Search Results for author:
Found 32 papers, 15 papers with code
OpenRL: A Unified Reinforcement Learning Framework
We present OpenRL, an advanced reinforcement learning (RL) framework designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems.
Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform
A public instance of Codabench (https://www. codabench. org/) is open to everyone, free of charge, and allows benchmark organizers to compare fairly submissions, under the same setting (software, hardware, data, algorithms), with custom protocols and data formats.
Efficient Neural Architecture Search via Proximal Iterations
Recently, DARTS, which constructs a differentiable search space and then optimizes it by gradient descent, can obtain high-performance architecture and reduces the search time to several days.
TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play
Multi-agent football poses an unsolved challenge in AI research.
Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models
The rise of deepfake images, especially of well-known personalities, poses a serious threat to the dissemination of authentic information.
Auto-KWS 2021 Challenge: Task, Datasets, and Baselines
Auto-KWS 2021 challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to a customized keyword spotting task.
OmniPrint: A Configurable Printed Character Synthesizer
We introduce OmniPrint, a synthetic data generator of isolated printed characters, geared toward machine learning research.
Comparison of Spatio-Temporal Models for Human Motion and Pose Forecasting in Face-to-Face Interaction Scenarios
In this work, we present the first systematic comparison of state-of-the-art approaches for behavior forecasting.
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge
Driven by business scenarios, we organized the first Automated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020.
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation
Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+2
LTU Attacker for Membership Inference
In both cases classification accuracy or error rate are used as the metric: Utility is evaluated with the classification accuracy of the Defender model; Privacy is evaluated with the membership prediction error of a so-called "Leave-Two-Unlabeled" LTU Attacker, having access to all of the Defender and Reserved data, except for the membership label of one sample from each.
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization
We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task.
SAdam: A Variant of Adam for Strongly Convex Functions
In this paper, we give an affirmative answer by developing a variant of Adam (referred to as SAdam) which achieves a data-dependant $O(\log T)$ regret bound for strongly convex functions.
Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction
To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency.
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce the human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search.
AutoML @ NeurIPS 2018 challenge: Design and Results
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018.
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications
Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses.
Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions
Along this line of research, this paper presents the first universal algorithm for minimizing the adaptive regret of convex functions.
Towards AutoML in the presence of Drift: first results
We extendAuto-Sklearn with sound and intuitive mechanisms that allow it to cope with this sort ofproblems.
Network On Network for Tabular Data Classification in Real-world Applications
As a result, the intra-field information and the non-linear interactions between those operations (e. g. neural network and factorization machines) are ignored.
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity
To deal with complicated constraints via locally light computation in distributed online learning, recent study has presented a projection-free algorithm called distributed online conditional gradient (D-OCG), and achieved an $O(T^{3/4})$ regret bound, where $T$ is the number of prediction rounds.
Improving Tail Label Prediction for Extreme Multi-label Learning
Extreme multi-label learning (XML) works to annotate objects with relevant labels from an extremely large label set.
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification
The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks.
Online Strongly Convex Optimization with Unknown Delays
Specifically, we first extend the delayed variant of OGD for strongly convex functions, and establish a better regret bound of $O(d\log T)$, where $d$ is the maximum delay.
Projection-free Distributed Online Learning with Sublinear Communication Complexity
In this paper, we propose an improved variant of D-OCG, namely D-BOCG, which can attain the same $O(T^{3/4})$ regret bound with only $O(\sqrt{T})$ communication rounds for convex losses, and a better regret bound of $O(T^{2/3}(\log T)^{1/3})$ with fewer $O(T^{1/3}(\log T)^{2/3})$ communication rounds for strongly convex losses.
Robust Long-Tailed Learning under Label Noise
To overcome this limitation, we establish a new prototypical noise detection method by designing a distance-based metric that is resistant to label noise.
Ranked #25 on
Image Classification
on mini WebVision 1.0
Didn't see that coming: a survey on non-verbal social human behavior forecasting
Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years.
Projection-free Online Learning with Arbitrary Delays
Projection-free online learning, which eschews the projection operation via less expensive computations such as linear optimization (LO), has received much interest recently due to its efficiency in handling high-dimensional problems with complex constraints.
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data
TRAS transforms the imbalanced pseudo-label distribution of a traditional SSL model via a delicate function to enhance the supervisory signals for minority classes.
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization
Inspired by their work, we investigate the theoretical guarantees of optimistic online mirror descent (OMD) for the SEA model.
Efficient Stochastic Approximation of Minimax Excess Risk Optimization
While traditional distributionally robust optimization (DRO) aims to minimize the maximal risk over a set of distributions, Agarwal and Zhang (2022) recently proposed a variant that replaces risk with excess risk.
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments
Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence.
