Worst Case

December 11, 2023

Compute-in-Memory based Neural Network Accelerators for Safety-Critical Systems: Worst-Case Scenarios and Protections
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Worst Case DNN Accelerator Safety Critical System Neural Network Accelerator Compute in Memory Deep Neural Network Performance Worst Case Performance

December 6, 2023

Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing
Lucas Monteiro Paes, Ananda Theertha Suresh, Alex Beutel, Flavio P. Calmon, Ahmad Beirami
Machine Learning Sample Complexity Group Fairness Worst Case Value at Risk Protected Attribute Performance Disparity
Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization
Yuanshi Liu, Hanzhen Zhao, Yang Xu, Pengyun Yue, Cong Fang
Optimization Purpose Worst Case Accelerated Gradient Method Gradient Complexity Gradient Based Algorithm Active Subspace

November 22, 2023

Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel
Large Language Model Code Generation Worst Case Risk Management Rigorous Framework Risk Measure

November 21, 2023

Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments
Trung Dang, Jasper C. H. Lee, Maoyuan Song, Paul Valiant
Near Optimality Worst Case Temporal Moment Mean Estimation Sub Gaussian

October 31, 2023

Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms
Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant Shenoy
Hidden CoST Worst Case Automated Conversion Balancing Efficiency Learning Augmented Algorithm Deterministic Online

October 30, 2023

October 15, 2023

Worst-Case Analysis is Maximum-A-Posteriori Estimation
Hongjun Wu, Di Wang
Worst Case Fuzz Testing Worst Case Performance

October 6, 2023

Adjustable Robust Reinforcement Learning for Online 3D Bin Packing
Yuxin Pan, Yize Chen, Fangzhen Lin
Worst Case Robust Reinforcement Learning Online 3D Bin Packing

September 30, 2023

Memorization with neural nets: going beyond the worst case
Sjoerd Dirksen, Patrick Finke, Martin Genzel
Neural Network Neural Network Architecture Interpolation Regime Limited Memorization Worst Case Three Layer Neural Network Memorization Capacity

September 28, 2023

Stackelberg Batch Policy Learning
Wenzhuo Zhou, Annie Qu
Game Theoretic Worst Case Regret Guarantee Instance Dependent Regret Batch Reinforcement Learning Batch Policy

August 15, 2023

July 29, 2023

Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noise
Zheyu Yan, Yifan Qin, Wujie Wen, Xiaobo Sharon Hu, Yiyu Shi
Training Data Gaussian Noise Worst Case DNN Accelerator DNN Robustness Compute in Memory

July 15, 2023

Sharp Convergence Rates for Matching Pursuit
Jason M. Klusowski, Jonathan W. Siegel
Lower Bound Worst Case Greedy Algorithm Pursuit Algorithm Sparse Resultant

May 31, 2023

Active causal structure learning with advice
Davin Choo, Themis Gouleakis, Arnab Bhattacharyya
Learning Algorithm Worst Case Causal Direction Advice Quality Temporal Causal Ancestral Graph

May 16, 2023

Sorting and Hypergraph Orientation under Uncertainty with Predictions
Thomas Erlebach, Murilo Santos de Lima, Nicole Megow, Jens Schlöter
Human Prediction High Uncertainty Anticipation Worst Case Auxiliary Sorting Network Rearranges Learning Augmented Algorithm Input Uncertainty Veracity Prediction Uncertainty Aware Exploration

May 2, 2023

Random Function Descent
Felix Benning, Leif Döring
Bayesian Optimization Stochastic Approximation Worst Case Classical Optimization

April 15, 2023

Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees
Nicholas Schiefer, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal, Tal Wagner
Data Stream Quantile Regression Worst Case Better Approximation Quantile Estimation Interpolation Learning

Papers

Compute-in-Memory based Neural Network Accelerators for Safety-Critical Systems: Worst-Case Scenarios and Protections

Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing

Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization

Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models

Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments

Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms

Worst-Case Optimal Multi-Armed Gaussian Best Arm Identification with a Fixed Budget

Flow-based Distributionally Robust Optimization

Worst-Case Analysis is Maximum-A-Posteriori Estimation

Adjustable Robust Reinforcement Learning for Online 3D Bin Packing

Memorization with neural nets: going beyond the worst case

Stackelberg Batch Policy Learning

Correct and Optimal: the Regular Expression Inference Challenge

NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients

Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noise

Sharp Convergence Rates for Matching Pursuit

Active causal structure learning with advice

Sorting and Hypergraph Orientation under Uncertainty with Predictions

Random Function Descent

Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees