A new buffer system improves continual learning in RL agents by adapting to changing environments.
problem Improving RL agents' ability to learn from changing environments over time.
method Multi-timescale replay buffer combined with invariant risk minimization.
result The method shows improvement over baselines in continual learning settings.
New method scales Bayesian inference for nonlinear SSMs using buffered stochastic gradient.
problem Inference for nonlinear, non-Gaussian SSMs is computationally challenging and particle degeneracy increases with longer series.
method Extends stochastic gradient MCMC to nonlinear SSMs using particle methods and error bounds.
result Demonstrates the importance of particle buffered stochastic gradient for long sequential data.
Paper introduces a dynamic reference frame strategy to predict events with a buffer time.
problem Lack of time buffer for predictions to enable timely action.
method Introduces a new concept of dynamic reference frame creation.
result Enables organizations to act on predictions with a buffer time.
Efficiently combines autoregressive and set-based models for joint distributions.
problem Joint distributions over multiple predictions from set-based models.
method Causal autoregressive buffer that caches context and captures dependencies.
result Up to 20x faster joint sampling and density evaluation, up to 7x lower memory usage.
A new model for predicting market order book dynamics using a buffer Hawkes process.
problem Predicting the evolution of limit order books in financial markets.
method Introducing a Markovian single point process with a buffer mechanism and self-exciting effect.
result The model accurately predicts market order book dynamics and converges to Brownian motion.
Secure aggregation for buffered asynchronous federated learning without TEEs.
problem Privacy and convergence in buffered asynchronous federated learning.
method Developed a new protocol (BASecAgg) that ensures privacy without TEEs by carefully designing masks.
result BASecAgg achieves similar convergence guarantees as FedBuff without TEEs.
Investigates multi-period portfolio optimization for DC plans using buffered Probability of Exceedance.
problem Optimizing long-term Defined Contribution plans with realistic constraints and dynamic dynamics.
method Formulates and solves bilevel optimization problems for pre-commitment and time-consistent Mean-bPoE and Mean-CVaR portfolio optimization.
result Time-consistent Mean-bPoE strategies maintain investor preferences for minimum terminal wealth, unlike Mean-CVaR.
MER algorithm speeds up VI solving with Markovian data.
problem Solving stochastic variational inequalities with Markovian data.
method MER algorithm using multi-scale sampling from a Markovian buffer.
result Achieves faster convergence without knowing Markov chain mixing time.
MAPO uses a memory buffer to improve policy optimization in structured prediction tasks.
problem Improving sample efficiency and robustness in policy optimization for structured prediction tasks.
method Memory Augmented Policy Optimization (MAPO) uses a memory buffer to reduce policy gradient variance.
result MAPO achieves state-of-the-art results in program synthesis and semantic parsing tasks.
Hierarchical GANs reduce anomaly detection costs.
problem Balancing anomaly detection accuracy and sampling costs.
method Hierarchical GANs for nonuniform sampling and buffer zones.
result Proposed GAN-based detector outperforms baseline in detection delay and average cost of error.
The paper optimizes pension policies with guarantees and sustainability constraints.
problem Designing optimal pension policies with guarantees and sustainability constraints.
method Dynamic utility model, stochastic domain, overlapping generations, time-consistent decision criterion.
result Optimal investment/pension policy computed for a general framework.
This work improves continual learning by selecting diverse samples for replay buffers.
problem Overcoming catastrophic forgetting in online continual learning.
method Formulates sample selection as a constraint reduction problem and uses gradient-based diversity maximization.
result Demonstrates improved performance compared to existing methods that rely on task boundaries.
This study examines how risky investments affect insurance capital valuation.
problem Standard cost-of-capital assumptions do not account for risky investments.
method Analyzed effects of allowing buffer capital investments in risky assets.
result Decomposition of buffer capital contributions varies with riskiness.
BUZz defends images from adversarial attacks using simple transformations.
problem Adversarial attacks on deep neural networks for image classification.
method Combination of deep neural networks and simple image transformations.
result Achieves significant improvement over state-of-the-art defenses with a modest drop in clean accuracy.
Regulator allocates buffers to prevent financial contagion in networks with common assets.
problem Containment of default contagion in financial networks with common asset exposures.
method Allocates nonnegative buffer vectors under linear budget constraints to maximize default or insolvency resilience margins or minimize worst-case systemic losses.
result Exact synthesis results for buffer allocation under ℓ∞ and ℓ1 uncertainty sets, showing significant gains over uniform and exposure-proportional allocations. Paper improves online time series forecasting by combining natural gradient and robust t-distribution.
problem Online time series forecasting challenges in rapidly adapting to evolving data.
method Reframed neural network optimization as a parameter filtering problem, using natural gradient and Student's t likelihood.
result Natural Score-driven Replay (NatSR) achieves stronger forecasting performance than state-of-the-art methods.
Deployment of emerging technologies and rapid change in industries has created a lot of risk for initiating the new projects. Many techniques and suggestions have been introduced but still lack the gap from various prospective. This paper proposes a reliable project scheduling approach. The objectives of project schedu…
A distributed system identification method for LTI systems using reverse experience replay.
problem Online system identification of LTI systems over multi-agent networks.
method DSGD-RER, a distributed variant of SGD-RER with backward updates.
result The estimation error decreases as the network size grows.
The paper proposes a method to learn from both simulation and real-world data.
problem Training autonomous systems in simulation and applying them to real-world environments.
method Balancing samples from simulation and real-world data using a replay buffer.
result The method achieves better performance in real-world tasks compared to training only in simulation.
SOCP uses SOM to find groups and local calibration buffers for better regional coverage.
problem Heterogeneous regional coverage gaps in conformal prediction.
method Self-Organizing Map (SOM) for group discovery; local calibration buffers at BMU or fixed grid.
result Reduces regional coverage gaps on 7/8 benchmarks by 7.1%.
A mean-reverting financial instrument is optimally traded by buying it when it is sufficiently below the estimated `mean level' and selling it when it is above. In the presence of linear transaction costs, a large amount of value is paid away crossing bid-offers unless one devises a `buffer' through which the price mus…
This paper considers a transmission control problem in network-coded two-way relay channels (NC-TWRC), where the relay buffers random symbol arrivals from two users, and the channels are assumed to be fading. The problem is modeled by a discounted infinite horizon Markov decision process (MDP). The objective is to find…
Study compares AI and static analysis for detecting buffer overflows.
problem Detecting buffer overflows in code.
method Developed s-bAbI to generate code samples, compared AI with static analysis tools.
result AI system requires extensive training data to match static analysis precision and recall.
PipeDream-2BW accelerates large model training by 20x with minimal memory usage.
problem Training large models requires memory beyond single accelerator capacity.
method Pipeline parallelism, weight gradient coalescing, double buffering.
result Accelerates large model training by up to 20x.
New method allows real-time audio synthesis using non-causal convolutions.
problem Real-time audio synthesis limitations due to offline model constraints.
method Post-training reconfiguration of non-causal models for real-time buffer-based processing.
result Non-causal streaming models can be transformed from offline-trained models without quality loss.
Improves on-policy RL by reusing data from multiple policies.
problem Lack of reuse of data from previous policies in on-policy RL.
method Adapts replay buffer concept to combine on- and off-policy learning.
result Method outperforms state-of-the-art on-policy RL algorithms.
DAC enhances exploration in reinforcement learning with entropy regularization.
problem Improving exploration efficiency in reinforcement learning.
method Sample-aware entropy regularization using replay buffer action distributions.
result DAC significantly outperforms existing algorithms in reinforcement learning tasks.
The study examines Cox models for lifetime loan default risk, addressing biased estimates by incorporating recurrent events.
problem Ignoring recurrent default events in Cox models leads to biased and inaccurate PD estimates.
method Investigates and compares different Cox models (Andersen-Gill and Prentice-Williams-Peterson) for lifetime loan default risk.
result The Andersen-Gill model underperforms compared to the Prentice-Williams-Person model and the time to first default model.
This work improves reinforcement learning with sparse rewards by following diverse past trajectories.
problem Challenges in reinforcement learning with sparse rewards and myopic behavior.
method Proposes a trajectory-conditioned policy to learn from a memory buffer of diverse past trajectories.
result Significantly outperforms existing methods on complex tasks with local optima.
Flexible VHDL design for multiple neural networks on FPGAs.
problem Inflexible neural network designs for FPGAs.
method Proposes a flexible VHDL structure with multiple processor groups.
result Allows training and testing of multiple neural networks on multiple FPGAs.
New algorithm optimizes linear system estimation from single trajectory.
problem Estimating LTI systems from a single trajectory.
method SGD with Reverse Experience Replay (SGD−RER) result Optimal guarantees for parameter and prediction errors.
Investigates optimal pension policies in PAYG systems with forward utility and ageing population.
problem Optimal investment and pension policies in PAYG systems with sustainability and adequacy constraints.
method Non-zero volatility forward CRRA utilities, closed-form optimal policies, detailed numerical analysis.
result Characterization of optimal policies and detailed impact analysis under various scenarios.
Proposes a new sampling method for deep Q-learning to improve efficiency and convergence.
problem Challenges in learning state-action value function from replay buffer.
method State distribution-aware sampling method to balance replay times for transitions.
result Reduces unnecessary TD updates and increases updates for uncertain state-action values.
Proportional transaction costs present difficult theoretical problems in trading algorithm design, on account of their lack of analytical tractability. The author derives a solution of DT-NT-DT form for an arbitrary model in which the the traded asset has diffusive dynamics described by one or more stochastic risk fact…
Novel asynchronous SGD method resists Byzantine attacks without server storage.
problem Asynchronous distributed learning with Byzantine attacks and failures.
method Buffered Asynchronous SGD (BASGD) and its momentum variant (BASGDm).
result BASGD and BASGDm resist non-omniscient and omniscient attacks without server storage.
Tiled Squeeze-and-Excite improves channel attention with local spatial context.
problem Improving channel attention mechanisms in neural networks.
method Proposes tiled squeeze-and-excite (TSE) framework for channel attention.
result Local context of 7 rows or columns is sufficient for matching global context performance.
Improved neural architecture search techniques fail to learn structural similarity.
problem NAS techniques fail to learn structural similarity.
method Investigated ENAS controller's hidden state and proposed a solution by training with a memory buffer.
result Models sampled from identical controller hidden states have no correlation with graph similarity metrics.
Improved diffusion models for sampling from given distributions.
problem Training diffusion models to sample from a given distribution.
method Benchmarked and improved off-policy methods for diffusion sampling.
result A novel exploration strategy improves sample quality.
Experience replay helps neural networks learn new tasks without forgetting old knowledge.
problem Catastrophic forgetting in neural networks trained on non-stationary data.
method Experience replay buffers with a mixture of on- and off-policy learning.
result Experience replay can learn new tasks quickly and reduce catastrophic forgetting.
Paper proposes efficient BNN inference techniques on FPGA.
problem Redundancy in BNN inference leading to high computation and data access costs.
method Analyzed image similarity and BNN kernel weights to exploit redundancy. Proposed two types of fast and energy-efficient architectures.
result 80% reduction in computation and 40% in buffer access, achieving 17% power reduction.
LiDER refreshes past experiences in RL by dreaming about them.
problem Improving data efficiency in off-policy RL algorithms.
method Refreshing past experiences in a replay buffer using the current policy.
result LiDER consistently improves performance in Atari games.
Proposes a new policy gradient algorithm to improve reinforcement learning efficiency and stability.
problem Inefficiency and instability of DDPG in practical applications, and difficulty in controlling Q estimation bias and variance.
method Introduces a Regularly Updated Deterministic (RUD) policy gradient algorithm.
result The RUD algorithm makes better use of new data and has lower Q value variance, leading to improved performance.
Improves VAE training by refining variational parameters with BSVI.
problem Amortized inference in VAEs leads to suboptimal variational parameters and the amortization gap.
method Proposes BSVI, a refinement procedure using SVI's importance weights.
result Training VAEs with BSVI yields improved performance compared to SVI.
Credit risk management in Italy is characterized, in the period June 2008 to June 2012, by frequent (frequency=0.5 cycles per year) and intense (peak amplitude: mean=39.2 billion Euros, s.e.=2.83 billion Euros) quarterly contractions and expansions around the mean (915.4 billion Euros, s.e.=3.59 billion Euros) of the n…
ETGL-DDPG improves DDPG for sparse reward control with new exploration and replay techniques.
problem Sparse reward continuous control in reinforcement learning.
method Introduces εt-greedy search and GDRB framework for efficient exploration and reward use. result ETGL-DDPG outperforms DDPG and other methods on sparse-reward continuous benchmarks.
Poor economies face frequent disruptions that trap them in producing simpler goods.
problem Frequent disruptions in poor economies prevent them from producing complex goods.
method Modeling an evolving input-output network with optimizing agents that adapt to disruptions.
result A poverty trap emerges where disruptions persist despite agents producing simpler goods.
We propose a streaming submodular maximization algorithm "stream clipper" that performs as well as the offline greedy algorithm on document/video summarization in practice. It adds elements from a stream either to a solution set S or to an extra buffer B based on two adaptive thresholds, and improves S by a final…
It had been believed in the conventional practice that the risk of a bank going bankrupt is lessened in a straightforward manner by transferring the risk of loan defaults. But the failure of American International Group in 2008 posed a more complex aspect of financial contagion. This study presents an extension of the …