Goldstein, Tom

161 publications

NeurIPS 2025 A Technical Report on “Erasing the Invisible”: The 2024 NeurIPS Competition on Stress Testing Image Watermarks Mucong Ding, Bang An, Tahseen Rabbani, Chenghao Deng, Anirudh Satheesh, Souradip Chakraborty, Mehrdad Saberi, Yuxin Wen, Kyle Rui Sang, Aakriti Agrawal, Xuandong Zhao, Mo Zhou, Mary-Anne Hartley, Lei Li, Yu-Xiang Wang, Vishal M. Patel, Soheil Feizi, Tom Goldstein, Furong Huang

ICCV 2025 ARGUS: Hallucination and Omission Evaluation in Video-LLMs Ruchit Rawal, Reza Shirkavand, Heng Huang, Gowthami Somepalli, Tom Goldstein

ICLRW 2025 AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security Zikui Cai, Shayan Shabihi, Bang An, Zora Che, Brian R. Bartoldson, Bhavya Kailkhura, Tom Goldstein, Furong Huang

ICLRW 2025 Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs Abhimanyu Hans, Yuxin Wen, Neel Jain, John Kirchenbauer, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, Tom Goldstein

AAAI 2025 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

NeurIPS 2025 Dense Backpropagation Improves Training for Sparse Mixture-of-Experts Ashwinee Panda, Vatsal Baherwani, Zain Sarwar, Benjamin Thérien, Sambit Sahu, Tom Goldstein, Supriyo Chakraborty

CVPR 2025 Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Reza Shirkavand, Peiran Yu, Shangqian Gao, Gowthami Somepalli, Tom Goldstein, Heng Huang

NeurIPS 2025 FineGRAIN: Evaluating Failure Modes of Text-to-Image Models with Vision Language Model Judges Kevin David Hayes, Micah Goldblum, Vikash Sehwag, Gowthami Somepalli, Ashwinee Panda, Tom Goldstein

NeurIPS 2025 Gemstones: A Model Suite for Multi-Faceted Scaling Laws Sean Michael McLeish, John Kirchenbauer, David Yu Miller, Siddharth Singh, Abhinav Bhatele, Micah Goldblum, Ashwinee Panda, Tom Goldstein

ICLRW 2025 Has My System Prompt Been Used? Large Language Model Prompt Membership Inference Roman Levin, Valeriia Cherepanova, Abhimanyu Hans, Avi Schwarzschild, Tom Goldstein

ICLR 2025 LiveBench: A Challenging, Contamination-Limited LLM Benchmark Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Sreemanti Dey, Shubh-Agrawal, Sandeep Singh Sandha, Siddartha Venkat Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum

ICLRW 2025 LoRA Without Forgetting: Freezing and Sparse Masking for Low-Rank Adaptation Juzheng Zhang, Jiacheng You, Ashwinee Panda, Tom Goldstein

CVPR 2025 PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting Alex Hanson, Allen Tu, Vasu Singla, Mayuka Jayawardhana, Matthias Zwicker, Tom Goldstein

NeurIPS 2025 Quantifying Cross-Modality Memorization in Vision-Language Models Yuxin Wen, Yangsibo Huang, Tom Goldstein, Ravi Kumar, Badih Ghazi, Chiyuan Zhang

NeurIPS 2025 Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach Jonas Geiping, Sean Michael McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Tom Goldstein

CVPR 2025 Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives Alex Hanson, Allen Tu, Geng Lin, Vasu Singla, Matthias Zwicker, Tom Goldstein

NeurIPS 2025 The Common Pile V0.1: An 8TB Dataset of Public Domain and Openly Licensed Text Nikhil Kandpal, Brian Lester, Colin Raffel, Sebastian Majstorovic, Stella Biderman, Baber Abbasi, Luca Soldaini, Enrico Shippole, A. Feder Cooper, Aviya Skowron, Shayne Longpre, Lintang Sutawika, Alon Albalak, Zhenlin Xu, Guilherme Penedo, Loubna Ben Allal, Elie Bakouch, John David Pressman, Honglu Fan, Dashiell Stander, Guangyu Song, Aaron Gokaslan, John Kirchenbauer, Tom Goldstein, Brian R. Bartoldson, Bhavya Kailkhura, Tyler Murray

ICCV 2025 Zero-Shot Vision Encoder Grafting via LLM Surrogates Kaiyu Yue, Vasu Singla, Menglin Jia, John Kirchenbauer, Rifaa Qadri, Zikui Cai, Abhinav Bhatele, Furong Huang, Tom Goldstein

NeurIPS 2024 Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs Abhimanyu Hans, Yuxin Wen, Neel Jain, John Kirchenbauer, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, Tom Goldstein

NeurIPS 2024 CALVIN: Improved Contextual Video Captioning via Instruction Tuning Gowthami Somepalli, Arkabandhu Chowdhury, Ronen Basri, Jonas Geiping, Tom Goldstein, David Jacobs

ICMLW 2024 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

NeurIPSW 2024 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

NeurIPSW 2024 CinePile: A Long Video Question Answering Dataset and Benchmark Ruchit Rawal, Khalid Saifullah, Ronen Basri, David Jacobs, Gowthami Somepalli, Tom Goldstein

ICLRW 2024 Coercing LLMs to Do and Reveal (almost) Anything Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, Tom Goldstein

NeurIPSW 2024 Dense Backpropagation Improves Routing for Sparsely-Gated Mixture-of-Experts Ashwinee Panda, Vatsal Baherwani, Zain Sarwar, Benjamin Thérien, Stephen Rawls, Sambit Sahu, Supriyo Chakraborty, Tom Goldstein

NeurIPS 2024 Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang

ICMLW 2024 Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam H Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum

TMLR 2024 Graph Neural Networks Formed via Layer-Wise Ensembles of Heterogeneous Base Models Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf

NeurIPSW 2024 Has My System Prompt Been Used? Large Language Model Prompt Membership Inference Roman Levin, Valeriia Cherepanova, Abhimanyu Hans, Avi Schwarzschild, Tom Goldstein

ICML 2024 InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou

ECCV 2024 Investigating Style Similarity in Diffusion Models Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas A. Geiping, Abhinav Shrivastava, Tom Goldstein

ICLR 2024 NEFTune: Noisy Embeddings Improve Instruction Finetuning Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

ICML 2024 ODIN: Disentangled Reward Mitigates Hacking in RLHF Lichang Chen, Chen Zhu, Jiuhai Chen, Davit Soselia, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro

CVPR 2024 Object Recognition as Next Token Prediction Kaiyu Yue, Bor-Chun Chen, Jonas Geiping, Hengduo Li, Tom Goldstein, Ser-Nam Lim

ICLR 2024 On the Reliability of Watermarks for Large Language Models John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein

NeurIPS 2024 Privacy Backdoors: Enhancing Membership Inference Through Poisoning Pre-Trained Models Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping, Tom Goldstein, Nicholas Carlini

NeurIPSW 2024 Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models Neel Jain, Aditya Shrivastava, Chenyang Zhu, Daben Liu, Alfy Samuel, Ashwinee Panda, Anoop Kumar, Micah Goldblum, Tom Goldstein

NeurIPS 2024 Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang

ICLRW 2024 Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang

ICML 2024 Spotting LLMs with Binoculars: Zero-Shot Detection of Machine-Generated Text Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

NeurIPS 2024 Transformers Can Do Arithmetic with the Right Embeddings Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein

ICMLW 2024 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

NeurIPSW 2024 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

ICLR 2024 Universal Guidance for Diffusion Models Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Roni Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein

ICML 2024 WAVES: Benchmarking the Robustness of Image Watermarks Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang

ICLRW 2024 WAVES: Benchmarking the Robustness of Image Watermarks Mucong Ding, Tahseen Rabbani, Bang An, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang

NeurIPSW 2024 What Do We Learn from Inverting CLIP Models? Hamid Kazemi, Atoosa Chegini, Jonas Geiping, Soheil Feizi, Tom Goldstein

NeurIPS 2023 A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew G Wilson, Tom Goldstein, Micah Goldblum

NeurIPSW 2023 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

ICML 2023 A Watermark for Large Language Models John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein

NeurIPS 2023 Battle of the Backbones: A Large-Scale Comparison of Pretrained Models Across Computer Vision Tasks Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew G Wilson, Tom Goldstein

ICLR 2023 Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein

NeurIPS 2023 Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein

ICMLW 2023 Cramming: Training a Language Model on a Single GPU in One Day Jonas Geiping, Tom Goldstein

ICML 2023 Cramming: Training a Language Model on a Single GPU in One Day. Jonas Geiping, Tom Goldstein

ICLR 2023 Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models Liam H Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein

CVPR 2023 Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein

ICLR 2023 Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang

ICML 2023 GOAT: A Global Transformer on Large-Scale Graphs Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein

NeurIPS 2023 Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein

ICLR 2023 How Much Data Are Augmentations Worth? an Investigation into Scaling Laws, Invariance, and Implicit Regularization Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson

ICLR 2023 Loss Landscapes Are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, Tom Goldstein

NeurIPS 2023 On the Exploitability of Instruction Tuning Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein

ICLR 2023 Panning for Gold in Federated Learning: Targeted Text Extraction Under Arbitrarily Large-Scale Aggregation Hong-Min Chu, Jonas Geiping, Liam H Fowl, Micah Goldblum, Tom Goldstein

ICLR 2023 Provable Robustness Against Wasserstein Distribution Shifts via Input Randomization Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi

ICLR 2023 Transfer Learning with Deep Tabular Models Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum

NeurIPS 2023 Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein

ICMLW 2023 Understanding Data Replication in Diffusion Models Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein

NeurIPS 2023 Understanding and Mitigating Copying in Diffusion Models Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein

CVPRW 2023 Universal Guidance for Diffusion Models Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein

NeurIPS 2023 What Can We Learn from Unlearnable Datasets? Pedro Sandoval-Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein

AISTATS 2022 Learning Revenue-Maximizing Auctions with Differentiable Matching Michael J. Curry, Uro Lyi, Tom Goldstein, John P. Dickerson

NeurIPSW 2022 A Deep Dive into Dataset Imbalance and Bias in Face Identification Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, John P Dickerson, Micah Goldblum, Tom Goldstein

NeurIPS 2022 Autoregressive Perturbations for Data Poisoning Pedro Sandoval-Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David Jacobs

CVPR 2022 Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping Yeh-Chiang, Yehuda Dar, Richard Baraniuk, Micah Goldblum, Tom Goldstein

NeurIPSW 2022 Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein

ICML 2022 Certified Neural Network Watermarks with Randomized Smoothing Arpit Bansal, Ping-Yeh Chiang, Michael J Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P Dickerson, Tom Goldstein

NeurIPSW 2022 DP-InstaHide: Data Augmentations Provably Enhance Guarantees Against Dataset Manipulations Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam H Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein

NeurIPSW 2022 Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models Liam H Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein

ICLR 2022 Diurnal or Nocturnal? Federated Learning of Multi-Branch Networks from Periodically Shifting Distributions Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečný, Andrew Hard, Tom Goldstein

ICLR 2022 Does Your Graph Need a Confidence Boost? Convergent Boosted Smoothing on Graphs with Tabular Node Features Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

NeurIPS 2022 End-to-End Algorithm Synthesis with Recurrent Networks: Extrapolation Without Overthinking Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

ICML 2022 Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification Yuxin Wen, Jonas A. Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein

ICMLW 2022 How Much Data Is Augmentation Worth? Jonas Geiping, Gowthami Somepalli, Ravid Shwartz-Ziv, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum

NeurIPSW 2022 Panning for Gold in Federated Learning: Targeted Text Extraction Under Arbitrarily Large-Scale Aggregation Hong-Min Chu, Jonas Geiping, Liam H Fowl, Micah Goldblum, Tom Goldstein

ICML 2022 Plug-in Inversion: Model-Agnostic Inversion for Vision with Data Augmentations Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein

CVPRW 2022 Poisons That Are Learned Faster Are More Effective Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein

ICLR 2022 Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models Liam H Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, Tom Goldstein

CVPR 2022 Robust Optimization as Data Augmentation for Large-Scale Graphs Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein

NeurIPS 2022 Robustness Disparities in Face Detection Samuel Dooley, George Z Wei, Tom Goldstein, John Dickerson

NeurIPSW 2022 SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training Gowthami Somepalli, Avi Schwarzschild, Micah Goldblum, C. Bayan Bruss, Tom Goldstein

NeurIPS 2022 Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein

ICLR 2022 Stochastic Training Is Not Necessary for Generalization Jonas Geiping, Micah Goldblum, Phil Pope, Michael Moeller, Tom Goldstein

NeurIPS 2022 Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao

ICLR 2022 The Close Relationship Between Contrastive Learning and Meta-Learning Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein

ICLR 2022 The Uncanny Similarity of Recurrence and Depth Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein

AAAI 2022 Towards Transferable Adversarial Attacks on Vision Transformers Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang

NeurIPSW 2022 Transfer Learning with Deep Tabular Models Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum

NeurIPS 2022 Where Do Models Go Wrong? Parameter-Space Saliency Maps for Explainability Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein

NeurIPSW 2021 A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, Tom Goldstein

NeurIPS 2021 Adversarial Examples Make Strong Poisons Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein

AAAI 2021 Are Adversarial Examples Created Equal? a Learnable Weighted Minimax Risk for Robustness Under Non-Uniform Attacks Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang

NeurIPS 2021 Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein

NeurIPS 2021 Center Smoothing: Certified Robustness for Networks with Structured Outputs Aounon Kumar, Tom Goldstein

ICML 2021 Data Augmentation for Meta-Learning Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein

NeurIPSW 2021 Diurnal or Nocturnal? Federated Learning from Periodically Shifting Distributions Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečný, Andrew Hard, Tom Goldstein

NeurIPS 2021 Encoding Robustness to Image Style via Adversarial Feature Perturbations Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein

NeurIPS 2021 GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein

NeurIPS 2021 Gradient-Free Adversarial Training Against Image Corruption for Learning-Based Steering Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming Lin

ICML 2021 Just How Toxic Is Data Poisoning? a Unified Benchmark for Backdoor and Data Poisoning Attacks Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P Dickerson, Tom Goldstein

NeurIPS 2021 Long-Short Transformer: Efficient Transformers for Language and Vision Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro

ICLR 2021 LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P Dickerson, Gavin Taylor, Tom Goldstein

ECML-PKDD 2021 MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein

ICLR 2021 The Intrinsic Dimension of Images and Its Impact on Learning Phil Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein

NeurIPS 2021 VQ-GNN: A Universal Framework to Scale up Graph Neural Networks Using Vector Quantization Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein

ICLR 2021 Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching Jonas Geiping, Liam H Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein

ICLR 2021 WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic Renkun Ni, Hong-min Chu, Oscar Castaneda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein

ICML 2020 Adversarial Attacks on Copyright Detection Systems Parsa Saadatpanah, Ali Shafahi, Tom Goldstein

AAAI 2020 Adversarially Robust Distillation Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein

NeurIPS 2020 Adversarially Robust Few-Shot Learning: A Meta-Learning Approach Micah Goldblum, Liam Fowl, Tom Goldstein

ICLR 2020 Adversarially Robust Transfer Learning Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David Jacobs, Tom Goldstein

ICLR 2020 Breaking Certified Defenses: Semantic Adversarial Examples with Spoofed Robustness Certificates Amin Ghiasi, Ali Shafahi, Tom Goldstein

ICML 2020 Certified Data Removal from Machine Learning Models Chuan Guo, Tom Goldstein, Awni Hannun, Laurens Van Der Maaten

ICLR 2020 Certified Defenses for Adversarial Patches Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studor, Tom Goldstein

NeurIPS 2020 Certifying Confidence via Randomized Smoothing Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein

NeurIPS 2020 Certifying Strategyproof Auction Networks Michael Curry, Ping-yeh Chiang, Tom Goldstein, John Dickerson

ICML 2020 Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi

ECCVW 2020 Deep k-NN Defense Against Clean-Label Data Poisoning Attacks Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson

NeurIPS 2020 Detection as Regression: Certified Object Detection with Median Smoothing Ping-yeh Chiang, Michael Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein

ICLR 2020 FreeLB: Enhanced Adversarial Training for Natural Language Understanding Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu

ECCV 2020 Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors Zuxuan Wu, Ser-Nam Lim, Larry S. Davis, Tom Goldstein

NeurIPS 2020 MetaPoison: Practical General-Purpose Clean-Label Data Poisoning W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein

ICLR 2020 Network Deconvolution Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Tom Goldstein, James A. Yorke, Cornelia Fermüller, Yiannis Aloimonos

ICML 2020 The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein

ICLR 2020 Truth or Backpropaganda? an Empirical Investigation of Deep Learning Theory Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein

NeurIPSW 2020 Understanding Generalization Through Visualizations W Ronny Huang, Zeyad Emam, Micah Goldblum, Liam H Fowl, J K Terry, Furong Huang, Tom Goldstein

AAAI 2020 Universal Adversarial Training Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein

ICML 2020 Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein

NeurIPS 2019 Adversarial Training for Free! Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein

ICLR 2019 Are Adversarial Examples Inevitable? Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein

ICML 2019 Transferable Clean-Label Poisoning Attacks on Deep Neural Nets Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein

ECCV 2018 DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation Zuxuan Wu, Xintong Han, Yen-Liang Lin, Mustafa Gokhan Uzunbas, Tom Goldstein, Ser Nam Lim, Larry S. Davis

ICML 2018 Linear Spectral Estimators and an Application to Phase Retrieval Ramina Ghods, Andrew Lan, Tom Goldstein, Christoph Studer

NeurIPS 2018 Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein

ICLR 2018 Stabilizing Adversarial Nets with Prediction Methods Abhay Yadav, Sohil Shah, Zheng Xu, David Jacobs, Tom Goldstein

NeurIPS 2018 Visualizing the Loss Landscape of Neural Nets Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein

CVPR 2017 A New Rank Constraint on Multi-View Fundamental Matrices, and Its Application to Camera Location Recovery Soumyadip Sengupta, Tal Amir, Meirav Galun, Tom Goldstein, David W. Jacobs, Amit Singer, Ronen Basri

AISTATS 2017 Adaptive ADMM with Spectral Penalty Parameter Selection Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein

ICML 2017 Adaptive Consensus ADMM for Distributed Optimization Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein

CVPR 2017 Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation Zheng Xu, Mario A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein

AISTATS 2017 Automated Inference with Adaptive Batches Soham De, Abhay Kumar Yadav, David W. Jacobs, Tom Goldstein

ICML 2017 Convex Phase Retrieval Without Lifting via PhaseMax Tom Goldstein, Christoph Studer

NeurIPS 2017 Training Quantized Nets: A Deeper Understanding Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein

ECCV 2016 Biconvex Relaxation for Semidefinite Programming in Computer Vision Sohil Shah, Abhay Kumar Yadav, Carlos Domingo Castillo, David W. Jacobs, Christoph Studer, Tom Goldstein

ICML 2016 Dealbreaker: A Nonlinear Latent Variable Model for Educational Data Andrew Lan, Tom Goldstein, Richard Baraniuk, Christoph Studer

CVPR 2016 Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity Sohil Shah, Tom Goldstein, Christoph Studer

ICML 2016 Training Neural Networks Without Gradients: A Scalable ADMM Approach Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein

AISTATS 2016 Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre

NeurIPS 2015 Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing Tom Goldstein, Min Li, Xiaoming Yuan