research
∙
09/16/2023
Efficient Methods for Non-stationary Online Learning
Non-stationary online learning has drawn much attention in recent years....
share
research
∙
08/11/2023
TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models
Robust pedestrian trajectory forecasting is crucial to developing safe a...
research
∙
08/08/2023
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs
Recently, there has been extensive study of cooperative multi-agent mult...
research
∙
07/07/2023
Hyperspectral and Multispectral Image Fusion Using the Conditional Denoising Diffusion Probabilistic Model
Hyperspectral images (HSI) have a large amount of spectral information r...
research
∙
07/07/2023
Unsupervised Hyperspectral and Multispectral Images Fusion Based on the Cycle Consistency
Hyperspectral images (HSI) with abundant spectral information reflected ...
research
∙
06/13/2023
Learning Unnormalized Statistical Models via Compositional Optimization
Learning unnormalized statistical models (e.g., energy-based models) is ...
research
∙
05/30/2023
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
In this paper, we consider non-convex multi-block bilevel optimization (...
research
∙
05/20/2023
Non-stationary Online Convex Optimization with Arbitrary Delays
Online convex optimization (OCO) with arbitrary delays, in which gradien...
research
∙
05/20/2023
Dynamic Gradient Balancing for Enhanced Adversarial Attacks on Multi-Task Models
Multi-task learning (MTL) creates a single machine learning model called...
research
∙
05/19/2023
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization
In this paper, we aim to optimize a contrastive loss with individualized...
research
∙
05/19/2023
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees
Projection-free online learning has drawn increasing interest due to its...
research
∙
04/17/2023
Scenario Approach for Parametric Markov Models
In this paper, we propose an approximating framework for analyzing param...
research
∙
04/13/2023
Structured Pruning for Multi-Task Deep Neural Networks
Although multi-task deep neural network (DNN) models have computation an...
research
∙
02/18/2023
Stochastic Approximation Approaches to Group Distributionally Robust Optimization
This paper investigates group distributionally robust optimization (GDRO...
research
∙
02/11/2023
Improved Dynamic Regret for Online Frank-Wolfe
To deal with non-stationary online problems with complex constraints, we...
research
∙
02/10/2023
Incremental Satisfiability Modulo Theory for Verification of Deep Neural Networks
Constraint solving is an elementary way for verification of deep neural ...
research
∙
02/09/2023
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization
Stochastically Extended Adversarial (SEA) model is introduced by Sachs e...
research
∙
11/23/2022
Safety Analysis of Autonomous Driving Systems Based on Model Learning
We present a practical verification method for safety analysis of the au...
research
∙
10/08/2022
Break the Wall Between Homophily and Heterophily for Graph Representation Learning
Homophily and heterophily are intrinsic properties of graphs that descri...
research
∙
09/21/2022
ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving
Critical scenario generation requires the ability of sampling critical c...
research
∙
07/18/2022
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization
Variance reduction techniques such as SPIDER/SARAH/STORM have been exten...
research
∙
06/01/2022
Defensive Design of Saturating Counters Based on Differential Privacy
The saturating counter is the basic module of the dynamic branch predict...
research
∙
05/02/2022
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor
In this paper, we investigate an online prediction strategy named as Dis...
research
∙
04/11/2022
Online Frank-Wolfe with Unknown Delays
The online Frank-Wolfe (OFW) method has gained much popularity for onlin...
research
∙
03/10/2022
A Tree-Structured Multi-Task Model Recommender
Tree-structured multi-task architectures have been employed to jointly t...
research
∙
02/24/2022
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance
In this paper, we study contrastive learning from an optimization perspe...
research
∙
02/24/2022
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence
NDCG, namely Normalized Discounted Cumulative Gain, is a widely used ran...
research
∙
02/15/2022
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization
In this paper, we investigate the problem of stochastic multi-level comp...
research
∙
01/23/2022
Weight Expansion: A New Perspective on Dropout and Generalization
While dropout is known to be a successful regularization technique, insi...
research
∙
12/29/2021
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization
We investigate online convex optimization in non-stationary environments...
research
∙
11/19/2021
Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression
This paper investigates deep neural network (DNN) compression from the p...
research
∙
10/25/2021
AutoMTL: A Programming Framework for Automated Multi-Task Learning
Multi-task learning (MTL) jointly learns a set of tasks. It is a promisi...
research
∙
07/26/2021
HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration
Point cloud registration is a fundamental problem in 3D computer vision....
research
∙
07/23/2021
Rethinking Hard-Parameter Sharing in Multi-Task Learning
Hard parameter sharing in multi-task learning (MTL) allows tasks to shar...
research
∙
07/18/2021
Probabilistic Verification of Neural Networks Against Group Fairness
Fairness is crucial for neural networks which are used in applications w...
research
∙
07/02/2021
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization
In this paper, we study stochastic optimization of areas under precision...
research
∙
06/05/2021
Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness
In this paper, we propose a framework of filter-based ensemble of deep n...
research
∙
05/08/2021
A Simple yet Universal Strategy for Online Convex Optimization
Recently, several universal methods have been proposed for online convex...
research
∙
05/07/2021
What do all these Buttons do? Statically Mining Android User Interfaces at Scale
We introduce FRONTMATTER: a tool to automatically mine both user interfa...
research
∙
04/07/2021
ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization
Weakly-supervised temporal action localization aims to localize action i...
research
∙
03/21/2021
Online Convex Optimization with Continuous Switching Constraint
In many sequential decision making applications, the change of decision ...
research
∙
03/21/2021
Online Strongly Convex Optimization with Unknown Delays
We investigate the problem of online convex optimization with unknown de...
research
∙
03/20/2021
Projection-free Distributed Online Learning with Strongly Convex Losses
To efficiently solve distributed online learning problems with complicat...
research
∙
03/09/2021
Non-stationary Linear Bandits Revisited
In this note, we revisit non-stationary linear bandits, a variant of sto...
research
∙
02/13/2021
Revisiting Smoothed Online Learning
In this paper, we revisit the problem of smoothed online learning, in wh...
research
∙
02/10/2021
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting
Although Transformer has made breakthrough success in widespread domains...
research
∙
01/25/2021
Probabilistic Robustness Analysis for DNNs based on PAC Learning
This paper proposes a black box based approach for analysing deep neural...
research
∙
12/16/2020
SID-NISM: A Self-supervised Low-light Image Enhancement Framework
When capturing images in low-light conditions, the images often suffer f...
research
∙
10/16/2020
Projection-free Online Learning over Strongly Convex Sets
To efficiently solve online problems with complicated constraints, proje...
research
∙
10/15/2020
