diff --git a/benchmarks/regressor.jl b/benchmarks/regressor.jl
index e0d35b90..ecc39d0a 100644
--- a/benchmarks/regressor.jl
+++ b/benchmarks/regressor.jl
@@ -19,11 +19,11 @@ import CUDA
 #threads
 # laptop depth 6: 12.717845 seconds (2.08 M allocations: 466.228 MiB)
 
-nobs = Int(10e6)
+nobs = Int(1e6)
 num_feat = Int(100)
 nrounds = 200
 max_depth = 6
-tree_type = "binary"
+tree_type = "oblivious"
 T = Float64
 nthread = Base.Threads.nthreads()
 @info "testing with: $nobs observations | $num_feat features. nthread: $nthread | tree_type : $tree_type | max_depth : $max_depth"
@@ -55,7 +55,7 @@ params_xgb = Dict(
     :print_every_n => 5,
     :subsample => 0.5,
     :colsample_bytree => 0.5,
-    :tree_method => "gpu_hist", # hist/gpu_hist
+    :tree_method => "hist", # hist/gpu_hist
     :max_bin => 64,
 )
 
@@ -149,15 +149,3 @@ CUDA.@time m_evo = fit_evotree(params_evo; x_train, y_train, x_eval=x_train, y_e
 CUDA.@time pred_evo = m_evo(x_train; device);
 CUDA.@time pred_evo = m_evo(x_train; device);
 # @btime m_evo($x_train; device);
-
-# CUDA v4: 0.439869 seconds (29.93 k CPU allocations: 181.869 MiB, 2.28% gc time) (16 GPU allocations: 138.430 MiB, 0.06% memmgmt time)
-# CUDA v5: 0.365197 seconds (27.93 k CPU allocations: 181.763 MiB, 3.17% gc time) (16 GPU allocations: 138.430 MiB, 0.03% memmgmt time)
-CUDA.@time m, cache = EvoTrees.init(params_evo, x_train, y_train, EvoTrees.GPU);
-CUDA.@time EvoTrees.grow_evotree!(m, cache, params_evo, EvoTrees.GPU);
-# CUDA v4: 3.345438 seconds (3.57 M CPU allocations: 151.870 MiB, 0.60% gc time) (38.74 k GPU allocations: 1.227 GiB, 1.39% memmgmt time)
-# CUDA v5: 5.275562 seconds (6.92 M CPU allocations: 296.344 MiB, 1.04% gc time) (38.73 k GPU allocations: 1.227 GiB, 1.58% memmgmt time)
-CUDA.@time begin
-    for i in 1:200
-        EvoTrees.grow_evotree!(m, cache, params_evo, EvoTrees.GPU)
-    end
-end
diff --git a/experiments/readme_plots-df-cpu.jl b/experiments/readme_plots-df-cpu.jl
index 878c6d7f..8e7a8df7 100644
--- a/experiments/readme_plots-df-cpu.jl
+++ b/experiments/readme_plots-df-cpu.jl
@@ -121,535 +121,3 @@ plot!(
     linewidth=1.5,
     label="Linear - C",
 )
-
-# logistic / cross-entropy
-params1 = EvoTreeRegressor(
-    loss=:logistic,
-    nrounds=200,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.1,
-    eta=0.05,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1,
-    dtrain;
-    fnames=["x_num", "x_cat"],
-    target_name="y",
-    deval,
-    metric=:logloss,
-    print_every_n=25,
-    early_stopping_rounds=20,
-    verbosity=0
-);
-# 218.040 ms (123372 allocations: 34.71 MiB)
-# @btime model = fit_evotree($params1, $X_train, $Y_train, X_eval = $X_eval, Y_eval = $Y_eval)
-plot(
-    dtrain.x_num,
-    dtrain.y,
-    msize=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-dinfer = dtrain[dtrain.x_cat.=="A", :]
-pred = model(dinfer)
-x_perm = sortperm(dinfer.x_num)
-plot!(
-    dinfer.x_num[x_perm],
-    pred[x_perm],
-    color="lightblue",
-    linewidth=1.5,
-    label="Linear - A",
-)
-dinfer = dtrain[dtrain.x_cat.=="B", :]
-pred = model(dinfer);
-x_perm = sortperm(dinfer.x_num)
-plot!(
-    dinfer.x_num[x_perm],
-    pred[x_perm],
-    color="blue",
-    linewidth=1.5,
-    label="Linear - B",
-)
-dinfer = dtrain[dtrain.x_cat.=="C", :]
-pred = model(dinfer);
-x_perm = sortperm(dinfer.x_num)
-plot!(
-    dinfer.x_num[x_perm],
-    pred[x_perm],
-    color="navy",
-    linewidth=1.5,
-    label="Linear - C",
-)
-
-# L1
-params1 = EvoTreeRegressor(
-    loss=:L1,
-    alpha=0.5,
-    nrounds=500,
-    nbins=64,
-    lambda=0.0,
-    gamma=0.0,
-    eta=0.1,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:mae
-);
-@time pred_train_L1 = predict(model, x_train)
-@time pred_eval_L1 = predict(model, x_eval)
-sqrt(mean((pred_train_L1 .- y_train) .^ 2))
-
-x_perm = sortperm(x_train[:, 1])
-plot(
-    x_train,
-    y_train,
-    msize=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_linear[x_perm],
-    color="navy",
-    linewidth=1.5,
-    label="Linear",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_linear_w[x_perm],
-    color="lightblue",
-    linewidth=1.5,
-    label="LinearW",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_logistic[x_perm],
-    color="darkred",
-    linewidth=1.5,
-    label="Logistic",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_L1[x_perm],
-    color="darkgreen",
-    linewidth=1.5,
-    label="L1",
-)
-savefig("figures/regression_sinus.png")
-
-# Poisson
-params1 = EvoTreeCount(
-    loss=:poisson,
-    nrounds=500,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.1,
-    eta=0.1,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:poisson
-);
-@time pred_train_poisson = predict(model, x_train);
-sqrt(mean((pred_train_poisson .- y_train) .^ 2))
-
-# Gamma
-params1 = EvoTreeRegressor(
-    loss=:gamma,
-    nrounds=500,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.1,
-    eta=0.02,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:gamma
-);
-@time pred_train_gamma = predict(model, x_train);
-sqrt(mean((pred_train_gamma .- y_train) .^ 2))
-
-# Tweedie
-params1 = EvoTreeRegressor(
-    loss=:tweedie,
-    nrounds=500,
-    nbins=64,
-    lambda=0.5,
-    gamma=0.1,
-    eta=0.1,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:tweedie
-);
-@time pred_train_tweedie = predict(model, x_train);
-sqrt(mean((pred_train_tweedie .- y_train) .^ 2))
-
-x_perm = sortperm(x_train[:, 1])
-plot(
-    x_train,
-    y_train,
-    msize=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_poisson[x_perm],
-    color="navy",
-    linewidth=1.5,
-    label="Poisson",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_gamma[x_perm],
-    color="lightblue",
-    linewidth=1.5,
-    label="Gamma",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_tweedie[x_perm],
-    color="darkred",
-    linewidth=1.5,
-    label="Tweedie",
-)
-savefig("figures/regression_sinus2.png")
-
-
-###############################
-## Quantiles
-###############################
-# q50
-params1 = EvoTreeRegressor(
-    loss=:quantile,
-    alpha=0.5,
-    nrounds=500,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.0,
-    eta=0.05,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:mae
-);
-# 116.822 ms (74496 allocations: 36.41 MiB) for 100 iterations
-# @btime model = grow_gbtree($X_train, $Y_train, $params1, X_eval = $X_eval, Y_eval = $Y_eval)
-@time pred_train_q50 = predict(model, x_train)
-sum(pred_train_q50 .< y_train) / length(y_train)
-
-# q20
-params1 = EvoTreeRegressor(
-    loss=:quantile,
-    alpha=0.2,
-    nrounds=300,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.0,
-    eta=0.05,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(params1; x_train, y_train, x_eval, y_eval, print_every_n=25);
-@time pred_train_q20 = predict(model, x_train)
-sum(pred_train_q20 .< y_train) / length(y_train)
-
-# q80
-params1 = EvoTreeRegressor(
-    loss=:quantile,
-    alpha=0.8,
-    nrounds=300,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.0,
-    eta=0.05,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(params1; x_train, y_train, x_eval, y_eval, print_every_n=25)
-@time pred_train_q80 = predict(model, x_train)
-sum(pred_train_q80 .< y_train) / length(y_train)
-
-x_perm = sortperm(x_train[:, 1])
-plot(
-    x_train,
-    y_train,
-    ms=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_q50[x_perm],
-    color="navy",
-    linewidth=1.5,
-    label="Median",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_q20[x_perm],
-    color="darkred",
-    linewidth=1.5,
-    label="Q20",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_q80[x_perm],
-    color="darkgreen",
-    linewidth=1.5,
-    label="Q80",
-)
-savefig("figures/quantiles_sinus.png")
-
-
-###############################
-## gaussian
-###############################
-params1 = EvoTreeMLE(
-    loss=:gaussian,
-    nrounds=500,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.1,
-    eta=0.05,
-    max_depth=6,
-    min_weight=10.0,
-    rowsample=1.0,
-    colsample=1.0,
-    rng=123,
-)
-
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:gaussian
-);
-# @time model = fit_evotree(params1, X_train, Y_train, print_every_n = 10);
-@time pred_train = EvoTrees.predict(model, x_train);
-# @btime pred_train = EvoTrees.predict(model, X_train);
-
-pred_gauss =
-    [Distributions.Normal(pred_train[i, 1], pred_train[i, 2]) for i in axes(pred_train, 1)]
-pred_q80 = quantile.(pred_gauss, 0.8)
-pred_q20 = quantile.(pred_gauss, 0.2)
-
-mean(y_train .< pred_q80)
-mean(y_train .< pred_q20)
-
-x_perm = sortperm(x_train[:, 1])
-plot(
-    x_train[:, 1],
-    y_train,
-    ms=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train[x_perm, 1],
-    color="navy",
-    linewidth=1.5,
-    label="mu",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train[x_perm, 2],
-    color="darkred",
-    linewidth=1.5,
-    label="sigma",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_q20[x_perm, 1],
-    color="darkgreen",
-    linewidth=1.5,
-    label="q20",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_q80[x_perm, 1],
-    color="darkgreen",
-    linewidth=1.5,
-    label="q80",
-)
-savefig("figures/gaussian-sinus.png")
-
-
-###############################
-## Logistic
-###############################
-params1 = EvoTrees.EvoTreeMLE(
-    loss=:logistic,
-    nrounds=500,
-    nbins=64,
-    lambda=1.0,
-    gamma=0.1,
-    eta=0.03,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=1.0,
-    colsample=1.0,
-    rng=123,
-)
-
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:logistic_mle
-);
-# @time model = fit_evotree(params1, X_train, Y_train, print_every_n = 10);
-@time pred_train = EvoTrees.predict(model, x_train);
-# @btime pred_train = EvoTrees.predict(model, X_train);
-
-pred_logistic = [
-    Distributions.Logistic(pred_train[i, 1], pred_train[i, 2]) for i in axes(pred_train, 1)
-]
-pred_q80 = quantile.(pred_logistic, 0.8)
-pred_q20 = quantile.(pred_logistic, 0.2)
-
-mean(y_train .< pred_q80)
-mean(y_train .< pred_q20)
-
-x_perm = sortperm(x_train[:, 1])
-plot(
-    x_train[:, 1],
-    y_train,
-    ms=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train[x_perm, 1],
-    color="navy",
-    linewidth=1.5,
-    label="mu",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train[x_perm, 2],
-    color="darkred",
-    linewidth=1.5,
-    label="s",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_q20[x_perm, 1],
-    color="darkgreen",
-    linewidth=1.5,
-    label="q20",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_q80[x_perm, 1],
-    color="darkgreen",
-    linewidth=1.5,
-    label="q80",
-)
-savefig("figures/logistic-sinus.png")
diff --git a/experiments/readme_plots-df-gpu.jl b/experiments/readme_plots-df-gpu.jl
index bff06ae4..0e623a28 100644
--- a/experiments/readme_plots-df-gpu.jl
+++ b/experiments/readme_plots-df-gpu.jl
@@ -5,6 +5,7 @@ using StatsBase: sample, quantile
 using Distributions
 using Random
 using Plots
+using CUDA
 using EvoTrees
 using DataFrames
 using CategoricalArrays
@@ -124,495 +125,3 @@ plot!(
     linewidth=1.5,
     label="Linear - C",
 )
-
-# logistic / cross-entropy
-params1 = EvoTreeRegressor(
-    loss=:logistic,
-    nrounds=200,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.1,
-    eta=0.05,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:logloss
-);
-# 218.040 ms (123372 allocations: 34.71 MiB)
-# @btime model = fit_evotree($params1, $X_train, $Y_train, X_eval = $X_eval, Y_eval = $Y_eval)
-@time pred_train_logistic = predict(model, x_train);
-@time pred_eval_logistic = predict(model, x_eval)
-sqrt(mean((pred_train_logistic .- y_train) .^ 2))
-
-# L1
-params1 = EvoTreeRegressor(
-    loss=:L1,
-    alpha=0.5,
-    nrounds=500,
-    nbins=64,
-    lambda=0.0,
-    gamma=0.0,
-    eta=0.1,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:mae
-);
-@time pred_train_L1 = predict(model, x_train)
-@time pred_eval_L1 = predict(model, x_eval)
-sqrt(mean((pred_train_L1 .- y_train) .^ 2))
-
-x_perm = sortperm(x_train[:, 1])
-plot(
-    x_train,
-    y_train,
-    msize=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_linear[x_perm],
-    color="navy",
-    linewidth=1.5,
-    label="Linear",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_linear_w[x_perm],
-    color="lightblue",
-    linewidth=1.5,
-    label="LinearW",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_logistic[x_perm],
-    color="darkred",
-    linewidth=1.5,
-    label="Logistic",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_L1[x_perm],
-    color="darkgreen",
-    linewidth=1.5,
-    label="L1",
-)
-savefig("figures/regression_sinus.png")
-
-# Poisson
-params1 = EvoTreeCount(
-    loss=:poisson,
-    nrounds=500,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.1,
-    eta=0.1,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:poisson
-);
-@time pred_train_poisson = predict(model, x_train);
-sqrt(mean((pred_train_poisson .- y_train) .^ 2))
-
-# Gamma
-params1 = EvoTreeRegressor(
-    loss=:gamma,
-    nrounds=500,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.1,
-    eta=0.02,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:gamma
-);
-@time pred_train_gamma = predict(model, x_train);
-sqrt(mean((pred_train_gamma .- y_train) .^ 2))
-
-# Tweedie
-params1 = EvoTreeRegressor(
-    loss=:tweedie,
-    nrounds=500,
-    nbins=64,
-    lambda=0.5,
-    gamma=0.1,
-    eta=0.1,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:tweedie
-);
-@time pred_train_tweedie = predict(model, x_train);
-sqrt(mean((pred_train_tweedie .- y_train) .^ 2))
-
-x_perm = sortperm(x_train[:, 1])
-plot(
-    x_train,
-    y_train,
-    msize=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_poisson[x_perm],
-    color="navy",
-    linewidth=1.5,
-    label="Poisson",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_gamma[x_perm],
-    color="lightblue",
-    linewidth=1.5,
-    label="Gamma",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_tweedie[x_perm],
-    color="darkred",
-    linewidth=1.5,
-    label="Tweedie",
-)
-savefig("figures/regression_sinus2.png")
-
-
-###############################
-## Quantiles
-###############################
-# q50
-params1 = EvoTreeRegressor(
-    loss=:quantile,
-    alpha=0.5,
-    nrounds=500,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.0,
-    eta=0.05,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:mae
-);
-# 116.822 ms (74496 allocations: 36.41 MiB) for 100 iterations
-# @btime model = grow_gbtree($X_train, $Y_train, $params1, X_eval = $X_eval, Y_eval = $Y_eval)
-@time pred_train_q50 = predict(model, x_train)
-sum(pred_train_q50 .< y_train) / length(y_train)
-
-# q20
-params1 = EvoTreeRegressor(
-    loss=:quantile,
-    alpha=0.2,
-    nrounds=300,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.0,
-    eta=0.05,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(params1; x_train, y_train, x_eval, y_eval, print_every_n=25);
-@time pred_train_q20 = predict(model, x_train)
-sum(pred_train_q20 .< y_train) / length(y_train)
-
-# q80
-params1 = EvoTreeRegressor(
-    loss=:quantile,
-    alpha=0.8,
-    nrounds=300,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.0,
-    eta=0.05,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=0.5,
-    colsample=1.0,
-)
-@time model = fit_evotree(params1; x_train, y_train, x_eval, y_eval, print_every_n=25)
-@time pred_train_q80 = predict(model, x_train)
-sum(pred_train_q80 .< y_train) / length(y_train)
-
-x_perm = sortperm(x_train[:, 1])
-plot(
-    x_train,
-    y_train,
-    ms=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_q50[x_perm],
-    color="navy",
-    linewidth=1.5,
-    label="Median",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_q20[x_perm],
-    color="darkred",
-    linewidth=1.5,
-    label="Q20",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train_q80[x_perm],
-    color="darkgreen",
-    linewidth=1.5,
-    label="Q80",
-)
-savefig("figures/quantiles_sinus.png")
-
-
-###############################
-## gaussian
-###############################
-params1 = EvoTreeMLE(
-    loss=:gaussian,
-    nrounds=500,
-    nbins=64,
-    lambda=0.1,
-    gamma=0.1,
-    eta=0.05,
-    max_depth=6,
-    min_weight=10.0,
-    rowsample=1.0,
-    colsample=1.0,
-    rng=123,
-)
-
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:gaussian
-);
-# @time model = fit_evotree(params1, X_train, Y_train, print_every_n = 10);
-@time pred_train = EvoTrees.predict(model, x_train);
-# @btime pred_train = EvoTrees.predict(model, X_train);
-
-pred_gauss =
-    [Distributions.Normal(pred_train[i, 1], pred_train[i, 2]) for i in axes(pred_train, 1)]
-pred_q80 = quantile.(pred_gauss, 0.8)
-pred_q20 = quantile.(pred_gauss, 0.2)
-
-mean(y_train .< pred_q80)
-mean(y_train .< pred_q20)
-
-x_perm = sortperm(x_train[:, 1])
-plot(
-    x_train[:, 1],
-    y_train,
-    ms=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train[x_perm, 1],
-    color="navy",
-    linewidth=1.5,
-    label="mu",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train[x_perm, 2],
-    color="darkred",
-    linewidth=1.5,
-    label="sigma",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_q20[x_perm, 1],
-    color="darkgreen",
-    linewidth=1.5,
-    label="q20",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_q80[x_perm, 1],
-    color="darkgreen",
-    linewidth=1.5,
-    label="q80",
-)
-savefig("figures/gaussian-sinus.png")
-
-
-###############################
-## Logistic
-###############################
-params1 = EvoTrees.EvoTreeMLE(
-    loss=:logistic,
-    nrounds=500,
-    nbins=64,
-    lambda=1.0,
-    gamma=0.1,
-    eta=0.03,
-    max_depth=6,
-    min_weight=1.0,
-    rowsample=1.0,
-    colsample=1.0,
-    rng=123,
-)
-
-@time model = fit_evotree(
-    params1;
-    x_train,
-    y_train,
-    x_eval,
-    y_eval,
-    print_every_n=25,
-    early_stopping_rounds=50,
-    metric=:logistic_mle
-);
-# @time model = fit_evotree(params1, X_train, Y_train, print_every_n = 10);
-@time pred_train = EvoTrees.predict(model, x_train);
-# @btime pred_train = EvoTrees.predict(model, X_train);
-
-pred_logistic = [
-    Distributions.Logistic(pred_train[i, 1], pred_train[i, 2]) for i in axes(pred_train, 1)
-]
-pred_q80 = quantile.(pred_logistic, 0.8)
-pred_q20 = quantile.(pred_logistic, 0.2)
-
-mean(y_train .< pred_q80)
-mean(y_train .< pred_q20)
-
-x_perm = sortperm(x_train[:, 1])
-plot(
-    x_train[:, 1],
-    y_train,
-    ms=0.5,
-    mcolor="darkgray",
-    mswidth=0,
-    background_color=RGB(1, 1, 1),
-    seriestype=:scatter,
-    xaxis=("feature"),
-    yaxis=("target"),
-    legend=true,
-    label="",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train[x_perm, 1],
-    color="navy",
-    linewidth=1.5,
-    label="mu",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_train[x_perm, 2],
-    color="darkred",
-    linewidth=1.5,
-    label="s",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_q20[x_perm, 1],
-    color="darkgreen",
-    linewidth=1.5,
-    label="q20",
-)
-plot!(
-    x_train[:, 1][x_perm],
-    pred_q80[x_perm, 1],
-    color="darkgreen",
-    linewidth=1.5,
-    label="q80",
-)
-savefig("figures/logistic-sinus.png")
diff --git a/experiments/readme_plots_gpu.jl b/experiments/readme_plots_gpu.jl
index 33b89c92..89dc1f07 100644
--- a/experiments/readme_plots_gpu.jl
+++ b/experiments/readme_plots_gpu.jl
@@ -5,12 +5,13 @@ using Distributions
 using Random
 using Plots
 using Revise
+using CUDA
 using EvoTrees
 using EvoTrees: predict, sigmoid, logit
 # using ProfileView
 
 # prepare a dataset
-tree_type = "binary"
+tree_type = "oblivious" # binary/oblivious
 device = "gpu"
 
 Random.seed!(123)
diff --git a/ext/EvoTreesCUDAExt/eval.jl b/ext/EvoTreesCUDAExt/eval.jl
index 6ea10153..f041c18e 100644
--- a/ext/EvoTreesCUDAExt/eval.jl
+++ b/ext/EvoTreesCUDAExt/eval.jl
@@ -46,7 +46,7 @@ end
 function eval_logloss_kernel!(eval::CuDeviceVector{T}, p::CuDeviceMatrix{T}, y::CuDeviceVector{T}, w::CuDeviceVector{T}) where {T<:AbstractFloat}
     i = threadIdx().x + (blockIdx().x - 1) * blockDim().x
     if i <= length(y)
-        @inbounds pred = sigmoid(p[1, i])
+        @inbounds pred = EvoTrees.sigmoid(p[1, i])
         @inbounds eval[i] = w[i] * (-y[i] * log(pred) + (y[i] - 1) * log(1 - pred))
     end
     return nothing
diff --git a/ext/EvoTreesCUDAExt/fit.jl b/ext/EvoTreesCUDAExt/fit.jl
index e4f4e330..1f454254 100644
--- a/ext/EvoTreesCUDAExt/fit.jl
+++ b/ext/EvoTreesCUDAExt/fit.jl
@@ -162,6 +162,7 @@ function grow_otree!(
     out,
     left,
     right,
+    h∇_cpu::Array{Float64,3},
     h∇::CuArray{Float64,3},
     x_bin::CuMatrix,
     feattypes::Vector{Bool},
diff --git a/figures/gaussian-sinus-oblivious-gpu.png b/figures/gaussian-sinus-oblivious-gpu.png
index abde7cdd..c62c1267 100644
Binary files a/figures/gaussian-sinus-oblivious-gpu.png and b/figures/gaussian-sinus-oblivious-gpu.png differ
diff --git a/figures/regression-sinus-oblivious-gpu.png b/figures/regression-sinus-oblivious-gpu.png
index 44644279..eb60970d 100644
Binary files a/figures/regression-sinus-oblivious-gpu.png and b/figures/regression-sinus-oblivious-gpu.png differ