A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A new conformal prediction framework for two-stage models identifies stage-wise uncertainty.
A new algorithm for identifying the best arm in linear feedback with safety constraints.
Study contextual bandits with stage-wise constraints, proving regret bounds and extending results.
New RL algorithm reduces policy switching cost to loglog(T) with similar regret.
Recently, researchers utilize Knowledge Graph (KG) as side information in recommendation system to address cold start and sparsity issue and improve the recommendation performance. Existing KG-aware recommendation model use the feature of neighboring entities and structural information to update the embedding of curren…
Effective medical test suggestions benefit both patients and physicians to conserve time and improve diagnosis accuracy. In this work, we show that an agent can learn to suggest effective medical tests. We formulate the problem as a stage-wise Markov decision process and propose a reinforcement learning method to train…
Two new algorithms optimize rewards while respecting safety constraints in sequential decisions.
AGGLIO optimizes non-convex functions with local convexity guarantees.
Deep reinforcement learning for high dimensional, hierarchical control tasks usually requires the use of complex neural networks as functional approximators, which can lead to inefficiency, instability and even divergence in the training process. Here, we introduce stacked deep Q learning (SDQL), a flexible modularized…
As machine learning algorithms enter applications in industrial settings, there is increased interest in controlling their cpu-time during testing. The cpu-time consists of the running time of the algorithm and the extraction time of the features. The latter can vary drastically when the feature set is diverse. In this…
We study the problem of collaborative filtering where ranking information is available. Focusing on the core of the collaborative ranking process, the user and their community, we propose new models for representation of the underlying permutations and prediction of ranks. The first approach is based on the assumption …
This paper presents a simple method for a posteriori (historical) multi-variate multi-stage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, we analyze the stage-wise optimal allocation over a time horizon with potential investments in mul…
Guided Learning improves end-to-end modeling for multi-stage decision-making.
The design and performance analysis of bandit algorithms in the presence of stage-wise safety or reliability constraints has recently garnered significant interest. In this work, we consider the linear stochastic bandit problem under additional \textit{linear safety constraints} that need to be satisfied at each round.…
We propose a neural architecture search (NAS) algorithm, Petridish, to iteratively add shortcut connections to existing network layers. The added shortcut connections effectively perform gradient boosting on the augmented layers. The proposed algorithm is motivated by the feature selection algorithm forward stage-wise …
DeRisk improves credit risk prediction using deep learning.
We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we present an efficient and effective SGPC design method to perform a stage-wise optimization of a predictive loss function. We introduce new me…
MOMENT selects and estimates mixed-effects models using moment identities.
Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use of a single generator to perform one-stage enhancement mapping. In this work, we propose to use multiple generators that are chained to perf…
TSL learns separable models to avoid signal cancellation and off-support extrapolation.
Transformers learn to integrate information from past positions incrementally, specializing heads in distinct patterns.
EPC curriculum improves MARL performance as agent population grows.
IBP-R improves verified adversarial robustness with simple, effective interval bound propagation.
Differentially private method for estimating individualized treatment rules.
The paper develops a method to learn cost-optimal sequential testing policies from retrospective data.
The paper tackles fast rates in batch active learning with pool-based data.
A semi-supervised framework using stochastic interpolation and latent representations.
CycleFQI tackles offline reinforcement learning for cyclic MDPs, mitigating state distribution mismatch.
This paper proposes a boosting-based solution addressing metric learning problems for high-dimensional data. Distance measures have been used as natural measures of (dis)similarity and served as the foundation of various learning methods. The efficiency of distance-based learning methods heavily depends on the chosen d…
Unified framework for constrained online decision-making.
Inspired by the unsupervised learning or self-organization in the machine learning context, here we attempt to draw `learning curve' for the collective behavior of job-seeking `zero-intelligence' labors in successive job-hunting processes. Our labor market is supposed to be opened especially for university graduates in…
Transformers learn unseen tasks via prompts without fine-tuning.
In this paper, we develop a novel {\bf ho}moto{\bf p}y {\bf s}moothing (HOPS) algorithm for solving a family of non-smooth problems that is composed of a non-smooth term with an explicit max-structure and a smooth term or a simple non-smooth term whose proximal mapping is easy to compute. The best known iteration compl…
BYOL-Explore learns to explore visually-rich environments by predicting world dynamics.
A new multi-objective RL framework improves intrinsic exploration performance.
Study incentivizes exploration in non-stationary MAB with compensation.
Effective and intelligent exploration has been an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as -greedy exploration or adding Gaussian noise to actions. These heuristics, however, are unable to intelligently distinguish the well …
New exploration bonuses improve reinforcement learning efficiency.
New findings on when to use action space exploration in reinforcement learning.
Proposes a method to avoid excessive exploration in reinforcement learning.
Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL). State-of-the-art RL algorithms suffer from high sample complexity, particularly in the sparse reward case, where they can do no better than to explore in all directions until the first positive rewards are found. To mitigate …
A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, whic…
New algorithm reduces regret by allowing free exploration in multi-armed bandits.
New meta-RL method avoids exploration-exploitation trade-off.
The paper tackles pure exploration in multi-armed bandits with low rank structure using oblivious sampling.
Regularization-induced exploration improves contextual bandit performance.
The Exploration-Exploitation tradeoff arises in Reinforcement Learning when one cannot tell if a policy is optimal. Then, there is a constant need to explore new actions instead of exploiting past experience. In practice, it is common to resolve the tradeoff by using a fixed exploration mechanism, such as -greedy ex…
We introduce the community exploration problem that has many real-world applications such as online advertising. In the problem, an explorer allocates limited budget to explore communities so as to maximize the number of members he could meet. We provide a systematic study of the community exploration problem, from off…