arXiv research

A new cost-frugal HPO method controls training cost during optimization.

New model considers varying costs in learning, outperforming existing methods.

Frugal method predicts multiple local electricity loads efficiently.

A new optimizer saves significant shots in quantum machine learning.

Frugal Flows learn complex data and infer marginal causal effects.

Spectral regularization improves learning over combinatorial spaces with limited data.

Frengression models causal data flexibly and faithfully.

Sampling a fraction of pairs can match full evaluation in machine learning losses.

Proposes a method to evaluate generalizability in causal inference models.

New approach selects sparse features without validation.

Model learns brevity by exposing to easy problems, improving efficiency without explicit length penalties.

We present an LDA approach to entity disambiguation. Each topic is associated with a Wikipedia article and topics generate either content words or entity mentions. Training such models is challenging because of the topic and vocabulary size, both in the millions. We tackle these problems using a novel distributed infer…

The family of temporal difference (TD) methods span a spectrum from computationally frugal linear methods like TD(λ) to data efficient least squares methods. Least square methods make the best use of available data directly computing the TD solution and thus do not require tuning a typically highly sensitive learning r…

In many real-world scenarios where data is high dimensional, test time acquisition of features is a non-trivial task due to costs associated with feature acquisition and evaluating feature value. The need for highly confident models with an extremely frugal acquisition of features can be addressed by allowing a feature…

We provide a mathematical definition of fragility and antifragility as negative or positive sensitivity to a semi-measure of dispersion and volatility (a variant of negative or positive "vega") and examine the link to nonlinear effects. We integrate model error (and biases) into the fragile or antifragile context. Unli…

Paper introduces SPADE method to protect classifiers from OOD and adversarial samples.

Algorithms typically come with tunable parameters that have a considerable impact on the computational resources they consume. Too often, practitioners must hand-tune the parameters, a tedious and error-prone task. A recent line of research provides algorithms that return nearly-optimal parameters from within a finite …

A new method finds DAG models without ground truth.

Optimal transport aggregation combines distributed MoE models efficiently.

Unimodal approach for group-level emotion recognition without individual features.

ASPIRE improves amortized posterior inference for Bayesian inverse problems.

When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers. We consider this challenge in the co…