvelle, Benjamin Wild, Roland Eils, Spiros Denaxas Differentiable Tree Operations Promote Compositional Generalization
Paul Soulos, Edward J Hu, Kate McCurdy, Yunmo Chen, Roland Fernandez, Paul Smolensky, Jianfeng Gao Differentially Private Clustering in Data Streams
Alessandro Epasto, Tamalika Mukherjee, Peilin Zhong Differentially Private Generation of High Fidelity Samples from Diffusion Models
Vikash Sehwag, Ashwinee Panda, Ashwini Pokle, Xinyu Tang, Saeed Mahloujifar, Mung Chiang, J Zico Kolter, Prateek Mittal Differentially Private Heavy Hitters Using Federated Analytics
Karan Chadha, Junye Chen, John Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar Differentiating Metropolis-Hastings to Optimize Intractable Densities
Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash Mansinghka, Jonathan Ragan-Kelley, Christopher Vincent Rackauckas, Moritz Schauer Diffusion Based Causal Representation Learning
Amir Mohammad Karimi Mamaghan, Andrea Dittadi, Stefan Bauer, Francesco Quinzan Diffusion Generative Inverse Design
Marin Vlastelica, Tatiana Lopez-Guevara, Kelsey R Allen, Peter Battaglia, Arnaud Doucet, Kim Stachenfeld Diffusion Model-Augmented Behavioral Cloning
Hsiang-Chun Wang, Shang-Fu Chen, Ming-Hao Hsu, Chun-Mao Lai, Shao-Hua Sun Diffusion on the Probability Simplex
Griffin Floto, Thorsteinn Jonsson, Mihai Nica, Scott Sanner, Eric Zhengyu Zhu Dimensionality Reduction as Probabilistic Inference
Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D Lawrence DIP-RL: Demonstration-Inferred Preference Learning in Minecraft
Ellen Novoseller, Vinicius G. Goecks, David Watkins, Josh Miller, Nicholas R Waytowich Direct Preference Optimization: Your Language Model Is Secretly a Reward Model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, Chelsea Finn Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning
Lars Lien Ankile, Brian Ham, Kevin Mao, Eura Shin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan Discovering Variable Binding Circuitry with Desiderata
Xander Davies, Max Nadeau, Nikhil Prakash, Tamar Rott Shaham, David Bau DISCS: A Benchmark for Discrete Sampling
Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Sussman Grathwohl, Dale Schuurmans, Hanjun Dai Dissecting Efficient Architectures for Wake-Word Detection
Cody Berger, Juncheng B Li, Yiyuan Li, Aaron Berger, Dmitri Berger, Karthik Ganesan, Emma Strubell, Florian Metze Distributions for Compositionally Differentiating Parametric Discontinuities
Jesse Michel, Kevin Mu, Xuanda Yang, Sai Praveen Bangaru, Elias Rojas Collins, Gilbert Bernstein, Jonathan Ragan-Kelley, Michael Carbin, Tzu-Mao Li Do LLMs Selectively Encode the Goal of an Agent's Reach?
Laura Ruis, Arduin Findeis, Herbie Bradley, Hossein A. Rahmani, Kyoung Whan Choe, Edward Grefenstette, Tim Rocktäschel Do Users Write More Insecure Code with AI Assistants?
Neil Perry, Megha Srivastava, Deepak Kumar, Dan Boneh Don't Trust Your Eyes: On the (un)reliability of Feature Visualizations
Robert Geirhos, Roland S. Zimmermann, Blair Bilodeau, Wieland Brendel, Been Kim Early Exiting for Accelerated Inference in Diffusion Models
Taehong Moon, Moonseok Choi, EungGu Yun, Jongmin Yoon, Gayoung Lee, Juho Lee Efficient Location Sampling Algorithms for Road Networks
Sara Ahmadian, Kostas Kollias, Ameya Velingker, Sreenivas Gollapudi, Vivek Kumar, Santhoshini Velusamy Emergent Deception and Skepticism via Theory of Mind
Lion Schulz, Nitay Alon, Jeffrey Rosenschein, Peter Dayan End-to-End Differentiable Clustering with Associative Memories
Bishwajit Saha, Dmitry Krotov, Mohammed J Zaki, Parikshit Ram Energy-Based Learning Algorithms: A Comparative Study
Benjamin Scellier, Maxence Ernoult, Jack Kendall, Suhas Kumar Entropy Coding of Unordered Data Structures
Julius Kunze, Daniel Severo, Giulio Zani, Jan-Willem van de Meent, James Townsend Equal Long-Term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making
Yuancheng Xu, Chenghao Deng, Yanchao Sun, Ruijie Zheng, Xiyao Wang, Jieyu Zhao, Furong Huang Equivalence Class Learning for GENERIC Systems
Baige Xu, Yuhan Chen, Takashi Matsubara, Takaharu Yaguchi Evading Black-Box Classifiers Without Breaking Eggs
Edoardo Debenedetti, Nicholas Carlini, Florian Tramèr Evaluating the Diversity and Utility of Materials Proposed by Generative Models
Alexander New, Michael Pekala, Elizabeth A Pogue, Nam Q Le, Janna Domenico, Christine D. Piatko, Christopher D Stiles Evaluation Metrics for Protein Structure Generation
Joshua Southern, Arne Schneuing, Michael M. Bronstein, Bruno Correia Exact Optimality in Communication-Privacy-Utility Tradeoffs
Berivan Isik, Wei-Ning Chen, Ayfer Ozgur, Tsachy Weissman, Albert No Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, Tatsunori Hashimoto Exploring Exchangeable Dataset Amortization for Bayesian Posterior Inference
Sarthak Mittal, Niels Leif Bracher, Guillaume Lajoie, Priyank Jaini, Marcus A Brubaker Exposing Attention Glitches with Flip-Flop Language Modeling
Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
Francesco Campi, Lukas Gosch, Tom Wollschläger, Yan Scholten, Stephan Günnemann Extracting Reward Functions from Diffusion Models
Felipe Pinto Coelho Nuti, Tim Franzmeyer, Joao F. Henriques FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation
Dhruv Bhandarkar Pai, Andres Carranza, Rylan Schaeffer, Arnuv Tandon, Sanmi Koyejo FAM: Relative Flatness Aware Minimization
Linara Adilova, Amr Abourayya, Jianning Li, Amin Dada, Henning Petzka, Jan Egger, Jens Kleesiek, Michael Kamp Fast and Functional Structured Data Generator
Alessandra Carbone, Aurélien Decelle, Lorenzo Rosset, Beatriz Seoane Fast Causal Attention with Dynamic Sparsity
Daniele Paliotta, Matteo Pagliardini, Martin Jaggi, François Fleuret Federated Conformal Predictors for Distributed Uncertainty Quantification
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael Jordan, Ramesh Raskar Federated Ensemble-Directed Offline Reinforcement Learning
Desik Rengarajan, Nitin Ragothaman, Dileep Kalathil, Srinivas Shakkottai Federated Experiment Design Under Distributed Differential Privacy
Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, Ayfer Ozgur Federated Heavy Hitter Recovery Under Linear Sketching
Adria Gascon, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh Federated Learning with Regularized Client Participation
Grigory Malinovsky, Samuel Horváth, Konstantin Pavlovich Burlachenko, Peter Richtárik Federated Optimization Algorithms with Random Reshuffling and Gradient Compression
Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Pavlovich Burlachenko, Peter Richtárik Federated, Fast, and Private Visualization of Decentralized Data
Debbrata Kumar Saha, Vince Calhoun, Soo Min Kwon, Anand Sarwate, Rekha Saha, Sergey Plis FedFwd: Federated Learning Without Backpropagation
Seonghwan Park, Dahun Shin, Jinseok Chung, Namhoon Lee Few-Shot Anomaly Detection via Personalization
Sangkyung Kwak, Jongheon Jeong, Hankook Lee, Woohyuck Kim, Jinwoo Shin Fine-Tuning Language Models with Just Forward Passes
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora Fine-Tuning Language Models with Just Forward Passes
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Jason D. Lee, Danqi Chen, Sanjeev Arora Flow Matching for Scalable Simulation-Based Inference
Jonas Bernhard Wildberger, Maximilian Dax, Simon Buchholz, Stephen R Green, Jakob H. Macke, Bernhard Schölkopf Game Theoretic Neural ODE Optimizer
Panagiotis Theodoropoulos, Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Xiangyu Liu, Tuomas Sandholm, Furong Huang, Stephen Marcus McAleer Generative Marginalization Models
Sulin Liu, Peter Ramadge, Ryan P Adams Generative Semi-Supervised Learning with a Neural Seq2seq Noisy Channel
Soroosh Mariooryad, Matt Shannon, Siyuan Ma, Tom Bagby, David Teh-Hwa Kao, Daisy Stanton, Eric Battenberg, Rj Skerry-Ryan Geometric Algebra Transformers
Johann Brehmer, Pim De Haan, Sönke Behrends, Taco Cohen Geometrically Regularized Wasserstein Dictionary Learning
Marshall Mueller, Shuchin Aeron, James M. Murphy, Abiy Tasissa GFlowNets for Causal Discovery: An Overview
Dragos Cristian Manta, Edward J Hu, Yoshua Bengio GFlowNets for Causal Discovery: An Overview
Dragos Cristian Manta, Edward J Hu, Yoshua Bengio Global Optimality in Bivariate Gradient-Based DAG Learning
Chang Deng, Kevin Bello, Pradeep Kumar Ravikumar, Bryon Aragam GPT-Zip: Deep Compression of Finetuned Large Language Models
Berivan Isik, Hermann Kumbong, Wanyi Ning, Xiaozhe Yao, Sanmi Koyejo, Ce Zhang Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information
Seongsik Park, Jeonghee Jo, Jongkil Park, Yeonjoo Jeong, Jaewook Kim, Suyoun Lee, Joon young Kwak, Inho Kim, Jong-keuk Park, Kyeong seok Lee, Hwang gyu Weon, Hyun Jae Jang Graph Neural Network Powered Bayesian Optimization for Large Molecular Spaces
Miles Wang-Henderson, Bartu Soyuer, Parnian Kassraie, Andreas Krause, Ilija Bogunovic GraphChef: Learning the Recipe of Your Dataset
Peter Müller, Lukas Faber, Karolis Martinkus, Roger Wattenhofer Green Federated Learning
Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael Rabbat, Carole-Jean Wu, Ilya Mironov Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces
Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ismael Morales, Ingmar Posner, Xiaowen Dong Group Invariant Global Pooling
Kamil Bujel, Yonatan Gideoni, Chaitanya K. Joshi, Pietro Lio Guide Your Agent with Adaptive Multimodal Rewards
Changyeon Kim, Younggyo Seo, Hao Liu, Lisa Lee, Jinwoo Shin, Honglak Lee, Kimin Lee Guided Evolution with Binary Predictors for ML Program Search
John D Co-Reyes, Yingjie Miao, George Tucker, Aleksandra Faust, Esteban Real Guiding the Last Layer in Federated Learning with Pre-Trained Models
Gwen Legate, Nicolas Bernier, Lucas Caccia, Edouard Oyallon, Eugene Belilovsky H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Re, Clark Barrett, Zhangyang Wang, Beidi Chen Hierarchical Decomposition Framework for Feasibility-Hard Combinatorial Optimization
Hanbum Ko, Minu Kim, Han-Seul Jeong, Sunghoon Hong, Deunsol Yoon, Youngjoon Park, Woohyung Lim, Honglak Lee, Moontae Lee, Kanghoon Lee, Sungbin Lim, Sungryull Sohn HINT: Hierarchical Coherent Networks for Constrained Probabilistic Forecasting
Kin G. Olivares, David Luo, Cristian Ignacio Challu, Stefania La Vattiata, Max Mergenthaler Canseco, Artur Dubrawski How Can Neuroscience Help Us Build More Robust Deep Neural Networks?
Sayanton V. Dibbo, Siddharth Mansingh, Jocelyn Rego, Garrett T. Kenyon, Juston Moore, Michael Teti How to Query Human Feedback Efficiently in RL?
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee How to Query Human Feedback Efficiently in RL?
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee How to Select Physics-Informed Neural Networks in the Absence of Ground Truth: A Pareto Front-Based Strategy
Zhao Wei, Jian Cheng Wong, Nicholas Wei Yong Sung, Abhishek Gupta, Chin Chun Ooi, Pao-Hsiung Chiu, My Ha Dao, Yew-Soon Ong Identifying Implicit Social Biases in Vision-Language Models
Kimia Hamidieh, Haoran Zhang, Thomas Hartvigsen, Marzyeh Ghassemi Identifying Inequity in Treatment Allocation
Yewon Byun, Dylan Sam, Zachary Chase Lipton, Bryan Wilder Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers
Tim Franzmeyer, Stephen Marcus McAleer, Joao F. Henriques, Jakob Nicolaus Foerster, Philip Torr, Adel Bibi, Christian Schroeder de Witt Imitation Learning with Human Eye Gaze via Multi-Objective Prediction
Ravi Kumar Thakur, Md Sunbeam, Vinicius G. Goecks, Ellen Novoseller, Ritwik Bera, Vernon Lawhern, Greg Gremillion, John Valasek, Nicholas R Waytowich Implementing Block-Sparse Matrix Multiplication Kernels Using Triton
Priya Mishra, Trevor Gale, Matei Zaharia, Cliff Young, Deepak Narayanan Improved Sampling via Learned Diffusions
Lorenz Richter, Julius Berner, Guan-Horng Liu Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport
Alexander Tong, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Kilian Fatras, Guy Wolf, Yoshua Bengio In-Context Decision-Making from Supervised Pretraining
Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill Incentivizing Honesty Among Competitors in Collaborative Learning
Florian E. Dorner, Nikola Konstantinov, Georgi Stoyanov Pashaliev, Martin Vechev Incremental Low-Rank Learning
Jiawei Zhao, Yifei Zhang, Beidi Chen, Florian Tobias Schaefer, Anima Anandkumar Inferring the Future by Imagining the past
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