arXiv research

Paper explains why small-loss criterion works for learning from noisy labels.

This paper explains why distributional reinforcement learning is better than vanilla RL using small-loss bounds.

New bounds for bandits with graph feedback, improving previous results.

New method achieves small-loss regret bounds in random-order model.

Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach "Co-teaching" that cross-trains two deep neural ne…

New algorithm achieves small-loss bounds in online learning with improved rates.

New algorithms adapt to friendly environments in online learning.

INN method refines clean labeled data from noisy labels.

BCD algorithm finds global minima in neural networks.

Mirror descent with an entropic regularizer is known to achieve shifting regret bounds that are logarithmic in the dimension. This is done using either a carefully designed projection or by a weight sharing technique. Via a novel unified analysis, we show that these two approaches deliver essentially equivalent bounds …

We investigate online convex optimization in changing environments, and choose the adaptive regret as the performance measure. The goal is to achieve a small regret over every interval so that the comparator is allowed to change over time. Different from previous works that only utilize the convexity condition, this pa…

Since deep neural networks are over-parameterized, they can memorize noisy examples. We address such a memorization issue in the presence of label noise. From the fact that deep neural networks cannot generalize to neighborhoods of memorized features, we hypothesize that noisy examples do not consistently incur small l…

Bernard et al. (2015) study an optimal insurance design problem where an individual's preference is of the rank-dependent utility (RDU) type, and show that in general an optimal contract covers both large and small losses. However, their contracts suffer from a problem of moral hazard for paying more compensation for a…

New bounds for online portfolio selection without smoothness assumptions.

Paper introduces a new $G^\star$ regret measure for online convex optimization with smooth losses.

Paper proposes a diagnostic tool for evaluating model performance out-of-sample.

This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constr…

We present a filter correlation based model compression approach for deep convolutional neural networks. Our approach iteratively identifies pairs of filters with the largest pairwise correlations and drops one of the filters from each such pair. However, instead of discarding one of the filters from each such pair naï…

Paper proposes a federated learning framework for relative fairness.

Data aggregation improves HAC for resource-constrained systems.

How can we control for latent discrimination in predictive models? How can we provably remove it? Such questions are at the heart of algorithmic fairness and its impacts on society. In this paper, we define a new operational fairness criteria, inspired by the well-understood notion of omitted variable-bias in statistic…

We propose a general framework for studying adaptive regret bounds in the online learning framework, including model selection bounds and data-dependent bounds. Given a data- or model-dependent bound we ask, "Does there exist some algorithm achieving this bound?" We show that modifications to recently introduced sequen…

Given a similarity graph between items, correlation clustering (CC) groups similar items together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of vertices, and obtains a 3-approximation ratio. Unfortunately, KwikCluster in practice re…

As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded controllers. These platforms are often resource-constrained, especially in energy storage and power, but state-of-the-art NN architectures are de…

Paper defends sensitive attributes in GNNs from inference attacks.

This paper tackles fairness in PCA by balancing it with reconstruction error.

We investigate how price variations of a stock are transformed into profits and losses (P&Ls) of a trend following strategy. In the frame of a Gaussian model, we derive the probability distribution of P&Ls and analyze its moments (mean, variance, skewness and kurtosis) and asymptotic behavior (quantiles). We show that …

Emotional aspects play an important part in our interaction with music. However, modelling these aspects in MIR systems have been notoriously challenging since emotion is an inherently abstract and subjective experience, thus making it difficult to quantify or predict in the first place, and to make sense of the predic…

JoCoR improves deep learning with noisy labels by reducing network diversity.

Paper compresses deep neural networks by eliminating redundant neurons.

We introduce a new principle for model selection in regression and classification. Many regression models are controlled by some smoothness or flexibility or complexity parameter c, e.g. the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polyno…

Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse RNNs are easier to deploy on devices and high-end server processors. …

The paper explores optimal insurance contracts using various deviation measures.

New approach for online learning with adaptive adversaries, simpler and more effective.

Improves bandit convex optimization with gradient variations.

New DP algorithms achieve near-optimal regret bounds for online learning problems.

We develop a novel and generic algorithm for the adversarial multi-armed bandit problem (or more generally the combinatorial semi-bandit problem). When instantiated differently, our algorithm achieves various new data-dependent regret bounds improving previous work. Examples include: 1) a regret bound depending on the …

Efficient deep learning for wireless source identification using test SNR estimates.

Paper provides new bounds for risk aggregation and sharing.

A trading system uses LLMs to adapt to volatile crypto markets.

Empirical risk minimization (ERM) is a fundamental learning rule for statistical learning problems where the data is generated according to some unknown distribution $\mathsf{P}$ and returns a hypothesis $f$ chosen from a fixed class $\mathcal{F}$ with small loss $\ell$. In the parametric setting, depending upon $(\ell…

Paper proposes an online learning method with multi-level adaptivity for diverse loss functions.

Efficient algorithm for contextual bandits with first-order guarantees.

DINOSAUR improves retrieval by accounting for embedding uncertainty in recommender systems.

Optimizes investment under uncertain time horizons with non-concave utility.

New algorithm tackles multi-armed bandit with arbitrary delays and general bounded losses.

Paper improves COCO problem, reducing constraint violation at the cost of slightly more regret.