← Authors

Tom Goldstein

118 papers · 2015–2025 · 9 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (12) πŸƒ Academic Marathon (10) 🌍 Conference Polyglot (9) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (14) 🌟 Keyword Trendsetter Combo (3) 🏠 Conference Loyalist (38) πŸ† Keyword Champion 🀝 Dynamic Duo (44) πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (21) 🧬 Topic Evolution πŸ—ƒοΈ Keyword Collector (343) ❓ The Questioner (13) πŸ“ˆ Trend Setter πŸ’Ž Century Club (118) πŸš€ Conference Pioneer ⚑ Prolific Year (15) πŸ”₯ Unstoppable (11)

Conferences

NIPS (38) ICLR (30) ICML (23) CVPR (10) AAAI (5) AISTATS (4) ECCV (3) ICCV (3) NAACL (2)

Top co-authors

Micah Goldblum (44) Jonas Geiping (32) Chen Zhu (14) Furong Huang (13) Christoph Studer (13) Gowthami Somepalli (12) Zheng Xu (12) Yuxin Wen (11) Avi Schwarzschild (11) Gavin Taylor (10)

Research topics

Privacy (4) Education (1)

Keywords

neural network (10) adversarial attack (9) data poisoning (8) image classification (7) adversarial robustness (7) adversarial training (7) large language model (6) vision-language model (5) alternating direction method of multiplier (5) randomized smoothing (5) convex optimization (4) certified robustness (4) few-shot learning (4) model compression (4) data augmentation (4) generative model (3) diffusion model (3) stochastic gradient descent (3) backdoor attack (3) text generation (3)

Papers

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models CVPR 2025
ARGUS: Hallucination and Omission Evaluation in Video-LLMs ICCV 2025
Zero-Shot Vision Encoder Grafting via LLM Surrogates ICCV 2025
Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives CVPR 2025
LLM-Generated Passphrases That Are Secure and Easy to Remember NAACL 2025
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement NAACL 2025
PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting CVPR 2025
LiveBench: A Challenging, Contamination-Limited LLM Benchmark ICLR 2025
Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data? AAAI 2025
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models ICML 2024
WAVES: Benchmarking the Robustness of Image Watermarks ICML 2024
Object Recognition as Next Token Prediction CVPR 2024
NEFTune: Noisy Embeddings Improve Instruction Finetuning ICLR 2024
Universal Guidance for Diffusion Models ICLR 2024
On the Reliability of Watermarks for Large Language Models ICLR 2024
CALVIN: Improved Contextual Video Captioning via Instruction Tuning NIPS 2024
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text ICML 2024
ODIN: Disentangled Reward Mitigates Hacking in RLHF ICML 2024
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs NIPS 2024
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization NIPS 2024
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models NIPS 2024
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models NIPS 2024
Investigating Style Similarity in Diffusion Models ECCV 2024
Transformers Can Do Arithmetic with the Right Embeddings NIPS 2024
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks NIPS 2023
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation ICLR 2023
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization ICLR 2023
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models ICLR 2023
GOAT: A Global Transformer on Large-scale Graphs ICML 2023
A Watermark for Large Language Models ICML 2023
Provable Robustness against Wasserstein Distribution Shifts via Input Randomization ICLR 2023
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise NIPS 2023
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning NIPS 2023
Understanding and Mitigating Copying in Diffusion Models NIPS 2023
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery NIPS 2023
Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images NIPS 2023
On the Exploitability of Instruction Tuning NIPS 2023
What Can We Learn from Unlearnable Datasets? NIPS 2023
Cramming: Training a Language Model on a single GPU in one day. ICML 2023
Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent ICLR 2023
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models CVPR 2023
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries ICLR 2023
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness ICLR 2023
Transfer Learning with Deep Tabular Models ICLR 2023
Autoregressive Perturbations for Data Poisoning NIPS 2022
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking NIPS 2022
Learning Revenue-Maximizing Auctions With Differentiable Matching AISTATS 2022
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch NIPS 2022
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability NIPS 2022
The Uncanny Similarity of Recurrence and Depth ICLR 2022
Robust Optimization As Data Augmentation for Large-Scale Graphs CVPR 2022
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent From the Decision Boundary Perspective CVPR 2022
The Close Relationship Between Contrastive Learning and Meta-Learning ICLR 2022
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models NIPS 2022
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models ICLR 2022
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions ICLR 2022
Stochastic Training is Not Necessary for Generalization ICLR 2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification ICML 2022
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations ICML 2022
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features ICLR 2022
Towards Transferable Adversarial Attacks on Vision Transformers AAAI 2022
Robustness Disparities in Face Detection NIPS 2022
Certified Neural Network Watermarks with Randomized Smoothing ICML 2022
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic ICLR 2021
Center Smoothing: Certified Robustness for Networks with Structured Outputs NIPS 2021
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks NIPS 2021
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization NIPS 2021
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training NIPS 2021
Long-Short Transformer: Efficient Transformers for Language and Vision NIPS 2021
Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering NIPS 2021
Encoding Robustness to Image Style via Adversarial Feature Perturbations NIPS 2021
Adversarial Examples Make Strong Poisons NIPS 2021
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks AAAI 2021
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching ICLR 2021
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition ICLR 2021
The Intrinsic Dimension of Images and Its Impact on Learning ICLR 2021
Data Augmentation for Meta-Learning ICML 2021
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks ICML 2021
Network Deconvolution ICLR 2020
FreeLB: Enhanced Adversarial Training for Natural Language Understanding ICLR 2020
Certified Defenses for Adversarial Patches ICLR 2020
Truth or backpropaganda? An empirical investigation of deep learning theory ICLR 2020
BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES ICLR 2020
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors ECCV 2020
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks ICML 2020
Certified Data Removal from Machine Learning Models ICML 2020
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness ICML 2020
Adversarial Attacks on Copyright Detection Systems ICML 2020
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent ICML 2020
Universal Adversarial Training AAAI 2020
Adversarially Robust Distillation AAAI 2020
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach NIPS 2020
MetaPoison: Practical General-purpose Clean-label Data Poisoning NIPS 2020
Certifying Confidence via Randomized Smoothing NIPS 2020
Certifying Strategyproof Auction Networks NIPS 2020
Detection as Regression: Certified Object Detection with Median Smoothing NIPS 2020
Adversarially robust transfer learning ICLR 2020
Adversarial training for free! NIPS 2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets ICML 2019
ACE: Adapting to Changing Environments for Semantic Segmentation ICCV 2019
Are adversarial examples inevitable? ICLR 2019
Linear Spectral Estimators and an Application to Phase Retrieval ICML 2018
Visualizing the Loss Landscape of Neural Nets NIPS 2018
Stabilizing Adversarial Nets with Prediction Methods ICLR 2018
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks NIPS 2018
DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation ECCV 2018
Training Quantized Nets: A Deeper Understanding NIPS 2017
Convex Phase Retrieval without Lifting via PhaseMax ICML 2017
Adaptive Consensus ADMM for Distributed Optimization ICML 2017
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation CVPR 2017
A New Rank Constraint on Multi-View Fundamental Matrices, and Its Application to Camera Location Recovery CVPR 2017
Automated Inference with Adaptive Batches AISTATS 2017
Adaptive ADMM with Spectral Penalty Parameter Selection AISTATS 2017
Training Neural Networks Without Gradients: A Scalable ADMM Approach ICML 2016
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data ICML 2016
Estimating Sparse Signals With Smooth Support via Convex Programming and Block Sparsity CVPR 2016
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction AISTATS 2016
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing NIPS 2015