ICMLW 2024
1500 papers
(Deep) Generative Geodesics
Beomsu Kim, Michael Anthony Puthawala, Jong Chul Ye, Emanuele Sansone $\alpha$-Fair Contextual Bandits
Siddhant Chaudhary, Abhishek Sinha $\mathbb{X}$-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs
Vlad Sobal, Mark Ibrahim, Randall Balestriero, Vivien Cabannes, Diane Bouchacourt, Pietro Astolfi, Kyunghyun Cho, Yann LeCun $\nabla \tau$: Gradient-Based and Task-Agnostic Machine Unlearning
Daniel Trippa, Cesare Campagnano, Maria Sofia Bucarelli, Gabriele Tolomei, Fabrizio Silvestri 3D Reconstruction of Dark Matter Fields with Diffusion Models: Towards Application to Galaxy Surveys
Core Francisco Park, Nayantara Mudur, Carolina Cuesta-Lazaro, Yueying Ni, Victoria Ono, Douglas Finkbeiner 3D Shape Completion with Test-Time Training
Michael Schopf-Kuester, Zorah Lähner, Michael Moeller A Bayesian Approach to Adversarially Robust Life Testing
Dorina Weichert, Alexander Kister, Sebastian Houben, Gunar Ernis, Tim Wirtz A Critical Look at Tokenwise Reward-Guided Text Generation
Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi, Pascal Poupart A Deeper Look at Depth Pruning of LLMs
Shoaib Ahmed Siddiqui, Xin Dong, Greg Heinrich, Thomas Breuel, Jan Kautz, David Krueger, Pavlo Molchanov A Differentiable Approach to Multi-Scale Brain Modeling
Chaoming Wang, Muyang Lyu, Tianqiu Zhang, Sichao He, Si Wu A Differentiable Topological Notion of Local Maxima for Keypoint Detection
Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno, Marco Guerra, Gabriele Berton, Carlo Masone A Framework for Differentiable Supervised Graph Prediction
Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d'Alché-Buc, Charlotte Laclau, Matthieu Labeau A Generative Foundation Model for Antibody Sequence Understanding
Justin Barton, Aretas Gaspariunas, David A Yadin, Jorge Dias, Francesca L Nice, Danielle H Minns, Olivia Snudden, Chelsea Povall, Sara Valle Tomas, Harry Dobson, James H R Farmery, Jinwoo Leem, Jacob D Galson A Geometric Framework for Understanding Memorization in Generative Models
Brendan Leigh Ross, Hamidreza Kamkari, Zhaoyan Liu, Tongzi Wu, George Stein, Gabriel Loaiza-Ganem, Jesse C. Cresswell A Geometric Framework for Understanding Memorization in Generative Models
Brendan Leigh Ross, Hamidreza Kamkari, Zhaoyan Liu, Tongzi Wu, George Stein, Gabriel Loaiza-Ganem, Jesse C. Cresswell A Multi-View Mixture-of-Experts Based on Language and Graphs for Molecular Properties Prediction
Victor Yukio Shirasuna, Eduardo Soares, Emilio Vital Brazil, Karen Fiorella Aquino Gutierrez, Renato Cerqueira, Seiji Takeda, Akihiro Kishimoto A Peek into Token Bias: Large Language Models Are Not yet Genuine Reasoners
Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie J Su, Camillo Jose Taylor, Dan Roth A Pontryagin Perspective on Reinforcement Learning
Onno Eberhard, Claire Vernade, Michael Muehlebach A Recipe for Charge Density Prediction
Xiang Fu, Andrew Scott Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess Smidt, Tommi Jaakkola A Statistical Framework for Weak-to-Strong Generalization
Seamus Somerstep, Felipe Maia Polo, Moulinath Banerjee, Yaacov Ritov, Mikhail Yurochkin, Yuekai Sun A Systematic Comparison of fMRI-to-Video Reconstruction Techniques
Camilo Luciano Fosco, Ben Lahner, Alex J Andonian, Bowen Pan, Aude Oliva A Tractable Inference Perspective of Offline RL
Xuejie Liu, Anji Liu, Guy Van den Broeck, Yitao Liang A Universal Class of Sharpness-Aware Minimization Algorithms
Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri, Stefanie Jegelka, Patrick Jaillet AbFlex: Predicting the Conformational Flexibility of Antibody CDRs
Fabian C Spoendlin, Wing Ki Wong, Guy Georges, Alexander Bujotzek, Charlotte Deane ABodyBuilder3: Improved and Scalable Antibody Structure Predictions
Henry Kenlay, Frederic A Dreyer, Daniel Cutting, Daniel Allen Nissley, Charlotte Deane Accelerating Best-of-N via Speculative Rejection
Ruiqi Zhang, Momin Haider, Ming Yin, Jiahao Qiu, Mengdi Wang, Peter Bartlett, Andrea Zanette Accelerating Best-of-N via Speculative Rejection
Ruiqi Zhang, Momin Haider, Ming Yin, Jiahao Qiu, Mengdi Wang, Peter Bartlett, Andrea Zanette Accelerating Best-of-N via Speculative Rejection
Ruiqi Zhang, Momin Haider, Ming Yin, Jiahao Qiu, Mengdi Wang, Peter Bartlett, Andrea Zanette Active Preference Optimization for Sample Efficient RLHF
Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury Active Propulsion Noise Shaping for Multi-Rotor Aircraft Localization
Tamir Shor, Gabriele Serussi, Tom Hirshberg, Chaim Baskin, Alex M. Bronstein AdaInf: Adaptive Inference for Resource-Constrained Foundation Models
Zhuoyan Xu, Khoi Duc Nguyen, Preeti Mukherjee, Somali Chaterji, Yingyu Liang, Yin Li Adam-Mini: Use Fewer Learning Rates to Gain More
Yushun Zhang, Congliang Chen, Ziniu Li, Tian Ding, Chenwei Wu, Yinyu Ye, Zhi-Quan Luo, Ruoyu Sun Adapting LLM Agents with Universal Feedback in Communication
Kuan Wang, Yadong Lu, Michael Santacroce, Yeyun Gong, Chao Zhang, Yelong Shen Adaptive Concept Bottleneck for Foundation Models
Jihye Choi, Jayaram Raghuram, Yixuan Li, Suman Banerjee, Somesh Jha Adaptive Model Pruning in Federated Learning Through Loss Exploration
Christian Internò, Elena Raponi, Niki van Stein, Thomas Bäck, Markus Olhofer, Yaochu Jin, Barbara Hammer Adaptive Two-Level Quasi-Monte Carlo for Soft Actor-Critic
Du Ouyang, Zhenpeng Shi, Aodong Guo, Huaze Tang, Hejin Wang, Chao Wang, Wenbo Ding Advancing LLM Reasoning Generalists with Preference Trees
Lifan Yuan, Ganqu Cui, Hanbin Wang, Ning Ding, Xingyao Wang, Jia Deng, Boji Shan, Huimin Chen, Ruobing Xie, Yankai Lin, Zhenghao Liu, Bowen Zhou, Hao Peng, Zhiyuan Liu, Maosong Sun Advantage Alignment Algorithms
Juan Agustin Duque, Milad Aghajohari, Tim Cooijmans, Tianyu Zhang, Aaron Courville Adversarial Circuit Evaluation
Niels uit de Bos, Adrià Garriga-Alonso Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura AI Agents with Formal Security Guarantees
Mislav Balunovic, Luca Beurer-Kellner, Marc Fischer, Martin Vechev AI Alignment with Changing and Influenceable Reward Functions
Micah Carroll, Davis Foote, Anand Siththaranjan, Stuart Russell, Anca Dragan AI Alignment with Changing and Influenceable Reward Functions
Micah Carroll, Davis Foote, Anand Siththaranjan, Stuart Russell, Anca Dragan AI for an Inverse Problem: Physical Model Solving Quantum Gravity
Koji Hashimoto, Koshiro Matsuo, Masaki Murata, Gakuto Ogiwara, Daichi Takeda Aligned Diffusion Models for Retrosynthesis
Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg Aligned Diffusion Models for Retrosynthesis
Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg Aligning Crowd Feedback via Distributional Preference Reward Modeling
Dexun Li, Cong Zhang, Kuicai Dong, Derrick Goh Xin Deik, Ruiming Tang, Yong Liu Aligning Large Language Models with Representation Editing: A Control Perspective
Lingkai Kong, Haorui Wang, Wenhao Mu, Yuanqi Du, Yuchen Zhuang, Yifei Zhou, Yue Song, Rongzhi Zhang, Kai Wang, Chao Zhang Alignment Calibration: Machine Unlearning for Contrastive Learning Under Auditing
Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao Alignment of MPNNs and Graph Transformers
Bao Nguyen, Anjana Yodaiken, Petar Veličković Altared Environments: The Role of Normative Infrastructure in AI Alignment
Rakshit Trivedi, Nikhil Chandak, Carter Blair, Atrisha Sarkar, Tehilla Weltman, Dylan Hadfield-Menell, Gillian K Hadfield AMBER: An Entropy Maximizing Environment Design Algorithm for Inverse Reinforcement Learning
Paul Nitschke, Lars Lien Ankile, Eura Nofshin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan Amortized Probabilistic Detection of Communities in Graphs
Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski, Ari Pakman An Analytical Approach to Enhancing DNN Efficiency and Accuracy Using Approximate Multiplication
Salar Shakibhamedan, Anice Jahanjoo, Amin Aminifar, Nima Amirafshar, Nima TaheriNejad, Axel Jantsch An Auditing Test to Detect Behavioral Shift in Language Models
Leo Richter, Nitin Agrawal, Xuanli He, Pasquale Minervini, Matt Kusner An Embodied Generalist Agent in 3D World
Jiangyong Huang, Silong Yong, Xiaojian Ma, Xiongkun Linghu, Puhao Li, Yan Wang, Qing Li, Song-Chun Zhu, Baoxiong Jia, Siyuan Huang An Exactly Solvable Model for Emergence and Scaling Laws
Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee, Chris Mingard, Ard A. Louis Analyzing GFlowNets: Stability, Expressiveness, and Assessment
Tiago Silva, Eliezer de Souza da Silva, Rodrigo Barreto Alves, Luiz Max Carvalho, Amauri H Souza, Samuel Kaski, Vikas Garg, Diego Mesquita Analyzing the Generalization and Reliability of Steering Vectors
Daniel Chee Hian Tan, David Chanin, Aengus Lynch, Adrià Garriga-Alonso, Dimitrios Kanoulas, Brooks Paige, Robert Kirk Antigen-Specific Antibody Design via Direct Energy-Based Preference Optimization
Xiangxin Zhou, Dongyu Xue, Ruizhe Chen, Zaixiang Zheng, Liang Wang, Quanquan Gu Are Large Language Models Chameleons?
Mingmeng Geng, Sihong He, Roberto Trotta Are Protein Language Models Compute Optimal?
Yaiza Serrano, Alvaro Ciudad Serrano, Alexis Molina AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields
Louis Serrano, Thomas X Wang, Etienne Le Naour, Jean-Noël Vittaut, Patrick Gallinari Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL
Eduardo Pignatelli, Johan Ferret, Davide Paglieri, Samuel Coward, Tim Rocktäschel, Edward Grefenstette, Laura Toni AssistanceZero: Scalably Solving Assistance Games
Cassidy Laidlaw, Eli Bronstein, Timothy Guo, Dylan Feng, Lukas Berglund, Justin Svegliato, Stuart Russell, Anca Dragan AstroPT: Scaling Large Observation Models for Astronomy
Michael J. Smith, Ryan J. Roberts, Eirini Angeloudi, Marc Huertas-Company Asymptotic Dynamics for Delayed Feature Learning in a Toy Model
Blake Bordelon, Tanishq Kumar, Samuel J. Gershman, Cengiz Pehlevan Asynchronous Local-SGD Training for Language Modeling
Bo Liu, Rachita Chhaparia, Arthur Douillard, Satyen Kale, Andrei Alex Rusu, Jiajun Shen, Arthur Szlam, MarcAurelio Ranzato Asynchrony Invariance Loss Functions for Graph Neural Networks
Pablo Monteagudo-Lago, Arielle Rosinski, Andrew Joseph Dudzik, Petar Veličković Attacking Large Language Models with Projected Gradient Descent
Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann Attention with Markov: A Curious Case of Single-Layer Transformers
Ashok Vardhan Makkuva, Marco Bondaschi, Alliot Nagle, Adway Girish, Hyeji Kim, Martin Jaggi, Michael Gastpar Augmenting Evolutionary Models with Structure-Based Retrieval
Yining Huang, Zuobai Zhang, Jian Tang, Debora Susan Marks, Pascal Notin Automatic Jailbreaking of the Text-to-Image Generative AI Systems
Minseon Kim, Hyomin Lee, Boqing Gong, Huishuai Zhang, Sung Ju Hwang Baba Is AI: Break the Rules to Beat the Benchmark
Nathan Cloos, Meagan Jens, Michelangelo Naim, Yen-Ling Kuo, Ignacio Cases, Andrei Barbu, Christopher J Cueva BAM! Just like That: Simple and Efficient Parameter Upcycling for Mixture of Experts
Qizhen Zhang, Nikolas Gritsch, Dwaraknath Gnaneshwar, Simon Guo, David Cairuz, Bharat Venkitesh, Jakob Nicolaus Foerster, Phil Blunsom, Sebastian Ruder, Ahmet Üstün, Acyr Locatelli Bandits with Abstention Under Expert Advice
Stephen Pasteris, Alberto Rumi, Maximilian Thiessen, Shota Saito, Atsushi Miyauchi, Fabio Vitale, Mark Herbster Base-Change at Prediction: Inference-Time Update of Fine-Tuned Models
Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida, Kuniko Saito, Susumu Takeuchi Batch Learning via Log-Sum-Exponential Estimator from Logged Bandit Feedback
Armin Behnamnia, Gholamali Aminian, Alireza Aghaei, Chengchun Shi, Vincent Y. F. Tan, Hamid R. Rabiee Bayesian Optimization for the Discovery of Redox Active Quinones
Giacomo De Gobbi, Reyhan Yagmur, Janine Maier, Stefan Spirk, Robert Peharz Bayesian Reward Models for LLM Alignment
Adam X. Yang, Maxime Robeyns, Thomas Coste, Zhengyan Shi, Jun Wang, Haitham Bou Ammar, Laurence Aitchison Behavior Generation with Latent Actions
Seungjae Lee, Yibin Wang, Haritheja Etukuru, H. Jin Kim, Nur Muhammad Mahi Shafiullah, Lerrel Pinto Benchmarking Mental State Representations in Language Models
Matteo Bortoletto, Constantin Ruhdorfer, Lei Shi, Andreas Bulling Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks
Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation
Katherine M. Collins, Najoung Kim, Yonatan Bitton, Verena Rieser, Shayegan Omidshafiei, Yushi Hu, Sherol Chen, Senjuti Dutta, Minsuk Chang, Kimin Lee, Youwei Liang, Georgina Evans, Sahil Singla, Gang Li, Adrian Weller, Junfeng He, Deepak Ramachandran, Krishnamurthy Dj Dvijotham Bilevel Optimization with Lower-Level Contextual MDPs
Vinzenz Thoma, Barna Pásztor, Andreas Krause, Giorgia Ramponi, Yifan Hu Bilingual Adaptation of Monolingual Foundation Models
Gurpreet Gosal, Yishi Xu, Gokulakrishnan Ramakrishnan, Rituraj Joshi, Avraham Sheinin, Zhiming Chen, Biswajit Mishra, Sunil Kumar Sahu, Neha Sengupta, Natalia Vassilieva, Joel Hestness, Samujjwal Ghosh, Bokang Jia, Onkar Arun Pandit, Satheesh Katipomu, Samta Kamboj, Rahul Pal, Parvez Mullah, Soundar Balaji Doraiswamy, Karim Chami, Preslav Nakov BioinformaticsBench: A Collaboratively Built Large Language Model Benchmark for Bioinformatics Reasoning
Varuni Sarwal, Seungmo Lee, Rosemary He, Aingela Kattapuram, Xiaoxuan Wang, Eleazar Eskin, Wei Wang, Serghei Mangul BiPer: Binary Neural Networks Using a Periodic Function
Edwin Vargas, Claudia V. Correa, Carlos Hinojosa, Henry Arguello Black-Box Detection of Language Model Watermarks
Thibaud Gloaguen, Nikola Jovanović, Robin Staab, Martin Vechev Black-Box Detection of Language Model Watermarks
Thibaud Gloaguen, Nikola Jovanović, Robin Staab, Martin Vechev Block Verification Accelerates Speculative Decoding
Ziteng Sun, Uri Mendlovic, Yaniv Leviathan, Asaf Aharoni, Ahmad Beirami, Jae Hun Ro, Ananda Theertha Suresh Boolean Logic for Low-Energy Deep Learning
Van Minh Nguyen, Cristian Ocampo, Aymen Askri, Ba-Hien Tran Bootstrapping Language Models with DPO Implicit Rewards
Changyu Chen, Zichen Liu, Chao Du, Tianyu Pang, Qian Liu, Arunesh Sinha, Pradeep Varakantham, Min Lin Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity
Zhuo Zhi, Ziquan Liu, Moe Elbadawi, Adam Daneshmend, Mine Orlu, Abdul W Basit, Andreas Demosthenous, Miguel R. D. Rodrigues BUILD: Buffer-Free Incremental Learning with OOD Detection for the Wild
Srishti Gupta, Daniele Angioni, Lea Schönherr, Ambra Demontis, Battista Biggio Bundle Neural Networks for Message Diffusion on Graphs
Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael M. Bronstein Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Yair Schiff, Chia Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Yair Schiff, Chia Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Yair Schiff, Chia Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov Calibrated Self-Rewarding Vision Language Models
Yiyang Zhou, Zhiyuan Fan, Dongjie Cheng, Sihan Yang, Zhaorun Chen, Chenhang Cui, Xiyao Wang, Yun Li, Linjun Zhang, Huaxiu Yao Can Editing LLMs Inject Harm?
Canyu Chen, Baixiang Huang, Zekun Li, Zhaorun Chen, Shiyang Lai, Xiongxiao Xu, Jia-Chen Gu, Jindong Gu, Huaxiu Yao, Chaowei Xiao, Xifeng Yan, William Yang Wang, Philip Torr, Dawn Song, Kai Shu Can Editing LLMs Inject Harm?
Canyu Chen, Baixiang Huang, Zekun Li, Zhaorun Chen, Shiyang Lai, Xiongxiao Xu, Jia-Chen Gu, Jindong Gu, Huaxiu Yao, Chaowei Xiao, Xifeng Yan, William Yang Wang, Philip Torr, Dawn Song, Kai Shu Can Go AIs Be Adversarially Robust?
Tom Tseng, Euan McLean, Kellin Pelrine, Tony Tong Wang, Adam Gleave Can Large Language Models Explore In-Context?
Akshay Krishnamurthy, Keegan Harris, Dylan J Foster, Cyril Zhang, Aleksandrs Slivkins Can Learned Optimization Make Reinforcement Learning Less Difficult?
Alexander D. Goldie, Chris Lu, Matthew Thomas Jackson, Shimon Whiteson, Jakob Nicolaus Foerster Can LLMs Enhance Performance Prediction for Deep Learning Models?
Karthick Panner Selvam, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Bryan Perozzi, Mats Brorsson Can Mamba In-Context Learn Task Mixtures?
Yingcong Li, Xupeng Wei, Haonan Zhao, Taigao Ma Can Models Learn Skill Composition from Examples?
Haoyu Zhao, Simran Kaur, Dingli Yu, Anirudh Goyal, Sanjeev Arora Can Transformers Solve Least Squares to High Precision?
Jerry Weihong Liu, Jessica Grogan, Owen M Dugan, Simran Arora, Atri Rudra, Christopher Re Can Transformers Solve Least Squares to High Precision?
Jerry Weihong Liu, Jessica Grogan, Owen M Dugan, Simran Arora, Atri Rudra, Christopher Re Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
Michael-Andrei Panaitescu-Liess, Zora Che, Bang An, Yuancheng Xu, Pankayaraj Pathmanathan, Souradip Chakraborty, Sicheng Zhu, Tom Goldstein, Furong Huang CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models
Peng Xia, Ze Chen, Juanxi Tian, Gong Yangrui, Ruibo Hou, Yue Xu, Zhenbang Wu, Zhiyuan Fan, Yiyang Zhou, Kangyu Zhu, Wenhao Zheng, Zhaoyang Wang, Xiao Wang, Xuchao Zhang, Chetan Bansal, Marc Niethammer, Junzhou Huang, Hongtu Zhu, Yun Li, Jimeng Sun, Zongyuan Ge, Gang Li, James Zou, Huaxiu Yao Cell Morphology-Guided Small Molecule Generation with GFlowNets
Stephen Zhewen Lu, Ziqing Lu, Ehsan Hajiramezanali, Tommaso Biancalani, Yoshua Bengio, Gabriele Scalia, Michał Koziarski Cell Morphology-Guided Small Molecule Generation with GFlowNets
Stephen Zhewen Lu, Ziqing Lu, Ehsan Hajiramezanali, Tommaso Biancalani, Yoshua Bengio, Gabriele Scalia, Michał Koziarski Cell Morphology-Guided Small Molecule Generation with GFlowNets
Stephen Zhewen Lu, Ziqing Lu, Ehsan Hajiramezanali, Tommaso Biancalani, Yoshua Bengio, Gabriele Scalia, Michał Koziarski Certifiably Robust RAG Against Retrieval Corruption
Chong Xiang, Tong Wu, Zexuan Zhong, David Wagner, Danqi Chen, Prateek Mittal Certifying Robustness to Adaptive Data Poisoning
Avinandan Bose, Madeleine Udell, Laurent Lessard, Maryam Fazel, Krishnamurthy Dj Dvijotham Chained Tuning Leads to Biased Forgetting
Megan Ung, Alicia Yi Sun, Samuel Bell, Levent Sagun, Adina Williams Chained Tuning Leads to Biased Forgetting
Megan Ung, Alicia Yi Sun, Samuel Bell, Levent Sagun, Adina Williams Characterizing Prompt Compression Methods for Long Context Inference
Siddharth Jha, Lutfi Eren Erdogan, Sehoon Kim, Kurt Keutzer, Amir Gholami CharED: Character-Wise Ensemble Decoding for Large Language Models
Kevin Gu, Eva Tuecke, Dmitriy A Katz, Raya Horesh, David Alvarez-Melis, Mikhail Yurochkin Chemical Language Modeling with Structured State Spaces
Rıza Özçelik, Sarah de Ruiter, Emanuele Criscuolo, Francesca Grisoni CLAM: Unifying Finetuning, Quantization, and Pruning by Chaining LLM Adapter Modules
Neelay Velingker, Jason Liu, Amish Sethi, William Dodds, Zhiqiu Xu, Saikat Dutta, Mayur Naik, Eric Wong Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms
Samuel Don Stanton, Robert G Alberstein, Nathan C. Frey, Andrew Martin Watkins, Kyunghyun Cho Cluster-Norm for Unsupervised Probing of Knowledge
Walter Laurito, Sharan Maiya, Grégoire Dhimoïla, Owen Ho Wan Yeung, Kaarel Hänni Code Agents Are State of the Art Software Testers
Niels Mündler, Mark Niklas Mueller, Jingxuan He, Martin Vechev Code Agents Are State of the Art Software Testers
Niels Mündler, Mark Niklas Mueller, Jingxuan He, Martin Vechev Cognitive Assessment of Language Models
Daniel McDuff, David Munday, Xin Liu, Isaac Galatzer-Levy Color Style Transfer with Modulated Flows
Maria Larchenko, Alexander Lobashev, Dmitry Guskov, Vladimir Vladimirovich Palyulin Comgra: A Tool for Analyzing and Debugging Neural Networks
Florian Dietz, Sophie Fellenz, Dietrich Klakow, Marius Kloft Commute-Time-Optimised Graphs for GNNs
Igor Sterner, Shiye Su, Petar Veličković Compact Proofs of Model Performance via Mechanistic Interpretability
Jason Gross, Rajashree Agrawal, Thomas Kwa, Euan Ong, Chun Hei Yip, Alex Gibson, Soufiane Noubir, Lawrence Chan Compress Then Serve: Serving Thousands of LoRA Adapters with Little Overhead
Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin, Justin Solomon Conditional Flow Matching for Time Series Modelling
Ella Tamir, Najwa Laabid, Markus Heinonen, Vikas Garg, Arno Solin Confidence Regulation Neurons in Language Models
Alessandro Stolfo, Ben Peng Wu, Wes Gurnee, Yonatan Belinkov, Xingyi Song, Mrinmaya Sachan, Neel Nanda Conformalized Credal Set Predictors
Alireza Javanmardi, David Stutz, Eyke Hüllermeier Consistency Checks for Language Model Forecasters
Abhimanyu Pallavi Sudhir, Alejandro Alvarez, Adam Shen, Daniel Paleka Consistency Checks for Language Model Forecasters
Abhimanyu Pallavi Sudhir, Alejandro Alvarez, Adam Shen, Daniel Paleka ContextCite: Attributing Model Generation to Context
Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev, Aleksander Madry ContextCite: Attributing Model Generation to Context
Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev, Aleksander Madry Coordination Failure in Cooperative Offline MARL
Callum Rhys Tilbury, Juan Claude Formanek, Louise Beyers, Jonathan Phillip Shock, Arnu Pretorius CoSy: Evaluating Textual Explanations of Neurons
Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina MC Höhne, Kirill Bykov CoSy: Evaluating Textual Explanations of Neurons
Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina MC Höhne, Kirill Bykov Cramming Protein Language Model Training in 24 GPU Hours
Nathan C. Frey, Taylor Joren, Aya Abdelsalam Ismail, Allen Goodman, Richard Bonneau, Kyunghyun Cho, Vladimir Gligorijevic DACO: Towards Application-Driven and Comprehensive Data Analysis via Code Generation
Xueqing Wu, Rui Zheng, Jingzhen Sha, Te-Lin Wu, Hanyu Zhou, Tang Mohan, Kai-Wei Chang, Nanyun Peng, Haoran Huang DARE: The Deep Adaptive Regulator for Control of Uncertain Continuous-Time Systems
Harrison Waldon, Fayçal Drissi, Yannick Limmer, Uljad Berdica, Jakob Nicolaus Foerster, Alvaro Cartea Data as a Consumable Resource
Dar Gilboa, Siddhartha Jain, Jarrod Ryan McClean DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
Jianxiong Li, Jinliang Zheng, Yinan Zheng, Liyuan Mao, Xiao Hu, Sijie Cheng, Haoyi Niu, Jihao Liu, Yu Liu, Jingjing Liu, Ya-Qin Zhang, Xianyuan Zhan Decoding-Time Language Model Alignment with Multiple Objectives
Ruizhe Shi, Yifang Chen, Yushi Hu, Alisa Liu, Hannaneh Hajishirzi, Noah A. Smith, Simon Shaolei Du Decomposed Evaluations of Geographic Disparities in Text-to-Image Models
Abhishek Sureddy, Dishant Padalia, Nandhinee Periyakaruppan, Oindrila Saha, Adina Williams, Adriana Romero-Soriano, Megan Richards, Polina Kirichenko, Melissa Hall Decomposed Evaluations of Geographic Disparities in Text-to-Image Models
Abhishek Sureddy, Dishant Padalia, Nandhinee Periyakaruppan, Oindrila Saha, Adina Williams, Adriana Romero-Soriano, Megan Richards, Polina Kirichenko, Melissa Hall Decoupled Stochastic Gradient Descent for N-Player Games
Ali Zindari, Parham Yazdkhasti, Tatjana Chavdarova, Sebastian U Stich Deep Networks Always Grok and Here Is Why
Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk Deep Supramolecular Language Processing for Co-Crystal Prediction
Rebecca Birolo, Rıza Özçelik, Andrea Aramini, Michele R. Chierotti, Roberto Gobetto, Francesca Grisoni Delay Embedding Theory of Neural Sequence Models
Mitchell Ostrow, Adam Joseph Eisen, Ila R Fiete Demystifying Amortized Causal Discovery with Transformers
Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar, Francesco Locatello DETAIL: Task DEmonsTration Attribution for Interpretable In-Context Learning
Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low Detecting Critical Treatment Effect Bias in Small Subgroups
Piersilvio De Bartolomeis, Javier Abad, Konstantin Donhauser, Fanny Yang Detrimental Memories in Transfer Learning
Amal Alnouri, Timothy J Wroge, Bilal Alsallakh Differentiable Cluster Graph Neural Network
Yanfei Dong, Mohammed Haroon Dupty, Lambert Deng, Zhuanghua Liu, Yong Liang Goh, Wee Sun Lee Differentiable Cost-Parameterized Monge mAP Estimators
Samuel Howard, George Deligiannidis, Patrick Rebeschini, James Thornton Differentiable Local Intrinsic Dimension Estimation with Diffusion Models
Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem Differentiable Weighted Automata
Anand Balakrishnan, Jyotirmoy V. Deshmukh Differentiable Wireless Simulation with Geometric Transformers
Thomas Hehn, Markus Peschl, Tribhuvanesh Orekondy, Arash Behboodi, Johann Brehmer DiffFit: Differentiable Fitting of Molecule Structures to a Cryo-EM mAP
Deng Luo, Zainab Alsuwaykit, Dawar Khan, Ondrej Strnad, Tobias Isenberg, Ivan Viola Diffusion Models with Group Equivariance
Haoye Lu, Spencer Szabados, Yaoliang Yu DiffusionGuard: A Robust Defense Against Malicious Diffusion-Based Image Editing
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Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborova, Bruno Loureiro, Florent Krzakala Future-Proof Vaccine Design with a Generative Model of Antibody Cross-Reactivity
Noor Youssef, Sarah Gurev, Hannah Rivka Pierce-Hoffman, Alexander A Cohen, Luis F Caldera, Pamela J Bjorkman, Debora Susan Marks Gaussian Process-Based Representation Learning via Timeseries Symmetries
Petar Bevanda, Max Beier, Armin Lederer, Alexandre Capone, Stefan Georg Sosnowski, Sandra Hirche Gene-Centric Evaluation of Causal Variant Prediction for DNA Models
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Ayush Sawarni, Nirjhar Das, Siddharth Barman, Gaurav Sinha Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion
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Anish Acharya, Inderjit S Dhillon, Sujay Sanghavi Geometric Wireless Simulation with Equivariant Transformers
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Beom Seok Kang, Vignesh C Bhethanabotla, Mohammadamin Tavakoli, William Goddard, Anima Anandkumar Geometry Fidelity for Spherical Images
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Raunaq Bhirangi, Chenyu Wang, Venkatesh Pattabiraman, Carmel Majidi, Abhinav Gupta, Tess Hellebrekers, Lerrel Pinto Higher Order and Self-Referential Evolution for Population-Based Methods
Samuel Coward, Chris Lu, Alistair Letcher, Minqi Jiang, Jack Parker-Holder, Jakob Nicolaus Foerster HLSTransform: Energy-Efficient Llama 2 Inference on FPGAs via High Level Synthesis
Darren Yan Key, Andy He, Mason Bulling, Andrew Chang, Skyler Shapiro, Everett Lee Hummer: Towards Limited Competitive Preference Dataset
Li Jiang, Yusen Wu, Junwu Xiong, Jingqing Ruan, Qingpei Guo, Zujie Wen, Jun Zhou, Xiaotie Deng Hummer: Towards Limited Competitive Preference Dataset
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Jordan Juravsky, Bradley Brown, Ryan Saul Ehrlich, Daniel Y Fu, Christopher Re, Azalia Mirhoseini Hyperspectral Unmixing for Raman Spectroscopy via Physics-Constrained Autoencoders
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Ali Ramlaoui, Théo Saulus, Basile Terver, Victor Schmidt, David Rolnick, Fragkiskos D. Malliaros, Alexandre AGM Duval Improving Route Development Using Convergent Retrosynthesis Planning
Paula Torren-Peraire, Jonas Verhoeven, Dorota Herman, Hugo Ceulemans, Igor V. Tetko, Jörg K. Wegner Improving Sparse Decomposition of Language Model Activations with Gated Sparse Autoencoders
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Sophie Xhonneux, David Dobre, Michael Noukhovitch, Jian Tang, Gauthier Gidel, Dhanya Sridhar In-Context Principle Learning from Mistakes
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Juncheng Dong, Moyang Guo, Ethan X Fang, Zhuoran Yang, Vahid Tarokh Incorporating Stability into Flow Matching
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Chaojun Xiao, Pengle Zhang, Xu Han, Guangxuan Xiao, Yankai Lin, Zhengyan Zhang, Zhiyuan Liu, Maosong Sun InfoNCE: Identifying the Gap Between Theory and Practice
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Kasia Kobalczyk, Mihaela van der Schaar Informed Meta-Learning
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Antoine Bourdin, Ronan Legin, Matthew Ho, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur Instruction Tuning with Loss over Instructions
Zhengyan Shi, Adam X. Yang, Bin Wu, Laurence Aitchison, Emine Yilmaz, Aldo Lipani Instruction-Guided Visual Masking
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Sean Wu, Jordan Hong, Keybai, Gregor Bachmann Interpretability Analysis on a Pathology Foundation Model Reveals Biologically Relevant Embeddings Across Modalities
Nhat Le, Ciyue Shen, Chintan Shah, Blake Martin, Daniel Shenker, Harshith Padigela, Jennifer A. Hipp, Sean Grullon, John Abel, Harsha Vardhan Pokkalla, Dinkar Juyal Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agent
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Connor Kissane, Robert Krzyzanowski, Joseph Isaac Bloom, Arthur Conmy, Neel Nanda Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
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TaiMing Lu, Lingfeng Shen, Xinyu Yang, Weiting Tan, Beidi Chen, Huaxiu Yao Iteration Head: A Mechanistic Study of Chain-of-Thought
Vivien Cabannes, Charles Arnal, Wassim Bouaziz, Xingyu Alice Yang, Francois Charton, Julia Kempe Iterative Sizing Field Prediction for Adaptive Mesh Generation from Expert Demonstrations
Niklas Freymuth, Philipp Dahlinger, Tobias Würth, Philipp Becker, Aleksandar Taranovic, Onno Grönheim, Luise Kärger, Gerhard Neumann Iterative Theory of Mind Assay of Multimodal AI Models
Rohini Elora Das, Rajarshi Das, Niharika Maity, Sreerupa Das Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent
Quentin Gallouédec, Edward Emanuel Beeching, Clément Romac, Emmanuel Dellandrea Jafar: An Open-Source Genie Reimplemention in JAX
Timon Willi, Matthew Thomas Jackson, Jakob Nicolaus Foerster JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks
Ferran Hernandez Caralt, Guillermo Bernardez, Iulia Duta, Eduard Alarcon, Pietro Lio Just Read Twice: Closing the Recall Gap for Recurrent Language Models
Simran Arora, Aman Timalsina, Aaryan Singhal, Sabri Eyuboglu, Xinyi Zhao, Ashish Rao, Atri Rudra, Christopher Re Landscaping Linear Mode Connectivity
Sidak Pal Singh, Linara Adilova, Michael Kamp, Asja Fischer, Bernhard Schölkopf, Thomas Hofmann Language Models Linearly Represent Sentiment
Curt Tigges, Oskar John Hollinsworth, Atticus Geiger, Neel Nanda Large Language Models as Misleading Assistants in Conversation
Betty Li Hou, Kejian Shi, Jason Phang, James Aung, Steven Adler, Rosie Campbell Large Language Models Can Self-Correct with Minimal Effort
Zhenyu Wu, Qingkai Zeng, Zhihan Zhang, Zhaoxuan Tan, Chao Shen, Meng Jiang Latent Functional Maps
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Abolfazl Malekahmadi, Mohammad Taha Teimuri Jervakani, Armin Behnamnia, Zahra Dehghanian, Amir Shamloo, Hamid R. Rabiee Parameter Tuning and Modeling of a Rotary Kiln Using Physics-Informed Neural Networks
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Xinyi Chen, Angelica Chen, Dean Foster, Elad Hazan PLINDER: The Protein-Ligand Interactions Dataset and Evaluation Resource
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George Tsoukalas, Jasper Lee, John Jennings, Jimmy Xin, Michelle Ding, Michael Jennings, Amitayush Thakur, Swarat Chaudhuri QGFN: Controllable Greediness with Action Values
Elaine Lau, Stephen Zhewen Lu, Ling Pan, Doina Precup, Emmanuel Bengio Quality-Diversity for One-Shot Biological Sequence Design
Jérémie Dona, Arthur Flajolet, Andrei Marginean, Antoine Cully, Thomas Pierrot Quantized Representations Prevent Dimensional Collapse in Self-Predictive RL
Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaeinezhadfard, Arno Solin, Joni Pajarinen Quantum Circuit Synthesis with Diffusion Models
Florian Fürruter, Gorka Muñoz-Gil, Hans J Briegel Quantum-PEFT: Ultra Parameter-Efficient Fine-Tuning
Toshiaki Koike-Akino, Francesco Tonin, Yongtao Wu, Leyla Naz Candogan, Volkan Cevher RamanSPy: Augmenting Raman Spectroscopy Data Analysis with AI
Dimitar Georgiev, Simon Vilms Pedersen, Ruoxiao Xie, Álvaro Fernández-Galiana, Molly M. Stevens, Mauricio Barahona RamanSPy: Augmenting Raman Spectroscopy Data Analysis with AI
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David Yunis, Kumar Kshitij Patel, Samuel Wheeler, Pedro Henrique Pamplona Savarese, Gal Vardi, Karen Livescu, Michael Maire, Matthew Walter Rapid Switching and Multi-Adapter Fusion via Sparse High Rank Adapters
Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi, Viswanath Ganapathy, Rafael Esteves, Shreya Kadambi, Shubhankar Borse, Paul Whatmough, Risheek Garrepalli, Mart Van Baalen, Harris Teague, Markus Nagel REBEL: Reinforcement Learning via Regressing Relative Rewards
Zhaolin Gao, Jonathan Daniel Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun REBEL: Reinforcement Learning via Regressing Relative Rewards
Zhaolin Gao, Jonathan Daniel Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun Recommender System Design via Online Feedback Optimization
Sanjay Chandrasekaran, Giulia De Pasquale, Giuseppe Belgioioso, Florian Dorfler Refusal in Language Models Is Mediated by a Single Direction
Andy Arditi, Oscar Balcells Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, Neel Nanda Reinforcement Learning from Bagged Reward
Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding, Qiyu Wu, Guoqing Liu, Masashi Sugiyama Reinforcement Learning in the Wild with Maximum Likelihood-Based Model Transfer
Hannes Eriksson, Tommy Tram, Debabrota Basu, Mina Alibeigi, Christos Dimitrakakis Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
Gianluigi Silvestri, Fabio Valerio Massoli, Tribhuvanesh Orekondy, Afshin Abdi, Arash Behboodi Relatively Rational: Learning Utilities and Rationalities Jointly from Pairwise Preferences
Taku Yamagata, Tobias Oberkofler, Timo Kaufmann, Viktor Bengs, Eyke Hüllermeier, Raul Santos-Rodriguez Relaxed Equivariant Graph Neural Networks
Elyssa Hofgard, Rui Wang, Robin Walters, Tess Smidt Relaxing Graph Transformers for Adversarial Attacks
Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann RepoQA: Evaluating Long Context Code Understanding
Jiawei Liu, Jia Le Tian, Vijay Daita, Yuxiang Wei, Yifeng Ding, Yuhan Katherine Wang, Jun Yang, Lingming Zhang Resolving Discrepancies in Compute-Optimal Scaling of Language Models
Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, Yair Carmon Rethinking Invariance in In-Context Learning
Lizhe Fang, Yifei Wang, Khashayar Gatmiry, Lei Fang, Yisen Wang Retrieval & Fine-Tuning for In-Context Tabular Models
Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini Retrieve to Explain: Evidence-Driven Predictions with Language Models
Ravi Patel, Angus Brayne, Rogier Hintzen, Daniel Jaroslawicz, Georgiana Neculae, Dane S. Corneil Revisiting Cascaded Ensembles for Efficient Inference
Steven Kolawole, Don Dennis, Ameet Talwalkar, Virginia Smith Revisiting Random Walks for Learning on Graphs
Jinwoo Kim, Olga Zaghen, Ayhan Suleymanzade, Youngmin Ryou, Seunghoon Hong Revisiting Successor Features for Inverse Reinforcement Learning
Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother, Irina Rish, Glen Berseth, Sanjiban Choudhury Reward Centering
Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton RGFN: Synthesizable Molecular Generation Using GFlowNets
Michał Koziarski, Andrei Rekesh, Dmytro Shevchuk, Almer M. van der Sloot, Piotr Gaiński, Yoshua Bengio, Cheng-Hao Liu, Mike Tyers, Robert A. Batey RIO-CPD: A Riemannian Geometric Method for Correlation-Aware Online Change Point Detection
Chengyuan Deng, Zhengzhang Chen, Xujiang Zhao, Haoyu Wang, Junxiang Wang, Haifeng Chen, Jie Gao RLHF and IIA: Perverse Incentives
Wanqiao Xu, Shi Dong, Xiuyuan Lu, Grace Lam, Zheng Wen, Benjamin Van Roy RNA-FrameFlow for De Novo 3D RNA Backbone Design
Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian Rokkum Jamasb, Charles Harris, Simon V Mathis, Kieran Didi, Bryan Hooi, Pietro Lio RNA-FrameFlow for De Novo 3D RNA Backbone Design
Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian Rokkum Jamasb, Charles Harris, Simon V Mathis, Kieran Didi, Bryan Hooi, Pietro Lio RNR: Teaching Large Language Models to Follow Roles and Rules
Kuan Wang, Alexander Bukharin, Haoming Jiang, Qingyu Yin, Zhengyang Wang, Tuo Zhao, Jingbo Shang, Chao Zhang, Bing Yin, Xian Li, Jianshu Chen, Shiyang Li RoboGolf: Mastering Real-World Minigolf with a Reflective Multi-Modality Vision-Language Model
Hantao Zhou, Tianying Ji, Lukas Sommerhalder, Michael Görner, Norman Hendrich, Fuchun Sun, Jianwei Dr. Zhang, Huazhe Xu Robust Knowledge Unlearning via Mechanistic Localizations
Phillip Huang Guo, Aaquib Syed, Abhay Sheshadri, Aidan Ewart, Gintare Karolina Dziugaite Robust Unlearning via Mechanistic Localizations
Phillip Huang Guo, Aaquib Syed, Abhay Sheshadri, Aidan Ewart, Gintare Karolina Dziugaite Robustness Analysis of AI Models in Critical Energy Systems
Pantelis Dogoulis, Matthieu Jimenez, Maxime Cordy, Salah Ghamizi, Yves Le Traon Robustness of Explainable Artificial Intelligence in Industrial Process Modelling
Benedikt Kantz, Clemens Staudinger, Christoph Feilmayr, Johannes Wachlmayr, Alexander Haberl, Stefan Schuster, Franz Pernkopf RouteFinder: Towards Foundation Models for Vehicle Routing Problems
Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda, André Hottung, Niels Wouda, Leon Lan, Kevin Tierney, Jinkyoo Park RouterBench: A Benchmark for Multi-LLM Routing System
Qitian Jason Hu, Jacob Bieker, Xiuyu Li, Nan Jiang, Benjamin Keigwin, Gaurav Ranganath, Kurt Keutzer, Shriyash Kaustubh Upadhyay Rule Based Rewards for Fine-Grained LLM Safety
Tong Mu, Alec Helyar, Johannes Heidecke, Joshua Achiam, Andrea Vallone, Ian D Kivlichan, Molly Lin, Alex Beutel, John Schulman, Lilian Weng Rule-Enhanced Graph Learning
Ali Khazraee, Abdolreza Mirzaei, Majjid Farhadi, Parmis Nadaff, Kiarash Zahirnia, Mohammad Salameh, Kevin Cannons, Richard Mar, Mingyi Wu, Oliver Schulte SA-DQAS: Self-Attention Enhanced Differentiable Quantum Architecture Search
Yize Sun, Jiarui Liu, Zixin Wu, Zifeng Ding, Yunpu Ma, Thomas Seidl, Volker Tresp Safe Exploration in Reproducing Kernel Hilbert Spaces
Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön, Dominik Baumann Safe Online Nonstochastic Control from Data
Sebastian Kerz, Armin Lederer, Marion Leibold, Dirk Wollherr SAIL: Self-Improving Efficient Online Alignment of Large Language Models
Mucong Ding, Souradip Chakraborty, Vibhu Agrawal, Zora Che, Alec Koppel, Mengdi Wang, Amrit Bedi, Furong Huang Scalable AI Safety via Doubly-Efficient Debate
Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras Scalable Anomaly Detection in Batch Polishing Processes for Inertial Confinement Fusion Shells
Shashank Galla, Akash Tiwari, Kshitij Bhardwaj, Sean Michael Hayes, Satish Bukkapatnam, Suhas Bhandarkar Scalable Approaches for a Theory of Many Minds
Maximilian Puelma Touzel, Amin Memarian, Matthew Riemer, Andrei Mircea, Andrew Robert Williams, Elin Ahlstrand, Lucas Lehnert, Rupali Bhati, Guillaume Dumas, Irina Rish Scalable Local Intrinsic Dimension Estimation with Diffusion Models
Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem Scalable Multi-Task Transfer Learning for Molecular Property Prediction
Chanhui Lee, Dae-Woong Jeong, Sung Moon Ko, Sumin Lee, Hyunseung Kim, Soorin Yim, Sehui Han, Sungwoong Kim, Sungbin Lim Scalable Unsupervised Alignment of Metric and Nonmetric Structures
Sanketh Vedula, Valentino Maiorca, Lorenzo Basile, Francesco Locatello, Alexander Bronstein Scalably Solving Assistance Games
Cassidy Laidlaw, Eli Bronstein, Timothy Guo, Dylan Feng, Lukas Berglund, Justin Svegliato, Stuart Russell, Anca Dragan ScaLES: Scalable Latent Exploration Score for Pre-Trained Generative Networks
Omer Ronen, Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk, Bin Yu Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
Alexander Hägele, Elie Bakouch, Atli Kosson, Loubna Ben Allal, Leandro Von Werra, Martin Jaggi Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
Rafael Rafailov, Yaswanth Chittepu, Ryan Park, Harshit Sikchi, Joey Hejna, W. Bradley Knox, Chelsea Finn, Scott Niekum Scaling the Vocabulary of Non-Autoregressive Models for Efficient Generative Retrieval
Ravisri Valluri, Akash Kumar Mohankumar, Kushal S. Dave, Amit S, Jian Jiao, Manik Varma, Gaurav Sinha Scanning Tunneling Microscopy (STM) Image Segmentation Using Unsupervised and Few-Shot Learning
Nikola Kolev, Emily Hofmann, Geoff Thornton, Max Trouton, Filippo Federici, David Gao, Steven Schofield, Taylor Stock, Neil Curson Scavenging Hyena: Distilling Transformers into Long Convolution Models
Tokiniaina Raharison Ralambomihanta, Shahrad Mohammadzadeh, Sami Nur Islam, Wassim Jabbour, Laurence Liang Scoreformer: A Surrogate Model for Large-Scale Prediction of Docking Scores
Alvaro Ciudad Serrano, Adrian Morales-Pastor, Laura Malo, Isaac Filella-Merce, Victor Guallar, Alexis Molina SE(3)-Hyena Operator for Scalable Equivariant Learning
Artem Moskalev, Mangal Prakash, Rui Liao, Tommaso Mansi SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound
Rishit Dagli, Shivesh Prakash, Robert Wu, Houman Khosravani Segmentation CNNs Are Denoising Models
Luis A. Zavala-Mondragón, Ruud Van Sloun, Peter H.N. de With, Fons van der Sommen Self-Cognition in Large Language Models: An Exploratory Study
Dongping Chen, Jiawen Shi, Neil Zhenqiang Gong, Yao Wan, Pan Zhou, Lichao Sun Self-Exploring Language Models: Active Preference Elicitation for Online Alignment
Shenao Zhang, Donghan Yu, Hiteshi Sharma, Ziyi Yang, Shuohang Wang, Hany Hassan Awadalla, Zhaoran Wang Self-Play Preference Optimization for Language Model Alignment
Yue Wu, Zhiqing Sun, Huizhuo Yuan, Kaixuan Ji, Yiming Yang, Quanquan Gu Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
Jiatong Han, Jannik Kossen, Muhammed Razzak, Lisa Schut, Shreshth A Malik, Yarin Gal Sequential Decision Making with Expert Demonstrations Under Unobserved Heterogeneity
Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul Krishnan, Vasilis Syrgkanis Setting the Record Straight on Transformer Oversmoothing
Gbetondji Jean-Sebastien Dovonon, Michael M. Bronstein, Matt Kusner SGD vs GD: Rank Deficiency in Linear Networks
Aditya Varre, Margarita Sagitova, Nicolas Flammarion Shall We Team up: Exploring Spontaneous Cooperation of Competing LLM Agents
Zengqing Wu, Brian I. Kwon, Shuyuan Zheng, Qianying Liu, Xu Han, Makoto Onizuka, Shaojie Tang, Run Peng, Chuan Xiao Sheaf Diffusion Goes Nonlinear: Enhancing GNNs with Adaptive Sheaf Laplacians
Olga Zaghen, Antonio Longa, Steve Azzolin, Lev Telyatnikov, Andrea Passerini, Pietro Lio Should You Trust DQN?
Aditya Gopalan, Gugan Thoppe Simple and Effective Masked Diffusion Language Models
Subham Sekhar Sahoo, Marianne Arriola, Aaron Gokaslan, Edgar Mariano Marroquin, Alexander M Rush, Yair Schiff, Justin T Chiu, Volodymyr Kuleshov Simple and Effective Masked Diffusion Language Models
Subham Sekhar Sahoo, Marianne Arriola, Aaron Gokaslan, Edgar Mariano Marroquin, Alexander M Rush, Yair Schiff, Justin T Chiu, Volodymyr Kuleshov Simple Linear Attention Language Models Balance the Recall-Throughput Tradeoff
Simran Arora, Sabri Eyuboglu, Michael Zhang, Aman Timalsina, Silas Alberti, Dylan Zinsley, James Zou, Atri Rudra, Christopher Re SkillAct: Using Skill Abstractions Improves LLM Agents
Anthony Zhe Liu, Jongwook Choi, Sungryull Sohn, Yao Fu, Jaekyeom Kim, Dong-Ki Kim, Xinhe Wang, Jaewon Yoo, Honglak Lee Slow Games
D Reusche, Christopher Goes, Nicolas Della Penna Smoke and Mirrors in Causal Downstream Tasks
Riccardo Cadei, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, Francesco Locatello SMX: Sequential Monte Carlo Planning for Expert Iteration
Edan Toledo, Matthew Macfarlane, Donal John Byrne, Siddarth Singh, Paul Duckworth, Alexandre Laterre Sorting Out Quantum Monte Carlo
Jack Richter-Powell, Luca Thiede, Alan Aspuru-Guzik, David Duvenaud Spatio-Spectral Graph Neural Networks
Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann Spectral State Space Models
Naman Agarwal, Daniel Suo, Xinyi Chen, Elad Hazan Stable Differentiable Causal Discovery
Achille Nazaret, Justin Hong, Elham Azizi, David Blei Steering Language Models with Game-Theoretic Solvers
Ian Gemp, Roma Patel, Yoram Bachrach, Marc Lanctot, Vibhavari Dasagi, Luke Marris, Georgios Piliouras, Siqi Liu, Karl Tuyls Stein Variational Newton Neural Network Ensembles
Klemens Flöge, Muhammad Abdul Moeed, Vincent Fortuin Step-on-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
Haoyu Wang, Guozheng Ma, Ziqiao Meng, Zeyu Qin, Li Shen, Zhong Zhang, Bingzhe Wu, Liu Liu, Yatao Bian, Tingyang Xu, Xueqian Wang, Peilin Zhao Stochastic Concept Bottleneck Models
Moritz Vandenhirtz, Sonia Laguna, Ričards Marcinkevičs, Julia E Vogt Stochastic Concept Bottleneck Models
Moritz Vandenhirtz, Sonia Laguna, Ričards Marcinkevičs, Julia E Vogt Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search
Jonathan Light, Min Cai, Weiqin Chen, Guanzhi Wang, Xiusi Chen, Wei Cheng, Yisong Yue, Ziniu Hu STREAM: Embodied Reasoning Through Code Generation
Daniil Cherniavskii, Phillip Lippe, Andrii Zadaianchuk, Efstratios Gavves STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making
Chuanhao Li, Runhan Yang, Tiankai Li, Milad Bafarassat, Kourosh Sharifi, Dirk Bergemann, Zhuoran Yang STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making
Chuanhao Li, Runhan Yang, Tiankai Li, Milad Bafarassat, Kourosh Sharifi, Dirk Bergemann, Zhuoran Yang Structural Activity Prediction Models Recover Known Binding Modes (Poster Abstract)
Michael Backenköhler, Joschka Groß, Paula Linh Kramer, Verena Wolf, Andrea Volkamer Structure-Based Drug Design Benchmark: Do 3D Methods Really Dominate?
Kangyu Zheng, Yingzhou Lu, Zaixi Zhang, Zhongwei Wan, Yao Ma, Marinka Zitnik, Tianfan Fu Sum-Max Submodular Bandits
Stephen Pasteris, Alberto Rumi, Fabio Vitale, Nicolò Cesa-Bianchi Survival of the Fittest Representation: A Case Study with Modular Addition
Xiaoman Delores Ding, Zifan Carl Guo, Eric J Michaud, Ziming Liu, Max Tegmark SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
Vijay Lingam, Atula Tejaswi Neerkaje, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Eunsol Choi, Alex Dimakis, Aleksandar Bojchevski, Sujay Sanghavi SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
Vijay Lingam, Atula Tejaswi Neerkaje, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Alex Dimakis, Eunsol Choi, Aleksandar Bojchevski, Sujay Sanghavi Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
Yuyang Wang, Ahmed A. A. Elhag, Navdeep Jaitly, Joshua M. Susskind, Miguel Ángel Bautista Symbolic Autoencoding for Self-Supervised Sequence Learning
Mohammad Hossein Amani, Nicolas Baldwin, Amin Mansouri, Martin Josifoski, Maxime Peyrard, Robert West Symbolic Regression with a Learned Concept Library
Arya Grayeli, Atharva Sehgal, Omar Costilla Reyes, Miles Cranmer, Swarat Chaudhuri Synthetic Data-Driven Prediction of Height for Childhood Malnutrition
David Berthiaume, Yuan Tang, Chau Nguyen, Siyu Gai, Emilia Mazzolenis, Weiwei Pan TAGMol: Target-Aware Gradient-Guided Molecule Generation
Vineeth Dorna, D. Subhalingam, Keshav Kolluru, Shreshth Tuli, Mrityunjay Singh, Saurabh Singal, N M Anoop Krishnan, Sayan Ranu Task Addition in Multi-Task Learning by Geometrical Alignment
Soorin Yim, Dae-Woong Jeong, Sung Moon Ko, Sumin Lee, Hyunseung Kim, Chanhui Lee, Sehui Han Teaching Dark Matter Simulations to Speak the Halo Language
Shivam Pandey, Francois Lanusse, Chirag Modi, Benjamin Dan Wandelt Teaching Large Language Models to Reason with Reinforcement Learning
Alexander Havrilla, Yuqing Du, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Eric Hambro, Sainbayar Sukhbaatar, Roberta Raileanu Teaching Transformers Causal Reasoning Through Axiomatic Training
Aniket Vashishtha, Abhinav Kumar, Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian, Amit Sharma Temporal Graph Rewiring with Expander Graphs
Katarina Petrović, Shenyang Huang, Farimah Poursafaei, Petar Veličković The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
Derek Lim, Theo Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka The GAN Is Dead; Long Live the GAN! a Modern Baseline GAN
Nick Huang, Aaron Gokaslan, Volodymyr Kuleshov, James Tompkin The Geometry of Diffusion Models: Tubular Neighbourhoods and Singularities
Kotaro Sakamoto, Ryosuke Sakamoto, Masato Tanabe, Masatomo Akagawa, Yusuke Hayashi, Manato Yaguchi, Masahiro Suzuki, Yutaka Matsuo The Hidden Pitfalls of the Cosine Similarity Loss
Andrew Draganov, Sharvaree Vadgama, Erik J Bekkers The Mamba in the Llama: Distilling and Accelerating Hybrid Models
Junxiong Wang, Daniele Paliotta, Avner May, Alexander M Rush, Tri Dao The NGT200 Dataset - Geometric Multi-View Isolated Sign Recognition
Oline Ranum, David Wessels, Gomèr Otterspeer, Erik J Bekkers, Floris Roelofsen, Jari I. Andersen The Optimization Landscape of Spectral Neural Network
Chenghui Li, Rishi Sonthalia, Nicolas Garcia Trillos The Pupil Becomes the Master: Eye-Tracking Feedback for Tuning LLMs
Samuel Kiegeland, David Robert Reich, Ryan Cotterell, Lena Ann Jäger, Ethan Wilcox The Scaling Law in Astronomical Time Series Data
Jia-Shu Pan, Yuan-Sen Ting, Jie Yu, Yang Huang, Ji-Feng Liu Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding
Benjamin Bergner, Andrii Skliar, Amelie Royer, Tijmen Blankevoort, Yuki M Asano, Babak Ehteshami Bejnordi Thinking Out-of-the-Box: A Comparative Investigation of Human and LLMs in Creative Problem-Solving
Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas L. Griffiths, Faeze Brahman Topology-Informed Graph Transformer
Yun Young Choi, Sun Woo Park, Minho Lee, Youngho Woo Towards Aligning Language Models with Textual Feedback
Saüc Abadal Lloret, Shehzaad Dhuliawala, Keerthiram Murugesan, Mrinmaya Sachan Towards Efficient and Scalable Training of Differentially Private Deep Learning
Sebastian Rodriguez Beltran, Marlon Tobaben, Niki Andreas Loppi, Antti Honkela Towards Efficient Large-Scale Language-3D Representation Learning
Shentong Mo, Xiaogang Xu, Tongzhou Wang, Antonio Torralba, Shuang Li Towards Enforcing Hard Physics Constraints in Operator Learning Frameworks
Valentin Duruisseaux, Miguel Liu-Schiaffini, Julius Berner, Anima Anandkumar Towards Generalizable Particle Picking in Cryo-EM Images by Leveraging Masked AutoEncoder
Andreas Zamanos, Panagiotis Koromilas, Giorgos Bouritsas, Panagiotis L. Kastritis, Yannis Panagakis Towards Safe Large Language Models for Medicine
Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju Towards Safe Large Language Models for Medicine
Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju Towards Safe Large Language Models for Medicine
Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju Towards Smaller Language Models via Layer Looping
Sabri Eyuboglu, Dylan Zinsley, Jon Saad-Falcon, Simran Arora, Atri Rudra, James Zou, Christopher Re Training Compute-Optimal Protein Language Models
Xingyi Cheng, Bo Chen, Pan Li, Jing Gong, Jie Tang, Le Song Training Compute-Optimal Protein Language Models
Xingyi Cheng, Bo Chen, Pan Li, Jing Gong, Jie Tang, Le Song Training-Free Acceleration of ViTs with Delayed Spatial Merging
Jung Hwan Heo, Seyedarmin Azizi, Arash Fayyazi, Massoud Pedram Training-Free Design of Augmentations with Data-Centric Principles
Jieke Wu, Wei Huang, Mingyuan Bai, Xiaoling Hu, Yi Duan, Wuyang Chen Transfer Learning in Multi-Fidelity Surrogate Modeling: A Wind Farm Case
Dichang Zhang, Zexia Zhang, Christian Santoni, Ali Khosronejad, Dimitris Samaras Transferability for Graph Convolutional Networks
Christian Koke, Abhishek Saroha, Yuesong Shen, Marvin Eisenberger, Michael M. Bronstein, Daniel Cremers Transferable Reinforcement Learning via Generalized Occupancy Models
Chuning Zhu, Xinqi Wang, Tyler Han, Simon Shaolei Du, Abhishek Gupta Transferable Reinforcement Learning via Generalized Occupancy Models
Chuning Zhu, Xinqi Wang, Tyler Han, Simon Shaolei Du, Abhishek Gupta Transformer Neural Autoregressive Flows
Massimiliano Patacchiola, Aliaksandra Shysheya, Katja Hofmann, Richard E. Turner Transformers Can Do Arithmetic with the Right Embeddings
Sean Michael McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein Transformers Need Glasses! Information Over-Squashing in Language Tasks
Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan Kumaran, João Guilherme Madeira Araújo, Alex Vitvitskyi, Razvan Pascanu, Petar Veličković Transformers on Markov Data: Constant Depth Suffices
Nived Rajaraman, Marco Bondaschi, Ashok Vardhan Makkuva, Kannan Ramchandran, Michael Gastpar Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum S Anderson, Yaron Singer, Amin Karbasi Truly No-Regret Learning in Constrained MDPs
Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He TrustAgent: Towards Safe and Trustworthy LLM-Based Agents Through Agent Constitution
Wenyue Hua, Xianjun Yang, Mingyu Jin, Zelong Li, Wei Cheng, Ruixiang Tang, Yongfeng Zhang Tuning-Free Alignment of Diffusion Models with Direct Noise Optimization
Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang, Mingyi Hong, Fan Wang, Tsung-Hui Chang U-μP: The Unit-Scaled Maximal Update Parametrization
Charlie Blake, Constantin Eichenberg, Josef Dean, Lukas Balles, Luke Yuri Prince, Björn Deiseroth, Andres Felipe Cruz-Salinas, Carlo Luschi, Samuel Weinbach, Douglas Orr U-μP: The Unit-Scaled Maximal Update Parametrization
Charlie Blake, Constantin Eichenberg, Josef Dean, Lukas Balles, Luke Yuri Prince, Björn Deiseroth, Andres Felipe Cruz-Salinas, Carlo Luschi, Samuel Weinbach, Douglas Orr Understanding and Minimising Outlier Features in Neural Network Training
Bobby He, Lorenzo Noci, Daniele Paliotta, Imanol Schlag, Thomas Hofmann Understanding and Minimising Outlier Features in Neural Network Training
Bobby He, Lorenzo Noci, Daniele Paliotta, Imanol Schlag, Thomas Hofmann Understanding Inhibition Through Maximally Tense Images
Christopher J Hamblin, Srijani Saha, Talia Konkle, George A. Alvarez Understanding the Cognitive Complexity in Language Elicited by Product Images
Yan-Ying Chen, Shabnam Hakimi, Monica P Van, Francine Chen, Matthew K Hong, Matthew Klenk, Charlene C. Wu Understanding the Role of Equivariance in Self-Supervised Learning
Yifei Wang, Kaiwen Hu, Sharut Gupta, Ziyu Ye, Yisen Wang, Stefanie Jegelka Universal Self-Consistency for Large Language Models
Xinyun Chen, Renat Aksitov, Uri Alon, Jie Ren, Kefan Xiao, Pengcheng Yin, Sushant Prakash, Charles Sutton, Xuezhi Wang, Denny Zhou Unlocking the Global Synergies in Low-Rank Adapters
Zixi Zhang, Cheng Zhang, Xitong Gao, Robert D. Mullins, George Anthony Constantinides, Yiren Zhao Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
Sanae Lotfi, Yilun Kuang, Marc Anton Finzi, Brandon Amos, Micah Goldblum, Andrew Gordon Wilson Unsupervised Feature Extraction from a Foundation Model Zoo for Cell Similarity Search in Oncological Microscopy Across Devices
Gabriel Kalweit, Anusha Klett, Mehdi Naouar, Jens Rahnfeld, Yannick Vogt, Diana Laura Infante Ramirez, Rebecca Berger, Jesus Duque Afonso, Tanja Nicole Hartmann, Marie Follo, Michael Luebbert, Roland Mertelsmann, Evelyn Ullrich, Joschka Boedecker, Maria Kalweit Unveiling CLIP Dynamics: Linear Mode Connectivity and Generalization
Alireza Abdollahpourrostam, Amartya Sanyal, Seyed-Mohsen Moosavi-Dezfooli USCILab3D: A Large-Scale, Long-Term, Semantically Annotated Outdoor Dataset
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