PRESTIGE improves privacy in stochastic optimization by combining private sampling and CL.
problem Privacy leakage in stochastic optimizations for large-scale sensitive data.
method Introduces PRESTIGE, a robust stochastic optimization framework that combines private sampling and gradual curriculum learning.
result PRESTIGE achieves a good tradeoff between privacy preservation and robustness over baselines.
Curriculum learning strategies improve GAN training speed and quality.
problem Training GANs is notoriously difficult and time-consuming.
method Proposes three curriculum learning strategies based on difficulty scores.
result Curriculum learning strategies lead to faster convergence and superior results.
We introduce Mix&Match (M&M) - a training framework designed to facilitate rapid and effective learning in RL agents, especially those that would be too slow or too challenging to train otherwise. The key innovation is a procedure that allows us to automatically form a curriculum over agents. Through such a curriculum …
MaMiC proposes a dual curriculum for robot manipulation tasks with sparse rewards.
problem Overcoming exploratory constraints in robot manipulation tasks with sparse rewards.
method Includes a macro curriculum scheme and a micro curriculum scheme to guide learning.
result Combining macro and micro curriculum strategies improves performance in robot manipulation tasks.
New algorithm combines curriculum learning with HER for complex object manipulation tasks.
problem Learning complex sequential object manipulation tasks from scratch is challenging.
method Curriculum learning with Hindsight Experience Replay (HER) for recurrent object manipulation tasks.
result Significant improvement in learning sequential object manipulation tasks compared to vanilla-HER.
Unified framework for multi-objective curriculum learning in robotics.
problem Improving sample efficiency and final performance in robotic policy learning.
method Unified automatic curriculum learning framework with multi-task hyper-net and flexible memory mechanism.
result Superior performance compared to state-of-the-art methods in robotic manipulation tasks.
The paper analyzes how curriculum learning improves machine learning performance.
problem Lack of theoretical analysis for curriculum learning in machine learning.
method Formulated an ideal difficulty score and analyzed its contribution in convex problems.
result The expected convergence rate decreases with the ideal difficulty score.
CP-DRL improves curriculum reinforcement learning by leveraging causal relationships.
problem Designing effective task sequences for reinforcement learning.
method Causal-Paced Deep Reinforcement Learning (CP-DRL) that approximates SCM differences based on interaction data.
result CP-DRL outperforms existing methods on benchmarks, achieving faster convergence and higher returns.
Proposes a curriculum-based dropout discriminator for domain adaptation.
problem Improving domain adaptation using deep learning networks trained on large labeled datasets.
method Introduces a curriculum-based dropout discriminator that gradually increases sample variance and uses reverse gradients to align source and target feature representations.
result The proposed model outperforms state-of-the-art results in domain adaptation tasks.
Algorithm learns to solve tasks by gradually expanding policy space.
problem Reinforcement learning difficulty progression.
method Decreasingly constrained policy space expansion.
result Superior learning rate in Tetris tasks.
Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability discourages over-specific co-adaptations of feature detectors, preventing overfitting and improving network generalization. Besides, Dropout can be interpreted as an approximate model aggregat…
Proposes a method to mitigate compounding errors in sequence prediction models.
problem Compounding errors in generated sequences at test time due to exposure bias.
method Curriculum learning based method that gradually replaces deterministic teacher policy with stochastic policy.
result Improves generalization in neural language models and performs well when used with scheduled sampling.
Improves sample efficiency in reinforcement learning by controlling task distribution.
problem Learning and generalization of behaviors across related tasks in intelligent robots.
method Introduces a novel relative entropy reinforcement learning algorithm that allows the agent to control the intermediate task distribution.
result The proposed curriculum learning scheme drastically improves sample efficiency and enables learning in challenging scenarios.
SAT improves adversarial training by smoothing the loss landscape through curriculum learning.
problem Adversarial training sacrifices clean accuracy for robustness and suffers from large generalization error.
method SAT uses curriculum learning to smooth the adversarial loss landscape, improving both clean and robust accuracy.
result SAT models improve clean and robust accuracy significantly compared to adversarial training and other baselines.
Proposes a curriculum-based scheme to smooth CNN feature embeddings.
problem Distortion artifacts in early training stages of CNNs.
method Smoothes feature embedding using Gaussian kernels to control high-frequency information.
result Significant performance improvements on various vision tasks.
Improved NODEs for long-term time series forecasting.
problem Dealing with complex, multi-frequency data.
method Progressive learning paradigm with curriculum learning.
result Performance improved by over 64%.
SelectNet selectively adds unlabelled data to balance imbalanced training data.
problem Training from imbalanced data in machine learning.
method SelectNet trains a deep neural network to add unlabelled data with predicted labels to the training dataset.
result SelectNet effectively balances imbalanced training data, improving classifier performance.
A new method for solving complex sequential decision-making problems by decomposing them into multiple levels.
problem Sequential decision-making with natural multi-level structure.
method Multi-level meta-reinforcement learning with skill-based curriculum.
result Efficiently reduces stochasticity and policy search space, leading to fewer iterations and computations.
This paper explores the trade-off between spatial and adversarial robustness in neural networks.
problem Understanding the trade-off between spatial and adversarial robustness in neural networks.
method Quantitative analysis and empirical testing with curriculum learning.
result Spatial robustness and adversarial robustness are quantitatively related and can be improved simultaneously.
Paper introduces a new curriculum generation method for reinforcement learning.
problem Improving reinforcement learning performance and speed through curriculum learning.
method The paper proposes a novel curriculum generation paradigm based on progression and mapping functions.
result Empirical results show the new approach outperforms state-of-the-art algorithms.
Analyzes when and why curricula help neural networks learn.
problem Why and when does curriculum learning help neural networks?
method Statistical physics methods applied to a neural network model.
result Curricula can modestly speed up learning but do not provide generalization benefit.
Curriculum learning shows marginal benefits over random ordering on standard datasets.
problem Investigating the benefits of curriculum learning over random ordering in training neural networks.
method Experiments with curriculum, anti-curriculum, and random-curriculum orderings on various datasets and conditions.
result Curriculum learning only marginally improves performance on standard datasets and is outperformed by random ordering.
Paper develops a method to automatically create learning curricula for reinforcement learning.
problem Designing an effective sequence of simpler tasks to prepare for a more complex task.
method Formulated as a Markov Decision Process, extended to handle multiple transfer learning algorithms.
result A curriculum policy can be learned from experience, improving training speed for reinforcement learning agents.
Automatically generates a deep RL curriculum for faster and more stable learning.
problem How to automatically generate a curriculum for deep RL agents.
method Interprets curriculum generation as an inference problem, learning task distributions progressively.
result Curricula significantly improve learning performance across various environments and deep RL algorithms.
Curriculum learning speeds up agent learning in Minecraft, a complex visual domain.
problem Training agents to learn multiple tasks in a complex, visual domain.
method Learning-progress based curriculum and dynamic exploration bonuses.
result Curriculum learning improves agent performance in a complex reinforcement learning problem.
Optimizes task sequencing in reinforcement learning to improve exploration and policy discovery.
problem Improving exploration and policy discovery in reinforcement learning.
method General optimization framework for task sequencing using different objective functions.
result Curriculum learning can improve initial performance, reduce suboptimal actions, and discover better policies.
Curriculum learning and imitation learning improve control over financial time-series data.
problem Improving control performance over complex financial time-series data.
method Data augmentation for curriculum learning and policy distillation for imitation learning.
result Curriculum learning shows significant improvement over time-series control tasks.
Improved reinforcement learning by using a structured training curriculum.
problem Machine learning players learn from the entire game at all times, not using a structured training curriculum.
method Employing an end-game-first training curriculum to train an AlphaZero inspired player.
result The rate of learning of an artificial player can be improved during the early stages of training.
The paper studies the benefits of curriculum learning in linear regression tasks.
problem Theoretical understanding of curriculum learning's benefits in machine learning.
method Theoretical analysis of curriculum learning in structured and unstructured multitask linear regression problems.
result Adaptive learning in the unstructured setting is fundamentally harder than oracle learning, but not in the structured setting.
This paper introduces curriculum loss to improve robustness and generalization in deep neural networks against label corruption.
problem Improving robustness and generalization in deep neural networks against label corruption.
method Proposes curriculum loss, a tighter upper bound of the 0-1 loss, which optimizes robust properties while adaptively selecting samples.
result Curriculum loss enhances robustness and generalization in deep neural networks compared to conventional losses.
Survey on curriculum learning for reinforcement learning.
problem Efficiently learning complex tasks in reinforcement learning.
method Framework for curriculum learning in reinforcement learning.
result Survey and classification of existing CL methods.
Curriculum learning improves deep generative models for noisy data.
problem Training deep generative models with noisy data is challenging.
method Proposes a curriculum learning algorithm based on data point centrality.
result The algorithm learns optimal generative models for noisy data.
Q-CurL optimizes quantum learning with a curriculum design.
problem Efficiently training quantum models with limited resources.
method Quantum curriculum learning framework.
result Q-CurL enhances training convergence and generalization.
In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning. Using a reinforcement learning agent based on the Deep Deterministic Policy Gradient algorithm and addressing the Reacher environment, we first show …
This work analyzes how curriculum learning improves deep network training.
problem Training deep networks with non-uniform sampling of mini-batches.
method Two methods to address curriculum learning: transfer learning and bootstrapping. Different pacing functions to guide mini-batch sampling.
result Curriculum learning leads to faster learning and better final performance.
EPC curriculum improves MARL performance as agent population grows.
problem Challenges in learning good policies for large multi-agent systems.
method Evolutionary Population Curriculum (EPC) for scaling MARL.
result EPC consistently outperforms baselines as agent population increases.
Proposes a curriculum learning algorithm to maximize cumulative return in reinforcement learning.
problem Maximizing cumulative return in reinforcement learning tasks.
method Task sequencing algorithm maximizing cumulative return, using curriculum learning to minimize suboptimal actions.
result Significantly better performance on cumulative return maximization compared to metaheuristic algorithms.
Agent learns complex web tasks by breaking them into simpler sub-tasks.
problem Learning from large, sparse reward environments like web navigation.
method Decompose complex instructions into sub-instructions, schedule a curriculum, and use meta-learning for new tasks.
result DQN agent achieves 100% success rate on complex web tasks without human demonstrations.
New method learns optimal environment and goal difficulty for reinforcement learning.
problem Lack of robust transfer in reinforcement learning.
method Self-Supervised Active Domain Randomization (SS-ADR).
result Optimal domain randomization strategy improves transfer in goal-directed tasks.
Paper proposes a curriculum for RL agents to learn from expert demonstrations.
problem Training RL agents efficiently from expert demonstrations.
method Develops a training curriculum that starts from the goal and works backwards.
result Improves sample efficiency for some tasks compared to PPO.
Curriculum learning helps neural networks learn parities more efficiently.
problem Improving learning efficiency for neural networks on parity targets.
method Using a curriculum learning approach with a mixture of sparse and dense inputs.
result A 2-layer ReLU neural network can learn parities more efficiently than a fully connected network.
ACL improves DRL by adapting task difficulty to agent's capabilities.
problem Improving sample efficiency and performance in DRL.
method Adapting task difficulty based on agent's capacity.
result Enhanced sample efficiency and asymptotic performance.
Bi-directional Curriculum Learning improves graph anomaly detection by considering both homogeneity and heterogeneity.
problem Existing graph anomaly detection methods often ignore the different contributions of nodes to training.
method Introduces Bi-directional Curriculum Learning (BCL) to optimize GAD methods by considering both homogeneity and heterogeneity of nodes.
result Extensive experiments show that BCL significantly improves the performance of GAD anomaly detection models.
In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the course of training, thereby making the learning task progressively more difficult for the generator. We demonstrate that this strategy is key …
This work improves NAT translation accuracy through fine-tuning with curriculum learning.
problem Improving NAT translation accuracy while maintaining inference speed.
method Curriculum learning applied to fine-tuning of a pre-trained AT model to a NAT model.
result Significant improvement in translation accuracy (over 1 BLEU score) and speedup in inference.
New syllabuses improve LSTM learning speed but not generalization.
problem Comparing syllabuses for curriculum learning in LSTM networks.
method Empirical comparison of identified syllabuses on three sequential learning tasks.
result Automated curriculum learning approach performs competitively.
SAMPLR optimizes for ground truth in aleatoric parameters to avoid curriculum-induced covariate shift.
problem Curriculum learning shifts training distribution, leading to suboptimal policies in aleatoric settings.
method SAMPLR optimizes ground-truth utility function, avoiding curriculum-induced covariate shift.
result SAMPLR preserves optimality under ground-truth distribution, promoting robustness across various environments.
Recent variants improve knowledge distillation performance.
problem Improving the performance of knowledge distillation.
method Introducing additional components or changing the learning process.
result These variants have shown promising results.