NeurIPSW 2023
2139 papers
#InsTag: Instruction Tagging for Analyzing Supervised Fine-Tuning of Large Language Models
Keming Lu, Hongyi Yuan, Zheng Yuan, Runji Lin, Junyang Lin, Chuanqi Tan, Chang Zhou, Jingren Zhou $\texttt{PREMIER-TACO}$ Is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé Iii, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Basu, Furong Huang $d^3$: Detoxing Deep Learning Dataset
Lu Yan, Siyuan Cheng, Guangyu Shen, Guanhong Tao, Xuan Chen, Kaiyuan Zhang, Yunshu Mao, Xiangyu Zhang $S^2Ac$: Energy-Based Reinforcement Learning with Stein Soft Actor Critic
Safa Messaoud, Billel Mokeddem, Zhenghai Xue, Linsey Pang, Bo An, Haipeng Chen, Sanjay Chawla A Bayesian Approach to Designing Microstructures and Processing Pathways for Tailored Material Properties
Adam P. Generale, Conlain Kelly, Grayson Harrington, Andreas Euan Robertson, Michael Buzzy, Surya Kalidindi A Brief Tutorial on Sample Size Calculations for Fairness Audits
Harvineet Singh, Fan Xia, Mi-Ok Kim, Romain Pirracchio, Rumi Chunara, Jean Feng A Case Study of Instruction Tuning with Mixture of Parameter-Efficient Experts
Oleksiy Ostapenko, Lucas Caccia, Zhan Su, Nicolas Le Roux, Laurent Charlin, Alessandro Sordoni A Causal Ordering Prior for Unsupervised Representation Learning
Avinash Kori, Pedro Sanchez, Konstantinos Vilouras, Ben Glocker, Sotirios A. Tsaftaris A Computational Framework for Solving Wasserstein Lagrangian Flows
Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani A Critical Survey on Fairness Benefits of XAI
Luca Deck, Jakob Schoeffer, Maria De-Arteaga, Niklas Kuehl A Data-Driven Measure of Relative Uncertainty for Misclassification Detection
Eduardo Dadalto Câmara Gomes, Marco Romanelli, Georg Pichler, Pablo Piantanida A Framework for Conditional Diffusion Modelling with Applications in Motif Scaffolding for Protein Design
Kieran Didi, Francisco Vargas, Simon Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio A Generative Flow Model for Conditional Sampling via Optimal Transport
Jason Alfonso, Ricardo Baptista, Anupam Bhakta, Noam Gal, Alfin Hou, Vasilisa Lyubimova, Daniel Pocklington, Josef Sajonz, Giulio Trigila, Ryan Tsai A Natural Experiment on LLM Data Contamination in Code Generation
Manley Roberts, Himanshu Thakur, Christine Herlihy, Colin White, Samuel Dooley A New Framework for Measuring Re-Identification Risk
Cj Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Munoz Medina, Vahab Mirrokni, Gabriel Nunes, Sergei Vassilvitskii, Peilin Zhong A PAC-Bayesian Perspective on the Interpolating Information Criterion
Liam Hodgkinson, Chris van der Heide, Robert Salomone, Fred Roosta, Michael Mahoney A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning
Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bruss, Andrew Wilson, Tom Goldstein, Micah Goldblum A Quadratic Synchronization Rule for Distributed Deep Learning
Xinran Gu, Kaifeng Lyu, Sanjeev Arora, Jingzhao Zhang, Longbo Huang A Semi-Automated System to Annotate Communal Roosts in Large-Scale Weather Radar Data
Wenlong Zhao, Gustavo Perez, Zezhou Cheng, Maria Carolina Tiburcio Dias Belotti, Yuting Deng, Victoria Simons, Elske K Tielens, Jeffrey Kelly, Kyle Horton, Subhransu Maji, Daniel Sheldon A Sparse Null Code Emerges in Deep Neural Networks
Brian S Robinson, Nathan Drenkow, Colin Conwell, Michael Bonner A Sparsity Principle for Partially Observable Causal Representation Learning
Danru Xu, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane A Study of Generalization in Offline Reinforcement Learning
Ishita Mediratta, Qingfei You, Minqi Jiang, Roberta Raileanu A Study on Improving Reasoning in Language Models
Yuqing Du, Alexander Havrilla, Sainbayar Sukhbaatar, Pieter Abbeel, Roberta Raileanu A Study on the Calibration of In-Context Learning
Hanlin Zhang, YiFan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade A Transformer Model for Symbolic Regression Towards Scientific Discovery
Florian Lalande, Yoshitomo Matsubara, Naoya Chiba, Tatsunori Taniai, Ryo Igarashi, Yoshitaka Ushiku A Unified Analysis of Label Inference Attacks
Andres Munoz Medina, Travis Dick, Claudio Gentile, Robert Istvan Busa-Fekete, Marika Swanberg Accelerating Deep Learning Using Ivy
Guillermo Sanchez-Brizuela, Ved Patwardhan, Matthew Barrett, Paul Anderson, Mustafa Hani, Daniel James Lenton Accelerating Inexact HyperGradient Descent for Bilevel Optimization
Haikuo Yang, Luo Luo, Chris Junchi Li, Michael Jordan, Maryam Fazel Accelerating Motion Planning via Optimal Transport
An Le, Georgia Chalvatzaki, Armin Biess, Jan Peters Accurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction
Samuel J. Yang, Shutong Li, Subhashini Venugopalan, Vahe Tshitoyan, Muratahan Aykol, Amil Merchant, Ekin Dogus Cubuk, Gowoon Cheon Active Learning for Iterative Offline Reinforcement Learning
Lan Zhang, Luigi Franco Tedesco, Pankaj Rajak, Youcef Zemmouri, Hakan Brunzell Active Learning Policies for Solving Inverse Problems
Tim Bakker, Thomas Hehn, Tribhuvanesh Orekondy, Arash Behboodi, Fabio Valerio Massoli Active Learning with Missing Not at Random Outcomes
Alan Mishler, Mohsen Ghassemi, Alec Koppel, Sumitra Ganesh Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference
Rishav Mukherji, Mark Schöne, Khaleelulla Khan Nazeer, Christian Mayr, Anand Subramoney ActSort: An Active-Learning Accelerated Cell Sorting Algorithm for Large-Scale Calcium Imaging Datasets
Hakki Orhun Akengin, Mehmet Anil Aslihak, Yiqi Jiang, Yang Li, Oscar Hernandez, Hakan Inan, Christopher Miranda, Marta Blanco Pozo, Fatih Dinc, Mark Schnitzer Adam Through a Second-Order Lens
Ross M Clarke, Baiyu Su, José Miguel Hernández-Lobato Adaptive Coalition Structure Generation
Lucia Cipolina-Kun, Ignacio Carlucho, Kalesha Bullard Adaptive Gradient Methods at the Edge of Stability
Jeremy Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, Zachary Nado, George E. Dahl, Justin Gilmer Adaptive Learning Acceleration for Nonlinear PDE Solvers
Vinicius Luiz Santos Silva, Pablo Salinas, Claire E Heaney, Matthew Jackson, Christopher Charles Pain Adaptive Resolution Residual Networks
Léa Demeule, Mahtab Sandhu, Glen Berseth Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
Anna Bair, Hongxu Yin, Maying Shen, Pavlo Molchanov, Jose M. Alvarez Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
Anna Bair, Hongxu Yin, Maying Shen, Pavlo Molchanov, Jose M. Alvarez Advancing Graph Neural Networks Through Joint Time-Space Dynamics
Qiyu Kang, Yanan Zhao, Kai Zhao, Xuhao Li, Qinxu Ding, Wee Peng Tay, Sijie Wang Agent-Centric State Discovery for Finite-Memory POMDPs
Lili Wu, Ben Evans, Riashat Islam, Raihan Seraj, Yonathan Efroni, Alex Lamb AgentTorch: Agent-Based Modeling with Automatic Differentiation
Ayush Chopra, Jayakumar Subramanian, Balaji Krishnamurthy, Ramesh Raskar Agile Modeling: From Concept to Classifier in Minutes
Otilia Stretcu, Edward Vendrow, Kenji Hata, Krishnamurthy Viswanathan, Vittorio Ferrari, Sasan Tavakkol, Wenlei Zhou, Aditya Avinash, Enming Luo, Neil Gordon Alldrin, Mohammadhossein Bateni, Gabriel Berger, Andrew Bunner, Chun-Ta Lu, Javier A Rey, Giulia DeSalvo, Ranjay Krishna, Ariel Fuxman AI for Mathematics: A Cognitive Science Perspective
Cedegao Zhang, Katherine Collins, Adrian Weller, Joshua Tenenbaum AI Framework for Generative Design of Computational Experiments with Structures in Physical Environment
Gleb Vitalevich Solovev, Anna Kalyuzhnaya, Alexander Hvatov, Nikita Starodubcev, Oleg Petrov, Nikolay Nikitin AI4HPC: Library to Train AI Models on HPC Systems Using CFD Datasets
Eray Inanc, Rakesh Sarma, Marcel Aach, Rocco Sedona, Andreas Lintermann An Alternative to Regulation: The Case for Public AI
Nicholas Vincent, David Bau, Sarah Schwettmann, Joshua Tan An Archival Perspective on Pretraining Data
Meera Desai, Abigail Z. Jacobs, Dallas Card An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
Yadong Lu, Chunyuan Li, Haotian Liu, Jianwei Yang, Jianfeng Gao, Yelong Shen An Emulator for Fine-Tuning Large Language Models Using Small Language Models
Eric Mitchell, Rafael Rafailov, Archit Sharma, Chelsea Finn, Christopher Manning An Information-Theoretic Understanding of Maximum Manifold Capacity Representations
Berivan Isik, Victor Lecomte, Rylan Schaeffer, Yann LeCun, Mikail Khona, Ravid Shwartz-Ziv, Sanmi Koyejo, Andrey Gromov An Information-Theoretic Understanding of Maximum Manifold Capacity Representations
Victor Lecomte, Rylan Schaeffer, Berivan Isik, Mikail Khona, Yann LeCun, Sanmi Koyejo, Andrey Gromov, Ravid Shwartz-Ziv An Information-Theoretic Understanding of Maximum Manifold Capacity Representations
Rylan Schaeffer, Berivan Isik, Victor Lecomte, Mikail Khona, Yann LeCun, Andrey Gromov, Ravid Shwartz-Ziv, Sanmi Koyejo An International Consortium for AI Risk Evaluations
Ross Gruetzemacher, Alan Chan, Štěpán Los, Kevin Frazier, Siméon Campos, Matija Franklin, James Fox, Jose Hernandez-Orallo, Christin Manning, Philip Tomei, Kyle Kilian Analysis of Cellular Phenotypes with Unbiased Image-Based Generative Models
Ruben Fonnegra, Mohammad Sanian, Zitong Chen, Lassi Paavolainen, Juan Caicedo Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, Huaxiu Yao Anomaly Detection in Continuous-Time Temporal Provenance Graphs
Jakub Reha, Giulio Lovisotto, Michele Russo, Alessio Gravina, Claas Grohnfeldt Anthropomorphization of AI: Opportunities and Risks
Ameet Deshpande, Tanmay Rajpurohit, Karthik Narasimhan, Ashwin Kalyan AntiFold: Improved Antibody Structure Design Using Inverse Folding
Magnus Høie, Alissa Hummer, Tobias Olsen, Morten Nielsen, Charlotte Deane Anytime Model Selection in Linear Bandits
Parnian Kassraie, Nicolas Emmenegger, Andreas Krause, Aldo Pacchiano ARB: Advanced Reasoning Benchmark for Large Language Models
Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, Pranav Tadepalli, Paula Vidas, Alexander Kranias, John Nay, Kshitij Gupta, Aran Komatsuzaki Are Graph Neural Networks Optimal Approximation Algorithms?
Morris Yau, Eric Lu, Nikolaos Karalias, Jessica Xu, Stefanie Jegelka Are Large Language Models Good Annotators?
Jay Mohta, Kenan Ak, Yan Xu, Mingwei Shen Are Large Language Models Post Hoc Explainers?
Nicholas Kroeger, Dan Ley, Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju Are Large Language Models Post Hoc Explainers?
Nicholas Kroeger, Dan Ley, Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju Are Large Language Models Really Robust to Word-Level Perturbations?
Haoyu Wang, Guozheng Ma, Cong Yu, Ning Gui, Linrui Zhang, Zhiqi Huang, Suwei Ma, Yongzhe Chang, Sen Zhang, Li Shen, Xueqian Wang, Peilin Zhao, Dacheng Tao Are Models Biased on Text Without Gender-Related Language?
Catarina Belém, Preethi Seshadri, Yasaman Razeghi, Sameer Singh Are VideoQA Models Truly Multimodal?
Ishaan Rawal, Shantanu Jaiswal, Basura Fernando, Cheston Tan Are We Going MAD? Benchmarking Multi-Agent Debate Between Language Models for Medical Q&A
Andries Petrus Smit, Paul Duckworth, Nathan Grinsztajn, Kale-ab Tessera, Thomas D Barrett, Arnu Pretorius Associative Memories with Heavy-Tailed Data
Vivien Cabannes, Elvis Dohmatob, Alberto Bietti Associative Memories with Heavy-Tailed Data
Vivien Cabannes, Elvis Dohmatob, Alberto Bietti Associative Transformer Is a Sparse Representation Learner
Yuwei Sun, Hideya Ochiai, Zhirong Wu, Stephen Lin, Ryota Kanai AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models
Francois Lanusse, Liam Holden Parker, Siavash Golkar, Alberto Bietti, Miles Cranmer, Michael Eickenberg, Geraud Krawezik, Michael McCabe, Ruben Ohana, Mariel Pettee, Bruno Régaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho ATAT: Automated Tissue Alignment and Traversal
Steven Song, Emaan Mohsin, Andrey Kuznetsov, Christopher Weber, Robert L. Grossman, Aly A Khan Attention for Causal Relationship Discovery from Biological Neural Dynamics
Ziyu Lu, Anika Tabassum, Shruti R. Kulkarn, Lu Mi, J. Nathan Kutz, Eric Todd SheaBrown, Seung-Hwan Lim Attention Schema in Neural Agents
Dianbo Liu, Samuele Bolotta, Mike He Zhu, Zahra Sheikhbahaee, Yoshua Bengio, Guillaume Dumas AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments
Yang Zhang, Yawei Li, Hannah Brown, Mina Rezaei, Bernd Bischl, Philip Torr, Ashkan Khakzar, Kenji Kawaguchi Augmenting Federated Learning with Pretrained Transformers
Xuechen Zhang, Mingchen Li, Xiangyu Chang, Jiasi Chen, Amit Roy-Chowdhury, Ananda Suresh, Samet Oymak Augmenting Large Language Models with Chemistry Tools
Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew White, Philippe Schwaller Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture
Yicheng Wang, Xiaotian Han, Chia-Yuan Chang, Daochen Zha, Ulisses Braga-Neto, Xia Hu AutoDAN: Automatic and Interpretable Adversarial Attacks on Large Language Models
Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, Tong Sun AutODEx: Automated Optimal Design of Experiments Platform with Data- and Time-Efficient Multi-Objective Optimization
Yunsheng Tian, Pavle Vanja Konakovic, Beichen Li, Ane Zuniga, Michael Foshey, Timothy Erps, Wojciech Matusik, Mina Konakovic Lukovic AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data
Caroline Choi, Yoonho Lee, Annie S Chen, Allan Zhou, Aditi Raghunathan, Chelsea Finn Automata Conditioned Reinforcement Learning with Experience Replay
Beyazit Yalcinkaya, Niklas Lauffer, Marcell Vazquez-Chanlatte, Sanjit Seshia Automated Clinical Coding Using Off-the-Shelf Large Language Models
Joseph Spartacus Boyle, Antanas Kascenas, Pat Lok, Maria Liakata, Alison Q O'Neil Automated Distillation of Genomic Equations Governing Single Cell Gene Expression
Edouardo Honig, Frederique Ruf-Zamojski, Stuart Sealfon, Ying Nian Wu, Zijun Frank Zhang Automating Reward Function Configuration for Drug Design
Temitope Ajileye, Paul Gainer, Marius Urbonas, Douglas Eduardo Valente Pires AutoMix: Mixing Models with Few-Shot Self and Meta Verification
Aman Madaan, Pranjal Aggarwal, Ankit Anand, Srividya Pranavi Potharaju, Swaroop Mishra, Pei Zhou, Aditya Gupta, Dheeraj Rajagopal, Yiming Yang, Shyam Upadhyay, Mausam, Manaal Faruqui AutoVP: An Automated Visual Prompting Framework and Benchmark
Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen, Sijia Liu, Tsung-Yi Ho AutoVP: An Automated Visual Prompting Framework and Benchmark
Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen, Sijia Liu, Tsung-Yi Ho Average-Constrained Policy Optimization
Akhil Agnihotri, Rahul Jain, Haipeng Luo Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, Hongxia Jin BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models
Zhen Xiang, Fengqing Jiang, Zidi Xiong, Bhaskar Ramasubramanian, Radha Poovendran, Bo Li BadFusion: 2D-Oriented Backdoor Attacks Against 3D Object Detection
Saket Sanjeev Chaturvedi, Lan Zhang, Wenbin Zhang, Pan He, Xiaoyong Yuan Baking Symmetry into GFlowNets
George Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Sets
Brandon Abreu Smith, Miguel Farinha, Siobhan Mackenzie Hall, Hannah Rose Kirk, Aleksandar Shtedritski, Max Bain Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Han Zhou, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine Heller, Subhrajit Roy Bayesian Low-Rank Adaptation for Large Language Models
Adam Yang, Maxime Robeyns, Xi Wang, Laurence Aitchison Bayesian Machine Scientist for Model Discovery in Psychology
Joshua Tomas Sealth Hewson, Younes Strittmatter, Ioana Marinescu, Chad C Williams, Sebastian Musslick Bayesian Metaplasticity from Synaptic Uncertainty
Djohan Bonnet, Tifenn Hirtzlin, Tarcisius Januel, Thomas Dalgaty, Damien Querlioz, Elisa Vianello Bending and Binding: Predicting Protein Flexibility upon Ligand Interaction Using Diffusion Models
Xuejin Zhang, Tomas Geffner, Matt McPartlon, Mehmet Akdel, Dylan Abramson, Graham Holt, Alexander Goncearenco, Luca Naef, Michael Bronstein BENO: Boundary-Embedded Neural Operators for Elliptic PDEs
Haixin Wang, Jiaxin Li, Anubhav Dwivedi, Kentaro Hara, Tailin Wu Better than Balancing: Debiasing Through Data Attribution
Saachi Jain, Kimia Hamidieh, Kristian Georgiev, Marzyeh Ghassemi, Aleksander Madry Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction
Eduardo Soares, Emilio Vital Brazil, Karen Fiorella Aquino Gutierrez, Renato Cerqueira, Daniel P Sanders, Kristin Schmidt, Dmitry Zubarev Beyond Erdos-Renyi: Generalization in Algorithmic Reasoning on Graphs
Dobrik Georgiev, Pietro Lio, Jakub Bachurski, Junhua Chen, Tunan Shi Beyond Erdos-Renyi: Generalization in Algorithmic Reasoning on Graphs
Dobrik Georgiev, Pietro Lio, Jakub Bachurski, Junhua Chen, Tunan Shi Beyond Parameter Averaging in Model Aggregation
Pol G. Recasens, Jordi Torres, Josep Lluis Berral, Søren Hauberg, Pablo Moreno-Muñoz Bi-Level Graphs for Cellular Pattern Discovery
Zhenzhen Wang, Aleksander S. Popel, Jeremias Sulam Binding Oracle: Fine-Tuning from Stability to Binding Free Energy
Chengyue Gong, Adam Klivans, Jordan Wells, James Loy, Qiang Liu, Alex Dimakis, Daniel Diaz Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains
Kyungeun Lee, Ye Seul Sim, Hyeseung Cho, Suhee Yoon, Sanghyu Yoon, Woohyung Lim Building Cooperative Embodied Agents Modularly with Large Language Models
Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua Tenenbaum, Tianmin Shu, Chuang Gan CALICO: Conversational Agent Localization via Synthetic Data Generation
Andy Rosenbaum, Pegah Kharazmi, Ershad Banijamali, Lu Zeng, Christopher DiPersio, Pan Wei, Gokmen Oz, Clement Chung, Karolina Owczarzak, Fabian Triefenbach, Wael Hamza Can Copyright Be Reduced to Privacy
Niva Elkin-Koren, Uri Hacohen, Roi Livni, Shay Moran Can Physics Informed Neural Operators Self Improve?
Ritam Majumdar, Amey Varhade, Shirish Karande, Lovekesh Vig Can Segment Anything Model Improve Semantic Segmentation?
Maryam Qamar, Donghoon Kim, Muhammad Salman Ali, Chaoning Zhang, Sung-Ho Bae Can Transformers In-Context Learn Task Mixtures?
Nilesh Tripuraneni, Lyric Doshi, Steve Yadlowsky Capture the Flag: Uncovering Data Insights with Large Language Models
Issam H. Laradji, Perouz Taslakian, Sai Rajeswar, Valentina Zantedeschi, Alexandre Lacoste, Nicolas Chapados, David Vazquez, Christopher Pal, Alexandre Drouin Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-Agent Dynamical Systems
Zijie Huang, Jeehyun Hwang, Junkai Zhang, Jinwoo Baik, Weitong Zhang, Dominik Wodarz, Yizhou Sun, Quanquan Gu, Wei Wang Causal Modeling with Stationary Diffusions
Lars Lorch, Andreas Krause, Bernhard Schölkopf Cayley Graph Propagation
Jj Wilson, Petar Veličković Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei-Fei, Fei Xia, Brian Ichter Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Jianfeng Gao Channel Selection for Test-Time Adaptation Under Distribution Shift
Pedro Vianna, Muawiz Sajjad Chaudhary, An Tang, Guy Cloutier, Guy Wolf, Michael Eickenberg, Eugene Belilovsky Characterizing Out-of-Distribution Error via Optimal Transport
Yuzhe Lu, Yilong Qin, Runtian Zhai, Andrew Shen, Ketong Chen, Zhenlin Wang, Soheil Kolouri, Simon Stepputtis, Joseph Campbell, Katia Sycara Characterizing Pre-Trained and Task-Adapted Molecular Representations
Celia Cintas, Payel Das, Jarret Ross, Brian Belgodere, Girmaw Abebe Tadesse, Vijil Chenthamarakshan, Jannis Born, Skyler Speakman CHARM: Creating Halos with Auto-Regressive Multi-Stage Networks
Shivam Pandey, Chirag Modi, Benjamin Dan Wandelt, Guilhem Lavaux ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Colin Bellinger, Mark Crowley, Isaac Tamblyn CHIRon: A Generative Foundation Model for Structured Sequential Medical Data
Brian L. Hill, Melikasadat Emami, Vijay S Nori, Aldo Cordova-Palomera, Robert E. Tillman, Eran Halperin CHORUS: Foundation Models for Unified Data Discovery and Exploration
Moe Kayali, Anton Lykov, Ilias Fountalis, Nikolaos Vasiloglou, Dan Olteanu, Dan Suciu CircuitVAE: Efficient and Scalable Latent Circuit Optimization
Jialin Song, Aidan Swope, Robert Kirby, Rajarshi Roy, Saad Godil, Jonathan Raiman, Bryan Catanzaro Clean-Label Backdoor Attacks by Selectively Poisoning with Limited Information from Target Class
Nguyen Hung-Quang, Ngoc-Hieu Nguyen, The-Anh Ta, Thanh Nguyen-Tang, Hoang Thanh-Tung, Khoa D Doan CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen, Oyvind Tafjord, Niket Tandon, Li Zhang, Chris Callison-Burch, Peter Clark CLIP Meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement
Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton, Fartash Faghri, Hadi Pouransari, Raviteja Vemulapalli, Oncel Tuzel, Ali Farhadi, Mohammad Rastegari, Sachin Mehta Coded Prompts for Large Language Models
Ziqian Lin, Yicong Chen, Yuchen Zeng, Kangwook Lee CodePlan: Repository-Level Coding Using LLMs and Planning
Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, D C Vageesh, Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B. Ashok, Shashank Shet CodonBERT: Large Language Models for mRNA Design and Optimization
Sizhen Li, Saeed Moayedpour, Ruijiang Li, Michael Bailey, Saleh Riahi, Milad Miladi, Jacob Miner, Dinghai Zheng, Jun Wang, Akshay Balsubramani, Khang Tran, Minnie, Monica Wu, Xiaobo Gu, Ryan Clinton, Carla Asquith, Joseph Skaleski, Lianne Boeglin, Sudha Chivukula, Anusha Dias, Fernando Ulloa Montoya, Vikram Agarwal, Ziv Bar-Joseph, Sven Jager ComboPath: A Model for Predicting Drug Combination Effects
Duminda S Ranasinghe, Nathan Sanders, Hok Hei Tam, Changchang Liu, Dan Spitz COMET: Neural Cost Model Explanation Framework
Isha Chaudhary, Alex Renda, Charith Mendis, Gagandeep Singh Comparing Optimization Targets for Contrast-Consistent Search
Hugo Fry, Seamus Fallows, Jamie Wright, Ian Fan, Nandi Schoots Compositional Foundation Models for Hierarchical Planning
Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi Jaakkola, Joshua Tenenbaum, Leslie Kaelbling, Akash Srivastava, Pulkit Agrawal Compositional Generative Inverse Design
Tailin Wu, Takashi Maruyama, Long Wei, Tao Zhang, Yilun Du, Gianluca Iaccarino, Jure Leskovec Compositional Preference Models for Alignment with Scalable Oversight
Dongyoung Go, Tomasz Korbak, Germàn Kruszewski, Jos Rozen, Marc Dymetman ConcatPlexer : Additional Dim1 Batching for Faster ViTs
Donghoon Han, Seunghyeon Seo, Donghyeon Jeon, Jiho Jang, Chaerin Kong, Nojun Kwak Conditional Generation of Antigen Specific T-Cell Receptor Sequences
Dhuvarakesh Karthikeyan, Colin Raffel, Benjamin Vincent, Alex Rubinsteyn Conformal Prediction via Regression-as-Classification
Etash Guha, Shlok Natarajan, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Eugene Ndiaye Confronting Reward Model Overoptimization with Constrained RLHF
Ted Moskovitz, Aaditya Singh, Dj Strouse, Tuomas Sandholm, Ruslan Salakhutdinov, Anca Dragan, Stephen McAleer Conservative World Models
Scott Jeen, Tom Bewley, Jonathan Cullen Context Is Environment
Sharut Gupta, David Lopez-Paz, Stefanie Jegelka, Kartik Ahuja Context Is Environment
Sharut Gupta, David Lopez-Paz, Stefanie Jegelka, Kartik Ahuja Context-Aware Meta-Learning
Christopher Fifty, Dennis Duan, Ronald Guenther Junkins, Ehsan Amid, Jure Leskovec, Christopher Re, Sebastian Thrun Continual Learning with Low Rank Adaptation
Martin Wistuba, Prabhu Teja S, Lukas Balles, Giovanni Zappella Continually Adapting Optimizers Improve Meta-Generalization
Wenyi Wang, Louis Kirsch, Francesco Faccio, Mingchen Zhuge, Jürgen Schmidhuber Continually Adapting Optimizers Improve Meta-Generalization
Wenyi Wang, Louis Kirsch, Francesco Faccio, Mingchen Zhuge, Jürgen Schmidhuber Contrasting Sequence with Structure: Pre-Training Graph Representations with PLMs
Louis Robinson, Timothy Atkinson, Liviu Copoiu, Patrick Bordes, Thomas Pierrot, Thomas D Barrett Contrastive Abstraction for Reinforcement Learning
Vihang Patil, Markus Hofmarcher, Elisabeth Rumetshofer, Sepp Hochreiter Contrastive Difference Predictive Coding
Chongyi Zheng, Ruslan Salakhutdinov, Benjamin Eysenbach Contrastive Power-Efficient Physical Learning in Resistor Networks
Menachem Stern, Sam Dillavou, Dinesh Jayaraman, Douglas Durian, Andrea Liu Contrastive Predict-and-Search for Mixed Integer Linear Programs
Taoan Huang, Aaron M Ferber, Arman Zharmagambetov, Yuandong Tian, Bistra Dilkina Contrastive Representations Make Planning Easy
Benjamin Eysenbach, Vivek Myers, Sergey Levine, Ruslan Salakhutdinov Controlled Decoding from Language Models
Sidharth Mudgal, Jong Lee, Harish Ganapathy, YaGuang Li, Tao Wang, Yanping Huang, Zhifeng Chen, Heng-Tze Cheng, Michael Collins, Jilin Chen, Alex Beutel, Ahmad Beirami Cooperative AI via Decentralized Commitment Devices
Xinyuan Sun, Davide Crapis, Matt Stephenson, Jonathan Passerat-Palmbach Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale?
Stephen Mak, Tim Pearce, Matthew Macfarlane, Liming Xu, Michael Ostroumov, Alexandra Brintrup COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
Xiyao Wang, Ruijie Zheng, Yanchao Sun, Ruonan Jia, Wichayaporn Wongkamjan, Huazhe Xu, Furong Huang Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making
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Simiao Zuo, Haoming Jiang, Qingyu Yin, Xianfeng Tang, Bing Yin, Tuo Zhao Discovering Environments with XRM
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz Discovering Lyapunov Functions with Transformers
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Michael Pekala, Elizabeth Ann Pogue, Kyle McElroy, Alexander New, Gregory Bassen, Brandon Wilfong, Janna Domenico, Tyrel McQueen, Christopher D Stiles Evaluating Large Language Models at Evaluating Instruction Following
Zhiyuan Zeng, Jiatong Yu, Tianyu Gao, Yu Meng, Tanya Goyal, Danqi Chen Evaluating Peripheral Vision as an Input Transformation to Understand Object Detection Model Behavior
Anne Harrington, Vasha DuTell, Mark Hamilton, Ayush Tewari, Simon Stent, William T. Freeman, Ruth Rosenholtz Evaluating the Structure of Cognitive Tasks with Transfer Learning
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Francisco Acosta, Colin Conwell, Sophia Sanborn, David A. Klindt, Nina Miolane Event-Based Contrastive Learning for Medical Time Series
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Jonas Jürß, Lucie Charlotte Magister, Pietro Barbiero, Pietro Lio, Nikola Simidjievski Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
Xinyu Hu, Pengfei Tang, Simiao Zuo, Zihan Wang, Bowen Song, Qiang Lou, Jian Jiao, Denis X Charles Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
Xinyu Hu, Pengfei Tang, Simiao Zuo, Zihan Wang, Bowen Song, Qiang Lou, Jian Jiao, Denis Charles Evolving Domain Adaptation of Pretrained Language Models for Text Classification
Yun-Shiuan Chuang, Rheeya Uppaal, Yi Wu, Luhang Sun, Makesh Narsimhan Sreedhar, Sijia Yang, Timothy T. Rogers, Junjie Hu Explainable Reinforcement Learning for Alzheimer’s Disease Progression Prediction.
Raja Farrukh Ali, Ayesha Farooq, Emmanuel Adeniji, John Woods, Vinny Sun, William Hsu Explaining Black Box Text Modules in Natural Language with Language Models
Chandan Singh, Aliyah Hsu, Richard Antonello, Shailee Jain, Alexander Huth, Bin Yu, Jianfeng Gao ExpLIMEable: An Exploratory Framework for LIME
Sonia Laguna, Julian Heidenreich, Jiugeng Sun, Nilüfer Cetin, Ibrahim Al Hazwani, Udo Schlegel, Furui Cheng, Mennatallah El-Assady Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training
Xi Chen, Chang Gao, Zuowen Wang, Longbiao Cheng, Sheng Zhou, Shih-Chii Liu, Tobi Delbruck Exploratory Training: When Annotators Learn About Data
Rajesh Shrestha, Omeed Habibelahian, Arash Termehchy, Paolo Papotti Explore to Generalize in Zero-Shot RL
Ev Zisselman, Itai Lavie, Daniel Soudry, Aviv Tamar Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery
Joseph Alejandro Gallego Mejia, Anna Jungbluth, Laura Martínez-Ferrer, Francisco Dorr, Matthew Allen, Freddie Kalaitzis, Raúl Ramos-Pollán Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction
Laura Martínez-Ferrer, Anna Jungbluth, Joseph Alejandro Gallego Mejia, Matt Allen, Francisco Dorr, Freddie Kalaitzis, Raúl Ramos-Pollán Exploring Modern Evolution Strategies in Portfolio Optimization
Ramin Hasani, Etan A Ehsanfar, Greg A Banis, Rusty Bealer, Amir Soroush Ahmadi Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models
Adhithya Prakash Saravanan, Rafal Kocielnik, Roy Jiang, Pengrui Han, Anima Anandkumar Exploring the Potential of Large Language Models (LLMs) in Learning on Graph
Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, Jiliang Tang Exploring the Temperature-Dependent Phase Transition in Modern Hopfield Networks
Felix Koulischer, Cédric Goemaere, Tom Van Der Meersch, Johannes Deleu, Thomas Demeester Exploring User-Level Gradient Inversion with a Diffusion Prior
Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Bradley A. Malin, Kieran Parsons, Ye Wang Expression Sampler as a Dynamic Benchmark for Symbolic Regression
Ioana Marinescu, Younes Strittmatter, Chad C Williams, Sebastian Musslick Extra Training Provides a Strong Baseline for CLIP
Alaa Khaddaj, Hadi Salman, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry FActScore: Fine-Grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, Hannaneh Hajishirzi Fair Wasserstein Coresets
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Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, Yann Ollivier Fast Temporal Wavelet Graph Neural Networks
Duc Thien Nguyen, Tuan Nguyen, Truong Son Hy, Risi Kondor Fast Temporal Wavelet Graph Neural Networks
Duc Thien Nguyen, Tuan Nguyen, Truong Son Hy, Risi Kondor Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR
Sheikh Shams Azam, Martin Pelikan, Vitaly Feldman, Kunal Talwar, Jan Silovsky, Tatiana Likhomanenko FedJETs: Efficient Just-in-Time Personalization with Federated Mixture of Experts
Chen Dun, Mirian Hipolito Garcia, Guoqing Zheng, Ahmed Awadallah, Robert Sim, Anastasios Kyrillidis, Dimitrios Dimitriadis FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
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Reyhane Askari Hemmat, Mohammad Pezeshki, Florian Bordes, Michal Drozdzal, Adriana Romero-Soriano Fewshot Learning on Global Multimodal Embeddings for Earth Observation Tasks
Matthew Allen, Francisco Dorr, Joseph Alejandro Gallego Mejia, Laura Martínez-Ferrer, Anna Jungbluth, Freddie Kalaitzis, Raúl Ramos-Pollán Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search
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Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary Ward Ulissi Fine-Tuned Protein Language Models Capture T Cell Receptor Stochasticity
Lewis Cornwall, Grisha Szep, James Day, S R Gokul Krishnan, David Carter, Jamie Blundell, Lilly Wollman, Neil Dalchau, Aaron Sim Fine-Tuning Language Models for Factuality
Katherine Tian, Eric Mitchell, Huaxiu Yao, Christopher Manning, Chelsea Finn Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning
Felix den Breejen, Sangmin Bae, Stephen Cha, Tae-Young Kim, Seoung Hyun Koh, Se-Young Yun FLASK: Fine-Grained Language Model Evaluation Based on Alignment Skill Sets
Seonghyeon Ye, Doyoung Kim, Sungdong Kim, Hyeonbin Hwang, Seungone Kim, Yongrae Jo, James Thorne, Juho Kim, Minjoon Seo Flexible Visual Prompts for in Context Learning in Computer Vision
Thomas Foster, Ioana Croitoru, Robert Dorfman, Christoffer Edlund, Thomas Varsavsky, Jon Almazán Flow-Based Distributionally Robust Optimization
Chen Xu, Jonghyeok Lee, Xiuyuan Cheng, Yao Xie FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
Wenda Chu, Chulin Xie, Boxin Wang, Linyi Li, Lang Yin, Arash Nourian, Han Zhao, Bo Li FOCUS: Object-Centric World Models for Robotic Manipulation
Stefano Ferraro, Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt For Distillation, Tokens Are Not All You Need
Mrigank Raman, Pranav Mani, Davis Liang, Zachary Lipton Foundation Models Can Robustify Themselves, for Free
Dyah Adila, Changho Shin, Linrong Cai, Frederic Sala FragXsiteDTI: An Interpretable Transformer-Based Model for Drug-Target Interaction Prediction
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Irene Cannistraci, Luca Moschella, Marco Fumero, Valentino Maiorca, Emanuele Rodolà From Charts to Atlas: Merging Latent Spaces into One
Donato Crisostomi, Irene Cannistraci, Luca Moschella, Pietro Barbiero, Marco Ciccone, Pietro Lio, Emanuele Rodolà From Child's Play to AI: Insights into Automated Causal Curriculum Learning
Annya Dahmani, Eunice Yiu, Tabitha Lee, Nan Ke, Oliver Kroemer, Alison Gopnik From Trojan Horses to Castle Walls: Unveiling Bilateral Backdoor Effects in Diffusion Models
Zhuoshi Pan, Yuguang Yao, Gaowen Liu, Bingquan Shen, H. Vicky Zhao, Ramana Rao Kompella, Sijia Liu Function-Constrained Program Synthesis
Patrick Anthony Hajali, Ignas Budvytis Fusing Models with Complementary Expertise
Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin GAD-EBM: Graph Anomaly Detection Using Energy-Based Models
Amit Roy, Juan Shu, Olivier Elshocht, Jeroen Smeets, Ruqi Zhang, Pan Li Generalisable Agents for Neural Network Optimisation
Kale-ab Tessera, Callum Rhys Tilbury, Sasha Abramowitz, Ruan John de Kock, Omayma Mahjoub, Benjamin Rosman, Sara Hooker, Arnu Pretorius Generalisable Agents for Neural Network Optimisation
Kale-ab Tessera, Callum Rhys Tilbury, Sasha Abramowitz, Ruan John de Kock, Omayma Mahjoub, Benjamin Rosman, Sara Hooker, Arnu Pretorius Generalization Guarantees of Deep ResNets in the Mean-Field Regime
Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios Chrysos, Volkan Cevher Generating Data Augmentation Queries Using Large Language Models
Christopher Buss, Jasmin Mousavi, Mikhail Tokarev, Arash Termehchy, David Maier, Stefan Lee Generating Human-like Goals by Synthesizing Reward-Producing Programs
Guy Davidson, Graham Todd, Todd Gureckis, Julian Togelius, Brenden Lake Generating Medical Instructions with Conditional Transformer
Samuel Belkadi, Nicolo Micheletti, Lifeng Han, Warren Del-Pinto, Goran Nenadic Generating Molecular Conformer Fields
Yuyang Wang, Ahmed Elhag, Navdeep Jaitly, Joshua Susskind, Miguel Bautista Generating Personalized Insulin Treatments Strategies with Conditional Generative Time Series Models
Manuel Schürch, Xiang Li, Ahmed Allam, Giulia Hofer, Amina Mollaysa, Claudia Cavelti-Weder, Michael Krauthammer Generating Privacy-Preserving Longitudinal Synthetic Data
Robin van Hoorn, Tom Bakkes, Zoi Tokoutsi, Ymke de Jong, R. Arthur Bouwman, Mykola Pechenizkiy Generation of 3D Realistic Soil Particles with Metaball Descriptor
Yifeng Zhao, Jinxin Liu, Xiangbo Gao, Pei Zhang, Stan Z. Li, Sergio Andres Galindo Torres Generative Design for Gene Therapy: An $\textit{in Vivo}$ Validated Method
Farhan Damani, David Brookes, Jeffrey Chan, Rishi Jajoo, Alexander Mijalis, Joyce Samson, Flaviu Vadan, Cameron Webster, Stephen Malina, Sam Sinai Generative Flow Networks Assisted Biological Sequence Editing
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Christian Koke, Abhishek Saroha, Yuesong Shen, Marvin Eisenberger, Daniel Cremers Rethinking Bayesian Optimization with Gaussian Processes: Insights from Hyperspectral Trait Search
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Amal Rannen-Triki, Jorg Bornschein, Razvan Pascanu, Alexandre Galashov, Michalis Titsias, Marcus Hutter, András György, Yee Whye Teh Revisiting Random Weight Perturbation for Efficiently Improving Generalization
Tao Li, Weihao Yan, Qinghua Tao, Zehao Lei, Yingwen Wu, Kun Fang, Mingzhen He, Xiaolin Huang Revisiting Supervision for Continual Representation Learning
Daniel Marczak, Sebastian Cygert, Tomasz Trzcinski, Bartłomiej Twardowski Revisiting the Noise Model of SGD
Barak Battash, Lior Wolf, Ofir Lindenbaum Reward Model Aggregation
Zihao Wang, Chirag Nagpal, Alexander D'Amour, Victor Veitch, Sanmi Koyejo Reward Model Ensembles Help Mitigate Overoptimization
Thomas Coste, Usman Anwar, Robert Kirk, David Krueger Reward Model Ensembles Help Mitigate Overoptimization
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Jacob Eisenstein, Jonathan Berant, Chirag Nagpal, Alekh Agarwal, Ahmad Beirami, Alexander Nicholas D'Amour, Krishnamurthy Dj Dvijotham, Katherine A Heller, Stephen Robert Pfohl, Deepak Ramachandran Riemannian Optimization for Euclidean Distance Geometry
Chandler Mack Smith, Samuel P. Lichtenberg, HanQin Cai, Abiy Tasissa Risk Assessment and Statistical Significance in the Age of Foundation Models
Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti, Kristjan Greenewald, Brian Belgodere, Mikhail Yurochkin, Jiri Navratil, Igor Melnyk, Jarret Ross RL4CO: A Unified Reinforcement Learning for Combinatorial Optimization Library
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Vindula Jayawardana, Sirui Li, Cathy Wu, Yashar Farid, Kentaro Oguchi Robust Hierarchical Scene Graph Generation
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Meirav Segal, Anne-Marie George, Ingrid Yu, Christos Dimitrakakis Robustness and Regularization in Reinforcement Learning
Esther Derman, Yevgeniy Men, Matthieu Geist, Shie Mannor Role of Structural and Conformational Diversity for Machine Learning Potentials
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Benjamin Maurel, Samy Blusseau, Santiago Velasco-Forero, Teodora Petrisor SAD: Segment Any RGBD
Jun Cen, Yizheng Wu, Kewei Wang, Xingyi Li, Jingkang Yang, Yixuan Pei, Lingdong Kong, Ziwei Liu, Qifeng Chen SALSA: Semantically-Aware Latent Space Autoencoder
Kathryn E Kirchoff, Travis Maxfield, Alexander Tropsha, Shawn M Gomez SAM Meets Gaze: Passive Eye Tracking for Prompt-Based Instance Segmentation
Daniel Beckmann, Jacqueline Kockwelp, Joerg Gromoll, Friedemann Kiefer, Benjamin Risse SAM-CLIP: Merging Vision Foundation Models Towards Semantic and Spatial Understanding
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Joseph Chahn Kim, David A Bloore, Karan Kapoor, Jun Feng, Ming-Hong Hao, Mengdi Wang Scalable Particle Generation for Granular Shape Study
Yifeng Zhao, Jinxin Liu, Xiangbo Gao, Sergio Torres, Stan Z. Li Scalar Invariant Networks with Zero Bias
Chuqin Geng, Xiaojie Xu, Haolin Ye, Xujie Si Scaling of Optical Transformers
Maxwell Anderson, Shi-Yuan Ma, Tianyu Wang, Logan G. Wright, Peter McMahon Scaling Offline Q-Learning with Vision Transformers
Yingjie Miao, Jordi Orbay, Rishabh Agarwal, Aviral Kumar, George Tucker, Aleksandra Faust Scaling-up Memristor Monte Carlo with Magnetic Domain-Wall Physics
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Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum SE(3) Equivariant Augmented Coupling Flows
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Felix Teufel, Carsten Stahlhut, Jan Refsgaard, Henrik Nielsen, Ole Winther, Dennis Madsen Seeking Truth and Beauty in Flavor Physics with Machine Learning
Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner Segment Any Stream: Scalable Water Extent Detection with the Segment Anything Model
Haozhen Zheng, Chenhui Zhang, Kaiyu Guan, Yawen Deng, Sherrie Wang, Bruce L. Rhoads, Andrew J Margenot, Shengnan Zhou, Sheng Wang Selective Perception: Learning Concise State Descriptions for Language Model Actors
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Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, Hannaneh Hajishirzi Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations
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Andrei Mancu, Wenqi Huang, Gastao Lima da Cruz, Daniel Rueckert, Kerstin Hammernik Self-Supervised Representation Learning from Random Data Projectors
Yi Sui, Tongzi Wu, Jesse Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs Semi-Supervised Diffusion Model for Brain Age Prediction
Ayodeji Ijishakin, Sophie A. Martin, Florence J Townend, James H. Cole, Andrea Malaspina Semi-Supervised Graph Imbalanced Regression
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Krzysztof Kacprzyk, Mihaela van der Schaar SHARCS: Shared Concept Space for\\Explainable Multimodal Learning
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Alex Owen Davies, Nirav Ajmeri, Telmo M Silva Filho Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks
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Mikail Khona, Maya Okawa, Rahul Ramesh, Kento Nishi, Robert P. Dick, Ekdeep Singh Lubana, Hidenori Tanaka STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
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Nithin Puthalath Manoj, Joel Cherian, Kevin Jude Concessao, Unnikrishnan Cheramangalath Stochastic Force Inference via Density Estimation
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Eric J Bigelow, Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka, Tomer Ullman Subtle Misogyny Detection and Mitigation: An Expert-Annotated Dataset
Anna Richter, Brooklyn Sheppard, Allison Cohen, Elizabeth Smith, Tamara Kneese, Carolyne Pelletier, Ioana Baldini, Yue Dong Sufficient Conditions for Offline Reactivation in Recurrent Neural Networks
Nanda H Krishna, Colin Bredenberg, Daniel Levenstein, Blake Aaron Richards, Guillaume Lajoie SuperHF: Supervised Iterative Learning from Human Feedback
Gabriel Mukobi, Peter Chatain, Su Fong, Robert Windesheim, Gitta Kutyniok, Kush Bhatia, Silas Alberti Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations
Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai, Takayuki R. Saitoh, Junichiro Makino, Shirley Ho Sustainable Concrete via Bayesian Optimization
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Tony Shen, Mohit Pandey, Jason Smith, Artem Cherkasov, Martin Ester TAIL: Task-Specific Adapters for Imitation Learning with Large Pretrained Models
Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems
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Uri Gadot, Kaixin Wang, Esther Derman, Navdeep Kumar, Kfir Levy, Shie Mannor Task Arithmetic with LoRA for Continual Learning
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Rajas Chitale, Ankit Vaidya, Aditya Kane, Archana Santosh Ghotkar TBoost: Gradient Boosting Temporal Graph Neural Networks
Pritam Nath, Govind Waghmare, Nancy Agrawal, Nitish Kumar, Siddhartha Asthana Teaching Arithmetic to Small Transformers
Nayoung Lee, Kartik Sreenivasan, Jason Lee, Kangwook Lee, Dimitris Papailiopoulos Teaching Language Models with Canonical Examples
John Hewitt, Sarah Li Chen, Percy Liang, Christopher D Manning Teaching Small Transformers to Rewrite ZX Diagrams
Francois Charton, Alexandre Krajenbrink, Konstantinos Meichanetzidis, Richie Yeung Tell Your Model Where to Attend: Post-Hoc Attention Steering for LLMs
Qingru Zhang, Chandan Singh, Liyuan Liu, Xiaodong Liu, Bin Yu, Jianfeng Gao, Tuo Zhao Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, Stuart Russell Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
Sam Toyer, Olivia Watkins, Ethan Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, Stuart Russell Testing Assumptions Underlying a Unified Theory for the Origin of Grid Cells
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Chen Qiu, Xingyu Li, Chaithanya Kumar Mummadi, Madan Ravi Ganesh, Zhenzhen Li, Lu Peng, Wan-Yi Lin The Disagreement Problem in Faithfulness Metrics
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