Learning Directed Graphical Models with Optimal Transport

Estimating the parameters of a probabilistic directed graphical model from incomplete data remains a long-standing challenge. This is because, in the presence of latent variables, both the likelihood function and posterior distribution are intractable without further assumptions about structural dependencies or model classes. While existing learning methods are fundamentally based on likelihood maximization, here we offer a new view of the parameter learning problem through the lens of optimal transport. This perspective licenses a general framework that operates on any directed graphs without making unrealistic assumptions on the posterior over the latent variables or resorting to black-box variational approximations. We develop a theoretical framework and support it with extensive empirical evidence demonstrating the flexibility and versatility of our approach. Across experiments, we show that not only can our method recover the ground-truth parameters but it also performs comparably or better on downstream applications, notably the non-trivial task of discrete representation learning

Interactive Visual Facets to Support Fluid Exploratory Search

Exploratory search starts with ill-defined goals and involves browsing, learning, and formulating new targets for search. To fluidly support such dynamic search behaviours, we focus on devising interactive visual facets (IVF), visualising information facets to support user comprehension and control of the information space. To do this, we reviewed existing faceted search interfaces and derived two design requirements (DR) that have not been fully addressed to sup- port fluid interactions in exploratory search. We then exemplified the requirements through devising an IVF tool, which coordinates a linear and a categorical facet representing the distribution and summarisation of items, respectively, and providing context for faceted exploration (DR1). To support rapid transitions between search criteria (DR2), the tool introduces a novel design concept of using facets to select items without filtering the item space. Particularly, we propose a filter-swipe technique that enables users to drag a categorical facet value sequentially over linear facet bars to view the items in the intersection of the two facets along with the categorical facet dynamically summarizing the items in the interaction. A user study of 11 participants with realistic email search tasks shows that dynamic suggestions through the timeline navigation can help discover useful suggestions for search; the novel design concept was favoured over using facet values as filters. Based on these practices, we derive IVF design implications for fluid, exploratory searches.Peer reviewe

Vector Quantized Wasserstein Auto-Encoder

Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks. Inspired by the seminal Vector Quantized Variational Auto-Encoder (VQ-VAE), most of work in learning deep discrete representations has mainly focused on improving the original VQ-VAE form and none of them has studied learning deep discrete representations from the generative viewpoint. In this work, we study learning deep discrete representations from the generative viewpoint. Specifically, we endow discrete distributions over sequences of codewords and learn a deterministic decoder that transports the distribution over the sequences of codewords to the data distribution via minimizing a WS distance between them. We develop further theories to connect it with the clustering viewpoint of WS distance, allowing us to have a better and more controllable clustering solution. Finally, we empirically evaluate our method on several well-known benchmarks, where it achieves better qualitative and quantitative performances than the other VQ-VAE variants in terms of the codebook utilization and image reconstruction/generation

Effectiveness of cervical pessary compared to cervical cerclage with or without vaginal progesterone for the prevention of preterm birth in women with twin pregnancies and a short cervix : Study protocol for a two-by-two factorial randomised clinical trial

Peer reviewedPublisher PD

Assessment of cataract surgical outcomes in settings where follow-up is poor: PRECOG, a multicentre observational study

Background Poor follow-up after cataract surgery in developing countries makes assessment of operative quality uncertain. We aimed to assess two strategies to measure visual outcome: recording the visual acuity of all patients 3 or fewer days postoperatively (early postoperative assessment), and recording that of only those patients who returned for the fi nal follow-up examination after 40 or more days without additional prompting. Methods Each of 40 centres in ten countries in Asia, Africa, and Latin America recruited 40–120 consecutive surgical cataract patients. Operative-eye best-corrected visual acuity and uncorrected visual acuity were recorded before surgery, 3 or fewer days postoperatively, and 40 or more days postoperatively. Clinics logged whether each patient had returned for the fi nal follow-up examination without additional prompting, had to be actively encouraged to return, or had to be examined at home. Visual outcome for each centre was defi ned as the proportion of patients with uncorrected visual acuity of 6/18 or better minus the proportion with uncorrected visual acuity of 6/60 or worse, and was calculated for each participating hospital with results from the early assessment of all patients and the late assessment of only those returning unprompted, with results from the fi nal follow-up assessment for all patients used as the standard. Findings Of 3708 participants, 3441 (93%) had fi nal follow-up vision data recorded 40 or more days after surgery, 1831 of whom (51% of the 3581 total participants for whom mode of follow-up was recorded) had returned to the clinic without additional prompting. Visual outcome by hospital from early postoperative and fi nal follow-up assessment for all patients were highly correlated (Spearman’s rs=0·74, p<0·0001). Visual outcome from fi nal followup assessment for all patients and for only those who returned without additional prompting were also highly correlated (rs=0·86, p<0·0001), even for the 17 hospitals with unprompted return rates of less than 50% (rs=0·71, p=0·002). When we divided hospitals into top 25%, middle 50%, and bottom 25% by visual outcome, classifi cation based on fi nal follow-up assessment for all patients was the same as that based on early postoperative assessment for 27 (68%) of 40 centres, and the same as that based on data from patients who returned without additional prompting in 31 (84%) of 37 centres. Use of glasses to optimise vision at the time of the early and late examinations did not further improve the correlations. Interpretation Early vision assessment for all patients and follow-up assessment only for patients who return to the clinic without prompting are valid measures of operative quality in settings where follow-up is poor

BridgeData V2: A Dataset for Robot Learning at Scale

We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors designed to facilitate research on scalable robot learning. BridgeData V2 contains 60,096 trajectories collected across 24 environments on a publicly available low-cost robot. BridgeData V2 provides extensive task and environment variability, leading to skills that can generalize across environments, domains, and institutions, making the dataset a useful resource for a broad range of researchers. Additionally, the dataset is compatible with a wide variety of open-vocabulary, multi-task learning methods conditioned on goal images or natural language instructions. In our experiments, we train 6 state-of-the-art imitation learning and offline reinforcement learning methods on our dataset, and find that they succeed on a suite of tasks requiring varying amounts of generalization. We also demonstrate that the performance of these methods improves with more data and higher capacity models, and that training on a greater variety of skills leads to improved generalization. By publicly sharing BridgeData V2 and our pre-trained models, we aim to accelerate research in scalable robot learning methods. Project page at https://rail-berkeley.github.io/bridgedataComment: 9 page

The extinction law for molecular clouds. Case study of B 335

We determine the extinction curve from the UV to the near-IR for molecular clouds and investigate whether current models can adequately explain this wavelength dependence of the extinction. The aim is also to interpret the extinction in terms of H2 column density. We applied five different methods, including a new method for simultaneously determining the reddening law and the classification of the background stars. Our method is based on multicolour observations and a grid of model atmospheres. We confirm that the extinction law can be adequately described by a single parameter, RV (the selective to absolute extinction), in accordance with earlier findings. The RV value for B 335 is RV = 4.8. The reddening curve can be accurately reproduced by model calculations. By assuming that all the silicon is bound in silicate grains, we can interpret the reddening in terms of column density, NH = 4.4 (\pm0.5) \times 1021 EI-Ks cm-2, corresponding to NH = 2.3 (\pm0.2) \times 1021 \cdot AV cm-2, close to that of the diffuse ISM, (1.8-2.2) \times 1021 cm-2 . We show that the density of the B 335 globule outer shells can be modelled as an evolved Ebert-Bonnor gas sphere with {\rho} \propto r-2, and estimate the mass of this globule to 2.5 Msu

Modeling asset returns under time-varying semi-nonparametric distributions

We extend the semi-nonparametric (SNP) density of León, Mencía and Sentana (2009) to time-varying higher-order moments for daily asset return innovations of stock indexes and foreign-exchange rates. We estimate robust tail-indexes for testing the existence of the unconditional higher-order moments. We obtain closed-form expressions of partial moments and expected shortfall under the time-varying SNP density with the GJR-GARCH for modeling returns. A comparative study between SNP and Hansen's skewed-t, based on skewness-kurtosis frontiers, in-sample and backtesting analyses, is also implemented. Finally, we conduct an out-of-sample portfolio selection exercise for the stocks of the S&P 100 index through an equity screening method based on our parametric one-sided reward/risk performance measures and compare with the Sharpe ratio portfolio