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Tom Goldstein
Thomas A. Goldstein
Person information
- affiliation: University of Maryland, Department of Computer Science, College Park, MD, USA
- affiliation (PhD 2010): University of California, Los Angeles, CA, USA
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2020 – today
- 2024
- [j19]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models. Trans. Mach. Learn. Res. 2024 (2024) - [c152]Kaiyu Yue, Bor-Chun Chen, Jonas Geiping, Hengduo Li, Tom Goldstein, Ser-Nam Lim:
Object Recognition as Next Token Prediction. CVPR 2024: 16645-16656 - [c151]Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, Tom Goldstein:
Investigating Style Similarity in Diffusion Models. ECCV (66) 2024: 143-160 - [c150]Renkun Ni, Yonghui Xiao, Phoenix Meadowlark, Oleg Rybakov, Tom Goldstein, Ananda Theertha Suresh, Ignacio López-Moreno, Mingqing Chen, Rajiv Mathews:
FedAQT: Accurate Quantized Training with Federated Learning. ICASSP 2024: 6100-6104 - [c149]Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Universal Guidance for Diffusion Models. ICLR 2024 - [c148]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:
NEFTune: Noisy Embeddings Improve Instruction Finetuning. ICLR 2024 - [c147]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein:
On the Reliability of Watermarks for Large Language Models. ICLR 2024 - [c146]Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang:
WAVES: Benchmarking the Robustness of Image Watermarks. ICML 2024 - [c145]Lichang Chen, Chen Zhu, Jiuhai Chen, Davit Soselia, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro:
ODIN: Disentangled Reward Mitigates Hacking in RLHF. ICML 2024 - [c144]Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou:
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models. ICML 2024 - [c143]Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text. ICML 2024 - [c142]Manli Shu, Le Xue, Ning Yu, Roberto Martín-Martín, Caiming Xiong, Tom Goldstein, Juan Carlos Niebles, Ran Xu:
Hierarchical Point Attention for Indoor 3D Object Detection. ICRA 2024: 4245-4251 - [c141]Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Animashree Anandkumar, Furong Huang:
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. NeurIPS 2024 - [c140]Abhimanyu Hans, John Kirchenbauer, Yuxin Wen, Neel Jain, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, Tom Goldstein:
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs. NeurIPS 2024 - [c139]Siddharth Singh, Prajwal Singhania, Aditya Ranjan, John Kirchenbauer, Jonas Geiping, Yuxin Wen, Neel Jain, Abhimanyu Hans, Manli Shu, Aditya Tomar, Tom Goldstein, Abhinav Bhatele:
Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers. SC 2024: 4 - [i205]Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang:
Benchmarking the Robustness of Image Watermarks. CoRR abs/2401.08573 (2024) - [i204]Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text. CoRR abs/2401.12070 (2024) - [i203]Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang:
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models. CoRR abs/2402.06659 (2024) - [i202]Lichang Chen, Chen Zhu, Davit Soselia, Jiuhai Chen, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro:
ODIN: Disentangled Reward Mitigates Hacking in RLHF. CoRR abs/2402.07319 (2024) - [i201]Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, Tom Goldstein:
Coercing LLMs to do and reveal (almost) anything. CoRR abs/2402.14020 (2024) - [i200]Hamid Kazemi, Atoosa Malemir Chegini, Jonas Geiping, Soheil Feizi, Tom Goldstein:
What do we learn from inverting CLIP models? CoRR abs/2403.02580 (2024) - [i199]Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum:
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. CoRR abs/2403.16365 (2024) - [i198]Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping, Tom Goldstein, Nicholas Carlini:
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models. CoRR abs/2404.01231 (2024) - [i197]Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta
, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, Tom Goldstein:
Measuring Style Similarity in Diffusion Models. CoRR abs/2404.01292 (2024) - [i196]Sean McLeish, Avi Schwarzschild, Tom Goldstein:
Benchmarking ChatGPT on Algorithmic Reasoning. CoRR abs/2404.03441 (2024) - [i195]John Kirchenbauer, Garrett Honke, Gowthami Somepalli, Jonas Geiping, Daphne Ippolito, Katherine Lee, Tom Goldstein, David Andre:
LMD3: Language Model Data Density Dependence. CoRR abs/2405.06331 (2024) - [i194]Ruchit Rawal, Khalid Saifullah, Ronen Basri, David Jacobs, Gowthami Somepalli, Tom Goldstein:
CinePile: A Long Video Question Answering Dataset and Benchmark. CoRR abs/2405.08813 (2024) - [i193]Xiyao Wang, Jiuhai Chen, Zhaoyang Wang, Yuhang Zhou, Yiyang Zhou, Huaxiu Yao, Tianyi Zhou, Tom Goldstein, Parminder Bhatia, Furong Huang, Cao Xiao:
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement. CoRR abs/2405.15973 (2024) - [i192]Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein:
Transformers Can Do Arithmetic with the Right Embeddings. CoRR abs/2405.17399 (2024) - [i191]Larisa Markeeva, Sean McLeish, Borja Ibarz, Wilfried Bounsi, Olga Kozlova, Alex Vitvitskyi, Charles Blundell, Tom Goldstein, Avi Schwarzschild, Petar Velickovic:
The CLRS-Text Algorithmic Reasoning Language Benchmark. CoRR abs/2406.04229 (2024) - [i190]Lichang Chen, Jiuhai Chen, Chenxi Liu, John Kirchenbauer, Davit Soselia, Chen Zhu, Tom Goldstein, Tianyi Zhou, Heng Huang:
OPTune: Efficient Online Preference Tuning. CoRR abs/2406.07657 (2024) - [i189]Abhimanyu Hans, Yuxin Wen, Neel Jain, John Kirchenbauer, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, Tom Goldstein:
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs. CoRR abs/2406.10209 (2024) - [i188]Alex Hanson, Allen Tu, Vasu Singla, Mayuka Jayawardhana, Matthias Zwicker, Tom Goldstein:
PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting. CoRR abs/2406.10219 (2024) - [i187]Jiuhai Chen, Rifaa Qadri, Yuxin Wen, Neel Jain, John Kirchenbauer, Tianyi Zhou, Tom Goldstein:
GenQA: Generating Millions of Instructions from a Handful of Prompts. CoRR abs/2406.10323 (2024) - [i186]Vasu Singla, Kaiyu Yue, Sukriti Paul, Reza Shirkavand, Mayuka Jayawardhana, Alireza Ganjdanesh, Heng Huang, Abhinav Bhatele, Gowthami Somepalli, Tom Goldstein:
From Pixels to Prose: A Large Dataset of Dense Image Captions. CoRR abs/2406.10328 (2024) - [i185]Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum:
LiveBench: A Challenging, Contamination-Free LLM Benchmark. CoRR abs/2406.19314 (2024) - [i184]Michael-Andrei Panaitescu-Liess, Zora Che, Bang An, Yuancheng Xu, Pankayaraj Pathmanathan, Souradip Chakraborty, Sicheng Zhu, Tom Goldstein, Furong Huang:
Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data? CoRR abs/2407.17417 (2024) - [i183]Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang:
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. CoRR abs/2409.18433 (2024) - [i182]Alex Stein, Samuel Sharpe, Doron Bergman, Senthil Kumar, C. Bayan Bruss, John Dickerson, Tom Goldstein, Micah Goldblum:
A Simple Baseline for Predicting Events with Auto-Regressive Tabular Transformers. CoRR abs/2410.10648 (2024) - [i181]Alex Hanson, Allen Tu, Geng Lin, Vasu Singla, Matthias Zwicker, Tom Goldstein:
Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives. CoRR abs/2412.00578 (2024) - [i180]Quang Nguyen, Truong Vu, Trong-Tung Nguyen, Yuxin Wen, Preston K. Robinette, Taylor T. Johnson, Tom Goldstein, Anh Tran, Khoi Nguyen:
EditScout: Locating Forged Regions from Diffusion-based Edited Images with Multimodal LLM. CoRR abs/2412.03809 (2024) - [i179]Neel Jain, Aditya Shrivastava, Chen Zhu, Daben Liu, Alfy Samuel, Ashwinee Panda, Anoop Kumar, Micah Goldblum, Tom Goldstein:
Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models. CoRR abs/2412.06748 (2024) - [i178]Reza Shirkavand, Peiran Yu, Shangqian Gao, Gowthami Somepalli, Tom Goldstein, Heng Huang:
Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models. CoRR abs/2412.15341 (2024) - 2023
- [j18]Micah Goldblum
, Dimitris Tsipras, Chulin Xie
, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein:
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 1563-1580 (2023) - [j17]Zhipeng Wei
, Jingjing Chen
, Micah Goldblum
, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang
, Larry S. Davis:
Towards Transferable Adversarial Attacks on Image and Video Transformers. IEEE Trans. Image Process. 32: 6346-6358 (2023) - [c138]Valeriia Cherepanova
, Steven Reich
, Samuel Dooley
, Hossein Souri
, John P. Dickerson
, Micah Goldblum
, Tom Goldstein
:
A Deep Dive into Dataset Imbalance and Bias in Face Identification. AIES 2023: 229-247 - [c137]Shishira R. Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava:
Unifying the Harmonic Analysis of Adversarial Attacks and Robustness. BMVC 2023: 620-621 - [c136]Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Universal Guidance for Diffusion Models. CVPR Workshops 2023: 843-852 - [c135]Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models. CVPR 2023: 6048-6058 - [c134]Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein:
STYX: Adaptive Poisoning Attacks Against Byzantine-Robust Defenses in Federated Learning. ICASSP 2023: 1-5 - [c133]Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, Tom Goldstein:
Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent. ICLR 2023 - [c132]Hong-Min Chu, Jonas Geiping, Liam H. Fowl, Micah Goldblum, Tom Goldstein:
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. ICLR 2023 - [c131]Liam H. Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein:
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. ICLR 2023 - [c130]Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson:
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. ICLR 2023 - [c129]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Provable Robustness against Wasserstein Distribution Shifts via Input Randomization. ICLR 2023 - [c128]Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum:
Transfer Learning with Deep Tabular Models. ICLR 2023 - [c127]Khalid Saifullah, Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein:
Seeing in Words: Learning to Classify through Language Bottlenecks. Tiny Papers @ ICLR 2023 - [c126]Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries. ICLR 2023 - [c125]Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang:
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. ICLR 2023 - [c124]Jonas Geiping, Tom Goldstein:
Cramming: Training a Language Model on a single GPU in one day. ICML 2023: 11117-11143 - [c123]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein:
A Watermark for Large Language Models. ICML 2023: 17061-17084 - [c122]Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein:
GOAT: A Global Transformer on Large-scale Graphs. ICML 2023: 17375-17390 - [c121]Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. NeurIPS 2023 - [c120]Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum:
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. NeurIPS 2023 - [c119]Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein:
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. NeurIPS 2023 - [c118]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein:
What Can We Learn from Unlearnable Datasets? NeurIPS 2023 - [c117]Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein:
On the Exploitability of Instruction Tuning. NeurIPS 2023 - [c116]Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Understanding and Mitigating Copying in Diffusion Models. NeurIPS 2023 - [c115]Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery. NeurIPS 2023 - [c114]Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein:
Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images. NeurIPS 2023 - [i177]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein:
A Watermark for Large Language Models. CoRR abs/2301.10226 (2023) - [i176]Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang:
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. CoRR abs/2302.03015 (2023) - [i175]Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery. CoRR abs/2302.03668 (2023) - [i174]Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Universal Guidance for Diffusion Models. CoRR abs/2302.07121 (2023) - [i173]Alex Stein, Avi Schwarzschild, Michael J. Curry
, Tom Goldstein, John P. Dickerson:
Neural Auctions Compromise Bidder Information. CoRR abs/2303.00116 (2023) - [i172]Pedro Sandoval Segura, Jonas Geiping, Tom Goldstein:
JPEG Compressed Images Can Bypass Protections Against AI Editing. CoRR abs/2304.02234 (2023) - [i171]Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum:
A Cookbook of Self-Supervised Learning. CoRR abs/2304.12210 (2023) - [i170]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein:
What Can We Learn from Unlearnable Datasets? CoRR abs/2305.19254 (2023) - [i169]Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein:
Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust. CoRR abs/2305.20030 (2023) - [i168]Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Understanding and Mitigating Copying in Diffusion Models. CoRR abs/2305.20086 (2023) - [i167]Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou:
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models. CoRR abs/2306.03082 (2023) - [i166]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando
, Aniruddha Saha, Micah Goldblum, Tom Goldstein:
On the Reliability of Watermarks for Large Language Models. CoRR abs/2306.04634 (2023) - [i165]Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models. CoRR abs/2306.13651 (2023) - [i164]Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein:
On the Exploitability of Instruction Tuning. CoRR abs/2306.17194 (2023) - [i163]Khalid Saifullah, Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein:
Seeing in Words: Learning to Classify through Language Bottlenecks. CoRR abs/2307.00028 (2023) - [i162]Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, Tom Goldstein:
Baseline Defenses for Adversarial Attacks Against Aligned Language Models. CoRR abs/2309.00614 (2023) - [i161]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:
NEFTune: Noisy Embeddings Improve Instruction Finetuning. CoRR abs/2310.05914 (2023) - [i160]Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein:
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. CoRR abs/2310.19909 (2023) - [i159]Vasu Singla, Pedro Sandoval Segura, Micah Goldblum, Jonas Geiping, Tom Goldstein:
A Simple and Efficient Baseline for Data Attribution on Images. CoRR abs/2311.03386 (2023) - [i158]Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum:
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. CoRR abs/2311.05877 (2023) - [i157]Kaiyu Yue, Bor-Chun Chen, Jonas Geiping, Hengduo Li, Tom Goldstein, Ser-Nam Lim:
Object Recognition as Next Token Prediction. CoRR abs/2312.02142 (2023) - [i156]Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson:
Perspectives on the State and Future of Deep Learning - 2023. CoRR abs/2312.09323 (2023) - [i155]Ping-yeh Chiang, Yipin Zhou, Omid Poursaeed, Satya Narayan Shukla, Ashish Shah, Tom Goldstein, Ser-Nam Lim:
Universal Pyramid Adversarial Training for Improved ViT Performance. CoRR abs/2312.16339 (2023) - 2022
- [j16]Haochuan Song
, Tom Goldstein, Xiaohu You
, Chuan Zhang
, Olav Tirkkonen
, Christoph Studer
:
Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems. IEEE Trans. Wirel. Commun. 21(6): 4068-4084 (2022) - [c113]Zhipeng Wei, Jingjing Chen
, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang:
Towards Transferable Adversarial Attacks on Vision Transformers. AAAI 2022: 2668-2676 - [c112]Michael J. Curry, Uro Lyi, Tom Goldstein, John P. Dickerson:
Learning Revenue-Maximizing Auctions With Differentiable Matching. AISTATS 2022: 6062-6073 - [c111]Kezhi Kong, Guohao Li
, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem
, Gavin Taylor, Tom Goldstein:
Robust Optimization as Data Augmentation for Large-scale Graphs. CVPR 2022: 60-69 - [c110]Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein:
Poisons that are learned faster are more effective. CVPR Workshops 2022: 197-204 - [c109]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CVPR 2022: 13689-13698 - [c108]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features. ICLR 2022 - [c107]Liam H. Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, Tom Goldstein:
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. ICLR 2022 - [c106]Jonas Geiping, Micah Goldblum, Phillip Pope, Michael Moeller, Tom Goldstein:
Stochastic Training is Not Necessary for Generalization. ICLR 2022 - [c105]Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein:
The Close Relationship Between Contrastive Learning and Meta-Learning. ICLR 2022 - [c104]Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein:
The Uncanny Similarity of Recurrence and Depth. ICLR 2022 - [c103]Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konecný, Andrew Hard, Tom Goldstein:
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. ICLR 2022 - [c102]Arpit Bansal, Ping-Yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein:
Certified Neural Network Watermarks with Randomized Smoothing. ICML 2022: 1450-1465 - [c101]Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein:
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. ICML 2022: 7484-7512 - [c100]Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein:
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. ICML 2022: 23668-23684 - [c99]Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking. NeurIPS 2022 - [c98]